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14 Artificial Intelligence Careers & Job Outlook [2025]
|
14 Artificial Intelligence Careers & Job Outlook [2025]
|
https://onlinedegrees.sandiego.edu
|
[] |
Most top-level AI jobs will typically require a master's degree, including research scientists, AI engineers and big data engineers. Most AI roles will require ...
|
What Is Artificial Intelligence?
Artificial intelligence is all around us, even in places you may not realize. From music preferences to home appliances and healthcare, the power of AI is far reaching. But first, let’s explore the basics of AI with this definition from Investopedia:
“AI refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning or problem-solving.”
Examples of artificial intelligence include:
Smart assistants like Siri and Alexa
Pandora and Netflix, which provide personalized song and entertainment recommendations
Chatbots
Robotic vacuum cleaners
Self-driving vehicles
Facial recognition software
Those are just a few of the many, many examples. Needless to say, artificial intelligence is everywhere, and the demand for AI — especially skilled, experienced AI professionals — is growing. Bernard Marr, a business and technology advisor to governments and companies, told Forbes that we now have access to more data than ever, which means AI has become smarter, faster, and more accurate.
“As a very simple example, think of Spotify recommendations,” he explained in the article. “The more music (or podcasts) you listen to via Spotify, the better able Spotify is to recommend other content that you might enjoy. Netflix and Amazon recommendations work on the same principle, of course.”
What Does an AI Professional Do?
Since artificial intelligence is an increasingly widespread and growing form of technology, professionals who specialize in AI are needed now more than ever. The good news is that the AI professional field is full of different career opportunities, which means you can take on different roles and responsibilities depending upon the position, your experience and your interests.
The need for skilled AI professionals spans nearly every industry, including:
Financial services
Healthcare
Technology
Media
Marketing
Government and military
National security
IoT-enabled systems
Agriculture
Gaming
Retail
Professional AI Skills in Demand for 2025
If you’re looking to enter the professional world of AI, it’s important to make sure you have the right skills, which will set you apart from other candidates and help you land the perfect position. First, competencies with calculus and linear algebra are extremely important. Also, if you’re interested in AI, you should have some knowledge and experience in at least one of the following programming languages:
Python
C/C++
MATLAB
According to ZipRecruiter, these are the top 5 skills required for AI jobs:
Communication skills
Knowledge and experience with Python specifically (in general, proficiency in programming language)
Digital marketing goals and strategies
Collaborating effectively with others
Analytical skills
The Intellipaat blog also recommends these additional skills for AI professionals:
Solid knowledge of applied mathematics and algorithms
Problem-solving skills
Industry knowledge
Management and leadership skills
Machine learning
7 Technical AI Skills in Demand for 2025
As the applications of artificial intelligence continue to expand across various industries, the demand for specific technical skills is rising. Staying current with these skills is crucial for anyone looking to succeed in this field. Here’s a look at the most in-demand technical AI skills and how you can acquire them through certifications and training programs:
To bolster these skills, consider obtaining AI certifications such as:
IBM Applied AI Professional Certificate
Build job-ready AI skills, including generative AI and chatbots, in this ten-course, self-paced program with hands-on labs. Complete it in six months at four hours per week to earn a Professional Certificate and digital badge from IBM.
Build job-ready AI skills, including generative AI and chatbots, in this ten-course, self-paced program with hands-on labs. Complete it in six months at four hours per week to earn a Professional Certificate and digital badge from IBM. IBM AI Engineering Professional Certificate
Master machine learning and deep learning techniques in this six-course program. Complete it in two months at 10 hours per week to earn a Professional Certificate and digital badge from IBM.
Master machine learning and deep learning techniques in this six-course program. Complete it in two months at 10 hours per week to earn a Professional Certificate and digital badge from IBM. Google Advanced Data Analytics Professional Certificate
Gain skills in statistical analysis, Python programming and machine learning. This seven-course program includes over 200 hours of instruction, completed in six months at 10 hours per week. Graduates receive a Professional Certificate from Google and access to career resources and job opportunities with over 150 U.S. employers.
How to Start a Career in Artificial Intelligence
Starting a career in AI requires a combination of education, practical experience and strategic job searching. If you aren’t already in the industry, the first step is to conduct research. This includes talking to current AI professionals and researching reputable colleges and programs.
According to Springboard, hiring managers will probably require at least a bachelor’s degree in math and basic computer technology, but in many cases, a bachelor’s degree will only qualify you for entry-level positions. Undergraduate degrees in computer science or engineering are good starting points.
Dan Ayoub, general manager for mixed reality education at Microsoft, explained in a Best Colleges article that “curiosity, confidence, and perseverance” will benefit students looking to break into an emerging field like AI. He noted that familiarity with data science, machine learning and Java are good places to begin, with specialized training offered through degree programs. There are many new undergraduate and graduate programs designed to prepare students specifically for AI careers.
For those looking to set themselves apart, a master’s degree in artificial intelligence can provide firsthand experience and knowledge from industry experts. Those interested in pursuing a master’s in AI should have a strong foundation in math, computer science and data analytics.
Once you have the necessary educational background, gaining hands-on experience is crucial. Entry-level roles within AI can provide a solid foundation for various career paths in the field. Here are some examples of entry-level artificial intelligence jobs:
AI Internships
An AI intern assists in developing and training machine learning models, analyzing data, and supporting various AI research and development projects. Many companies offer internships for current college students and recent graduates to gain hands-on experience.
An AI intern assists in developing and training machine learning models, analyzing data, and supporting various AI research and development projects. Many companies offer internships for current college students and recent graduates to gain hands-on experience. Junior Data Analyst
This role involves analyzing datasets to uncover patterns and insights.
This role involves analyzing datasets to uncover patterns and insights. Machine Learning Intern
This position focuses on assisting in building and training machine learning models.
This position focuses on assisting in building and training machine learning models. Research Assistant
The responsibilities of this role include supporting AI research projects at universities or tech companies.
The responsibilities of this role include supporting AI research projects at universities or tech companies. AI Support Specialist
This artificial intelligence job involves helping users troubleshoot AI applications.
To find entry-level roles like these, consider the following strategies:
Expand your professional network by attending AI conferences and meetups and joining online communities.
by attending AI conferences and meetups and joining online communities. Explore job portals by regularly checking AI-specific job boards such as AI Jobs, LinkedIn and Indeed.
by regularly checking AI-specific job boards such as AI Jobs, LinkedIn and Indeed. Browse the career sections on websites of companies known for their AI work.
on websites of companies known for their AI work. Join professional AI and data science associations to access job postings and networking opportunities
14 Career Paths in Artificial Intelligence
From developing algorithms to analyzing large datasets, AI professionals can choose from various roles that align with their skills and interests. Here are some additional AI career paths to consider:
Career Path Description Median Annual Salary Big Data Analyst Find meaningful patterns in data by looking at the past to help make predictions about the future. $123,210 User Experience (UX) Designer/Developer Work with products to help customers understand their function and can use them easily. Understand how people use equipment and how computer scientists can apply that understanding to produce more advanced software. $82,364 Natural Language Processing Engineer Explore the connection between human language and computational systems; this includes working on projects like chatbots and virtual assistants. $119,000 Researcher Work with computer science and AI research Discover ways to advance AI technology $82,165 Research Scientist Expert in applied math, machine learning, deep learning, and computational stats. Expected to have an advanced degree in computer science or an advanced degree in a related field supported by experience. $150,000 Software Engineer Develop programs in which AI tools function. The role may also be referred to as a Programmer or Artificial Intelligence Developer. $94,912 AI Engineer Build AI models from scratch and help product managers and stakeholders understand results. $204,000 Data Mining and Analysis Finding anomalies, patterns, etc. within large data sets to predict outcomes. $137,000 Machine Learning Engineer Using data to design, build and manage ML software applications. $157,969 Data Scientist Collect, analyze and interpret data sets. $163,000 Business Intelligence (BI) Developer Analyze complex data sets to identify business and market trends $99,780 Big Data Engineer/Architect Develop systems that allow businesses to communicate and collect data $205,000 Robotics Engineer Design, build and test robots or robotic systems. $64,728 Computer Vision Engineer Develop and work on projects and systems involving visual data. $127,000 Data Engineer Design and maintain data pipelines and ensure data is accessible for analysis. $109,675 AI Ethicist Ensures the ethical implications of AI technologies are considered in their development and deployment. $137,000 Algorithm Developer Specializes in creating algorithms for various AI applications. $158,499 UX Developer Focuses on creating user-friendly AI interfaces and experiences. $120,000 Disclaimer: The salary figures mentioned in this blog post are based on data available from job sites like Indeed, Glassdoor, LinkedIn, Zippia and ZipRecruiter as of August 2024. Please note that these figures may fluctuate over time as new salaries are entered. For the most up-to-date information, we recommend checking the original sources directly.
Artificial Intelligence Job Outlook
The job outlook for AI professionals is extremely promising, with ZipRecruiter predicting the industry to “grow explosively as it becomes capable of accomplishing more tasks.”
In an article on Built In, Satya Mallick, founder of Big Vision LLC/Interim CEO, OpenCV.org, likened AI to “a rocket ship that is taking off.” He also explained that even entry-level jobs can pay extremely well. “The reason is a huge demand for AI talent and not enough people with the right expertise,” he explained.
The U.S. Bureau of Labor Statistics expects employment of computer and information technology occupations to grow 26% from 2023 to 2033.
Companies Currently Hiring AI Positions
A recent search for artificial intelligence job openings on LinkedIn revealed thousands and thousands of results at a wide variety of companies. Here is a sample of some of the positions we found. (You can see similar LinkedIn search results here.)
Samsung Electronics America — Machine Learning Serving Engineer – VD
Pfizer — Director, Data Science and AI
Starbucks — Decision Scientist Sr – Workforce Management
Roku — Senior Manager, Data Science
Yahoo — Research Scientist
Thermo Fisher Scientific — Engineer III, Artificial Intelligence
United States Postal Service — Artificial Intelligence Architect
CVS Health — Conversational AI Principal Architect
As you can see from the list above, there are many different types of positions within artificial intelligence. Some of the most common AI-related job titles, courtesy of Glassdoor, include:
Software engineer
Data scientist
Software development engineer
Research scientist
In general, tech companies (both software and hardware) dominate the list of companies that are hiring AI professionals. But a quick search on any reputable job listing site will give you a list of positions that span a variety of industries. Here is a sample of some of the top companies that are hiring for these types of AI roles:
NVIDIA
Facebook
Deloitte
Amazon
Accenture
H&R Block
IBM
PwC
Fidelity Investments
PayPal
Major League Baseball
Harvard Business School
IKEA
Artificial Intelligence Salaries
Salaries are dynamic, which means the numbers we’ve listed below will fluctuate due to inflation, trends, the job market, demand and other factors.
According to our degree page, the average salary for an artificial intelligence programmer ranges from $100,000 to $150,000. Salaries are significantly higher for AI engineers, averaging $171,715 with the top 25% earning above $200,000.
There are a range of averages, depending on the position and the responsibilities, but here are the most popular:
According to Indeed, the salary for artificial intelligence careers ranges from approximately $124,427 for a full stack developer to $162,168 for a machine learning engineer.
The average annual base pay for artificial intelligence salaries in the United States is $170,000, according to Glassdoor.
According to Talent.com, the average artificial intelligence salary is $153,119 per year. Entry positions start at $115,000, and most experienced employees can make up to $204,000 per year.
Artificial Intelligence Career FAQs
Expand All If I’m interested in a career in AI, where do I start? The first step is to conduct research, which includes talking to current AI professionals and researching reputable colleges and programs that offer AI-related degrees. You at least need a bachelor’s degree in math and basic computer technology to start, and an advanced degree in artificial intelligence is also something to consider if you’re looking to stand out from other applicants and learn real-world experience from industry experts. What skills and background do I need to pursue a career in artificial intelligence? It’s important to have a strong background in math, science, engineering, and command of at least one of the following programming languages: Python, C and MATLAB. What is the outlook for a career in AI? The career outlook for AI professionals is promising. According to the U.S. Bureau of Labor Statistics, employment in computer and information technology occupations, which includes AI roles, is projected to grow faster than other occupations from 2022 to 2032, adding about 377,500 new jobs each year. This growth reflects the increasing demand for AI technologies across various industries, indicating robust opportunities for those entering the field. Do any AI careers require a master’s degree? Most top-level AI jobs will typically require a master’s degree, including research scientists, AI engineers and big data engineers. Most AI roles will require applicants to have solid knowledge and skills with MATLAB, C/C++ and Python programming. What is the benefit of a master’s degree in artificial intelligence? A master’s degree is a great way to expand the number of available AI job opportunities, especially since an advanced degree is often required, especially for higher-level jobs. A master’s degree will also give you greater earning potential and illustrate that you’re invested in your career and the industry. View All FAQs
Educational Preparation in Artificial Intelligence
Exciting, high-paying career opportunities in AI continue to expand across a variety of industries. The University of San Diego — a highly regarded industry thought leader and education provider — offers an innovative, online AI master’s degree program, the Master of Science in Applied Artificial Intelligence, which is designed to prepare graduates for success in this important fast-growing field. This program includes a significant emphasis on real-world applications, ethics, privacy, moral responsibility and social good in designing AI-enabled systems.
| 2021-07-22T00:00:00 |
2021/07/22
|
https://onlinedegrees.sandiego.edu/artificial-intelligence-jobs/
|
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Companies keep slashing jobs. How worried should workers be ...
|
Companies keep slashing jobs. How worried should workers be about AI replacing them?
|
https://www.latimes.com
|
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"Staff Writer",
"Queenie Wong Is A Technology Reporter For The Los Angeles Times. At Cnet",
"The Mercury News",
"She Wrote About The World S Largest Social Networks. Wong Also Covered Politics",
"Education For The Statesman Journal In Salem",
"Ore. Growing Up In Southern California",
"She Started Reading The Times As A Kid",
"Took Her First Journalism Class In Middle School. She Graduated Washington",
"Lee University",
"Where She Studied Journalism"
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Workers are anxious that artificial intelligence could wipe out their jobs as layoffs continue and employers are cautious about hiring.
|
Andy Jassy, chief executive of Amazon, has said that the e-commerce giant plans to shrink its workforce as employees use more AI tools and agents.
Tech companies that are cutting jobs and leaning more on artificial intelligence are also disrupting themselves.
Amazon’s Chief Executive Andy Jassy said last month that he expects the e-commerce giant will shrink its workforce as employees “get efficiency gains from using AI extensively.”
At Salesforce, a software company that helps businesses manage customer relationships, Chief Executive Marc Benioff said last week that AI is already doing 30% to 50% of the company’s work.
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Other tech leaders have chimed in. Earlier this year, Anthropic, an AI startup, flashed a big warning: AI could wipe out more than half of all entry-level white-collar jobs in the next one to five years.
Ready or not, AI is reshaping, displacing and creating new roles as technology’s impact on the job market ripples across multiple sectors. The AI frenzy has fueled anxiety from workers who fear their jobs could be automated. Roughly half of U.S. workers are worried about how AI may be used in the workplace in the future, and few think AI will lead to more job opportunities in the long run, according to a Pew Research Center report .
The heightened fear comes as major tech companies, such as Microsoft, Intel, Amazon and Meta cut workers, push for more efficiency and promote their AI tools. Tech companies have rolled out AI-powered features that can generate code, analyze data, develop apps and help complete other tedious tasks.
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“AI isn’t just taking jobs. It’s really rewriting the rule book on what work even looks like right now,” said Robert Lucido, senior director of strategic advisory at Magnit, a company based in Folsom, Calif., that helps tech giants and other businesses manage contractors, freelancers and other contingent workers.
Disruption debated
Exactly how big of a disruption AI will have on the job market is still being debated. Executives for OpenAI, the maker of popular chatbot ChatGPT, have pushed back against the prediction that a massive white-collar job bloodbath is coming.
“I do totally get not just the anxiety, but that there is going to be real pain here, in many cases,” said Sam Altman, chief executive of OpenAI, at an interview with “Hard Fork,” the tech podcast from the New York Times. ”In many more cases, though, I think we will find that the world is significantly underemployed. The world wants way more code than can get written right now.”
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As new economic policies, including those around tariffs, create more unease among businesses, companies are reining in costs while also being pickier about whom they hire.
“They’re trying to find what we call the purple unicorns rather than someone that they can ramp up and train,” Lucido said.
Before the 2022 launch of ChatGPT — a chatbot that can generate text, images, code and more —tech companies were already using AI to curate posts, flag offensive content and power virtual assistants. But the popularity and apparent superpowers of ChatGPT set off a fierce competition among tech companies to release even more powerful generative AI tools. They’re racing ahead, spending hundreds of billions of dollars on data centers , facilities that house computing equipment such as servers used to process the trove of information needed to train and maintain AI systems.
Economists and consultants have been trying to figure out how AI will affect engineers, lawyers, analysts and other professions. Some say the change won’t happen as soon as some tech executives expect.
“There have been many claims about new technologies displacing jobs, and although such displacement has occurred in the past, it tends to take longer than technologists typically expect,” economists for the U.S. Bureau of Labor Statistics said in a February report.
AI can help develop, test and write code, provide financial advice and sift through legal documents. The bureau, though, still projects that employment of software developers, financial advisors, aerospace engineers and lawyers will grow faster than the average for all occupations from 2023 to 2033. Companies will still need software developers to build AI tools for businesses or maintain AI systems.
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Worker bots
Tech executives have touted AI’s ability to write code. Meta Chief Executive Mark Zuckerberg has said that he thinks AI will be able to write code like a mid-level engineer in 2025. And Microsoft Chief Executive Satya Nadella has said that as much as 30% of the company’s code is written by AI.
Other roles could grow more slowly or shrink because of AI. The Bureau of Labor Statistics expects employment of paralegals and legal assistants to grow slower than the average for all occupations while roles for credit analysts, claims adjusters and insurance appraisers to decrease.
McKinsey Global Institute, the business and economics research arm of the global management consulting firm McKinsey & Co., predicts that by 2030 “activities that account for up to 30 percent of hours currently worked across the US economy could be automated.”
The institute expects that demand for science, technology, engineering and mathematics roles will grow in the United States and Europe but shrink for customer service and office support.
“A large part of that work involves skills, which are routine, predictable and can be easily done by machines,” said Anu Madgavkar, a partner with the McKinsey Global Institute.
Although generative AI fuels the potential for automation to eliminate jobs, AI can also enhance technical, creative, legal and business roles, the report said. There will be a lot of “noise and volatility” in hiring data, Madgavkar said, but what will separate the “winners and losers” is how people rethink their work flows and jobs themselves.
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Tech companies have announced 74,716 cuts from January to May, up 35% from the same period last year, according to a report from Challenger, Gray & Christmas, a firm that offers job search and career transition coaching.
Business AI a job killer? In California it’s complicated While the tech industry has been roiled by layoffs, the greater focus on AI could lead to new jobs in the future.
Tech companies say they’re reducing jobs for various reasons.
Autodesk, which makes software used by architects, designers and engineers, slashed 9% of its workforce, or 1,350 positions, this year. The San Francisco company cited geopolitical and macroeconomic factors along with its efforts to invest more heavily in AI as reasons for the cuts, according to a regulatory filing.
Other companies such as Oakland fintech company Block, which trimmed 8% of its workforce in March, told employees that the cuts were strategic not because they’re “replacing folks with AI.”
Diana Colella, executive vice president, entertainment and media solutions at Autodesk, said that it’s scary when people don’t know what their job will look like in a year. Still, she doesn’t think AI will replace humans or creativity but rather act as an assistant.
Companies are looking for more AI expertise. Autodesk found that mentions of AI in U.S. job listings surged in 2025 and some of the fastest-growing roles include AI engineer, AI content creator and AI solutions architect. The company partnered with analytics firm GlobalData to examine nearly 3 million job postings over two years across industries such as architecture, engineering and entertainment.
Workers have adapted to technology before. When the job of a door-to-door encyclopedia salesman was disrupted because of the rise of online search, those workers pivoted to selling other products, Colella said.
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“The skills are still key and important,” she said. “They just might be used for a different product or a different service.”
| 2025-07-05T00:00:00 |
2025/07/05
|
https://www.latimes.com/business/story/2025-07-05/workers-are-anxious-that-ai-will-take-their-jobs-amid-layoffs-how-worried-should-they-be
|
[
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{
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{
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{
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{
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},
{
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},
{
"date": "2025/07/05",
"position": 25,
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{
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},
{
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"query": "artificial intelligence workers"
}
] |
Using AI for Employment Purposes - SHRM
|
Using AI for Employment Purposes
|
https://www.shrm.org
|
[] |
Understand how artificial intelligence is being used in the workplace and the issues employers should be aware of.
|
Designed and delivered by HR experts to empower you with the knowledge and tools you need to drive lasting change in the workplace.
Demonstrate targeted competence and enhance credibility among peers and employers.
Gain a deeper understanding and develop critical skills.
| 2022-12-01T00:00:00 |
https://www.shrm.org/topics-tools/tools/toolkits/using-artificial-intelligence-employment-purposes
|
[
{
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"position": 26,
"query": "artificial intelligence employment"
},
{
"date": "2022/12/01",
"position": 25,
"query": "AI employers"
},
{
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"query": "AI employment"
},
{
"date": "2023/01/01",
"position": 25,
"query": "artificial intelligence employment"
},
{
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"query": "AI employers"
},
{
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},
{
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},
{
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},
{
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"query": "AI employers"
},
{
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"query": "AI employment"
},
{
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"query": "AI employment"
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{
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{
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{
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{
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},
{
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},
{
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{
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{
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{
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},
{
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},
{
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"query": "AI employers"
},
{
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},
{
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"query": "AI employers"
},
{
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{
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},
{
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{
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{
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},
{
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},
{
"date": "2024/12/01",
"position": 14,
"query": "artificial intelligence employers"
},
{
"date": "2025/01/01",
"position": 15,
"query": "AI employers"
},
{
"date": "2025/01/01",
"position": 11,
"query": "AI employment"
},
{
"date": "2025/02/01",
"position": 10,
"query": "AI employment"
},
{
"date": "2025/03/13",
"position": 5,
"query": "AI employers"
},
{
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"position": 11,
"query": "AI employment"
},
{
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"position": 40,
"query": "AI labor union"
},
{
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"query": "artificial intelligence employers"
},
{
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"position": 9,
"query": "artificial intelligence employment"
},
{
"date": "2025/03/13",
"position": 26,
"query": "artificial intelligence hiring"
},
{
"date": "2025/03/13",
"position": 44,
"query": "artificial intelligence workers"
},
{
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"query": "AI employers"
},
{
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"query": "artificial intelligence employers"
},
{
"date": "2025/05/01",
"position": 12,
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},
{
"date": "2025/05/01",
"position": 14,
"query": "artificial intelligence employment"
},
{
"date": "2025/06/01",
"position": 10,
"query": "AI employment"
}
] |
|
AI and the Workplace: Employment Considerations | Insights
|
AI and the Workplace: Employment Considerations
|
https://www.skadden.com
|
[
"David E. Schwartz",
"Anne Villanueva Jeffers",
"Emily D. Safko"
] |
An employer's use of AI must comply with existing employment laws, including the following that prohibit discrimination.
|
Key Points
As employers and employees are increasingly using AI in the workplace, government regulation is playing catch-up.
Existing EEO laws place some guardrails on an employer’s use of AI for traditional human resources tasks, and Illinois and New York City have enacted laws specifically regulating the use of AI in employment decisions.
Employers should monitor for updates and mitigate risks when using AI systems. That may entail training HR and other staff and, in some cases, commissioning audits of the impact of the systems.
The use of artificial intelligence (AI) in the workplace is growing exponentially. Companies are looking to harness AI to improve productivity and efficiency as AI’s capabilities continue to expand. In doing so, employers should be mindful of the evolving legal landscape surrounding the use of AI, including existing employment-related laws.
Artificial Intelligence
AI generally refers to a computer performing tasks historically done by a human. Generative AI refers to AI algorithms that create new content based on existing data. AI generally draws on existing data and practices that are used to train the technology. (See our Spring 2023 The Informed Board article “What Is Generative AI and How Does It Work?”)
In the workplace, AI can be used for many functions, including recruiting and hiring. It can streamline these processes by, for example, preparing job descriptions and analyzing internal employer data to best predict which applicants would be most successful at the company. AI systems can be used to analyze productivity, measure individual performance and select candidates for promotion.
AI proponents argue that AI reduces or eliminates human bias in screening and evaluating applicants and employees. However, because AI technologies generally draw on existing data and practices, some argue that use of AI for these functions may perpetuate any existing discriminatory practices.
In addition to human resources functions, employees are also using chatbots to augment their work. For example, employees can outsource time-consuming tasks such as drafting an email, writing code or doing research to an AI chatbot. AI proponents believe it will revolutionize the workforce and reduce — if not eliminate — the need for humans to perform certain tasks. But information provided by chatbots may be incorrect or outdated because chatbots survey and pull from troves of publicly available information, which may themselves be incorrect or outdated.
Chatbots also “hallucinate” or make things up. There are also privacy concerns associated with use of AI, as information (which may include confidential or proprietary information) entered into a company’s AI system might be inadvertently disclosed in the output given to a third party or, if such information is submitted in a query to a chatbot, it might be incorporated into the chatbot’s database and revealed in a subsequent response to an unrelated party’s query.
Existing Guardrails
An employer’s use of AI must comply with existing employment laws, including the following that prohibit discrimination:
Title VII of the Civil Rights Act of 1964 (Title VII) — on the basis of race, color, religion, sex or national origin.
Americans With Disabilities Act (ADA) — against individuals with disabilities.
Age Discrimination in Employment Act — against applicants and employees who are 40 years of age or older.
Similar state and local laws.
Both federal and state laws prohibit disparate treatment discrimination in the workplace (i.e., intentional discrimination of members of a protected class) and disparate impact discrimination (i.e., facially neutral practices or policies that may disproportionately affect members of a protected class).
To the extent an employer uses AI in connection with recruitment, hiring and promotion, there is a risk that the employer may run afoul of applicable anti-discrimination laws. For example, if the AI model bases hiring decisions on criteria such as ratings, pay and titles that skew in favor of white or male applicants, the AI system may screen for applicants meeting those characteristics.
There is also a risk that AI may make inferences based on an applicant’s religion, age, sexuality, genetic information or disability status learned from either the internet, social media or the applicant’s resume or interview. Though AI may not intentionally screen out or favor certain protected classes, AI’s choice could cause a discriminatory impact.
In the event that use of AI results in employee layoffs, employers should also ensure that they are mindful of anti-discrimination laws when selecting those who will be separated from employment. They must also pay close attention to the requirements of the federal Worker Adjustment and Retraining Notification and equivalent state and local laws, which govern notice obligations in connection with plant closings and mass layoffs.
Additionally, to the extent employers use AI in robotic systems or other machinery, they should be sure to comply with the Occupational Safety and Health Act and equivalent state laws and regulations, which require that employers provide a workplace free from recognized hazards.
New AI Legislation and Guidance
Increasingly, federal, state and local governments have considered legislation to supplement existing employment laws and taken other steps in response to the rapid increase in use of AI in connection with employment.
Federal Laws and Action
Although no comprehensive federal legislation has passed, there are several bills in Congress addressing different aspects of AI. For example, the Algorithmic Accountability Act of 2022 bill directs the Federal Trade Commission (FTC) to require entities to conduct impact assessments for bias, effectiveness and other factors when using automated decision systems to make critical decisions.
Additionally, the White House has released a Blueprint for an AI Bill of Rights and an AI Risk Management Framework, and President Joe Biden signed an executive order related to bias and discrimination with respect to the use of AI. (For more on the AI Risk Management Framework, see our May 18, 2023, client alert.)
On May 12, 2022, the Equal Employment Opportunity Commission (EEOC) issued technical guidance addressing the use of AI to assess job applicants and employees under the ADA. The guidance outlines several ways that utilizing AI tools can violate the ADA, including, for example, using AI to evaluate video interviews without providing notice and the opportunity for applicants to request a reasonable accommodation, in light of the potential for AI to negatively evaluate a candidate with a speech disorder.
On May 18, 2023, the EEOC issued technical guidance addressing the use of AI to assess job applicants and employees under Title VII. The guidance outlines how an employer’s use of AI tools can violate Title VII under a disparate impact analysis. It recommends that employers consider, among other things, whether the software has been evaluated to ensure it does not result in substantially lower selection rates for individuals with protected characteristics.
State and Local Laws
At the state level, Illinois enacted the Artificial Intelligence Video Interview Act in 2022, which imposes certain requirements on employers that analyze video interviews with AI. Many other states, including California, New Jersey, New York, Vermont and Washington, D.C., have proposed or are otherwise considering legislation to regulate AI use in hiring and promotion.
At the local level, New York City Local Law 144, which sets forth limitations and requirements for employers using automated employment decision tools (AEDTs) to screen candidates for hire or promotion, will be enforceable as of July 5, 2023.
The law prohibits use of AEDTs unless the tool has been the subject of an independent bias audit within the past year. Employers and employment agencies are also required to make public on their websites the date and a summary of the results of the most recent bias audit and the AEDT’s distribution date.
In addition, employers and employment agencies must provide a notice of the use of AEDTs to employees and candidates for employment who reside in New York City. The notice must include instructions for requesting an alternative selection process or reasonable accommodation under other laws.
Employers in violation will be liable for a civil penalty of up to $500 for a first-time violation and between $500 and $1,500 for successive violations.
Best Practices
Against this backdrop, employers should consider implementing policies and procedures governing employees’ use of AI at work. When using AI technologies for hiring, screening and promotion, employers should consider auditing the technology to ensure selection rates do not violate anti-discrimination laws, to the extent employers are not otherwise required by applicable law to conduct audits.
Employers should also consider clearly informing applicants about the type of technology that will be used and the information it is measuring. Training staff regarding their use of — and potential discriminatory issues associated with — AI tools and the need to verify the accuracy of any AI-generated content is also advisable.
See all of Skadden’s June 2023 Insights
This memorandum is provided by Skadden, Arps, Slate, Meagher & Flom LLP and its affiliates for educational and informational purposes only and is not intended and should not be construed as legal advice. This memorandum is considered advertising under applicable state laws.
| 2022-12-01T00:00:00 |
https://www.skadden.com/insights/publications/2023/06/quarterly-insights/ai-and-the-workplace
|
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Artificial intelligence in hiring - Wikipedia
|
Artificial intelligence in hiring
|
https://en.wikipedia.org
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[] |
Proponents of artificial intelligence in hiring claim it reduces bias, assists with finding qualified candidates, and frees up human resource workers' time for ...
|
AI application in work environments
Artificial intelligence can be used to automate aspects of the job recruitment process. Advances in artificial intelligence, such as the advent of machine learning and the growth of big data, enable AI to be utilized to recruit, screen, and predict the success of applicants.[1][2] Proponents of artificial intelligence in hiring claim it reduces bias, assists with finding qualified candidates, and frees up human resource workers' time for other tasks, while opponents worry that AI perpetuates inequalities in the workplace and will eliminate jobs. Despite the potential benefits, the ethical implications of AI in hiring remain a subject of debate, with concerns about algorithmic transparency, accountability, and the need for ongoing oversight to ensure fair and unbiased decision-making throughout the recruitment process.[3]
Background [ edit ]
Artificial intelligence has fascinated researchers since the term was coined in the mid-1950s.[4] Researchers[who?] have identified four main forms of intelligence that AI would need to possess to truly replace humans in the workplace: mechanical, analytical, intuitive, and empathetic.[5] Automation follows a predictable progression in which it will first be able to replace the mechanical tasks, then analytical tasks, then intuitive tasks, and finally empathy based tasks.[5] However, full automation is not the only potential outcome of AI advancements. Humans may instead work alongside machines, enhancing the effectiveness of both. In the hiring context, this means that AI has already replaced many basic human resource tasks in recruitment and screening, while freeing up time for human resource workers to do other more creative tasks that can not yet be automated or do not make fiscal sense to automate.[6] It also means that the type of jobs companies are recruiting and hiring form will continue to shift as the skillsets that are most valuable change.[7]
Human resources has been identified as one of the ten industries most affected by AI.[7] It is increasingly common for companies to use AI to automate aspects of their hiring process. The hospitality, finance, and tech industries in particular have incorporated AI into their hiring processes to significant extents.[8]
Human resources is fundamentally an industry based around making predictions.[9] Human resource specialists must predict which people would make quality candidates for a job, which marketing strategies would get those people to apply, which applicants would make the best employees, what kinds of compensation would get them to accept an offer, what is needed to retain an employee, which employees should be promoted, what a companies staffing needs, among others.[9] AI is particularly adept at prediction because it can analyze huge amounts of data. This enables AI to make insights many humans would miss and find connections between seemingly unrelated data points. This provides value to a company and has made it advantageous to use AI to automate or augment many human resource tasks.[9]
Uses [ edit ]
Screeners [ edit ]
Screeners are tests that allow companies to sift through a large applicant pool and extract applicants that have desirable features. Companies commonly screen through the use of questionnaires, coding tests, interviews, and resume analysis. Artificial Intelligence already plays a major role in the screening process. Resumes can be analyzed using AI for desirable characteristics, such as a certain amount of work experience or a relevant degree. Interviews can then be extended to applicant's whose resumes contain these characteristics.[9]
What factors are used to screen applicants is a concern to ethicists and civil rights activists. A screener that favors people who have similar characteristics to those already employed at a company may perpetuate inequalities. For example, if a company that is predominantly white and male uses its employees' data to train its screener it may accidentally create a screening process that favors white, male applicants. The automation of screeners also has the potential to reduce biases. Biases against applicants with African American sounding names have been shown in multiple studies.[10] An AI screener has the potential to limit human bias and error in the hiring process, allowing more minority applicants to be successful.[11]
Recruitment [ edit ]
Recruitment involves the identification of potential applicants and the marketing of positions. AI is commonly utilized in the recruitment process because it can help boost the number of qualified applicants for positions. Companies are able to use AI to target their marketing to applicants who are likely to be good fits for a position. This often involves the use of social media sites advertising tools, which rely on AI. Facebook allows advertisers to target ads based on demographics, location, interests, behavior, and connections. Facebook also allows companies to target a "look-a-like" audience, that is the company supplies Facebook with a data set, typically the company's current employees, and Facebook will target the ad to profiles that are similar to the profiles in the data set.[12] Additionally, job sites like Indeed, Glassdoor, and ZipRecruiter target job listings to applicants that have certain characteristics employers are looking for. Targeted advertising has many advantages for companies trying to recruit such being a more efficient use of resources, reaching a desired audience, and boosting qualified applicants. This has helped make it a mainstay in modern hiring.[12]
Who receives a targeted ad can be controversial. In hiring, the implications of targeted ads have to do with who is able to find out about and then apply to a position. Most targeted ad algorithms are proprietary information. Some platforms, like Facebook and Google, allow users to see why they were shown a specific ad, but users who do not receive the ad likely never know of its existence and also have no way of knowing why they were not shown the ad.[12]
Interviews [ edit ]
Chatbots were one of the first applications of AI and are commonly used in the hiring process. Interviewees interact with chatbots to answer interview questions, and their responses can then be analyzed by AI, providing prospective employers with a myriad of insights. Chatbots streamline the interview process and reduce the workload of human resource professionals.[13] Video interviews utilizing AI have become increasingly prevalent. Zappyhire, a recruitment automation startup, has developed a recruitment bot that ensures engagement with the most relevant candidates by leveraging AI-powered resume screening technology.[14] HireVue has created technology that analyzes interviewees' responses and gestures during recorded video interviews. Over 12 million interviewees have been screened by the more than 700 companies that utilize the service.[13]
Controversies [ edit ]
Artificial intelligence in hiring confers many benefits, but it also has some challenges which have concerned experts.[15] AI is only as good as the data it is using. Biases can inadvertently be baked into the data used in AI.[1] Often companies will use data from their employees to decide what people to recruit or hire. This can perpetuate bias and lead to more homogenous workforces. Facebook Ads was an example of a platform that created such controversy for allowing business owners to specify what type of employee they are looking for. For example, job advertisements for nursing and teach could be set such that only women of a specific age group would see the advertisements. Facebook Ads has since then removed this function from its platform, citing the potential problems with the function in perpetuating biases and stereotypes against minorities. The growing use of Artificial Intelligence-enabled hiring systems has become an important component of modern talent hiring, particularly through social networks such as LinkedIn and Facebook. However, data overflow embedded in the hiring systems, based on Natural Language Processing (NLP) methods, may result in unconscious gender bias. Utilizing data driven methods may mitigate some bias generated from these systems [16]
It can also be hard to quantify what makes a good employee.[1] This poses a challenge for training AI to predict which employees will be best. Commonly used metrics like performance reviews can be subjective and have been shown to favor white employees over black employees and men over women.[10] Another challenge is the limited amount of available data. Employers only collect certain details about candidates during the initial stages of the hiring process. This requires AI to make determinations about candidates with very limited information to go off of. Additionally, many employers do not hire employees frequently and so have limited firm specific data to go off.[1] To combat this, many firms will use algorithms and data from other firms in their industry.[1] AI's reliance on applicant and current employees personal data raises privacy issues. These issues effect both the applicants and current employees, but also may have implications for third parties who are linked through social media to applicants or current employees. For example, a sweep of someone's social media will also show their friends and people they have tagged in photos or posts.[1]
AI makes it easier for companies to search applicants social media accounts. A study conducted by Monash University found that 45% of hiring managers use social media to gain insight on applicants. Seventy percent of those surveyed said they had rejected an applicant because of things discovered on their applicant's social media, yet only 17% of hiring managers saw using social media in the hiring process as a violation of applicants privacy. Using social media in the hiring process is appealing to hiring managers because it offers them a less curated view of applicants lives. The privacy trade-off is significant. Social media profiles often reveal information about applicants that human resource departments are legally not allowed to require applicants to divulge like race, ability status, and sexual orientation.[17]
AI and the future of hiring [ edit ]
Artificial intelligence is changing the recruiting process by gradually replacing routine tasks performed by human recruiters. AI can reduce human involvement in hiring and reduce the human biases that hinder effective hiring decisions.[18] And some platforms such as TalAiro go further Talairo is an AI-powered Talent Impact Platform designed to optimize hiring for agencies and enterprises. It leverages patented AI models to match job descriptions with candidates, automate administrative tasks, and provide deep hiring insights, all in an effort to maximize business outcomes.
AI is changing the way work is done.[opinion] Artificial intelligence along with other technological advances such as improvements in robotics have placed 47% of jobs at risk of being eliminated in the near future.[19] Some classify the shifts in labor brought about by AI as a 4th industrial revolution, which they call Industrial Revolution 4.0.[7] According to some scholars, however, the transformative impact of AI on labor has been overstated. The "no-real-change" theory holds that an IT revolution has already occurred, but that the benefits of implementing new technologies does not outweigh the costs associated with adopting them. This theory claims that the result of the IT revolution is thus much less impactful than had originally been forecasted.[20] Other scholars refute this theory claiming that AI has already led to significant job loss for unskilled labor and that it will eliminate middle skill and high skill jobs in the future. This position is based around the idea that AI is not yet a technology of general use and that any potential 4th industrial revolution has not fully occurred.[20] A third theory holds that the effect of AI and other technological advances is too complicated to yet be understood. This theory is centered around the idea that while AI will likely eliminate jobs in the short term it will also likely increase the demand for other jobs. The question then becomes will the new jobs be accessible to people and will they emerge near when jobs are eliminated.[20]
Although robots can replace people to complete some tasks, there are still many tasks that cannot be done alone by robots that master artificial intelligence.[21] A study analyzed 2,000 work tasks in 800 different occupations globally, and concluded that half (totaling US$15 trillion in salaries) could be automated by adapting already existing technologies. Less than 5% of occupations could be fully automated and 60% have at least 30% automatable tasks.[22] In other words, in most cases, artificial intelligence is a tool rather than a substitute for labor. As artificial intelligence enters the field of human work, people have gradually discovered that artificial intelligence is incapable of unique tasks, and the advantage of human beings is to understand uniqueness and use tools rationally. At this time, human-machine reciprocal work came into being. Brandão discovers that people can form organic partnerships with machines. “Humans enable machines to do what they do best: doing repetitive tasks, analyzing significant volumes of data, and dealing with routine cases. Due to reciprocity, machines enable humans to have their potentialities "strengthened" for tasks such as resolving ambiguous information, exercising the judgment of difficult cases, and contacting dissatisfied clients.”[23] Daugherty and Wilson have observed successful new types of human-computer interaction in occupations and tasks in various fields.[24] In other words, even in activities and capabilities that are considered simpler, new technologies will not pose an imminent danger to workers. As far as General Electric is concerned, buyers of it and its equipment will always need maintenance workers. Entrepreneurs need these workers to work well with new systems that can integrate their skills with advanced technologies in novel ways.
Artificial intelligence has sped up the hiring process considerably, dramatically reducing costs.[opinion] For example, Unilever has reviewed over 250,000 applications using AI and reduced its hiring process from 4 months to 4 weeks. This saved the company 50,000 hours of labor.[13] The increased efficiency AI promises has sped up its adoption by human resource departments globally.[13]
Regulations on AI in hiring [ edit ]
The Artificial Intelligence Video Interview Act, effective in Illinois since 2020, regulates the use of AI to analyze and evaluate job applicants’ video interviews.[25] This law requires employers to follow guidelines to avoid any issues regarding using AI in the hiring process.[26]
| 2022-12-01T00:00:00 |
https://en.wikipedia.org/wiki/Artificial_intelligence_in_hiring
|
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{
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{
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{
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] |
|
Artificial intelligence | International Labour Organization
|
Artificial intelligence
|
https://www.ilo.org
|
[] |
There are two distinct types of application of AI technology in the workplace. The first is directed at automating tasks that workers perform.
|
In the world of work, there are two distinct types of application of AI technology in the workplace. The first is directed at automating tasks that workers perform; the second is to use AI-based analytics and algorithms to automate managerial functions – or what is commonly referred to as “algorithmic management”.
When AI is used to automate tasks, it doesn’t necessarily lead to redundancies, as the technology can also complement human labour when certain tasks are automated. Whether technological adoption leads to automation (job loss) or augmentation (job complementarity) depends on the centrality of the automated task to the occupation, how the technology is integrated into work processes and management’s desire to retain humans to perform or oversee some of the tasks, despite automation’s potential. As AI transforms occupations, a workforce equipped with necessary skills in machine learning, data science, and AI ethics is crucial for harnessing its potential.
In addition to the potential effects on workers, AI’s integration into the workplace can also have consequences for organizational performance, including productivity, with spillover effects on economic performance. For this reason, unequal access to the technology stemming from infrastructure bottlenecks, skill deficiencies or simply the cost of the technology can widen existing productivity divides between countries as well as between large and small or micro enterprises.
| 2024-04-23T00:00:00 |
2024/04/23
|
https://www.ilo.org/artificial-intelligence
|
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Top 65 Jobs Safest from AI & Robot Automation - U.S. Career Institute
|
The 65 Jobs With the Lowest Risk of Automation by Artificial Intelligence and Robots
|
https://www.uscareerinstitute.edu
|
[] |
The jobs safest from AI and automation are ones that require human qualities that a robot cannot replicate, such as social skills, emotional intelligence, and ...
|
By: U.S. Career Institute
February 2023
The age of artificial intelligence and automated robots is here, and with it comes many advancements for society. But the main concern on many people’s minds as we enter the age of automation led by robots and artificial intelligence (AI) is “Will AI take my job?” When selecting a career path, it’s important to think about what the future looks like for that field. As we begin to see more robots taking over jobs, it will become increasingly important to choose a career path that has a low risk of being automated in the future. If that career path is projected to grow over the next decade, even better!
Now, the question is, which jobs are the safest from artificial intelligence and robots? Using results that Will Robots Take My Job? generated with automation risk data, U.S. Career Institute created a chart of the 65 jobs that are the least likely to be replaced by robotic automation. The following 65 occupations were all determined to have a job automation risk probability of 0.0% based on the abilities, knowledge, skills, and activities that are required to perform the job well. With the same low risk of automation, they are ranked in order of their projected growth by 2032 to determine which occupations will continue to thrive through the age of AI and robotics.
Click on the image to view full size
Which Jobs Are Safest from AI and Automation?
The jobs safest from AI and automation are ones that require human qualities that a robot cannot replicate, such as social skills, emotional intelligence, and interpersonal relationships. Fields that are more creative and don’t stick to a rigid routine will also lower the risk of AI replacing jobs in that field. The most common jobs found to have a low risk of automation are jobs in the medical field, as they are complex and require flexibility; medical situations can be unpredictable. Jobs least likely to be affected by automation are commonly found in the following fields:
Health Care: Nurses, doctors, therapists, and counselors
Education: Teachers, instructors, and school administrators
Creative: Musicians, artists, writers, and journalists
Personal Services: Hairdressers, cosmetologists, personal trainers, and coaches
Which AI-Proof Job Is Projected to Grow the Most?
Of the list of AI-proof jobs, nurse practitioners are projected to grow the most, with an estimated increase of 45.7% by 2032, far faster than any of the other 64 jobs on the list. It’s a great option for a stable career in the medical field, but the path to becoming a nurse practitioner can be long, as they are required to earn a master’s or doctoral degree in nursing. It is a lucrative career choice, though, with a median annual wage of $120,680. Why are nurse practitioners less likely to be affected by AI automation? This type of job includes assisting and caring for others as well as persuasion, negotiation, and social perceptiveness, all qualities that would be very difficult to replicate with a robot or artificial intelligence.
Other jobs that are estimated to grow the most over the next decade include choreographers, with a projected growth of 29.7% by 2032, followed by physicians’ assistants, with a projected growth of 27.6%. A career as a choreographer requires creativity, originality, and social skills, all qualities that AI and robots cannot easily replicate. A job as a physician’s assistant requires similar skills as that of a nurse practitioner, making this a job with low risk of robot automation.
The 10 AI-Proof Jobs With the Highest Projected Growth by 2032
Nurse Practitioners: 45.7% Choreographers: 29.7% Physician Assistants: 27.6% Mental Health Counselors: 22.1% Nursing Instructors and Teachers, Post-Secondary: 21.5% Coaches and Scouts: 20% Athletic Trainers: 17.5% Physical Therapists: 16.9% Orthotists and Prosthetists: 16.8% Occupational Therapists: 13.9%
While there are many benefits of artificial intelligence and robots automating certain tasks to make them more efficient, one of the main drawbacks is how the future of artificial intelligence will affect the job market and the millions of people working jobs at risk of automation by robotics and AI. Automate-proof your future by choosing a career path that requires skills that cannot be easily automated by a robot or artificial intelligence. One way to stay ahead of job automation is to continue educating yourself and learning new, valuable skills that will allow you to to remain competitive in the job market for years to come.
The 65 AI-Proof Jobs and How Much They Are Projected to Grow
| 2022-12-01T00:00:00 |
https://www.uscareerinstitute.edu/blog/65-jobs-with-the-lowest-risk-of-automation-by-ai-and-robots
|
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|
JFFLabs Artificial Intelligence - Jobs for the Future (JFF)
|
JFFLabs Artificial Intelligence
|
https://www.jff.org
|
[] |
Center for Artificial Intelligence & the Future of Work. Shaping AI's impact on skills development, job quality, and equitable outcomes.
|
The explosive growth of artificial intelligence is reshaping how we learn, work, and live. The implications are profound for job creation, economic mobility, skill development, and more. We must act now to understand and influence the rapid evolution of AI adoption to drive equitable outcomes and ensure job quality.
The Center for Artificial Intelligence & the Future of Work incubated at JFFLabs will convene stakeholders across sectors to shape the national dialogue on AI and the future of work and learning. Leading with discovery, design, and action, this work will ensure AI accelerates access to quality jobs by shaping policy, practice, and investment in innovative solutions.
| 2022-12-01T00:00:00 |
https://www.jff.org/work/jff-labs/jfflabs-incubation/jfflabs-artificial-intelligence/
|
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"query": "AI workforce transformation"
},
{
"date": "2023/09/01",
"position": 78,
"query": "artificial intelligence employers"
},
{
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"query": "artificial intelligence employment"
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{
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"query": "artificial intelligence workers"
},
{
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{
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{
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},
{
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"position": 35,
"query": "AI impact jobs"
},
{
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"position": 100,
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{
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{
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"query": "artificial intelligence workers"
},
{
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"position": 31,
"query": "AI impact jobs"
},
{
"date": "2023/12/01",
"position": 93,
"query": "AI workforce transformation"
},
{
"date": "2023/12/01",
"position": 78,
"query": "artificial intelligence employers"
},
{
"date": "2024/01/01",
"position": 88,
"query": "AI workforce transformation"
},
{
"date": "2024/01/01",
"position": 62,
"query": "artificial intelligence workers"
},
{
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"position": 33,
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},
{
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"position": 98,
"query": "AI workforce transformation"
},
{
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"position": 39,
"query": "artificial intelligence employment"
},
{
"date": "2024/03/01",
"position": 100,
"query": "AI workforce transformation"
},
{
"date": "2024/03/01",
"position": 76,
"query": "artificial intelligence employers"
},
{
"date": "2024/03/01",
"position": 39,
"query": "artificial intelligence employment"
},
{
"date": "2024/03/01",
"position": 51,
"query": "artificial intelligence workers"
},
{
"date": "2024/03/01",
"position": 33,
"query": "future of work AI"
},
{
"date": "2024/04/01",
"position": 36,
"query": "AI impact jobs"
},
{
"date": "2024/05/01",
"position": 35,
"query": "AI impact jobs"
},
{
"date": "2024/06/01",
"position": 35,
"query": "artificial intelligence employment"
},
{
"date": "2024/07/01",
"position": 96,
"query": "artificial intelligence employers"
},
{
"date": "2024/07/01",
"position": 68,
"query": "artificial intelligence workers"
},
{
"date": "2024/07/01",
"position": 32,
"query": "future of work AI"
},
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"date": "2024/08/01",
"position": 83,
"query": "AI workforce transformation"
},
{
"date": "2024/09/01",
"position": 35,
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},
{
"date": "2024/09/01",
"position": 89,
"query": "AI workforce transformation"
},
{
"date": "2024/09/01",
"position": 34,
"query": "artificial intelligence employment"
},
{
"date": "2024/10/01",
"position": 35,
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{
"date": "2024/10/01",
"position": 90,
"query": "AI workforce transformation"
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{
"date": "2024/10/01",
"position": 37,
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{
"date": "2024/11/01",
"position": 34,
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"date": "2024/11/01",
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"date": "2024/11/01",
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"date": "2024/12/01",
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{
"date": "2025/01/01",
"position": 95,
"query": "AI workforce transformation"
},
{
"date": "2025/03/01",
"position": 89,
"query": "AI workforce transformation"
},
{
"date": "2025/03/01",
"position": 76,
"query": "artificial intelligence employers"
},
{
"date": "2025/03/01",
"position": 32,
"query": "artificial intelligence employment"
},
{
"date": "2025/04/01",
"position": 75,
"query": "artificial intelligence employers"
},
{
"date": "2025/05/01",
"position": 35,
"query": "AI impact jobs"
},
{
"date": "2025/05/01",
"position": 36,
"query": "artificial intelligence employment"
},
{
"date": "2025/05/01",
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"query": "artificial intelligence workers"
},
{
"date": "2025/06/01",
"position": 72,
"query": "artificial intelligence workers"
}
] |
|
ai usajobs
|
USAJOBS
|
https://ai.usajobs.gov
|
[] |
The US Government is hiring talent to ensure the US leads in safe, secure, and trustworthy AI innovation to harness the opportunities of AI.
|
You have reached the maximum number of saved jobs allowed (25). If you would like to save another job, you will need to go to your profile and remove a saved job first.
We want to help you find the right job. Try entering a keyword or location, or use the filters.
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| 2022-12-01T00:00:00 |
https://ai.usajobs.gov/
|
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|
Jobs That Are Growing With the Rise of AI - PartnerStack
|
Jobs That Are Growing With the Rise of AI
|
https://partnerstack.com
|
[] |
According to the US Bureau of Labor Statistics, careers in artificial intelligence are projected to grow 21 per cent from 2021 to 2031.
|
After bouncing back from a hard year in 2023, tech is on the rise. AI in specific has seen huge growth in adoption. According to a McKinsey report on The State of AI, adoption rates are up to 72 per cent of respondents citing that their organization are using AI.
If we follow the money further, it’s clear that AI is one of the fastest-growing technology fields and with that also comes increased demand in the artificial intelligence job market. The McKinsey report also finds that organizations are likely to invest more than 5 per cent of their digital budgets in generative AI and analytical AI
AI is being used in a wide range of industries, from healthcare to finance, so traditional companies are investing in the technology, creating roles for people with expertise in generative AI, machine learning, natural language processing, data science, software development and other AI-related areas. AI is also a helpful tool for automating time-consuming tasks so you can get back valuable time to focus on growth and revenue-driving activities, like implementing new partnership strategies.
In this article we will:
Share some of the career fields and roles that are growing with the increased demand for AI technology
Plus, we'll share their current salary bands in USD
Let's get started.
Which jobs will increase in demand with AI?
While many people are still focusing on potential job losses, AI job growth is reaching unprecedented heights. According to the US Bureau of Labor Statistics, careers in artificial intelligence are projected to grow 21 per cent from 2021 to 2031.
Companies aren’t necessarily using advancements in AI to replace people, but rather to improve workplace efficiency and productivity by automating processes, allowing their current employees to work smarter, not harder.
In the first half of 2024 alone, AI companies received 41 per cent of US VS deal value, according to PitchBook. Deals have grown on average and funding is still happening — and it seems that venture capital and other investors are placing their bets on generative AI. With funding comes opportunity — and as AI technology continues to evolve, here are ten technology-based occupations where demand is growing.
Related: How to talk to partners and prospects without AI chatbots.
Machine learning engineer
One of the most essential roles in the development of AI is the machine learning engineer. As companies seek ways to automate processes, increase efficiency and gain a competitive advantage, this role is responsible for designing and developing artificial intelligence and machine learning systems that can learn and adapt on their own.
Machine learning engineers create algorithms, build and manage data pipelines and deploy models. A strong background in computer science, statistics and mathematics along with the knowledge of programming languages like Python and Java are integral to this job. They should also have experience with data manipulation, analysis, and visualization.
Average salary: $103,000 to $251,000 USD annually
Natural language processing (NLP) scientist
While everyone has heard of ChatGPT (Chat Generative Pre-Trained Transformer) by OpenAI — this large language model-based chatbot has also become people’s first exposure to the work of a natural language processing scientist. Along with machine learning job growth, the demand for NLP scientists is increasing. An NLP scientist plays the important role of developing technology that allows computers to better understand, interpret, generate and interact with human language.
They specialize in developing computer programs and algorithms using statistical and machine learning techniques to understand human language data like text, speech, and audio which can then be used for automated translation, speech recognition, sentiment analysis, and chatbot applications. They work with software engineers, data scientists, and linguists to implement these systems while also staying up to date on advancements in this rapidly changing field.
Average salary: $154,000 to $253,000 USD annually
See more: Here's how automation saves you time and money.
Data scientist
Working closely with machine learning engineers and software developers, data scientists play a key role in the development and improvement of AI technology. Their role is to collect, analyze and interpret complex sets of data by using both statistical and mathematical models to identify patterns and trends in the data and programming languages like Python and SQL.
They design experiments and collect data that is used to train and test AI models so they can learn, become more accurate, reliable and effective. Data scientists take all that data and knowledge and recommend improvements to advance the technology forward.
Average salary: $77,000 to $198,000 USD annually
Business intelligence (BI) developer
When companies want to make data-driven decisions, they often look to a business intelligence developer, making this a key role to fill on the AI job market. BI developers help in designing and building the infrastructure needed for AI systems to function, including the implementation of data warehouses and other data storage systems.
These are key for training and operating AI algorithms. BI developers may also be responsible for the integration systems that ensure the data is of the highest quality possible so it can be used most effectively by AI algorithms. These are individuals with excellent communication and problem-solving skills as well as a background in data analysis, programming, and statistics.
Average salary: $71,000 to $145,000 USD annually
Director of Marketing
In the B2B SaaS industry, a director of marketing is a vital part of a company's success, bringing a deep understanding of both the technology and the target market, all while managing a team of marketing professionals. It is this person's responsibility to oversee all marketing activities related to the SaaS, including AI products, and to develop and implement marketing strategies to promote them.
They work with product development teams to ensure that the company's marketing strategy describes a product's features and capabilities clearly. Furthermore, they analyze market trends, track their competitors, and research the needs of their customers, implementing marketing strategies and partnerships to support the scale of their ecosystem.
Average salary: From $61,000 to $184,000 USD annually
Human-centered machine learning designer
To understand how people interact with technology, a human-centered machine learning designer is the person to consult. Specialists in the design and development of machine learning systems that focus on the needs of human users, these professionals work closely with data scientists, UX/UI designers and product managers.
They identify user needs and create solutions by ensuring AI technology is accurate, efficient, intuitive and user-friendly. Like UX/UI designers, they use a combination of user research, prototyping and usability testing in their work in order to ensure that machine learning systems enhance human capabilities rather than replace them.
Average salary: $90,000 to $120,000 USD annually
Did you know? Cloud 100 companies lean into partner-led GTM strategies more than the SaaS average.
Software engineer
Software engineers are critical in any AI development. They are responsible for designing, developing, and testing the software that powers AI systems. Algorithms, programming languages and software work together so that machines learn and make decisions based on data patterns and a software engineer creates the infrastructure that fuels AI technology. They write code that can absorb large amounts of data, interpret the data, and produce actionable results. They also design and implement algorithms that allow machines to learn and adapt, perform QA testing, debug code and look at data privacy and protection considerations across all kinds of SaaS companies.
Average salary: From $100,000 USD
You might also like: Why ChatGPT won’t replace your job (and how to use it to your advantage).
Software developer
AI systems would not exist without the software developers who design, code, and test the algorithms that make the technology possible. A software developer works with data scientists, AI researchers, and other key players to create systems that can perform intricate tasks like natural language processing, image recognition and decision-making.
They are also responsible for ensuring that AI systems are reliable, secure and scalable by performing tests that identify any bugs and ensuring that the systems can sustain large volumes of data and traffic. Their knowledge and expertise are vital to creating AI that will transform the B2B SaaS industry.
Average salary: $66,000 to $167,000 USD annually
Computational linguist
When it comes to AI job growth, demand is increasing for computational linguists. In a field that merges computer science and linguistics, a computational linguist develops computer algorithms and models that process and analyze human language to create AI systems that can understand and generate human language.
This is used for speech recognition and machine translation applications. Alongside NLP scientists, they design and implement natural language processing systems, develop machine learning algorithms for language processing and analyze linguistic data to identify patterns and trends. Some of the projects they work on include customer service chatbots and speech recognition software for virtual assistants, which are essential automation technologies for many SaaS companies.
Average salary: $101,000 to $130,000 USD annually
See more: Top growth channel tactics for summer 2023.
Product Manager
Product managers oversee the entire product lifecycle from conception to launch and beyond. This person evaluates go-to-market opportunities and customer needs that can be solved using AI and software. They work closely with engineers and data scientists to ensure the product is technically sound before it reaches the customer.
It is imperative that a product manager has a deep understanding of the technology behind the products in development and is able to communicate effectively across the business to ensure that the product aligns with the company's goals. Innovative and strategic thinkers, they can identify emerging trends or technologies that can be applied to the creation of new products or improvements of existing ones.
Average salary: $76,000to $190,000 USD annually
| 2022-12-01T00:00:00 |
https://partnerstack.com/articles/ai-job-growth
|
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{
"date": "2023/08/03",
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"query": "future of work AI"
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{
"date": "2023/08/03",
"position": 9,
"query": "generative AI jobs"
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{
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|
What Jobs Will AI Replace? | Built In
|
What Jobs Will AI Replace?
|
https://builtin.com
|
[] |
11 Jobs AI Will Replace · 1. Customer Service Representative · 2. Car and Truck Driver · 3. Computer Programmer · 4. Research Analyst · 5. Paralegal · 6.
|
Artificial intelligence (AI) is transforming the workforce, and not always for the better. As companies continue to adopt AI to boost productivity, many are also cutting human jobs and replacing them with tools like ChatGPT or GitHub Copilot.
Analysts at Goldman Sachs estimate that AI could replace two-thirds of all occupations, however, the new technology is also creating several new jobs, potentially easing the transition for affected workers.
Will Artificial Intelligence (AI) Replace Jobs? AI is replacing and will continue to replace some jobs. Workers in industries ranging from healthcare to agriculture and industrial sectors can all expect to see disruptions in hiring due to AI. But demand for workers in other sectors is also expected to rise, thanks to AI.
Some experts and industry insiders, however, aren’t too worried about the rise of AI and remain unimpressed with the technology. While discussing AI models with the BBC, Nick Clegg, Meta’s former president of global affairs, went so far as to say, “In many ways, they’re quite stupid.” Even OpenAI founder Sam Altman believes current AI tools are “wildly overhyped.”
It isn’t that the machines aren’t rising. It’s that they’re rising much more slowly than some of the more breathless media coverage might have you believe — which is great news for most of those who think AI-powered technology will soon steal their jobs.
“Most of” being the operative words.
11 Jobs AI Will Replace
The consensus among many experts is that a number of professions will be totally automated in the next five to 10 years. Below are a few roles that are at risk of being taken over by AI in the near future.
1. Customer Service Representative
The customer service role is becoming more automated, as tools like chatbots and virtual assistants handle a broader range of customer inquiries and requests. Advancements like self-checkouts also make human workers less relevant in settings like grocery stores, reducing the number of jobs within the customer service industry.
2. Car and Truck Driver
Improvements in autonomous vehicles reduce the need for human drivers, impacting those in both the taxi and rideshare industries. In fact, Uber has partnered with self-driving car companies like Waymo and Aurora to give its riders more options, potentially setting up a conflict with its human drivers.
3. Computer Programmer
Generative AI tools like ChatGPT and Gemini have raised questions about whether AI will replace writing-heavy jobs. While human language requires much more creativity and nuance, programming consists of more structured, straightforward language. ChatGPT can already be used to write code, so entry-level programming jobs may see reduction.
4. Research Analyst
Research-centric positions like market research analyst and financial analyst can be performed, at least to a degree, by AI. Machine learning has the ability to process large volumes of data, detect patterns and organize its findings into convenient visuals. This makes AI ideal for quickly delivering industry insights to teams without the need for human intervention.
5. Paralegal
Many administrative tasks completed by paralegals are within the range of AI’s capabilities. With its ability to process massive amounts of complex data, AI can write legal reports, gather facts for a case, sort through documents and conduct legal research. This promises greater efficiency for legal firms, perhaps one day rendering the paralegal role obsolete.
6. Factory or Warehouse Worker
AI powers lots of machines in factories, performing many actions with greater speed and consistency than humans. Additionally, machine vision enables AI-powered machines in warehouses to retrieve goods and navigate their surroundings, making logistics providers less dependent on human warehouse workers.
7. Financial Trader
Financial traders are in charge of analyzing markets and informing investors’ decisions, but AI can work through this process much faster. AI trading technologies can also predict market trends while remaining more accurate than human workers. These two factors combined with the fact that AI lowers hiring costs mean financial trader roles could disappear soon.
8. Travel Advisor
Travelers no longer need to rely on travel agents for personalized recommendations and travel tips. Travel platforms can leverage AI to power customer searches and make suggestions based on previous searches. Experiences like virtual tours and online informational videos also allow travelers to gather the information they want without turning to a travel agency.
9. Content Writer
AI content generators can already help brainstorm writing ideas and assist with repetitive content creation. Basic content marketing tasks like writing formulaic emails and short social media posts are no trouble for artificial intelligence. In some cases, AI can even produce a first draft for longer-form content, taking over many duties for human content marketers.
10. Graphic Designer
Graphic designers are in direct competition with AI-generated art, especially since the ability to produce this kind of art is available to the public. Tools like Lensa and DALL-E make it easy to create professional images without artistic expertise. As a result, businesses and individuals may depend less on graphic design services to produce eye-catching visuals moving forward.
11. Data Entry Clerk
Data entry clerks are in charge of entering, updating and maintaining information on databases and internal systems. However, AI systems have shown they’re capable of accurately processing massive amounts of structured and unstructured data, and at faster speeds than humans. Consulting firm, McKinsey & Co. estimate that AI could affect 38 percent of business process and data entry jobs.
9 Jobs AI Won’t Replace
We can take comfort in the fact that some jobs are less likely to be replaced by AI. The jobs that AI won’t be able to replicate range from creative fields to empathic jobs, as well as complex strategic jobs.
1. Teacher
AI-powered tools have made gains in the classroom, guiding small-group interactions and helping children grow their socio-emotional skills. But AI can’t build the trust and intimacy that human teachers are able to have with their students. Human teachers may also be more equipped to resolve arguments, reach out to students’ parents and handle other complex social interactions.
2. Nurse
Simple healthcare tasks like transporting medical supplies and retrieving patient data may fall to artificial intelligence. It’s the face-to-face interactions where a human touch is essential. Providing bedside care, having hard conversations with families and assuaging the fears of patients are all situations where nurses and other health workers may be preferred over AI.
3. Social Worker
Social workers probably won’t be replaced by AI anytime soon. The work they engage in — often with people from underserved or at-risk populations — requires a human touch and judgment. Understanding people’s unique circumstances and helping them navigate stressful situations are areas humans may be better prepared for than artificial intelligence.
4. Therapist
Therapists perform a great deal of emotional labor, listening to people’s problems and guiding them as they work through their feelings, thoughts and emotional responses. AI doesn’t have the ability to grasp this aspect of humans to such a degree. And with a mental health crisis on the rise, human-led therapy is more crucial than ever to aid those who feel stuck or isolated.
5. Handyperson
People who work in the trades, like plumbers and electricians, often have to perform a range of manual labor and handle more in-depth human interactions — two things AI doesn’t excel at. Plumbers, for example, have to demonstrate excellent eye-hand coordination to handle different appliances while displaying the soft skills needed to work with residents to resolve issues.
6. Lawyer
Although AI has become a major part of the legal industry, it’s unlikely to replace lawyers any time soon. Lawyers are expected to possess a strong grasp of morals and ethics, relying on this knowledge to inform their legal advice. AI giving legal advice raises many ethical questions since AI doesn’t have the same sense of ‘right’ and ‘wrong’ as humans.
7. HR Specialist
HR specialists oversee areas like recruiting, interviewing and onboarding — all processes that require high levels of personalized, human interactions. AI might be useful for screening resumes, but it may not be able to offer the kind of sensitivity and thoughtfulness required to navigate situations like layoffs, private questions about benefits and employee complaints.
8. Copywriter, UX Writer and Technical Writer
Despite enjoying a boom in popularity, AI writing tools struggle to match the quality and creativity of talented human writers in some cases. Copywriters, UX writers and technical writers alike routinely exercise critical thinking in their work, such as making decisions based on audience preferences and needs. While AI tools can assist with generating ideas, the quirks of writing and human language are much harder to master.
9. Artist
AI art generators are skilled at producing high-quality pieces, but these pieces are only based on artworks and styles that already exist. Human artists are the ones who develop new styles and ideas that drive innovation within the artistic landscape. The livelihoods of artists will still be impacted by AI, but artistic originality resides with humans.
Benefits of AI in the Workplace
Despite fears of job loss due to automation, AI does offer major advantages to companies and workers who embrace the technology.
More Focused and Engaging Tasks
Among AI’s biggest boons, many experts believe, is its ability to save humans from having to perform tedious repetitive tasks that are part of their overall duties so they’re free to focus on more complex and rewarding projects — or just take some much-needed time off.
“There’s always a concern that technology is displacing this current body of workers or tasks, and that’s true,” Sean Chou, co-founder of AI startup Catalytic, said. “But what always happens is that work, and that output, gets redirected to things that are much more productive.”
More Efficient and Shorter Workdays
Some think increased productivity and efficiency might even shorten the work week. Which seems good in theory but comes with its own set of issues. How will pay and benefits be affected? And who reaps the bulk of monetary rewards? Those remain unanswered questions.
“Up to this point, technology has created more work because it’s another thing you have to deal with,” said Justin Adams, former CEO at Digitize.AI and vice president at its parent company Waystar. “But I think there’s an inflection point where certain AI will get to a place where that actually flips.”
More Informed Decision-Making
Because AI and machine learning can gather and process large volumes of data, human workers can more quickly access data-based insights and understand the meaning behind trends and patterns. This takes the guesswork out of important decisions, ensuring employees rely on data-driven discoveries to help them make accurate decisions for their teams and businesses.
Increased Innovation and Problem-Solving
With AI taking care of redundant and mundane tasks, humans can dedicate their energy to addressing more complicated business challenges. AI-based tools like ChatGPT can also take on a collaborative role, allowing humans to bounce ideas off of them. As a result, AI can expand people’s capacity to solve problems and serve as collaborators that spur innovative approaches to lingering business issues.
More Personalized Customer Interactions
Employees can use AI technologies to gather data on users’ online behavior and save information on customer preferences. These abilities enable teams to tailor products and services to customers’ needs and cultivate more personalized, higher-quality customer interactions. By enhancing the customer experience, employees can help improve their company’s reputation and profitability.
How AI Will Create Jobs
The development of AI itself requires many humans to train and refine AI algorithms. This leads to the creation of roles that haven’t existed until now. Machine learning engineers must design and oversee AI systems while AI ethics specialists ensure AI is deployed responsibly.
Chou confirms the necessity for human workers. “The number of people that are necessary to deliver better and better technology grows massively,” Chou said. “When you look at AI, there’s this nonstop need for training, for data, for maintenance, for taking care of all the exceptions that are happening. How do we monitor AI? How do we train it? How do we make sure that AI’s not running amok? Those are all going to become new jobs.”
Rather than completely destroying jobs, AI is shifting jobs and changing the type of work that professionals do. It’s the kind of impact that the internet had upon its introduction. Chris Nicholson, CEO of machine learning company Skymind.AI, shares a similar view rooted in even more distant history.
“Everybody uses this analogy, but when the Industrial Revolution came, a certain kind of job disappeared,” Nicholson said. “But many jobs, and many [new] jobs, were created. So when you think about, say, England before and after the Industrial Revolution, it wasn’t a poorer place where there was less work. There was a lot more work, but it was a different kind of work.”
What Jobs Will AI Create?
While a number of jobs are AI-proof, the new technology will also create several new opportunities, especially for early adopters and individuals with tech skills. Below are a few new careers already created by AI.
1. Prompt Engineer
Prompt engineering is one of the most talked about AI-created jobs. Individuals in this type of role optimize text or code-based inputs to achieve their desired output on chatbot platforms. The process involves developing questions and utilizing prompt techniques to achieve a specific generative AI output. Their end goal is to feed proprietary models with useful data and context to improve their generation process.
2. AI Ethics Specialist
AI ethicists ensure that companies and their systems are fair and aligned with human values and rights. These individuals work throughout the development lifecycle and create policies for safe AI use and work alongside data scientists, developers and product managers to ensure data transparency and mitigate any potential risks that may arise from an AI platform.
3. Health Tech Implementation Specialist
As AI continues to expand in the healthcare industry, many large organizations are turning to health tech implementation specialists to adapt. Implementation specialists are like product managers in healthcare and help organizations implement new medical AI products or develop proprietary platforms.
4. AI Literacy Trainer
AI literacy trainers are knowledgeable educators who teach the fundamentals of artificial intelligence and help individuals understand functional and practical uses in the workforce. They provide curricula through in-person or virtual workshops and guide participants through hands-on training.
How to Prepare for AI in the Workplace
Amazon announced in 2021 that it would retrain 300,000 employees to the tune of $1.2 billion. Participation is voluntary in a program the company calls “Upskilling 2025,” which is designed to teach employees skills they can apply to work in technical roles inside or outside of Amazon. More cynical observers might chalk that up to an expensive public relations campaign in light of less-than-flattering reports about how the company allegedly treats its workers.
Skills to Cultivate in the Age of AI Basic mathematics
Strong verbal and written communication
Creativity
People management
Emotional intelligence
Critical thinking and problem-solving
Besides that, retraining warehouses for highly technical roles like engineering may be a significant challenge. Which isn’t to say there’s no value in additional education.
“I think that we should be trying to get people to understand a little bit about a lot of things so the jump is not very large and the opportunities come,” said Dan Platt, senior principal of market innovation at AI company Narrative Science (acquired by Salesforce). “You’re not going to train everybody to write in Python, but if you have people that are trained to understand the basics of engineering, or how things work, their chances [of not being displaced] are a lot higher.”
For Nicholson, surviving and thriving in an increasingly AI-powered world requires a multi-pronged approach. First and foremost, he advises, “Avoid bullshit jobs. If you’re bored in your job, it’s probably a bullshit job and the machines will probably eat it.”
Basic skills can be a crucial asset as well. For example, having solid verbal and written communication like listening, reading emotions, asking questions, writing clearly and structuring cogent arguments devoid of ambiguity.
It’s also a good idea, Nicholson says, to cultivate a decent understanding of statistical concepts, calculus and algebraic linear regression in order to comprehend the “output of AI algorithms.” Arming oneself with that sort of foundational knowledge is key to “being able to adapt.”
“People like to compare AI to electricity,” Chou said. “And I actually agree with that analogy. But electricity took one or two generations to go from idea to widespread adoption, whereas today we’re seeing the impact of technology occur much faster.”
Frequently Asked Questions
What jobs will AI replace? AI will replace both blue-collar and white-collar jobs that involve more straightforward and repetitive tasks. These jobs include drivers, factory workers, administrative assistants, paralegals and some copywriters.
What jobs are safe from AI? Jobs that involve more complex tasks and human interaction are mostly safe from AI. This includes teachers, nurses, therapists and people in the trades.
What jobs will AI create? Human workers are required to train and develop AI systems. Machine learning engineer, AI ethics specialist and AI and cybersecurity researcher are a few examples of jobs AI will create.
How many jobs will AI replace? AI could could expose up to 300 million full-time jobs to automation, with a quarter to half of the workload in these jobs being replaced by AI, according to a 2023 Goldman Sachs report.
Will AI replace jobs? Not all jobs will be replaced by AI, but many roles like customer service representative, truck driver and computer programmer could be automated. In addition, AI could lead to new jobs like machine learning engineer and prompt engineer.
Is AI replacing the role of humans? AI can’t completely replace humans and is more likely to augment many existing roles. It can change the nature of certain jobs by automating repetitive and mundane tasks, freeing up human workers to focus on more challenging problems.
| 2022-12-01T00:00:00 |
https://builtin.com/artificial-intelligence/ai-replacing-jobs-creating-jobs
|
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"date": "2025/05/15",
"position": 30,
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},
{
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"position": 77,
"query": "artificial intelligence layoffs"
},
{
"date": "2025/05/01",
"position": 69,
"query": "artificial intelligence workers"
},
{
"date": "2025/05/15",
"position": 13,
"query": "automation job displacement"
},
{
"date": "2025/05/15",
"position": 18,
"query": "future of work AI"
},
{
"date": "2025/05/15",
"position": 41,
"query": "generative AI jobs"
},
{
"date": "2025/05/15",
"position": 16,
"query": "machine learning workforce"
},
{
"date": "2025/05/01",
"position": 82,
"query": "robotics job displacement"
},
{
"date": "2025/05/15",
"position": 80,
"query": "workplace AI adoption"
},
{
"date": "2025/06/01",
"position": 15,
"query": "AI employment"
},
{
"date": "2025/06/01",
"position": 45,
"query": "AI job creation vs elimination"
},
{
"date": "2025/06/01",
"position": 29,
"query": "AI job losses"
},
{
"date": "2025/06/01",
"position": 18,
"query": "AI replacing workers"
},
{
"date": "2025/06/01",
"position": 70,
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}
] |
|
77,000+ Artificial Intelligence jobs in United States - LinkedIn
|
76,000+ Artificial Intelligence Jobs in United States
|
https://www.linkedin.com
|
[] |
Today's top 77000+ Artificial Intelligence jobs in United States. Leverage your professional network, and get hired. New Artificial Intelligence jobs added ...
|
This button displays the currently selected search type. When expanded it provides a list of search options that will switch the search inputs to match the current selection.
Jobs
People
Learning
| 2022-12-01T00:00:00 |
https://www.linkedin.com/jobs/artificial-intelligence-jobs
|
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{
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{
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{
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"position": 40,
"query": "artificial intelligence employment"
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{
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|
Machine Learning and AI - Careers at Apple
|
Machine Learning and AI
|
https://www.apple.com
|
[
"Apple Inc."
] |
A group of Apple machine learning and AI employees have a conversation in an office. The people working here in machine learning and AI are building amazing ...
|
Find a team and begin your own story here.
Machine Learning Infrastructure
Build the rock-solid foundation for some of Apple’s most innovative products. As part of this team, you’ll connect the world’s best researchers with the world’s best computing, storage, and analytics tools to take on the most challenging problems in machine learning. And this is Apple, so your team will innovate across the entire stack: hardware, software, algorithms — it’s all here. Areas of work include Back-End Engineering, Data Science, Platform Engineering, and Systems Engineering.
Find available Machine Learning Infrastructure roles
Deep Learning and Reinforcement Learning
Join a team of researchers and engineers with a proven track record in a variety of machine learning methods: supervised and unsupervised learning, generative models, temporal learning, multimodal input streams, deep reinforcement learning, inverse reinforcement learning, decision theory, and game theory. This team dives deep into deep learning and AI research to help solve real-world, large-scale problems. Areas of work include Deep Learning, Reinforcement Learning, and Research.
Find available Deep Learning and Reinforcement Learning roles
Natural Language Processing and Speech Technologies
This group is a collective of hands-on research scientists from a wide variety of fields related to natural language processing. Join them to work with natural language understanding, machine translation, named entity recognition, question answering, topic segmentation, and automatic speech recognition. This team’s research typically relies on very large quantities of data and innovative methods in deep learning to tackle user challenges around the world — in languages from around the world. Areas of work include Natural Language Engineering, Language Modeling, Text-to-Speech Software Engineering, Speech Frameworks Engineering, Data Science, and Research.
Find available Natural Language Processing and Speech Technologies
roles
| 2022-12-01T00:00:00 |
https://www.apple.com/careers/us/machine-learning-and-ai.html
|
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{
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"position": 19,
"query": "generative AI jobs"
},
{
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|
Artificial Intelligence and Employment: Growing Use, but Lack of ...
|
Artificial Intelligence and Employment: Growing Use, but Lack of Training
|
https://medium.com
|
[
"Marta Reyes"
] |
According to a recent study conducted by his agency, 35% of professionals claim to have minimal or very limited training in the use of AI, and ...
|
Artificial Intelligence and Employment: Growing Use, but Lack of Training Marta Reyes 2 min read · 5 days ago 5 days ago -- Listen Share
Francisco Scasserra, a graduate in International Relations from Torcuato Di Tella University and leader of recruitment teams in Argentina and Uruguay at the Michael Page agency, spoke with Regreso CNN, the program hosted by Mariana Arias and Pepe Gil Vidal, about the impact of artificial intelligence (AI) on the workplace and the level of preparation of Argentine professionals regarding this technology.
According to a recent study conducted by his agency, 35% of professionals claim to have minimal or very limited training in the use of AI, and 34% admit to having no training at all. “We are still at such an early stage that we all have to learn about artificial intelligence. At the same time, organizations also have to learn,” Scasserra warned.
For the specialist, AI should be understood as a tool that empowers workers: “I see artificial intelligence as an invitation to evolve to see what added value we can provide to companies or to our work. We can leave the transactional aspects to this tool that helps us.”
Despite technological advancement, Scasserra emphasized the irreplaceable role of humans: “The final human eye is still irreplaceable in certain matters. One has to be the one who corrects artificial intelligence.”
Regarding the educational landscape, he maintained that the arrival of AI exposes a structural weakness: “Artificial intelligence has come to show that we are behind. Behind in education in schools and universities. At the same time, it moves so fast that it is very difficult for curricula to keep up with artificial intelligence.”
However, he noted that this delay should not be an excuse: “Education will always lag behind, but companies must constantly train professionals.”
Scasserra also emphasized the need to advance public policies and appropriate institutional frameworks: “Regulations and frameworks must be created to determine where we can work with artificial intelligence.”
Finally, he highlighted a positive fact: the use of these tools is growing. “The survey shows that in 2024, 38% of professionals used artificial intelligence in their work, and now, in 2025, 46% will. It is becoming more natural,” he concluded.
| 2025-07-08T00:00:00 |
2025/07/08
|
https://medium.com/@martareyessuarez25/artificial-intelligence-and-employment-growing-use-but-lack-of-training-a49e699fbf04
|
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] |
Careers - Ai2
|
Careers
|
https://allenai.org
|
[] |
We're all united by a passion for applying our skills to breakthrough research projects and products that put us at the forefront of impactful AI. With ...
|
Our teams are actively reviewing applications. If your qualifications align with a role, we’ll reach out directly. In the meantime, we encourage you to monitor our Careers page and sign up for job alerts. Please also create a MyGreenhouse account to be able to track the status of your application and to easily apply to future positions by auto-populating your basic personal information onto your application.
| 2022-12-01T00:00:00 |
https://allenai.org/careers
|
[
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{
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{
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{
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{
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{
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{
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}
] |
|
Top 10 jobs in artificial intelligence (AI) & who's hiring - Handshake
|
Top 10 jobs in artificial intelligence (AI) & who’s hiring
|
https://joinhandshake.com
|
[
"Ben Nemeroff"
] |
Top 10 entry-level jobs in artificial intelligence · 1. Software engineer · 2. Data scientist · 3. Machine learning engineer · 4. Data engineer · 5. Natural ...
|
Artificial intelligence is a hot topic these days, with many people talking about how AI will change or replace existing jobs. However, it’s also important to note that AI will create many innovative and exciting opportunities in the working world.
If you’re a college student getting a degree in a technical field like computer science, a career path in AI could be for you. From AI research scientists to deep learning engineers, a variety of roles are available. We highlight some current full-time gigs available on Handshake right now.
Is artificial intelligence a good career path for you?
The use of AI technology has expanded significantly in the past years, more than doubling since 2017. While some people worry about AI replacing jobs, it’s important to note AI will also create many new jobs. All in all, AI is expected to create some 97 million new jobs by 2025.
Organizations actually report having trouble filling AI roles — which is good news for anyone looking for a gig! For example, many organizations report struggling to find fitting AI data scientists, machine learning engineers, AI product managers, and data architects.
What do you need to work in AI? You’ll benefit from the following mix of hard and soft skills:
Technical prowess. Programming, coding, and data analytics skills are in demand for many AI jobs.
Programming, coding, and data analytics skills are in demand for many AI jobs. Ability to collaborate. AI is a complex technology that isn’t harnessed by one person alone. Get ready to work in teams.
AI is a complex technology that isn’t harnessed by one person alone. Get ready to work in teams. Adaptability. AI capabilities are constantly evolving. Adapting to the changing tools can help you keep up.
AI capabilities are constantly evolving. Adapting to the changing tools can help you keep up. Communication skills. Communicating on AI complexities in writing and verbally will serve you well in the field.
Communicating on AI complexities in writing and verbally will serve you well in the field. Curiosity. An innovative mindset can help you explore AI’s full potential.
Best degrees for artificial intelligence
Whether you want to work for Big Tech or a little startup, a technical degree is advisable for anyone wanting to break into the AI field. Consider these options for your bachelor’s degree (bonus points if you go on to get a master's degree):
Computer science . This degree can pave the path to any of the tech jobs listed below. Discover additional jobs for computer science majors.
This degree can pave the path to any of the tech jobs listed below. Discover additional jobs for computer science majors. Statistics. A stats degree can lead to jobs like business intelligence developer or deep learning engineer.
A stats degree can lead to jobs like business intelligence developer or deep learning engineer. Engineering. An engineering degree can prepare you for many roles, such as an AI research scientist.
Ideally, you’ll complement the above degrees with AI-specific certificates or hands-on experience. For example, the United States Artificial Intelligence Institute offers a certification in AI engineering, while Google offers courses in data analytics.
Industries where you can build an AI career
Tech companies are at the forefront of AI optimization and hiring. However, you don’t have to work for a tech giant like Amazon or Google. New jobs are popping up in a diversity of fields as other industries harness AI software tools — from ChatGPT to automation technologies.
Areas looking for AI pros include:
Health care . AI is expected to help health care practitioners diagnose and treat patients in the future. Software engineers and AI research scientists are two job roles that can help.
AI is expected to help health care practitioners diagnose and treat patients in the future. Software engineers and AI research scientists are two job roles that can help. Finance. Banks are already using AI to detect fraud and understand consumer purchasing behavior. Financial services can use data engineers, natural language processing engineers, deep learning engineers, and more.
Banks are already using AI to detect fraud and understand consumer purchasing behavior. Financial services can use data engineers, natural language processing engineers, deep learning engineers, and more. Supply chain . Supply chain management is one field already reporting big savings thanks to AI. Available jobs range from machine learning engineer to business intelligence developer.
Supply chain management is one field already reporting big savings thanks to AI. Available jobs range from machine learning engineer to business intelligence developer. Government. Government agencies are already hiring for AI jobs in areas like engineering, cybersecurity, and compliance.
Government agencies are already hiring for AI jobs in areas like engineering, cybersecurity, and compliance. Human resources . HR professionals and recruiters may use cutting-edge AI tools to help with everything from sifting through resumes to writing job ads.
Scroll down to see who’s hiring in AI on Handshake. If you like the idea of working in tech but AI isn’t for you, check out these other tech jobs.
Top 10 entry-level jobs in artificial intelligence
Ready to launch your AI career? From programmer to data analyst, we highlight some real-world opportunities worth exploring.
1. Software engineer
Software engineers work in software development to create new products for the AI pipeline, from new-and-improved chatbots to shopping apps. They use programming languages like Python and Java. Learn about the day in the life of a software engineer.
Average salary: $76,018 annually
Qualifications:
Bachelor’s degree in computer science or similar
Skills:
Teamwork abilities
Coding and programming prowess
Problem-solving skills
2. Data scientist
Data scientists collect, organize, and analyze data used in AI. They may also label data to help improve generative AI for the future. They work for tech companies, engineering firms, software manufacturers, and more.
Average salary: $74,905 annually
Qualifications:
Bachelor’s degree in computer science or similar
Skills:
Detail-oriented
Analytical mindset
Team player
3. Machine learning engineer
Machine learning engineers use data and algorithms to enhance AI tools. They want to help AI improve accuracy and basically “think” more like a human. Duties include researching, analyzing, and tweaking machine learning formulas.
Average salary: $122,531 annually
Qualifications:
Bachelor’s degree in computer science or similar
Skills:
Innovative attitude
Ability to adapt
Research-oriented mindset
4. Data engineer
Data engineers develop, create, and test data storage architecture. They basically build the digital infrastructure that holds the data AI tools need to function properly.
Average salary: $114,260 annually
Qualifications:
Bachelor’s degree in computer science, data science, or similar
Skills:
Analytical mindset
Problem-solving abilities
Communication skills
5. Natural language processing (NLP) engineer
Natural language processing (NLP) engineers design NLP processing systems. For example, they might create tools that allow AI to recognize speech patterns (they’re the reason Alexa can follow your commands). In addition to developing new tools, they may improve existing tools to enhance the user experience.
Average salary: $115,008 annually
Qualifications:
Bachelor’s degree in computer science, data science, engineering, or similar
Skills:
Time management skills
Collaboration abilities
Organizational talent
6. Robotics engineer
Robotics engineers use tools like automation and AI to develop robotic systems. These systems may perform labor-intensive tasks previously done by humans — from picking warehouse items to mopping floors.
Average salary: $105,290 annually
Qualifications:
Bachelor’s degree in computer science or similar
Skills:
Hands-on attitude
Ability to pivot
Detail-oriented
7. Business intelligence (BI) developer
Business intelligence (BI) developers help bridge the gap between AI data and those working with it, including product managers, analysts, and executives. They organize, analyze, and report data in a comprehensible way, for example, by creating visualization models.
Average salary: $94,487 annually
Qualifications:
Bachelor’s degree in computer science, applied mathematics, statistics, or similar
Skills:
Excellent communication abilities
Collaborative mindset
Good people skills
8. Deep learning engineer
Deep learning is a type of machine learning that deals with artificial neural networks (i.e., digital versions of what you find in your own brain). They help improve AI so it can better mimic the way people acquire knowledge.
Average salary: $147,023 annually
Qualifications:
Bachelor’s degree in computer science, mathematics, statistics, or similar
Skills:
Research-driven mindset
Innovative outlook
Time management skills
9. AI research scientist
AI research scientists develop techniques and infrastructure to harness AI’s power in a range of industries, from health care to financial services. Their job is to stay at the forefront of AI technology and implement the latest into their organizations.
Average salary: $65,875 annually
Qualifications:
Bachelor’s degree in computer science, engineering, or similar
Skills:
Superior programming skills
Research experience
Good project management skills
10. Computer vision engineer
Computer vision engineers help AI-driven tools “see” like a human. They create programs that can create and interpret visual information similar to how a human brain would (e.g., scanning a QR code to see a restaurant menu).
Average salary: $105,531 annually
Qualifications:
Bachelor’s degree in computer science or similar
Skills:
Collaborative mindset
Ability to evolve
Analytical skills
Let Handshake help you find an AI job
Technology is a great career path. In particular, AI promises to offer an increasing number of opportunities in the years ahead. If you have the technical skills to enter the field, it’s a solid job choice. The above list gives you a peek at the types of roles available.
Start your AI job search today with Handshake. Simply create your profile and let potential employers come to you. You can connect with companies in all kinds of industries, including finance, health care, HR, tech, social media, and beyond.
Handshake also makes it easy to sort jobs based on details like location or commitment (like full versus part time). It’s fast, easy, and stress-free. Set up your Handshake profile today.
Sources:
| 2022-12-01T00:00:00 |
https://joinhandshake.com/blog/students/artificial-intelligence-jobs/
|
[
{
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{
"date": "2023/05/25",
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{
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{
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{
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{
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{
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{
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{
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{
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},
{
"date": "2025/03/01",
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},
{
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},
{
"date": "2025/04/01",
"position": 39,
"query": "artificial intelligence employers"
},
{
"date": "2025/05/01",
"position": 47,
"query": "artificial intelligence employment"
},
{
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"position": 64,
"query": "AI hiring"
},
{
"date": "2025/06/01",
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}
] |
|
TrainAI Community | Remote, Part Time, Work from Home Jobs - RWS
|
Remote, Part Time, Work from Home Jobs - RWS
|
https://www.rws.com
|
[] |
As an AI data specialist in our TrainAI community, you will have the opportunity to work on a variety of AI training data-related freelance, remote, part-time, ...
|
As an AI data specialist, you’ll have the opportunity to work as an Online Rater, Data Collector, Data Annotator, Search Engine Evaluator, Ad Evaluator, or on other project-specific opportunities to help train AI.
Below are just a few examples of the types of tasks you’ll have the opportunity to work on and get paid:
| 2022-12-01T00:00:00 |
https://www.rws.com/artificial-intelligence/train-ai-data-services/trainai-community/
|
[
{
"date": "2022/12/01",
"position": 66,
"query": "artificial intelligence employment"
},
{
"date": "2022/12/01",
"position": 87,
"query": "AI wages"
},
{
"date": "2023/01/01",
"position": 67,
"query": "artificial intelligence employment"
},
{
"date": "2023/01/01",
"position": 90,
"query": "AI wages"
},
{
"date": "2023/02/01",
"position": 67,
"query": "artificial intelligence employment"
},
{
"date": "2023/02/01",
"position": 80,
"query": "generative AI jobs"
},
{
"date": "2023/04/01",
"position": 16,
"query": "generative AI jobs"
},
{
"date": "2023/05/01",
"position": 83,
"query": "AI wages"
},
{
"date": "2023/05/01",
"position": 66,
"query": "artificial intelligence employment"
},
{
"date": "2023/05/01",
"position": 65,
"query": "generative AI jobs"
},
{
"date": "2023/06/01",
"position": 83,
"query": "AI wages"
},
{
"date": "2023/06/01",
"position": 17,
"query": "generative AI jobs"
},
{
"date": "2023/07/01",
"position": 23,
"query": "generative AI jobs"
},
{
"date": "2023/08/01",
"position": 98,
"query": "AI wages"
},
{
"date": "2023/09/01",
"position": 74,
"query": "artificial intelligence employment"
},
{
"date": "2023/09/01",
"position": 16,
"query": "generative AI jobs"
},
{
"date": "2023/10/01",
"position": 84,
"query": "AI wages"
},
{
"date": "2023/10/01",
"position": 16,
"query": "generative AI jobs"
},
{
"date": "2023/11/01",
"position": 70,
"query": "generative AI jobs"
},
{
"date": "2023/12/01",
"position": 17,
"query": "generative AI jobs"
},
{
"date": "2024/01/01",
"position": 18,
"query": "generative AI jobs"
},
{
"date": "2024/02/01",
"position": 17,
"query": "generative AI jobs"
},
{
"date": "2024/03/01",
"position": 66,
"query": "generative AI jobs"
},
{
"date": "2024/05/01",
"position": 95,
"query": "AI wages"
},
{
"date": "2024/07/01",
"position": 66,
"query": "generative AI jobs"
},
{
"date": "2024/08/01",
"position": 21,
"query": "generative AI jobs"
},
{
"date": "2024/09/01",
"position": 17,
"query": "generative AI jobs"
},
{
"date": "2024/11/01",
"position": 15,
"query": "generative AI jobs"
},
{
"date": "2024/12/01",
"position": 84,
"query": "AI wages"
},
{
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"position": 85,
"query": "AI wages"
},
{
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"position": 69,
"query": "generative AI jobs"
},
{
"date": "2025/02/01",
"position": 18,
"query": "generative AI jobs"
},
{
"date": "2025/03/01",
"position": 84,
"query": "AI wages"
},
{
"date": "2025/03/01",
"position": 62,
"query": "artificial intelligence employment"
},
{
"date": "2025/03/01",
"position": 20,
"query": "generative AI jobs"
},
{
"date": "2025/04/01",
"position": 84,
"query": "AI wages"
},
{
"date": "2025/04/01",
"position": 20,
"query": "generative AI jobs"
},
{
"date": "2025/05/01",
"position": 63,
"query": "artificial intelligence employment"
},
{
"date": "2025/06/01",
"position": 82,
"query": "AI wages"
},
{
"date": "2025/06/01",
"position": 24,
"query": "generative AI jobs"
}
] |
|
'Big Beautiful Bill' Leaves AI Regulation to States and Localities ...
|
‘Big Beautiful Bill’ Leaves AI Regulation to States and Localities … For Now
|
https://www.lawandtheworkplace.com
|
[
"Guy Brenner",
"Jonathan Slowik",
"July",
".Wp-Block-Co-Authors-Plus-Coauthors.Is-Layout-Flow",
"Class",
"Wp-Block-Co-Authors-Plus",
"Display Inline",
".Wp-Block-Co-Authors-Plus-Avatar",
"Where Img",
"Height Auto Max-Width"
] |
... artificial intelligence (“AI”) for the ... Employment Law Department and leads the Firm's Washington, D.C. Labor & Employment practice.
|
On July 4, 2025, President Trump signed the One Big Beautiful Bill Act into law—a budget reconciliation bill enacting several signature policies of the President’s second-term agenda. Left on the cutting-room floor, however, was an ambitious attempt to prohibit nearly all state and local regulation of artificial intelligence (“AI”) for the foreseeable future.
The version of the bill passed by the House of Representatives on May 22, 2025, contained a provision preventing states and localities, for a period of 10 years, from enforcing “any law or regulation … limiting, restricting, or otherwise regulating artificial intelligence models, artificial intelligence systems, or automated decision systems entered into interstate commerce.” Initially, a proposed version of the bill in the Senate included a similar provision that made the moratorium a condition of states receiving any of the $500 million in funds earmarked to support deployment of AI models or systems and underlying infrastructure.
However, the moratorium was ultimately stripped from the bill when it became clear the moratorium lacked majority support.
Thus, the version signed into law leaves states and localities free to continue regulating AI systems, including the use of such systems to make or assist with employment decisions such as hiring, firing, promotions, discipline, evaluations, compensation, and the like. Prominent statutes regulating such uses of AI have passed in Colorado, New York City, and Illinois, and are under consideration in several other states. However, the demise of the moratorium in the budget reconciliation law has renewed calls by some for federal regulation in the space that would prevent a patchwork of state and local laws regulating AI.
We will continue to monitor and report on developments in this space.
| 2025-07-08T00:00:00 |
2025/07/08
|
https://www.lawandtheworkplace.com/2025/07/big-beautiful-bill-leaves-ai-regulation-to-states-and-localities-for-now/
|
[
{
"date": "2022/12/01",
"position": 67,
"query": "artificial intelligence employment"
},
{
"date": "2022/12/01",
"position": 73,
"query": "AI regulation employment"
},
{
"date": "2022/12/01",
"position": 54,
"query": "AI employment"
},
{
"date": "2023/01/01",
"position": 69,
"query": "artificial intelligence employment"
},
{
"date": "2023/01/01",
"position": 41,
"query": "AI employment"
},
{
"date": "2023/02/01",
"position": 68,
"query": "artificial intelligence employment"
},
{
"date": "2023/02/01",
"position": 54,
"query": "AI employment"
},
{
"date": "2023/04/01",
"position": 73,
"query": "AI employment"
},
{
"date": "2023/05/01",
"position": 75,
"query": "AI employment"
},
{
"date": "2023/05/01",
"position": 80,
"query": "artificial intelligence employment"
},
{
"date": "2023/06/01",
"position": 78,
"query": "AI employment"
},
{
"date": "2023/07/01",
"position": 85,
"query": "AI employers"
},
{
"date": "2023/08/01",
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"query": "AI employment"
},
{
"date": "2023/09/01",
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"query": "artificial intelligence employment"
},
{
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"query": "artificial intelligence employment"
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{
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"position": 78,
"query": "AI employment"
},
{
"date": "2023/11/01",
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},
{
"date": "2023/12/01",
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"query": "AI employment"
},
{
"date": "2024/01/01",
"position": 81,
"query": "AI employment"
},
{
"date": "2024/02/01",
"position": 91,
"query": "AI employment"
},
{
"date": "2024/02/01",
"position": 83,
"query": "artificial intelligence employment"
},
{
"date": "2024/03/01",
"position": 87,
"query": "artificial intelligence employment"
},
{
"date": "2024/06/01",
"position": 78,
"query": "AI employment"
},
{
"date": "2024/06/01",
"position": 78,
"query": "artificial intelligence employment"
},
{
"date": "2024/07/01",
"position": 78,
"query": "AI employment"
},
{
"date": "2024/09/01",
"position": 83,
"query": "artificial intelligence employment"
},
{
"date": "2024/10/01",
"position": 90,
"query": "artificial intelligence employment"
},
{
"date": "2024/11/01",
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"query": "AI employment"
},
{
"date": "2024/11/01",
"position": 76,
"query": "artificial intelligence employment"
},
{
"date": "2025/01/01",
"position": 78,
"query": "AI employment"
},
{
"date": "2025/03/01",
"position": 99,
"query": "AI regulation employment"
},
{
"date": "2025/04/01",
"position": 99,
"query": "AI regulation employment"
}
] |
AI Software Developer - U-M Careers - University of Michigan
|
AI Software Developer
|
https://careers.umich.edu
|
[] |
In this position, you'll help design, build, and support innovative AI services, ranging from GenAI-powered tools to OCR and document processing ...
|
How to Apply
A cover letter and resume are important submissions for the hiring team to get a sense of your experience. In the cover letter, in one page or less, please let us know how this role aligns with your career aspirations and skills. Submit both a cover letter and resume as one file.
Competitive salary available based on qualifications, experience and education of the selected candidate.
Job Summary
Join the future of AI at the University of Michigan!
Information and Technology Services (ITS) is seeking curious, creative, and forward-thinking professionals to join us as an Emerging Technologies Engineer. If you're excited by the opportunity to work at the frontier of artificial intelligence in higher education, this is the role for you.
In this position, you'll help design, build, and support innovative AI services, ranging from GenAI-powered tools to OCR and document processing solutions that are secure, private, and impactful. You'll collaborate with multidisciplinary teams to explore new applications of AI and help ensure the university remains a leader in responsible and effective AI use.
We value diverse perspectives and welcome candidates from all backgrounds who bring bold ideas and a passion for emerging technology. If you thrive in a fast-moving environment and want to shape the next wave of AI solutions at Michigan, we want to hear from you.
Apply today and help build what's next.
Responsibilities*
Software Development: Collaborate on designing and building scalable, reliable systems that enable AI-driven capabilities. Develop and maintain back-end services with secure, efficient data pipelines, and contribute to intuitive, accessibility-compliant user interfaces. Work alongside senior engineers, adopting best practices and iterating quickly to meet project goals.
AI Project Support: Contribute to the success of the team by coordinating work with other team members and actively supporting project management processes. Assist in maintaining sprint momentum by helping with planning, facilitating communication, and participating in retrospectives to ensure milestones are achieved.
Strategic Contribution: Collaborate with senior staff on the department's technical and strategic planning. Support efforts to align strategies with university goals, particularly in the areas of secure, private, and accessible AI solutions.
Ongoing Learning & Professional Development: Stay current with trends in web development, AI, security, privacy, and accessibility. Participate in training and industry events to ensure the Michigan AI Platform remains state-of-the-art.
Required Qualifications*
Bachelor's Degree in Computer Science or related field: Or equivalent work experience in software development, with a focus on web technologies.
Minimum 2 years of professional experience in web development: Proficiency in modern web technologies like HTML, CSS, JavaScript, and familiarity with front-end and back-end frameworks.
Experience in web application development: Familiarity with web development tools and frameworks such as Django and Vue.js.
Agile Development Practices: Proficiency in working within an Agile framework, contributing to sprint planning, daily stand-ups, and retrospectives to ensure timely completion of tasks.
Understanding of Security, Privacy, and Accessibility: Experience or knowledge in implementing secure and private web solutions that are accessible to a diverse user base.
Effective Communication Skills: Ability to convey technical information clearly and effectively to both technical and non-technical stakeholders.
Continual Learning Mindset: Commitment to ongoing professional development in web development, AI technologies, and industry best practices.
Desired Qualifications*
Master's Degree in Computer Science or related field: Advanced degree with a focus on AI, machine learning, or web technologies is a plus but not required.
Experience with Cloud Technologies: Familiarity with cloud platforms such as AWS, Azure, or GCP, and exposure to DevOps practices, is highly desirable.
Experience with Containers: Proficiency in working with containers and container orchestration tools such as Docker and Kubernetes.
Experience with AI Frameworks: Exposure to GenAI, LangGraph, and similar AI frameworks for machine learning, with hands-on expertise in integrating AI technologies into web-based platforms or services, including using DRF for building robust APIs.
Modes of Work
Positions that are eligible for hybrid or mobile/remote work mode are at the discretion of the hiring department. Work agreements are reviewed annually at a minimum and are subject to change at any time, and for any reason, throughout the course of employment. Learn more about the work modes.
Additional Information
PHYSICAL DEMANDS/WORK ENVIRONMENT
Punctual, regular, and consistent attendance is required. Ability to work on technical equipment installed at heights of 10 feet or higher. Normal amount of being stationary, average mobility to move around an office environment, able to conduct normal amounts of work at a computer. Requires travel to various locations on the UM campuses and moves through buildings including stairways with test equipment weighing up to 40 lbs. utilizing proper safety techniques. May require working during non-business hours and on weekends. Responsible for protecting data and information from unauthorized release or from loss, alteration, or unauthorized deletion; and, following applicable regulations and instructions regarding access to computerized files, release of data, etc. as stated in a computer access agreement which the incumbent signs.
Benefits at the University of Michigan
In addition to a career filled with purpose and opportunity, The University of Michigan offers a comprehensive benefits package to help you stay well, protect yourself and your family and plan for a secure future. Benefits include:
Generous time off
A retirement plan that provides two-for-one matching contributions with immediate vesting
Many choices for comprehensive health insurance
Life insurance
Long-term disability coverage
Flexible spending accounts for healthcare and dependent care expenses
Dental and Vision Insurance
Parental and Maternity Leave
U-M EEO Statement
The University of Michigan is an equal employment opportunity employer.
| 2022-12-01T00:00:00 |
https://careers.umich.edu/job_detail/265830/ai-software-developer
|
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"position": 68,
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{
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},
{
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},
{
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},
{
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{
"date": "2024/01/01",
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{
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},
{
"date": "2024/02/01",
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"query": "artificial intelligence employment"
},
{
"date": "2024/04/01",
"position": 96,
"query": "future of work AI"
},
{
"date": "2024/06/01",
"position": 55,
"query": "AI employment"
},
{
"date": "2024/06/01",
"position": 89,
"query": "AI hiring"
},
{
"date": "2024/06/01",
"position": 92,
"query": "artificial intelligence employment"
},
{
"date": "2024/06/01",
"position": 95,
"query": "future of work AI"
},
{
"date": "2024/07/01",
"position": 99,
"query": "AI employment"
},
{
"date": "2024/07/01",
"position": 98,
"query": "future of work AI"
},
{
"date": "2024/09/01",
"position": 94,
"query": "artificial intelligence employment"
},
{
"date": "2024/10/01",
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"query": "AI hiring"
},
{
"date": "2024/11/01",
"position": 91,
"query": "artificial intelligence employment"
},
{
"date": "2025/01/01",
"position": 95,
"query": "AI employment"
},
{
"date": "2025/01/01",
"position": 90,
"query": "AI hiring"
}
] |
|
How Will Artificial Intelligence Create More Jobs by 2025?
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How Will Artificial Intelligence Create More Jobs by 2025?
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https://www.mygreatlearning.com
|
[
"Great Learning Editorial Team"
] |
According to a report by the World Economic Forum, 50% of tasks in the workplace will be automated by machine by 2025 compared to 29% as of now. Nearly 50% of ...
|
Table of contents
Know How AI Create more Jobs by 2025
Any new technology that has the potential to change the way humanity lives has always created a huge amount of debate. And this is especially true for Artificial Intelligence. The debate over Artificial Intelligence is never-ending. Researchers, thinkers, IT professionals, even the average layman has polarizing opinions on AI and its potential impact on humanity.
“I visualize a time when we will be to robots what dogs are to humans, and I’m rooting for the machines.” —Claude Shannon.
“There is no reason and no way that a human mind can keep up with an artificial intelligence machine by 2035.” —Gray Scott.
“Sooner or later, the U.S. will face mounting job losses due to advances in automation, artificial intelligence, and robotics.” —Oren Etzioni.
“I believe this artificial intelligence is going to be our partner. If we misuse it, it will be a risk. If we use it right, it can be our partner.”
The list goes on and on. But a recent study seems to provide a conclusion to this debate.
Know Why Artificial Intelligence will Not Destroy Jobs
Automation threatens 8 million jobs by the end of 2030 but at the same time technology is all set to create more jobs than ever. Artificial intelligence is not purely destructive. New jobs will be created, existing roles will be re-built, and switching careers will be a great opportunity, says a report.
Reports claim that over 30% of jobs are under potential threat to some people already losing their jobs. Digging in deeper, we establish that AI technology will also create over 2 million job opportunities worldwide by the end of 2020.
While there is cut-throat competition across all jobs, machine learning and artificial intelligence jobs face substantially less competition. These specialized engineering jobs are on a rise but still remain vacant. The critical factor is the scarcity of skilled talent.
This report talks about why is it necessary for IT professionals to upskill to stay relevant.
Read the entire report here.
The AI research spectrum is expanding. From autonomous cars to models for cancer detection, the use cases are touching almost every segment. At the same time, there is a plethora of platforms offering degrees and certifications in AI, Machine Learning, and Deep Learning but the number of potential employees for the same is very less.
Presently, with very few job seekers, the hiring processes have become sloppy, delaying deft adaptation of intelligent machines.
Automation Requires Creating New Skill Sets.
While unskilled jobs are under grave threat, AI will create room for a new category of jobs which can be mastered with training.
If you do not believe in this philosophy, now is the right to start believing it. While a lot of jobs have taken a hit due to artificial intelligence, you can still land yourself a job with constant learning and upskilling.
Robots and machines are becoming smarter with artificial intelligence and are taking over time-consuming, manual labour based jobs which might be threatening but we need to address the fact that this has been an on-going process. In the early 60’s, we would reach out to the nearest branch to withdraw money but with ATMs in place, the process is fastened.
Read Also: Reasons why Artificial Intelligence will create more jobs than it takes
Robots for painting cars, ATMs for cash disposal, computers for creating spreadsheets, did feel like a potential threat initially. Similar could be the case with AI, we never know.
A global survey by Allegis had fascinating insights- Twenty-one percent of the people viewed AI as something to be excited about. Seventeen percent considered it both disrupting and enabling, and a lower number, 9 percent, believed AI will displace most jobs in 10 years.
“This mixed view of AI is not surprising because the technology does more than automate tasks; it changes the nature of the work we do.” -Rachel Russell.
As AI becomes more and more able to carry human-like functions it will replace jobs with certain human attributes but will create new opportunities as well.
Machine learning engineer, Deep Learning engineer, AI trainers, Natural Language Processing engineer, AI specialist, Deep Learning engineer – Computer Vision followed by multiple permutations and combinations like AI & Dl, DL, and ML, DL and Data Scientist etc are among the trending job profiles to name a few.
Of all the advancements, Human judgment is less likely to be surpassed by AI. We need to start looking at Artificial intelligence in its purest form- Intelligence in augmented form instead of a job-hungry robot training to take over.
According to a report by the World Economic Forum, 50% of tasks in the workplace will be automated by machine by 2025 compared to 29% as of now. Nearly 50% of companies predict their workforce to reduce by 2022 but at the same time, automation is expected to create new roles in the industry. Another key takeaway is that the demand for roles varies across regions.
The entire hype about AI replacing jobs has a lot to do with the type of jobs under consideration. While certain aspects of a job are susceptible to automation, human intervention cannot be replaced.
AI can power multiple aspects which make up today’s jobs
Machines are great at cyclically performing a particular task with a high level of accuracy and consistency, thus we can surely say AI will take over a particular variety of tasks.
Read Also: Top Artificial Intelligence (AI) Trends
But complex problem solving remains a far-fetched thought among the goals of AI. Change is the only constant and as some jobs are replaced new ones will be created. As per some estimates, 65% of the kids who are in schools today will end up with jobs which do not exist today.
When it comes to tactical thinking, nuances of problem-solving, adaptive thinking, and thinking-out-of-the-box abilities, AI is still way behind the human brain. After all, AI can only mimic the human brain.
So it seems AI will cut off the monotonous jobs on the table (like data entry and a certain level of accounting) but human resource-based jobs like customer care, sales and marketing, innovation, and research will continue to be in high demand along with specialized jobs in the field of AI itself. Take up AI courses and enhance your knowledge today.
For those of you who are wondering what should you do to save your job- a little less worry and more upskill and training will set the game just right for you.
Great Learning’s free online courses are tailor-made for individuals like you. Gain an edge in your career with in-demand domains such as Cybersecurity, Management, Artificial Intelligence, Cloud Computing, IT, and Software. These courses are designed by industry experts to provide you with hands-on experience and practical knowledge. Whether you’re a beginner looking to start a new career path or a professional aiming to upskill, our courses offer a flexible and accessible way to learn.
| 2020-05-15T00:00:00 |
2020/05/15
|
https://www.mygreatlearning.com/blog/how-will-artificial-intelligence-create-more-jobs/
|
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Is generative AI a job killer? Evidence from the freelance market
|
Is generative AI a job killer? Evidence from the freelance market
|
https://www.brookings.edu
|
[
"Tania Babina",
"Anastassia Fedyk",
"Malihe Alikhani",
"Martin Neil Baily",
"Aidan T. Kane",
"Mark Maccarthy",
"John Villasenor",
"Darrell M. West"
] |
Much depends on whether AI complements or substitutes human labor. On the one hand, AI may improve worker outcomes by boosting productivity, ...
|
Sections Toggle section navigation Sections Print
Over the past few years, generative artificial intelligence (AI) and large language models (LLMs) have become some of the most rapidly adopted technologies in history. Tools such as OpenAI’s ChatGPT, Google’s Gemini, and Anthropic’s Claude now support a wide range of tasks and have been integrated across sectors, from education and media to law, marketing, and customer service. According to McKinsey’s 2024 report, 71% of organizations now regularly use generative AI in at least one business function. This rapid adoption has sparked a vibrant public debate among business leaders and policymakers about how to harness these tools while mitigating their risks. Perhaps the most alarming feature of generative AI is its potential to disrupt the labor market. Eloundou et al. (2024) estimate that around 80% of the U.S. workforce could see at least 10% of their tasks affected by LLMs, while approximately 19% of workers may have over half of their tasks impacted. To better understand the impact of generative AI on employment, we examined its effect on freelance workers using a popular online platform (Hui et al. 2024). We found that freelancers in occupations more exposed to generative AI have experienced a 2% decline in the number of contracts and a 5% drop in earnings following since the release of new AI software in 2022. These negative effects were especially pronounced among experienced freelancers who offered higher-priced, higher-quality services. Our findings suggest that existing labor policies may not be fully equipped to support workers, particularly freelancers and other nontraditional workers, in adapting to the disruptions posed by generative AI. To ensure long-term, inclusive benefits from AI adoption, policymakers should invest in workforce reskilling, modernize labor protections, and develop institutions that support human-AI complementarity across a rapidly evolving labor market.
How might AI affect employment? The effect of AI on employment remains theoretically ambiguous. As with past general-purpose technologies, such as the steam engine, the personal computer, or the internet, AI may fundamentally reshape employment structures, though it remains unclear whether AI will ultimately harm or improve worker outcomes (Agrawal et al. 2022). Much depends on whether AI complements or substitutes human labor. On the one hand, AI may improve worker outcomes by boosting productivity, work quality, and efficiency. It can take over routine or repetitive tasks, allowing humans to focus on strategic thinking, creativity, or interpersonal interactions. This optimistic view has been championed by scholars such as Brynjolfsson and McAfee (2014), who argue that technology can augment productivity and increase the value of human capital when paired with the right skills. Brynjolfsson et al. (2025) and Noy and Zhang (2023) find that access to AI tools increased productivity in customer support centers and writing tasks. Nevertheless, substitution remains a real risk. When AI can perform a particular set of tasks at equal quality and lower cost than a human employee, the demand for human labor in those areas may decline. Acemoglu and Restrepo (2020) argue that automation may reduce labor demand unless it is accompanied by the creation of new tasks in which humans maintain a comparative advantage. Full substitution may be cost-effective for firms but could lead to severe economic and social consequences such as widespread layoffs and unemployment. In contrast to past technologies, where the types of workers affected were relatively predictable, the impact of AI is harder to anticipate. As a general-purpose technology, AI may disrupt a broad range of occupations in varied and uneven ways. These dynamics are unlikely to affect all workers equally. High-skill workers with access to complementary tools may benefit, while mid-skill workers, whose tasks are more easily replicated by AI, may be displaced or pushed into lower-paying jobs. Conversely, if AI democratizes access to services and information and reduces the returns to specialized human capital, it could undermine the economic position of those previously seen as secure in creative or professional roles, potentially reducing inequality. Evaluating the direct effect of AI on employment in the short run empirically is challenging. To begin with, it is often difficult to determine whether changes in hiring or separations are driven by AI or by other unobserved industry-, organization-, or employee-level factors. In addition, traditional employment contracts tend to be rigid and cannot quickly adjust to technological changes. They also tend to involve a bundle of varied tasks such as responding to emails, attending meetings, managing subordinates, and interacting with clients. In its current form, AI may be effective at automating some of these tasks but is not yet advanced enough to fully replace a human worker. As a result, early adoption of AI might not be reflected in conventional employment statistics.
AI in online labor markets To overcome these limitations, our recent paper, published in Organization Science (Hui et al. 2024), adopts a different empirical strategy: We focus on online labor markets, namely Upwork, one of the world’s largest online freelancing platforms in the world. The platform operates as a spot market for short-term, usually remote, projects. Prospective employers on the platform can post various jobs offering either fixed or hourly compensation. Jobs span across a range of categories including web development, graphic design, administrative support, digital marketing, legal assistance, and so forth. They usually have a clear timeline and/or well-defined deliverables. Once the jobs are posted freelancers may submit bids offering their services, and, after some negotiation process, one or more freelancers are hired to complete the job. This setting offers several advantages: Job postings are typically short-term, contracts are flexible, and the platform provides detailed, transparent data on employment history and freelancer earnings. Freelancers often take on and complete multiple projects per month, generating high-frequency data ideal for short-term analysis. To examine how these interactions are affected by the release of generative AI, we focus on two types of AI models. First, image-based models, specifically DALL-E2 and Midjourney, which were launched within a month of each other in early 2022. These tools marked a major breakthrough in image-generation capabilities, offering the public unprecedented public access to AI tools that could produce high-quality visuals from text prompts. Second, text-based models, specifically the launch of ChatGPT in November 2022. ChatGPT was the first commercial-grade text-based AI model made broadly available. ChatGPT’s release was a watershed moment, attracting over 100 million active users within a couple of months and marking the beginning of mass adoption of generative AI. Using these model launches as natural experiments, we compare the change in freelancer outcomes in AI-affected and less-affected occupations before and after the launch of the AI tools. Building on previous research as well as exploratory data analysis, we identified specific freelancers offering services in domains more likely to be affected by the different types of AI. For example, copyeditors and proofreaders are likely to be impacted by text-based AI models like ChatGPT, while graphic designers are more likely to be affected by image-based models like DALL-E2. Other categories, such as administrative services, video editing, and data entry, expected to experience little or no direct impact from these early AI tools. Our analysis reveals that freelancers operating in domains more exposed to generative AI were disproportionately affected by the release of ChatGPT. Specifically, we find that freelancers providing services such as copyediting, proofreading, and other text-heavy tasks experienced a decline of approximately 2% in the number of new monthly contracts. In addition to reduced job flow, these freelancers also saw a roughly 5% decrease in their total monthly earnings on the platform. These effects suggest a significant disruption in the demand for services that can be replicated by AI. Importantly, we observe similar patterns following the release of image-based models such as DALL-E2 and Midjourney. Despite the fact that these tools were released at different times and affected a distinct set of services, the magnitude of the impact was identical to what we observe in text-based models. These are sizable effects, especially considering how recently these technologies became available. To put these changes in perspective, the observed declines are comparable in magnitude to those estimated in studies of other major automation technologies such as industrial robots and task automation (Acemoglu and Restrepo 2023). They are also similar to the labor market impacts of large-scale policy interventions, including changes in the minimum wage and access to unionization. Moreover, while our data covers only the first six to eight months following the release of these AI models, the negative trend has been persistent over that time. In fact, rather than fading after the initial release, the declines in both employment and compensation continue to grow, suggesting our findings represent more than merely short-term shocks or transitional responses. Instead, they likely reflect shifts in how certain services are valued and delivered in an AI-augmented economy. We conjecture that as AI capabilities improve and adoption expands, these trends will not only persist but may accelerate, potentially leading to broader reductions in employment and earnings across occupations.
The role of worker experience Having documented the negative average effect of generative AI on employment outcomes on the platform, we next turn to evaluating whether certain freelancer characteristics can mitigate, or potentially exacerbate, these effects. One particular dimension of interest is worker quality and experience. Prior research on technological change suggests that high-skill labor, particularly those engaged in cognitively demanding or creative tasks, tends to be more resilient to adverse technology shocks. The conventional wisdom holds that providing higher- services should, to some extent, shield freelancers from displacement, as their work may be harder to automate or replicate (Acemoglu and Autor 2011; Autor et al. 2003). Examining the impact of AI across the distribution of worker quality reveals a somewhat surprising pattern: Not only are high-skill freelancers not insulated from the adverse effects but they are, in fact, disproportionately affected. Among workers within the same occupation, those with stronger past performance—as measured by client feedback, contract history, and other platform-based reputational metrics—experience larger declines in both the number of new contracts and total monthly earnings. This finding highlights a critical and somewhat counterintuitive interaction between artificial and human expertise. Generative AI appears to be “leveling the playing field” by compressing performance differences across the skill spectrum. One potential explanation is that, with tools like ChatGPT and DALL-E2, less experienced or lower-rated freelancers can now produce outputs that in many cases approximate the quality associated only with top-tier talent. As a result, clients may no longer perceive as much value in paying a premium for high-reputation workers, particularly when lower-cost alternatives can generate comparable results. Thus, as discussed earlier, generative AI represents a fundamentally different kind of technological advance. This dynamic stands in contrast to prior waves of technological change, where advanced tools often complemented highly skilled labor and widened the productivity gap between top and bottom performers (Per Krusell et al. 2000). As a result, its disruptive potential extends across the entire skill distribution, including those at the very top. The early effects of generative AI suggest that it may reduce the dispersion of earnings and opportunities. This interpretation is consistent with earlier findings that the marginal returns to technology adoption are often highest for those with lower initial productivity who gain more from the new technology.
Implications for policy Our study provides some of the earliest empirical evidence on the labor market effects of generative AI, but it is also important to recognize its limitations. Examining the effect on freelancers is appealing for the reasons stated above but may not fully capture the dynamics of traditional employment arrangements or long-term contractual relationships. Still, the findings highlight the fact that certain worker groups, such as freelancers, who often lack formal labor protections and social safety nets, benefits, or bargaining power, are uniquely exposed to technological disruptions. For example, workers in more flexible work arrangements lack access to employer-sponsored retirement savings and unemployment insurance and have faced legal challenges in forming labor unions. Existing labor relations and regulations may thus not be well equipped to address the challenges posed by emerging technologies. As the nature of work continues to evolve, policies may need to be rethought to account for more fast-moving and AI-enhanced freelancer markets, especially in sectors highly vulnerable to automation. While our analysis focuses on well-defined, task-oriented freelance jobs, which are arguably more amenable to AI substitution, recent research finds that generative AI may also affect more complex, collaborative work. Dell’Acqua et al. (2025), for example, show that AI can even substitute for team-based professional problem-solving and contribute meaningfully to real-world business decisions. This suggests that the impact of AI may extend beyond routine or isolated tasks and begin to reshape how high-skilled, interdependent work is performed. Predicting the future trajectory of AI remains difficult, as the technology continues to evolve rapidly. As its capabilities grow, AI is likely to be adopted across a wider range of industries, including those once thought resistant to automation, further reshaping the relationship between labor and technology. Closely tracking these developments through initiatives like the Workforce Innovation and Opportunity Act (WIOA) and other federal labor data programs is essential for informing timely and effective policy. Historical evidence from past general-purpose technologies suggests that while short-term substitution effects can displace workers, longer-term gains often emerge through task reorganization, workforce reskilling, and the creation of entirely new roles. In the case of generative AI, true progress may come not just from automating existing tasks, but from fundamentally reshaping how organizations operate and the types of goods and services they offer. At the same time, reductions in task costs in one sector can spur innovation and economic activity in others. For example, Brynjolfsson et al. (2019) show that AI-driven machine translation at eBay significantly increased cross-border trade and improved consumer outcomes. Similarly, as generative AI continues to evolve, it may enable the emergence of new occupations, business models, and collaborative structures. Realizing these long-term benefits will require sustained investment in education, training, and institutional reform that promotes human-AI complementarity. Policymakers should not only help workers adapt to near-term disruptions but also foster an environment in which AI enhances, rather than replaces, human capabilities. It will also require creating conditions that incentivize firms to reorganize workflows and adopt AI in ways that amplify, rather than erode, the value of human labor. In addition, labor market institutions must evolve to keep pace with the new realities of work. This involves not only rethinking social safety nets but by also promoting inclusive access to AI tools and training opportunities. If designed thoughtfully, policy can ensure that the next wave of AI adoption delivers broad-based benefits rather than deepening existing disparities.
Authors Xiang Hui Assistant Professor of Marketing - Washington University in St. Louis Oren Reshef Assistant Professor of Strategy and Entrepreneurship - Washington University in St. Louis
References Acemoglu, Daron, and David Autor. 2011. “Skills, Tasks and Technologies: Implications for Employment and Earnings.” In Handbook of Labor Economics, 4:1043–1171. Elsevier. https://doi.org/10.1016/S0169-7218(11)02410-5. Acemoglu, Daron, and Pascual Restrepo. 2020. “Robots and Jobs: Evidence from US Labor Markets.” Journal of Political Economy 128 (6): 2188–2244. https://doi.org/10.1086/705716. Agrawal, Ajay B., Joshua S. Gans, and Avi Goldfarb. 2022. Power and Prediction: The Disruptive Economics of Artificial Intelligence. Boston, Mass: Harvard business review press. Autor, D. H., F. Levy, and R. J. Murnane. 2003. “The Skill Content of Recent Technological Change: An Empirical Exploration.” The Quarterly Journal of Economics 118 (4): 1279–1333. https://doi.org/10.1162/003355303322552801. Brynjolfsson, Erik, Xiang Hui, and Meng Liu. 2019. “Does Machine Translation Affect International Trade? Evidence from a Large Digital Platform.” Management Science 65 (12): 5449–60. https://doi.org/10.1287/mnsc.2019.3388. Brynjolfsson, Erik, Danielle Li, and Lindsey Raymond. 2025. “Generative AI at Work.” The Quarterly Journal of Economics 140 (2): 889–942. https://doi.org/10.1093/qje/qjae044. Brynjolfsson, Erik, and Andrew McAfee. 2016. The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. First published as a Norton paperback. New York London: W. W. Norton & Company. Dell’Acqua, Fabrizio, Charles Ayoubi, Hila Lifshitz-Assaf, Raffaella Sadun, Ethan R. Mollick, Lilach Mollick, Yi Han, et al. 2025. “The Cybernetic Teammate: A Field Experiment on Generative AI Reshaping Teamwork and Expertise.” Preprint. SSRN. https://doi.org/10.2139/ssrn.5188231. Eloundou, Tyna, Sam Manning, Pamela Mishkin, and Daniel Rock. 2024. “GPTs Are GPTs: Labor Market Impact Potential of LLMs.” Science 384 (6702): 1306–8. https://doi.org/10.1126/science.adj0998. Hui, Xiang, Oren Reshef, and Luofeng Zhou. 2024. “The Short-Term Effects of Generative Artificial Intelligence on Employment: Evidence from an Online Labor Market.” Organization Science 35 (6): 1977–89. https://doi.org/10.1287/orsc.2023.18441. Krusell, Per, Lee E. Ohanian, Jose-Victor Rios-Rull, and Giovanni L. Violante. 2000. “Capital-Skill Complementarity and Inequality: A Macroeconomic Analysis.” Econometrica 68 (5): 1029–53. https://doi.org/10.1111/1468-0262.00150. Noy, Shakked, and Whitney Zhang. 2023. “Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence.” Science 381 (6654): 187–92. https://doi.org/10.1126/science.adh2586.
| 2022-12-01T00:00:00 |
https://www.brookings.edu/articles/is-generative-ai-a-job-killer-evidence-from-the-freelance-market/
|
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},
{
"date": "2024/11/01",
"position": 87,
"query": "AI employment"
},
{
"date": "2024/11/01",
"position": 62,
"query": "AI impact jobs"
},
{
"date": "2024/11/01",
"position": 34,
"query": "generative AI jobs"
},
{
"date": "2024/12/01",
"position": 97,
"query": "artificial intelligence graphic design"
},
{
"date": "2025/01/01",
"position": 82,
"query": "AI employment"
},
{
"date": "2025/01/01",
"position": 97,
"query": "artificial intelligence graphic design"
},
{
"date": "2025/01/01",
"position": 37,
"query": "generative AI jobs"
},
{
"date": "2025/02/01",
"position": 62,
"query": "generative AI jobs"
},
{
"date": "2025/03/01",
"position": 68,
"query": "artificial intelligence employment"
},
{
"date": "2025/03/01",
"position": 77,
"query": "generative AI jobs"
},
{
"date": "2025/04/01",
"position": 63,
"query": "generative AI jobs"
},
{
"date": "2025/05/01",
"position": 67,
"query": "artificial intelligence employment"
},
{
"date": "2025/06/01",
"position": 77,
"query": "generative AI jobs"
}
] |
|
Open Positions - Microsoft Research
|
Microsoft Research
|
https://www.microsoft.com
|
[] |
空间智能组-工程实习生 · Senior Data Scientist – Microsoft Security · Research Intern for Machine Learning Group – Embodied AI research.
|
Career Opportunity
Come build community, explore your passions and do your best work at Microsoft with thousands of University interns from every corner of the world. As a PhD Researcher Intern, you will conduct research and leads…
| 2022-12-01T00:00:00 |
https://www.microsoft.com/en-us/research/careers/open-positions/
|
[
{
"date": "2022/12/01",
"position": 77,
"query": "artificial intelligence employment"
},
{
"date": "2023/01/01",
"position": 77,
"query": "artificial intelligence employment"
},
{
"date": "2023/02/01",
"position": 77,
"query": "artificial intelligence employment"
},
{
"date": "2023/02/01",
"position": 31,
"query": "generative AI jobs"
},
{
"date": "2023/04/01",
"position": 92,
"query": "AI employment"
},
{
"date": "2023/05/01",
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"query": "AI employment"
},
{
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{
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},
{
"date": "2023/08/01",
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"query": "AI employment"
},
{
"date": "2023/09/01",
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"query": "artificial intelligence employment"
},
{
"date": "2023/10/01",
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"query": "artificial intelligence employment"
},
{
"date": "2023/11/01",
"position": 93,
"query": "AI employment"
},
{
"date": "2023/11/01",
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},
{
"date": "2023/12/01",
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"query": "AI employment"
},
{
"date": "2024/01/01",
"position": 92,
"query": "AI employment"
},
{
"date": "2024/02/01",
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},
{
"date": "2024/03/01",
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},
{
"date": "2024/06/01",
"position": 80,
"query": "AI employment"
},
{
"date": "2024/06/01",
"position": 70,
"query": "artificial intelligence employment"
},
{
"date": "2024/07/01",
"position": 94,
"query": "AI employment"
},
{
"date": "2024/09/01",
"position": 72,
"query": "artificial intelligence employment"
},
{
"date": "2024/10/01",
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},
{
"date": "2024/11/01",
"position": 92,
"query": "AI employment"
},
{
"date": "2024/11/01",
"position": 67,
"query": "artificial intelligence employment"
}
] |
|
Why AI will never replace this one job - Rolling Out
|
Why AI will never replace this one job
|
https://rollingout.com
|
[
"Miriam Musa",
"Miriam Musa Is A Journalist Covering Health",
"Fitness",
"Tech",
"Food",
"Nutrition",
"News. She Specializes In Web Development",
"Cybersecurity",
"Content Writing. With An Hnd In Health Information Technology",
"A Bsc In Chemistry"
] |
Jobs heavy on these skills will likely remain human-dominated. Jobs that are more about following procedures, processing information, or ...
|
Everyone’s talking about which jobs AI will eliminate, but I stumbled across research that made me completely rethink the automation conversation. While machines are getting scary good at everything from writing code to diagnosing diseases, there’s one profession that seems genuinely immune to replacement — and the reason why reveals something profound about what it means to be human.
The job? Therapists and mental health counselors. And before you roll your eyes thinking this is obvious, hear me out on why this matters for everyone’s career future.
The human connection AI can’t fake
I initially thought therapy would be prime for AI disruption. After all, artificial intelligence can already analyze speech patterns, detect emotional states, and even provide cognitive behavioral therapy exercises. Some AI chatbots are surprisingly good at offering mental health support.
But here’s what I learned: therapy isn’t just about dispensing advice or following treatment protocols. It’s about creating a genuine human connection that validates someone’s experience and makes them feel truly understood.
When you’re struggling with depression, anxiety, or trauma, you don’t just need correct information — you need to feel like another human being genuinely cares about your pain. That moment when a therapist’s eyes show real understanding, when they respond to something you didn’t even say out loud, when they adjust their approach based on subtle cues you’re giving off — that’s irreplaceable.
AI can simulate empathy, but it can’t actually feel it. And somehow, people can sense the difference.
The improvisation factor that stumps machines
Every therapy session is like jazz improvisation. A therapist might walk into a session with a plan, but within five minutes, the conversation could go somewhere completely unexpected based on what the client brings up.
Maybe someone planned to discuss work stress but instead breaks down talking about their childhood. A human therapist reads the room, abandons their agenda, and follows the emotional thread that’s most important in that moment.
I watched a session where a client was describing a fight with their partner, but the therapist noticed they kept touching their wedding ring. That tiny observation led to a breakthrough about the client’s fear of divorce that they hadn’t even consciously acknowledged.
AI excels at pattern recognition, but human behavior in emotional crisis doesn’t follow predictable patterns. Each person’s psychological landscape is unique, shaped by countless experiences, traumas, and relationships that create responses no algorithm could anticipate.
The vulnerability paradox machines can’t solve
Here’s something fascinating: effective therapy requires clients to be vulnerable, but vulnerability requires trust, and trust requires believing that someone genuinely cares about your wellbeing rather than just processing your data.
People struggling with mental health often feel isolated and misunderstood. They need to believe that their therapist is a real person who has experienced human emotions, not a sophisticated computer program designed to help them.
The therapeutic relationship itself becomes part of the healing. When clients develop genuine connection with their therapist, they often experience their first healthy relationship in years. You can’t get that from interacting with an AI, no matter how sophisticated.
Why this matters beyond therapy
The reason therapists are irreplaceable reveals something crucial about the future job market: roles requiring genuine human connection, emotional intelligence, and adaptive creativity will remain human territory.
This doesn’t just apply to mental health. Teachers who inspire students, salespeople who build real relationships, managers who understand team dynamics, creative directors who capture cultural moments — these roles all require the same uniquely human skills that make therapists irreplaceable.
The key insight is that AI excels at tasks with clear inputs and outputs, but struggles with the messy, unpredictable, emotionally complex work that defines many human interactions.
The creativity and intuition gap
Therapy often involves creative problem-solving that combines professional training with personal intuition. A therapist might suggest an unconventional homework assignment, use a metaphor that perfectly captures a client’s situation, or try a completely different approach because something feels off.
These decisions aren’t based on data analysis — they’re based on years of human experience, emotional intelligence, and intuitive understanding of how people work. AI can suggest evidence-based interventions, but it can’t have that “aha” moment where a creative solution emerges from nowhere.
What this means for your career
If you’re worried about AI taking your job, look at what makes therapists irreplaceable: genuine empathy, creative problem-solving, ability to build trust, skill at reading subtle emotional cues, capacity for authentic human connection, and flexibility to adapt in unpredictable situations.
Jobs heavy on these skills will likely remain human-dominated. Jobs that are more about following procedures, processing information, or completing predictable tasks are more vulnerable to automation.
The surprising career lesson
The most AI-resistant careers aren’t necessarily the most technical ones — they’re the most human ones. While everyone’s rushing to learn coding or data analysis to stay relevant, the real job security might come from developing skills that make you more human, not more machine-like.
Emotional intelligence, creative problem-solving, authentic relationship-building, and the ability to provide genuine care and understanding — these aren’t just nice-to-have soft skills anymore. They might be the ultimate job security in an AI-dominated world.
The bigger picture about human value
Understanding why AI can’t replace therapists gives us a roadmap for thriving alongside artificial intelligence rather than competing with it. The future probably isn’t humans versus machines — it’s humans and machines working together, with each doing what they do best.
AI can handle the data processing, pattern recognition, and routine tasks. Humans can focus on creativity, emotional connection, complex problem-solving, and providing the care and understanding that makes life meaningful.
That division of labor doesn’t diminish human value — it highlights what makes us irreplaceable.
| 2025-07-05T00:00:00 |
2025/07/05
|
https://rollingout.com/2025/07/05/ai-never-replace-this-job-answer/
|
[
{
"date": "2022/12/01",
"position": 78,
"query": "artificial intelligence employment"
},
{
"date": "2023/01/01",
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"query": "artificial intelligence employment"
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"query": "artificial intelligence employment"
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{
"date": "2023/05/01",
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{
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{
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},
{
"date": "2023/11/01",
"position": 78,
"query": "artificial intelligence employment"
},
{
"date": "2024/02/01",
"position": 75,
"query": "artificial intelligence employment"
},
{
"date": "2024/03/01",
"position": 82,
"query": "artificial intelligence employment"
},
{
"date": "2024/06/01",
"position": 71,
"query": "artificial intelligence employment"
},
{
"date": "2024/09/01",
"position": 75,
"query": "artificial intelligence employment"
},
{
"date": "2024/10/01",
"position": 83,
"query": "artificial intelligence employment"
},
{
"date": "2024/11/01",
"position": 70,
"query": "artificial intelligence employment"
}
] |
Legislative Volatility, AI, Remote Work Among Challenges in ...
|
Legislative Volatility, AI, Remote Work Among Challenges in Employment Law
|
https://www.sdbj.com
|
[
"Madison Geering",
"Born",
"Raised In San Diego",
"Madison Takes Great Pride In Local Storytelling. Her Coverage At The San Diego Business Journal Includes Tourism",
"Hospitality",
"Nonprofits",
"Education",
"Retail. An Alumna Of San Diego State University S Journalism Program",
"She Has Written For Publications Including The San Diego Union-Tribune",
"The San Diego Sun. At The San Diego Press Club Awards"
] |
AI has been a major disruptor across sectors in the last five years. Across industries, both employees and managers alike have their concerns.
|
SAN DIEGO COUNTY – Last week, the Society for Human Resource Management (SHRM) hosted its annual conference at the San Diego Convention Center. During the 5-day conference, over 20,000 professionals gathered to discuss and learn about the trends and issues driving HR.
From artificial intelligence (AI) to pay transparency, the conference offered insights from over 400 speakers on a variety of subjects. However, one sentiment stood out: the landscape of HR and employment law is in a moment of flux.
“With so much change and volatility, our people are getting overwhelmed,” SHRM Chief of Staff Emily Dickens said.
SHRM’s conference provided resources, education and counsel on best practices handling topics like legislative changes, AI and remote work.
Legislative Volatility
The prerequisite to many of the current challenges in employment law is legislative volatility. As governmental leaders transition and set new agendas, HR professionals and legal counsel must adapt best practices. Fisher & Phillips Partner David Amaya weighs in on the impacts of this dynamic.
“The shifting legal landscape creates uncertainty and increased litigation risk for employers, particularly concerning issues like independent contractor definitions or joint-employer liability,” Amaya said. “Lawyers must also stay abreast of changing enforcement priorities from agencies like the U.S. Department of Labor, National Labor Relations Board (NLRB) and Equal Employment Opportunity Commission (EEOC), which reflect the current administration’s stance on worker protections or deregulation.”
According to Dickens, SHRM always discusses immigration policy as it relates to HR at its conference. This year, however, the organization put an emphasis on educating its members on compliance and procedures when approached by immigration enforcement.
“This was a great opportunity for us to dig deeper and to have more recent content for our members to access,” Dickens said.
Another major point discussed at the conference was the recent shift in diversity, equity and inclusion policies.
“DEI initiatives have shifted from corporate buzzwords to legal minefields— and DEI efforts are currently under a national microscope,” Gordon Rees Scully Mansukhani Partner Brandon Saxon said. “The presidential administration has issued a number of executive orders impacting DEI, and both the EEOC and California’s Civil Rights Department (CRD) are prioritizing enforcement around systemic discrimination, pay equity and underrepresentation.
“In California, employers are walking a legal tightrope, trying to advance diversity while avoiding claims of reverse discrimination,” he continued. “This has made documentation, neutrality in decision-making and legal vetting of DEI programs more important than ever.”
At the conference, Bowles Rice Attorney Mario Bordogna hosted a session called “An Employment Lawyer’s Top 10 Reasons Why Employers Get Sued by Employees (And How to Prevent Them All)!” During his presentation, Bordogna discussed many HR staples, like documentation, classification and work environment.
He also explored the impact of legislative volatility in practice.
“If you talk to employers and work with HR professionals on a regular basis, you will hear them have angst over the fact that they’re constantly having to switch their priorities,” Bordogna said. “It’d be better if they had a direct, clear, stable climate to operate in. Regulatory and legislative volatility is a huge deal. It’s a challenge for HR professionals that’s probably not going to go away.”
National inconsistency is a concern, according to Dickens.
“States are going to continue to pass laws on topics where they think the federal government is falling short, and they’re not going to look to each other to pass the same laws,” Dickens said. “It’d be great if they all talked to each other and said, ‘Regionally, these 10 states, we’re going to get together, and this is what paid leave is going to look like.’ If I’m a multi-state business, the impact on me is that I’ve got to hire more people, counsel in each state that’s going to help me interpret the laws.”
In order to address changing government priorities and stay out of legal trouble, Saxon said that vigilance is key for businesses.
“Under the current federal leadership, there’s been a clear shift toward pro-employee policies in general. In California, those changes layer on top of already progressive laws, creating a complex compliance environment,” Saxon said. “For HR teams and legal departments, this volatility demands constant updates to policies, handbooks and training—along with a heightened risk of liability for falling out of step.”
While much has changed in the last six months, Bordogna predicts that the dust still needs to settle.
“The Trump administration is still appointing some people,” Bordogna said. “The National Labor Relations Board still doesn’t have a quorum, the Equal Employment Opportunity Commission still needs members…When you start to get a handle on who will be in those roles, it’s easier to drill down on what the priorities and trends are going to be for the next number of years.”
AI in the Workplace
AI has been a major disruptor across sectors in the last five years. Across industries, both employees and managers alike have their concerns. Many professionals fear job displacement. Leadership is uncertain of how to navigate the issue as technology becomes increasingly synonymous with the workplace.
“SHRM has fully embraced the fact that AI can help us do our work,” Dickens said. “We actually have this mantra, ‘AI plus HI (human intelligence) is the ROI.’ We tell people that they are more likely to lose their job to an individual who knows how to leverage AI to get their job done than to the tool itself.”
In the field of HR, AI has been used to streamline and manage applicants.
“AI is transforming HR—from applicant screening to productivity monitoring—but it’s also creating legal risk,” Saxon said. “If left unchecked, algorithmic tools can unintentionally perpetuate bias or violate worker privacy laws. The EEOC has issued guidance on AI bias, and California is expected to follow suit with more robust regulations.”
If employers are not careful about AI use, it can spell detrimental repercussions.
“If historical data [that informs AI tools] reflects existing societal or organizational biases, the AI can perpetuate and even amplify discrimination against protected characteristics (e.g., race, gender, age, disability),” Amaya said. “Proactive risk management, continuous legal monitoring and a commitment to responsible AI deployment are paramount for businesses to leverage AI’s benefits while navigating its complex legal landscape.”
When first introduced, many businesses did not predict the far-reaching impact of advanced AI. Harnessing its benefits while mitigating its costs is a complex matter, one that professionals are still trying to understand.
“Employers need to vet vendors, audit AI outcomes for disparate impact, and ensure transparency with applicants and employees,” Saxon said. “This is an emerging area where legal compliance and ethical practices must go hand in hand.”
The Hybrid and Remote Era of Work
When the pandemic happened, every aspect of daily life and business was uprooted. Today, recovery is still underway.
“When you have a societal jolt like that, it forces everybody to rethink what they’re going to do, what’s going to happen next? Are we even going to be open tomorrow?” Bordogna said. “It triggers seismic changes that are irreversible. If you worked in this space, as I have for a long time, when that happened, you knew right away that whatever changes happened because of the pandemic, were going to be here [for a while].”
One of the major shifts in the workplace post-pandemic was the rise in remote and hybrid work. While necessary during the pandemic, employers have struggled to get employees to return to the office. As a result, many office buildings have closed or experience high vacancy. To accompany this shift, younger generations increasingly expect flexibility, said Bordogna.
“While no one has a crystal ball, based on the ever-changing policies, I would expect we are going to see more legislation—and litigation—focused on pay transparency, retaliation protections and the boundaries of remote work,” Saxon said. “The CRD has already shown it will treat remote harassment or discrimination as seriously as in-person conduct.
Whistleblower protections will continue to expand, especially tied to COVID leave, wage theft and public health.”
Saxon believes that California will lead the charge as the landscape of employment law and HR evolves.
“California has always set the pace for employment law nationwide, and that won’t change. What we’re seeing now is a perfect storm of political, social and technological forces reshaping the workplace,” Saxon said. “Employers that stay ahead of the curve—through education, compliance and a willingness to adapt—will be best positioned to thrive.”
| 2025-07-08T00:00:00 |
2025/07/08
|
https://www.sdbj.com/special-report/legislative-volatility-ai-remote-work-among-challenges-in-employment-law/
|
[
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{
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{
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{
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"query": "artificial intelligence employment"
},
{
"date": "2025/01/01",
"position": 99,
"query": "AI employment"
}
] |
California's AI Employment Discrimination Regs Receive Final ...
|
California’s AI Employment Discrimination Regs Receive Final Approval
|
https://ogletree.com
|
[] |
In a statement on the approval, the Civil Rights Council noted that “automated-decision systems” that rely on algorithms or AI “are increasingly ...
|
Quick Hits
The California Civil Rights Council has received final approval for comprehensive regulations governing the use of AI and “automated-decision systems” in employment, aimed at preventing discrimination.
These regulations clarify that employers must not use “automated-decision systems” that discriminate against applicants or employees based on protected characteristics under California antidiscrimination laws.
California joins other states in implementing AI regulations for employment decisions while continuing to explore additional legislation to manage the impact of emerging technologies in the workplace.
California’s new regulations are set to go into effect on October 1, 2025.
On June 27, 2025, the California Office of Administrative Law submitted a notice of approval on the latest modified text of the proposed regulations, which the California Civil Rights Department (CRD) advanced in March 2025. The Civil Rights Council, which is a branch of the CRD, has been considering the regulations for multiple years, going back to at least 2022. The Civil Rights Council said the final language was based on input from nonprofits, businesses, and others in more than forty public comment letters.
The final regulations clarify that it is unlawful for an employer to use an “automated-decision system” or selection criteria that discriminate against applicants or employees on a basis protected by the California Fair Employment and Housing Act (FEHA) and other California antidiscrimination laws.
Rationale
In a statement on the approval, the Civil Rights Council noted that “automated-decision systems” that rely on algorithms or AI “are increasingly used in employment settings to facilitate a wide range of decisions related to job applicants or employees, including concerning recruitment, hiring, and promotion.” While these technologies may have “myriad benefits, they can also exacerbate existing biases and contribute to discriminatory outcomes,” the Civil Rights Council stated.
“These new regulations on artificial intelligence in the workplace aim to help our state’s antidiscrimination protections keep pace,” CRD Director Kevin Kish said in a statement. “I applaud the Civil Rights Council for their commitment to protecting the rights of all Californians.”
Key Terms
The final regulations amend existing regulations to define key terms related to AI:
“automated-decision system[s]” are defined as any “computational process that makes a decision or facilitates human decision making regarding an employment benefit,” including processes that “may be derived from and/or use artificial intelligence, machine-learning, algorithms, statistics, and/or other data processing techniques.”
“agent” —The final regulations consider employers’ agents to be “employers” under FEHA regulations. Specifically, the regulations define “agent” as “any person acting on behalf of an employer, directly or indirectly, to exercise a function traditionally exercised by the employer or any other FEHA-regulated activity … including when such activities and decisions are conducted in whole or in part through the use of an automated decision system.” (Emphasis added.)
—The final regulations consider employers’ agents to be “employers” under FEHA regulations. Specifically, the regulations define “agent” as “any person acting on behalf of an employer, directly or indirectly, to exercise a function traditionally exercised by the employer or any other FEHA-regulated activity … including when such activities and decisions are conducted in whole or in part through the use of an automated decision system.” (Emphasis added.) “automated-decision system data” —The regulations cover “[a]ny data used to develop or customize an automated-decision system for use by a particular employer or other covered entity.”
—The regulations cover “[a]ny data used to develop or customize an automated-decision system for use by a particular employer or other covered entity.” “artificial intelligence” —The regulations define AI as “[a] machine-based system that infers, from the input it receives, how to generate outputs,” which can include “predictions, content, recommendations, or decisions.”
—The regulations define AI as “[a] machine-based system that infers, from the input it receives, how to generate outputs,” which can include “predictions, content, recommendations, or decisions.” “machine learning”—The term is defined as the “ability for a computer to use and learn from its analysis of data or experience and apply this learning automatically in future calculations or tasks.”
Unlawful Selection Criteria
The regulations further clarify that California laws prohibiting discriminatory hiring tools apply to automated-decisions systems or AI tools. The regulations state that it is “unlawful for an employer or other covered entity to use an automated-decision system or selection criteria (including a qualification standard, employment test, or proxy) that discriminates against an applicant or employee or a class of applicants or employees on a basis protected” by FEHA.
Next Steps
With the regulations, California joins a growing number of states and jurisdictions, including Colorado, Illinois, and New York City, in enacting laws or regulations concerning AI and similar technologies, including their use to make employment-related decisions. At the same time, California continues to mull overlapping pieces of legislation and proposed regulations to manage AI. Notably, a bill called the “No Robo Bosses Act” would require employers to provide human oversight over the use of AI.
The state laws come as President Donald Trump’s administration has sought to remove legal restrictions on AI to promote technology development in the United States. The administration’s spending bill included a ten-year moratorium prohibiting states from enacting or enforcing laws and regulations concerning AI. However, lawmakers in the U.S. Senate modified that proposal, and it was ultimately dropped from the bill as passed by the Senate.
The new California regulations are set to go into effect on October 1, 2025. Employers in California may want to review the new regulations and consider how they impact their operations and employment decision-making policies and practices, including recruitment, hiring, promotions, and disciplinary decisions.
Ogletree Deakins’ Technology Practice Group will continue to monitor developments and will provide updates on the California, Employment Law, and Technology blogs as additional information becomes available.
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| 2022-12-01T00:00:00 |
https://ogletree.com/insights-resources/blog-posts/californias-ai-employment-discrimination-regs-receive-final-approval/
|
[
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{
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},
{
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{
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{
"date": "2025/01/01",
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}
] |
|
Jobs at NVIDIA | NVIDIA Careers
|
Like No Place You’ve Ever Worked
|
https://www.nvidia.com
|
[] |
Our product lines range from consumer graphics to self-driving cars to the growing field of AI. We've built an exceptional team of people from around the globe ...
|
Join our hardware team for the opportunity to have real impact in a dynamic, technology-focused company. Our product lines range from consumer graphics to self-driving cars to the growing field of AI. We’ve built an exceptional team of people from around the globe whose mission is to push the frontiers of what’s possible and to define the platform for the future of computing.
| 2022-12-01T00:00:00 |
https://www.nvidia.com/en-us/about-nvidia/careers/
|
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|
California Finalizes AI Employment Rules - The National Law Review
|
California Finalizes AI Employment Rules
|
https://natlawreview.com
|
[] |
In a statement on the approval, the Civil Rights Council noted that “automated-decision systems” that rely on algorithms or AI “are increasingly ...
|
Quick Hits
The California Civil Rights Council has received final approval for comprehensive regulations governing the use of AI and “automated-decision systems” in employment, aimed at preventing discrimination.
These regulations clarify that employers must not use “automated-decision systems” that discriminate against applicants or employees based on protected characteristics under California antidiscrimination laws.
California joins other states in implementing AI regulations for employment decisions while continuing to explore additional legislation to manage the impact of emerging technologies in the workplace.
California’s new regulations are set to go into effect on October 1, 2025.
On June 27, 2025, the California Office of Administrative Law submitted a notice of approval on the latest modified text of the proposed regulations, which the California Civil Rights Department (CRD) advanced in March 2025. The Civil Rights Council, which is a branch of the CRD, has been considering the regulations for multiple years, going back to at least 2022. The Civil Rights Council said the final language was based on input from nonprofits, businesses, and others in more than forty public comment letters.
The final regulations clarify that it is unlawful for an employer to use an “automated-decision system” or selection criteria that discriminate against applicants or employees on a basis protected by the California Fair Employment and Housing Act (FEHA) and other California antidiscrimination laws.
Rationale
In a statement on the approval, the Civil Rights Council noted that “automated-decision systems” that rely on algorithms or AI “are increasingly used in employment settings to facilitate a wide range of decisions related to job applicants or employees, including concerning recruitment, hiring, and promotion.” While these technologies may have “myriad benefits, they can also exacerbate existing biases and contribute to discriminatory outcomes,” the Civil Rights Council stated.
“These new regulations on artificial intelligence in the workplace aim to help our state’s antidiscrimination protections keep pace,” CRD Director Kevin Kish said in a statement. “I applaud the Civil Rights Council for their commitment to protecting the rights of all Californians.”
Key Terms
The final regulations amend existing regulations to define key terms related to AI:
“ automated-decision system[s] ” are defined as any “computational process that makes a decision or facilitates human decision making regarding an employment benefit,” including processes that “may be derived from and/or use artificial intelligence, machine-learning, algorithms, statistics, and/or other data processing techniques.”
” are defined as any “computational process that makes a decision or facilitates human decision making regarding an employment benefit,” including processes that “may be derived from and/or use artificial intelligence, machine-learning, algorithms, statistics, and/or other data processing techniques.” “agent” —The final regulations consider employers’ agents to be “employers” under FEHA regulations. Specifically, the regulations define “agent” as “any person acting on behalf of an employer, directly or indirectly, to exercise a function traditionally exercised by the employer or any other FEHA-regulated activity … including when such activities and decisions are conducted in whole or in part through the use of an automated decision system.” (Emphasis added.)
—The final regulations consider employers’ agents to be “employers” under FEHA regulations. Specifically, the regulations define “agent” as “any person acting on behalf of an employer, directly or indirectly, to exercise a function traditionally exercised by the employer or any other FEHA-regulated activity … including when such activities and decisions are conducted in whole or in part through the use of an automated decision system.” (Emphasis added.) “automated-decision system data” —The regulations cover “[a]ny data used to develop or customize an automated-decision system for use by a particular employer or other covered entity.”
—The regulations cover “[a]ny data used to develop or customize an automated-decision system for use by a particular employer or other covered entity.” “artificial intelligence” —The regulations define AI as “[a] machine-based system that infers, from the input it receives, how to generate outputs,” which can include “predictions, content, recommendations, or decisions.”
—The regulations define AI as “[a] machine-based system that infers, from the input it receives, how to generate outputs,” which can include “predictions, content, recommendations, or decisions.” “machine learning”—The term is defined as the “ability for a computer to use and learn from its analysis of data or experience and apply this learning automatically in future calculations or tasks.”
Unlawful Selection Criteria
The regulations further clarify that California laws prohibiting discriminatory hiring tools apply to automated-decisions systems or AI tools. The regulations state that it is “unlawful for an employer or other covered entity to use an automated-decision system or selection criteria (including a qualification standard, employment test, or proxy) that discriminates against an applicant or employee or a class of applicants or employees on a basis protected” by FEHA.
Next Steps
With the regulations, California joins a growing number of states and jurisdictions, including Colorado, Illinois, and New York City, in enacting laws or regulations concerning AI and similar technologies, including their use to make employment-related decisions. At the same time, California continues to mull overlapping pieces of legislation and proposed regulations to manage AI. Notably, a bill called the “No Robo Bosses Act” would require employers to provide human oversight over the use of AI.
The state laws come as President Donald Trump’s administration has sought to remove legal restrictions on AI to promote technology development in the United States. The administration’s spending bill included a ten-year moratorium prohibiting states from enacting or enforcing laws and regulations concerning AI. However, lawmakers in the U.S. Senate modified that proposal, and it was ultimately dropped from the bill as passed by the Senate.
The new California regulations are set to go into effect on October 1, 2025. Employers in California may want to review the new regulations and consider how they impact their operations and employment decision-making policies and practices, including recruitment, hiring, promotions, and disciplinary decisions.
| 2022-12-01T00:00:00 |
https://natlawreview.com/article/californias-ai-employment-discrimination-regs-receive-final-approval
|
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{
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},
{
"date": "2024/11/01",
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{
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"position": 97,
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},
{
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] |
|
How is AI transforming employment in Mexico, according to PwC?
|
How is AI transforming employment in Mexico, according to PwC?
|
https://www.merca20.com
|
[
"Laura Islas",
"Seo. Periodista. Le Voy Al Cruz Azul. Runner. Fotografía. Literatura",
"Periodismo Y Gatos. Me Gusta Contar Historias. No Sé Vivir Sin Un Libro."
] |
The PwC Barometer reveals that jobs exposed to artificial intelligence have experienced significant growth. Between 2021 and 2024, vacancies in ...
|
Artificial intelligence (AI) is reshaping the global labor market, and Mexico is no exception. This is revealed by the latest AI Barometer in the Labor Market 2025, prepared by PwC, which provides a detailed analysis of the impact of this technology on employment, required skills, and formal education in the country.
The study offers revealing data on the most affected sectors, the emerging opportunities, and the challenges that companies, workers, and business leaders face in adopting artificial intelligence.
What is PwC’s AI Barometer in the Labor Market?
The AI Barometer in the Labor Market 2025, developed by PwC, is a global analysis that examines nearly one billion job postings and thousands of corporate reports across six continents. In Mexico’s case, more than 18 million observations were evaluated to understand how artificial intelligence is reshaping the labor landscape.
This report measures the current impact and identifies trends, projections, and opportunities that will shape the future of employment in Mexico. Its value lies in providing companies, leaders, and decision-makers with a roadmap to navigate the changes driven by artificial intelligence.
How has the demand for AI-related jobs evolved in Mexico?
Between 2021 and 2024, Mexico experienced sustained growth in job postings requiring artificial intelligence skills. According to PwC, the demand recorded a compound annual growth rate (CAGR) of 33.6%, reflecting the increasing interest in AI-trained professionals.
However, 2024 brought an economic slowdown that affected the overall labor market, causing a decline in the percentage of AI-related vacancies. While 55,000 job offers were posted in 2023, by 2024 the number dropped to 42,000, representing just 0.8% of total vacancies.
This data suggests that although artificial intelligence remains a strategic area, its integration faces ups and downs due to the national economic context.
Which sectors are looking for AI-skilled talent?
The PwC report identifies that the professional, scientific, and technical services sector remains the largest employer in Mexico, maintaining the highest proportion of vacancies, despite a slight decline from 10.6% in 2021 to 10.4% in 2024.
Meanwhile, the manufacturing sector has shown a significant rebound, rising from 8.2% of vacancies in 2023 to 10.1% in 2024, consolidating itself as the second sector with the most job offers.
In terms of specific AI-related skills, the information and communications sector leads the demand, with steady growth from 2.2% in 2021 to over 3.6% in 2024. Other sectors, such as finance, insurance, manufacturing, and healthcare services, still show a slower adoption of AI, with less than 1% of their vacancies focused on this area.
What impact does artificial intelligence have on different types of occupations?
The PwC Barometer reveals that jobs exposed to artificial intelligence have experienced significant growth. Between 2021 and 2024, vacancies in occupations highly exposed to AI increased by 88%.
Particularly striking is the case of CEO and senior management positions, where vacancies grew by 250%, reflecting that AI adoption not only transforms technical positions but also leadership levels.
In the specific case of generative AI, growth in exposed occupations reached 84%, with senior positions once again topping the list, with an astonishing 600% increase in job offers.
Artificial intelligence and educational requirements in the Mexican labor market
One of the most interesting findings from PwC’s report is the decline in degree requirements for AI-related jobs.
Between 2021 and 2024, jobs with high AI exposure saw university degree requirements drop from 14% to 12%, while for less exposed jobs, the decline was from 2% to 1%. Although a gap still exists, the data suggests that AI is helping to democratize access to skilled jobs by reducing formal entry barriers.
In the case of jobs related to augmented AI, degree requirements fell by 15%, dropping from 18% to 15%, while in automated jobs the decline was from 11% to 10%.
This phenomenon suggests that practical experience and technical skills in artificial intelligence may carry more weight than traditional academic degrees in the hiring process.
What are the implications of this outlook for business leaders in Mexico?
PwC identifies five key recommendations for business leaders facing the growing influence of artificial intelligence:
| 2025-07-07T00:00:00 |
2025/07/07
|
https://www.merca20.com/how-is-ai-transforming-employment-in-mexico-according-to-pwc/
|
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"date": "2024/09/01",
"position": 91,
"query": "artificial intelligence employment"
},
{
"date": "2024/10/01",
"position": 99,
"query": "artificial intelligence employment"
},
{
"date": "2024/11/01",
"position": 82,
"query": "artificial intelligence employment"
}
] |
Artificial Intelligence GTM Lead, Google Cloud (German, English)
|
Artificial Intelligence GTM Lead, Google Cloud (German, English) — Google Careers
|
https://www.google.com
|
[] |
To all recruitment agencies: Google does not accept agency resumes. Please do not forward resumes to our jobs alias, Google employees, or any other organization ...
|
Information collected and processed as part of your Google Careers profile, and any job applications you choose to submit is subject to Google's Applicant and Candidate Privacy Policy.
Google is proud to be an equal opportunity and affirmative action employer. We are committed to building a workforce that is representative of the users we serve, creating a culture of belonging, and providing an equal employment opportunity regardless of race, creed, color, religion, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition (including breastfeeding), expecting or parents-to-be, criminal histories consistent with legal requirements, or any other basis protected by law. See also Google's EEO Policy, Know your rights: workplace discrimination is illegal, Belonging at Google, and How we hire.
If you have a need that requires accommodation, please let us know by completing our Accommodations for Applicants form.
Google is a global company and, in order to facilitate efficient collaboration and communication globally, English proficiency is a requirement for all roles unless stated otherwise in the job posting.
To all recruitment agencies: Google does not accept agency resumes. Please do not forward resumes to our jobs alias, Google employees, or any other organization location. Google is not responsible for any fees related to unsolicited resumes.
| 2022-12-01T00:00:00 |
https://www.google.com/about/careers/applications/jobs/results/110668627488711366-artificial-intelligence-gtm-lead-google-cloud-german-english?page=117
|
[
{
"date": "2022/12/01",
"position": 90,
"query": "artificial intelligence employment"
},
{
"date": "2023/01/01",
"position": 90,
"query": "artificial intelligence employment"
},
{
"date": "2023/02/01",
"position": 91,
"query": "artificial intelligence employment"
}
] |
|
Federal AI Regulation Moratorium Dropped from Final Bill
|
Federal AI Regulation Moratorium Dropped from Final Bill
|
https://natlawreview.com
|
[] |
Thus, the version signed into law leaves states and localities free to continue regulating AI systems, including the use of such systems to make ...
|
On July 4, 2025, President Trump signed the One Big Beautiful Bill Act into law—a budget reconciliation bill enacting several signature policies of the President’s second-term agenda. Left on the cutting-room floor, however, was an ambitious attempt to prohibit nearly all state and local regulation of artificial intelligence (“AI”) for the foreseeable future.
The version of the bill passed by the House of Representatives on May 22, 2025, contained a provision preventing states and localities, for a period of 10 years, from enforcing “any law or regulation … limiting, restricting, or otherwise regulating artificial intelligence models, artificial intelligence systems, or automated decision systems entered into interstate commerce.” Initially, a proposed version of the bill in the Senate included a similar provision that made the moratorium a condition of states receiving any of the $500 million in funds earmarked to support deployment of AI models or systems and underlying infrastructure.
However, the moratorium was ultimately stripped from the bill when it became clear the moratorium lacked majority support.
Thus, the version signed into law leaves states and localities free to continue regulating AI systems, including the use of such systems to make or assist with employment decisions such as hiring, firing, promotions, discipline, evaluations, compensation, and the like. Prominent statutes regulating such uses of AI have passed in Colorado, New York City, and Illinois, and are under consideration in several other states. However, the demise of the moratorium in the budget reconciliation law has renewed calls by some for federal regulation in the space that would prevent a patchwork of state and local laws regulating AI.
We will continue to monitor and report on developments in this space.
| 2022-12-01T00:00:00 |
https://natlawreview.com/article/big-beautiful-bill-leaves-ai-regulation-states-and-localities-now
|
[
{
"date": "2022/12/01",
"position": 93,
"query": "artificial intelligence employment"
},
{
"date": "2023/01/01",
"position": 94,
"query": "artificial intelligence employment"
},
{
"date": "2023/02/01",
"position": 95,
"query": "artificial intelligence employment"
}
] |
|
Software Engineer (Technical Leadership) - AI Specialist
|
Software Engineer (Technical Leadership) - AI Specialist
|
https://www.metacareers.com
|
[] |
Meta is seeking Researchers and Engineers to join our Generative AI organizations. Candidates will have industry experience working on LLM related topics ...
|
Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today—beyond the constraints of screens, the limits of distance, and even the rules of physics.
| 2022-12-01T00:00:00 |
https://www.metacareers.com/jobs/4135917433304743
|
[
{
"date": "2022/12/01",
"position": 94,
"query": "artificial intelligence employment"
},
{
"date": "2023/01/01",
"position": 93,
"query": "artificial intelligence employment"
},
{
"date": "2023/02/01",
"position": 94,
"query": "artificial intelligence employment"
},
{
"date": "2023/02/01",
"position": 52,
"query": "generative AI jobs"
},
{
"date": "2023/04/01",
"position": 48,
"query": "generative AI jobs"
},
{
"date": "2023/05/01",
"position": 94,
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},
{
"date": "2023/05/01",
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"query": "generative AI jobs"
},
{
"date": "2023/06/01",
"position": 52,
"query": "generative AI jobs"
},
{
"date": "2023/07/01",
"position": 53,
"query": "generative AI jobs"
},
{
"date": "2023/09/01",
"position": 50,
"query": "generative AI jobs"
},
{
"date": "2023/10/01",
"position": 72,
"query": "generative AI jobs"
},
{
"date": "2023/11/01",
"position": 94,
"query": "artificial intelligence employment"
},
{
"date": "2023/11/01",
"position": 54,
"query": "generative AI jobs"
},
{
"date": "2023/12/01",
"position": 50,
"query": "generative AI jobs"
},
{
"date": "2024/01/01",
"position": 50,
"query": "generative AI jobs"
},
{
"date": "2024/02/01",
"position": 38,
"query": "generative AI jobs"
},
{
"date": "2024/03/01",
"position": 52,
"query": "generative AI jobs"
},
{
"date": "2024/06/01",
"position": 91,
"query": "artificial intelligence employment"
},
{
"date": "2024/07/01",
"position": 86,
"query": "generative AI jobs"
},
{
"date": "2024/08/01",
"position": 57,
"query": "generative AI jobs"
},
{
"date": "2024/09/01",
"position": 95,
"query": "artificial intelligence employment"
},
{
"date": "2024/09/01",
"position": 69,
"query": "generative AI jobs"
},
{
"date": "2024/11/01",
"position": 92,
"query": "artificial intelligence employment"
},
{
"date": "2024/11/01",
"position": 52,
"query": "generative AI jobs"
},
{
"date": "2025/01/01",
"position": 57,
"query": "generative AI jobs"
}
] |
|
National Security Agency Careers | Apply Now
|
National Security Agency Careers
|
https://www.nsa.gov
|
[] |
Artificial Intelligence Security Center · Press Room · Press Releases ... Answers to all your questions about the pre-employment process at NSA. Learn More ...
|
No Such Podcast
NSA is known as home to the world’s greatest codemakers and codebreakers — “No Such Podcast” will bring people to the table from across the agency to discuss our role as a combat support agency, our foreign signals intelligence and cybersecurity missions, and so much more.
| 2022-12-01T00:00:00 |
https://www.nsa.gov/careers/
|
[
{
"date": "2022/12/01",
"position": 95,
"query": "artificial intelligence employment"
},
{
"date": "2023/01/01",
"position": 95,
"query": "artificial intelligence employment"
},
{
"date": "2023/02/01",
"position": 96,
"query": "artificial intelligence employment"
},
{
"date": "2023/05/01",
"position": 95,
"query": "artificial intelligence employment"
},
{
"date": "2023/11/01",
"position": 95,
"query": "artificial intelligence employment"
},
{
"date": "2024/06/01",
"position": 93,
"query": "artificial intelligence employment"
},
{
"date": "2024/09/01",
"position": 96,
"query": "artificial intelligence employment"
},
{
"date": "2024/11/01",
"position": 93,
"query": "artificial intelligence employment"
},
{
"date": "2025/03/01",
"position": 92,
"query": "artificial intelligence employment"
},
{
"date": "2025/05/01",
"position": 88,
"query": "artificial intelligence employment"
}
] |
|
Turing AI Precision Engine PLO at Sanofi
|
Turing AI Precision Engine PLO at Sanofi
|
https://jobs.sanofi.com
|
[] |
... Artificial Intelligence. You will own the strategy, development and ... All qualified applicants will receive consideration for employment ...
|
Job Title: Turing AI Precision Engine PLO
Location: Cambridge, MA Morristown, NJ
About the Job
At Sanofi, we’re committed to providing the next-gen healthcare that patients and customers need. It’s about harnessing data insights and leveraging AI responsibly to search deeper and solve sooner than ever before.
At Sanofi, we are an innovative global healthcare company, driven by one purpose: we chase the miracles of science to improve people’s lives. Our team, across some 100 countries, is dedicated to transforming the practice of medicine by working to turn the impossible into the possible. We provide potentially life-changing treatment options and life-saving vaccine protection to millions of people globally, while putting sustainability and social responsibility at the center of our ambitions. We’re committed to providing the next-gen healthcare that patients and customers need. It’s about harnessing data insights and leveraging AI responsibly to search deeper and solve sooner than ever before.
Sanofi is driving an industry leading transformation toward AI powered Omnichannel experience for their customers, including Health Care Practitioners.
As the Turing AI Precision Engine PLO, which is driving the AI-/data-enabled HCP experience, she/he will be accountable for selected products combining Omnichannel capability with Data and Artificial Intelligence.
You will own the strategy, development and execution of the data-driven digital product that enhances engagement with Healthcare Professionals (HCPs). This role sits at the intersection of data/AI, digital product, and commercial strategy, ensuring that HCP interactions are personalized, measurable and impactful. You will lead cross-functional teams to build scalable, insights-driven experience that improves HCP satisfaction and commercial outcomes.
We are an innovative global healthcare company with one purpose: to chase the miracles of science to improve people’s lives. We’re also a company where you can flourish and grow your career, with countless opportunities to explore, make connections with people, and stretch the limits of what you thought was possible. Ready to get started?
Main Responsibilities:
Product Strategy and Ownership
Define and champion the data/AI strategy for HCP experience aligned with long-term go-to-market objectives, and contribute to best-in-class HCP experience
Timely present insights and product performance to senior leadership, advocating for data-driven strategies, and how it contributes to the NorthStar
Own the quality delivery of business (& experience) impact on time and under budget, in line with overall HCP experience roadmap and KPIs
Data-driven Decision Making
MUST areas to focus:
Secure Turing offering is adapted and enhance to support launch brand (TOP12) in prelaunch-/launch- phase, driving a company-wide measurable impact
Drive and transform early initiatives on Turing for Marketers and bridge AI with Marketing Automation
Finalise Turing standardization under Turing MAX, continue roll out and maintain ruthless automation efforts
Critical aspects to build muscle, continuously:
Establish measurement frameworks to track engagement effectiveness (e.g. NPS, conversion rates, content utilization, cost per touchpoint, etc.)
Partner with data scientist and BI teams to translate HCP engagement data into actionable insights
undefined
Cross-functional Leadership & Stakeholder Management
Communicate and collaborate transversally with business, GTMC & market to develop and execute digital strategies that support best-in-class HCP experience
Build consensus among diverse executive stakeholders on strategic and operational priorities
Innovation & Emerging technology
Explore and pilot AI-driven engagement practice (e.g. next-best-action model, agentic ai, etc.), while stay flexible in build-/buy- solutions
Stay ahead of trends in digital health, artificial intelligence, omnichannel marketing, and HCP analytics to drive innovation.
Product Delivery & Vendor management
Work closely with the Data teams both in COE as well as commercial performance analytics, and actively promote company standards.
Ensure the on time and on quality delivery of outcome, incl. performance monitoring, impact measurement, budget process, risk mitigation, etc.
Manage partnership strategy and execution with external partners (vendors, agencies, analytics/AI providers) to enhance capabilities from time to time
Team management
Manage a team of Product Owners responsible for functional domain of the product line
Coaching teams and build a capable team, and be able to shift and lift team capabilities along with the priorities evolve
About You
Required Qualifications:
8+ years in digital product management, commercial analytics or modern digital marketing, with 2-3 years focus on AI/ML application in customer experience, personalization, or predictive analytics.
Proven ability to lead agile product development and work with engineers, data scientist, data engineers, UX and compliance teams.
Proven track record leading enterprise-wide strategic initiatives with significant business impact, with strong business acumen and ability to connect technology investments to business outcomes
Exceptional leadership, influence, and communication skills at the executive level
Preferred Qualifications:
Experience in pharmaceutical or healthcare industry leadership roles
Experience with AI/ML technologies and their application in customer-facing sales & marketing operations
Education:
Advanced Scientific, Business or Engineering Degree, Marketing, Computer Science, Information Technology, or related discipline is required
Languages:
English a must, French a plus, other languages a plus
What Sets You Apart:
A passion for leveraging digital innovation to improve patient lives and transform healthcare delivery.
Ability to navigate complex stakeholder landscapes and drive consensus across diverse teams.
Agility in adapting to rapid technological changes and market shifts.
A global perspective with local insights, understanding the nuances of the North American healthcare market.
Commitment to ethical leadership and maintaining the highest standards of integrity in all aspects of work.
Why Choose Us?
Bring the miracles of science to life alongside a supportive, future-focused team.
Discover endless opportunities to grow your talent and drive your career, whether it’s through a promotion or lateral move, at home or internationally.
Enjoy a thoughtful, well-crafted rewards package that recognizes your contribution and amplifies your impact.
Take good care of yourself and your family, with a wide range of health and wellbeing benefits including high-quality healthcare, prevention and wellness programs and at least 14 weeks’ gender-neutral parental leave.
Sanofi Inc. and its U.S. affiliates are Equal Opportunity and Affirmative Action employers committed to a culturally diverse workforce. All qualified applicants will receive consideration for employment without regard to race; color; creed; religion; national origin; age; ancestry; nationality; marital, domestic partnership or civil union status; sex, gender, gender identity or expression; affectional or sexual orientation; disability; veteran or military status or liability for military status; domestic violence victim status; atypical cellular or blood trait; genetic information (including the refusal to submit to genetic testing) or any other characteristic protected by law.
#GD-SA
#LI-SA
#LI-Hybrid
#vhd
All compensation will be determined commensurate with demonstrated experience. Employees may be eligible to participate in Company employee benefit programs, and additional benefits information can be found here.
| 2022-12-01T00:00:00 |
https://jobs.sanofi.com/en/job/cambridge/turing-ai-precision-engine-plo/2649/27103715584
|
[
{
"date": "2022/12/01",
"position": 96,
"query": "artificial intelligence employment"
},
{
"date": "2023/01/01",
"position": 80,
"query": "AI impact jobs"
},
{
"date": "2023/01/01",
"position": 96,
"query": "artificial intelligence employment"
},
{
"date": "2023/02/01",
"position": 86,
"query": "AI impact jobs"
},
{
"date": "2023/02/01",
"position": 97,
"query": "artificial intelligence employment"
},
{
"date": "2023/03/01",
"position": 71,
"query": "AI impact jobs"
}
] |
|
Senior Software Engineer, AI/Machine Learning - Google Careers
|
Machine Learning — Google Careers
|
https://careers.google.com
|
[] |
We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking ...
|
About the job
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
We are seeking a highly motivated Engineer to join our team in Hyderabad, focused on building Artificial Intelligence (AI) agents. You will work with peers and software engineers developing agents. This will involve utilizing the latest advancements in large language models (LLMs). You will be developing more capable, factual, and helpful deflection agents which can auto-resolve support issues with the potential to impact millions of users.
In this role, you will have the opportunity to apply your experience such as LLM post-training and evaluation to create AI agents that can understand and interact with the world in unprecedented ways.
At Corp Eng, we build world-leading business solutions that scale a more helpful Google for everyone. As Google’s IT organization, we provide end-to-end solutions for organizations across Google. We deliver the right tools, platforms, and experiences for all Googlers as they create more helpful products and services for everyone. In the simplest terms, we are Google for Googlers.
| 2022-12-01T00:00:00 |
https://careers.google.com/jobs/results/109023139618267846-senior-software-engineer/
|
[
{
"date": "2022/12/01",
"position": 97,
"query": "artificial intelligence employment"
},
{
"date": "2023/01/01",
"position": 93,
"query": "AI impact jobs"
},
{
"date": "2023/01/01",
"position": 97,
"query": "artificial intelligence employment"
},
{
"date": "2023/01/01",
"position": 79,
"query": "AI employment"
},
{
"date": "2023/02/01",
"position": 92,
"query": "AI impact jobs"
},
{
"date": "2023/02/01",
"position": 99,
"query": "artificial intelligence employment"
},
{
"date": "2023/02/01",
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"query": "generative AI jobs"
},
{
"date": "2023/03/01",
"position": 94,
"query": "AI impact jobs"
},
{
"date": "2023/04/01",
"position": 65,
"query": "generative AI jobs"
},
{
"date": "2023/04/01",
"position": 62,
"query": "machine learning job market"
},
{
"date": "2023/05/01",
"position": 97,
"query": "artificial intelligence employment"
},
{
"date": "2023/05/01",
"position": 66,
"query": "generative AI jobs"
},
{
"date": "2023/06/01",
"position": 61,
"query": "generative AI jobs"
},
{
"date": "2023/07/01",
"position": 67,
"query": "generative AI jobs"
},
{
"date": "2023/07/01",
"position": 67,
"query": "machine learning job market"
},
{
"date": "2023/09/01",
"position": 62,
"query": "generative AI jobs"
},
{
"date": "2023/10/01",
"position": 69,
"query": "generative AI jobs"
},
{
"date": "2023/10/01",
"position": 66,
"query": "machine learning job market"
},
{
"date": "2023/11/01",
"position": 97,
"query": "artificial intelligence employment"
},
{
"date": "2023/11/01",
"position": 69,
"query": "generative AI jobs"
},
{
"date": "2023/12/01",
"position": 67,
"query": "generative AI jobs"
},
{
"date": "2023/12/01",
"position": 61,
"query": "machine learning job market"
},
{
"date": "2024/01/01",
"position": 70,
"query": "generative AI jobs"
},
{
"date": "2024/02/01",
"position": 66,
"query": "generative AI jobs"
},
{
"date": "2024/03/01",
"position": 67,
"query": "generative AI jobs"
},
{
"date": "2024/03/01",
"position": 61,
"query": "machine learning job market"
},
{
"date": "2024/04/01",
"position": 99,
"query": "AI impact jobs"
},
{
"date": "2024/06/01",
"position": 96,
"query": "artificial intelligence employment"
},
{
"date": "2024/07/01",
"position": 67,
"query": "generative AI jobs"
},
{
"date": "2024/07/01",
"position": 65,
"query": "machine learning job market"
},
{
"date": "2024/08/01",
"position": 67,
"query": "generative AI jobs"
},
{
"date": "2024/09/01",
"position": 99,
"query": "artificial intelligence employment"
},
{
"date": "2024/09/01",
"position": 64,
"query": "generative AI jobs"
},
{
"date": "2024/11/01",
"position": 95,
"query": "artificial intelligence employment"
},
{
"date": "2024/11/01",
"position": 66,
"query": "generative AI jobs"
},
{
"date": "2024/11/01",
"position": 60,
"query": "machine learning job market"
},
{
"date": "2025/01/01",
"position": 68,
"query": "generative AI jobs"
},
{
"date": "2025/01/01",
"position": 61,
"query": "machine learning job market"
}
] |
|
Data and modelling | Ministry of Business, Innovation & Employment
|
Data and modelling
|
https://www.mbie.govt.nz
|
[] |
Responsible training data collection and modelling practices support useful, secure and fair artificial intelligence systems.
|
Scenario B: BigBuild
BigBuild is a New Zealand construction firm looking to speed up their recruitment processes. They decide to trial a CV screening AI tool to scan applications and rank them based on desired skills, experience and other key words. Those that fall below a certain threshold are filtered out and do not progress further.
Following a risk assessment, BigBuild puts in place some risk mitigations, including being transparent with applicants about AI involvement in the shortlisting process, and implementing a human reviewer for all shortlisted applications.
After deployment, 50 candidates apply for a position at BigBuild. They are advised that AI is being used. The human reviewer notices that, despite there being an equal amount of women who applied, only 3 of the 10 shortlisted applications were women. This is not in line with BigBuild’s goals for gender diversity in hiring. They also receive correspondence from rejected female candidates questioning the AI system results, and upon evaluation their applications were at least as deserving as those that were shortlisted.
On investigation, BigBuild finds that the AI model was trained on historical ‘successful hire’ data – which underrepresented women, likely due to a mixture of historical human bias and societal factors. Therefore, despite the AI system being fed ‘gender blind’ CVs, it viewed factors and language moreso included on men’s CVs as positive (such as ‘executed’). Meanwhile, it assessed similar factors that appear moreso on women’s CVs (for example, language like ‘supported’ and ‘co-ordinated’) negatively. Career gaps (that may have been taken due to maternity leave), and certain hobbies, roles or institutions (such as ‘volleyball’, ‘girl’s school’) also contributed to the system being more likely to rank female candidates lower.
To address the issue, BigBuild apologises to impacted candidates and paused use of the tool while it works with the provider to retrain the model using more representative, bias-audited data. They also introduce a diverse set of ‘success profiles’ representing a range of career pathways and experiences, and lower the shortlisting threshold so more applications move to human review.
BigBuild regularly monitor and evaluate the tool’s performance throughout their trial, and continue to provide feedback to the developer as necessary.
| 2022-12-01T00:00:00 |
https://www.mbie.govt.nz/business-and-employment/business/support-for-business/responsible-ai-guidance-for-businesses/artificial-intelligence-system-specific-considerations/data-and-modelling
|
[
{
"date": "2022/12/01",
"position": 99,
"query": "artificial intelligence employment"
}
] |
|
Effects of technological change and automation on industry ...
|
Effects of technological change and automation on industry structure and (wage-)inequality: insights from a dynamic task-based model
|
https://link.springer.com
|
[
"Dawid",
"Hdawid Uni-Bielefeld.De",
"Department Of Business Administration",
"Economics",
"Center For Mathematical Economics",
"Bielefeld University",
"Bielefeld",
"Neugart",
"Michael.Neugart Tu-Darmstadt.De",
"Department Of Law"
] |
by H Dawid · 2023 · Cited by 21 — Job polarization is driven by automation that mainly affects manufacturing and triggers the emergence of high skill and low skill occupations in the service ...
|
4.1 Dynamics of the burn in phase
Choosing initial values for the variables of our model and parameters, as summarized in Table 1, without any technological change results in stationary outcomes, as exemplified in Fig. 1. The time series shown for various variables relate to the case where we allow for entry and exit of firms and initialize the model with 25 firms (which is the number of firms we keep constant for the case of no exit and entry of firms.) Initially, profits summarized over all firms in the market are positive, triggering firm entry. As the total number of firms settles at 39, total profits in the market become zero. The entry of firms initially drives up real output, which however eventually converges to a stationary level as the number of firms ceases to grow further. Firms partly produce with labor and partly with machines. Lower tasks are operated with machines as exemplified with the share of firms putting workers on Task 15, which becomes zero after a short burn in phase. Task 16, in contrast, is operated by almost all firms with workers. Hence, our model endogenously, based on independent decisions by the different firms, generates a rather sharp distinction between tasks that are carried out by machines and by workers. In the long run, firms allocate a fraction 45% of tasks to workers and the labor share in the industry settles at approximately 62%. Throughout our analysis, we use the Theil index as a measure of wage inequality, as it will allow us to easily decompose the overall wage inequality into the components of a within firm wage inequality and a between firm wage inequality. As can be seen in Fig. 1, the Theil index also converges to a stationary level, and most of the wage inequality can be attributed to within firm wage inequality.
Fig. 1 Time series. Notes: Panels show time series for various outcomes (solid lines) with 95% confidence intervals (dashed lines). Standard errors are calculated on the basis of 50 replications Full size image
4.2 Long-run effects
The main focus of our analysis is on the longer run effects of automation on various market outcomes. To analyze these effects, we shock the economy at iteration 400 and record outcomes briefly before the shock and 100 iterations after the shock. The results are summarized in box plots, where the coding on the vertical axis relates to the baseline (0), i.e. outcomes before the shock, and the experiments “deepening of automation” (1), “labor augmenting technological advances” (2), the combination of the two (3), and “shifts in tasks” (4). The box plots summarize the outcomes of 50 replications showing the 25th, 50th, and 75th percentile, and the upper and lower adjacent values. Dots refer to outside values.
In particular, we analyze the consequences arising from the various forms of automation for the labor share, real wages, employment, and the wage distribution. We compare the distributional effects when barriers to entry are so high that there is no entry and exit of firms in the market and when entry barriers are low such that there is entry and exit of firms in the market. These are two extreme forms of the competitiveness of industries that may, and as we will see, do matter for how automation affects market outcomes. Moreover, our model allows us to analyze the effects of automation on market outcomes along two further dimensions. First, in the model without entry and exit of firms, we introduce firm heterogeneity with respect to their wage setting behavior, cf. parameter ζ i in our model description. Rather than assuming that all firms have the same wage offer rule, we split firms randomly into one half that wants to keep a larger fraction of the joined surplus rather than transferring it to the workers, and another half that transfers a larger fraction of the joined surplus to the workers via the wage offer. Second, we analyze the outcome of our experiments for scenarios where workers have different general skills. In this scenario, as opposed to our baseline where all workers can acquire necessary specific skills for all tasks, there are high skill workers who can be employed on all tasks of a firm, and low skill workers who can only be assigned to tasks up to xmax and do not have the general skills needed to operate higher tasks.
4.2.1 No market entry or exit
Homogeneous firms
We start discussing our results by considering the scenario with no entry and exit of firms and homogeneous agents. Figure 2 summarizes the main findings. With all four forms of automation real output in the economy increases. However, the distributional consequences differ substantially. With respect to the baseline of no automation, we observe that a deepening of automation (Experiment (1)) decreases the labor share and labor augmenting technological advances (Experiment (2)) increase the labor share. If both types of automation occur simultaneously (Experiment (3)) the effects on the labor share more or less compensate each other, whereas a shift in tasks (Experiment (4)) raises the labor share. The signs of the effects of the four experiments on the fraction of tasks allocated to workers are equal to the signs of the effects on the labor share. We can also see that automation has distinct effects on the total profits in the industry. Compared to the baseline, automation deepening, i.e. higher machine productivity, increases total profits, whereas labor augmenting technological change, i.e. higher labor productivity, decreases total profits. When productivity rises for both production factors, total profits stay approximately the same, while they decrease with a shift in tasks. The wage distribution, measured with the Theil index, becomes more equal for a deepening of automation and becomes more unequal for higher labor productivities. The effects of automation on the wage distribution are almost exclusively related to a change in the within firm wage inequality and not to a change in the between firm wage inequality as the decomposition of the Theil index reveals. Average real wages slightly fall with automation deepening and increase with respect to the baseline for all other types of automation.
Fig. 2 Treatment effects on market outcomes without market entry and exit of firms. Notes: Panels show the long run effects of the four automation experiments, compared to a baseline before the automation shock. Box plots summarize the simulation results of 50 replications: upper hinge of box refers to 75th percentile, lower hinge of box refers to 25th percentile, line in box is median. There is no market entry or exit, and firms and workers are homogeneous. Treatments are: 0 – baseline, 1 – deepening of automation, 2 – labor augmenting technological advances, 3 – deepening of automation and labor augmenting technological advances, 4 – shift in tasks Full size image
The observation that real output increases in all four experiments is a straightforward consequence of the higher task productivities. Less obvious are the observations that the positive output effect is much stronger for labor augmenting technological advances compared to automation deepening (although our experiments are set up in a way that the average productivity increase on each task is the same in Experiments (1) and (2)), and that only automation deepening leads to an increase in firm profits, whereas an increase in labor productivity results in lower profits compared to the baseline. To gain an intuitive understanding of these observations, it should be noted that the decision whether a task is (planned to be) allocated to a machine or a worker is essentially based on the ratio between productivity and costs (i.e. machine price respectively expected wage) for the two options. For tasks close to the marginal task, where the allocation switches from machines to workers, this ratio is approximately identical under both allocations, but both productivity and costs are lower for a machine compared to a worker allocation. If machine productivity increases and hence firms re-allocate one (or several) tasks from workers to machines this reallocation implies a decrease in output (due to the lower productivity of machines) and in firm costs. In contrast, in the case of an increase of labor productivity the induced reallocation of tasks to workers induces a further increase of output (in addition to the increases triggered by the pure effect of a productivity increase) and at the same time an increase in costs. Hence, total output increase is larger under labor augmenting technological advances compared to automation deepening. The implications for firm profits are then driven by the resulting price effect. The stronger increase in output under labor augmenting technological change induces a stronger decrease in the price of the consumption good. Since at the same time the costs of the firms increase this induces a reduction in profits. In contrast, under automation deepening the reallocation of tasks to machines has a positive impact on the consumption good price and therefore induces an increase of firm profits.Footnote 6
The effects on the average real wages in the industry are the result of countervailing forces. As described above, a higher machine productivity reduces the average real wage. As firms substitute labor for machine, labor demand declines which exerts downward pressure on wages (labor demand effect). At the same time average real wages (net of a demand effect) increase because only those workers operating better paid higher tasks remain employed (composition effect). As workers are spread over fewer tasks also wage inequality declines. For a higher labor productivity the composition effect and the labor demand effect have opposite signs when compared to the experiment with a higher machine productivity. Labor demand increases pushing up wages. At the same time workers are spread over a larger set of tasks including less well paid tasks, which reduces average wages (net of the labor demand effect). As the labor demand effect is far stronger than the composition effect, real wages increase. Moreover, wage inequality increases as workers operate less well paying tasks that formerly have been done by machines.
Heterogeneous wage bargaining
So far we have shown the consequences of various forms of automation when firms are homogeneous with respect to how wages are determined. We proceed by altering our assumption on wage setting to investigate whether heterogeneity of firms in this respect changes the obtained results. We randomly divide the population of firms into two groups. Half of the firms have a wage setting parameter of ζ i = 0.25, whereas the remaining firms have ζ i = 0.75. In the former case, firms on average offer higher wages compared to the latter case, see Eq. 5.
Compared to the case with homogeneous firms, we observe that total real output, employment, and the labor share are hardly affected by heterogeneous wage setting rules across firms (see Fig. 3). Since half of the firms pay lower wages than the rest of the firms, these firms with lower wage costs make significantly higher profits. Average real wages are only slightly affected by the heterogeneous population of firms compared to a homogeneous population. As to be expected, the share of wage inequality (measured again by the Theil index) attributable to the between firm wage inequality increases substantially. How automation affects wage inequality and the share of within firm wage inequality in relation to total inequality is, however, unaffected by introducing heterogeneous firms.
Fig. 3 Treatment effects on market outcomes without entry and exit of firms and with heterogeneous wage bargaining. Notes: Panels show the long run effects of the four automation experiments, compared to a baseline before the automation shock. Box plots summarize the simulation results of 50 replications: upper hinge of box refers to 75th percentile, lower hinge of box refers to 25th percentile, line in box is median. There is no market entry or exit, firms are heterogeneous with respect to their wage offer rule, and workers are homogeneous. Treatments are: 0 – baseline, 1 – deepening of automation, 2 – labor augmenting technological advances, 3 – deepening of automation and labor augmenting technological advances, 4 – shift in tasks Full size image
Heterogeneous general skills of workers
Next, we consider the effect of heterogeneity on the worker side. More precisely, we assume that workers have heterogeneous general skills (and firms are homogeneous again). As explained in Section 2.1, workers with high general skills can carry out and acquire the necessary specific skills for all task, whereas workers with low skills can only carry out tasks up to level xmax. In our experiment, the share of high skill workers is 30% and we set xmax = 18, where the highest possible task is N t = 20 in Experiments (1) - (3) and N t = 21 for t ≥ 400 in Experiment (4).
The effects of automation with heterogeneous general skills on output, tasks assigned to workers, labor share, total profits, average real wages, and wage inequality are almost unchanged (figures are not shown) when compared to the case of homogeneous skills. However, the two skill groups are differently affected by automation. This becomes evident as we look into wages and employment by skill group (Fig. 4). As to be expected, the wages of the high skill workers relative to the low skill workers are always higher and so are relative employment rates. Different types of automation, however, affect these ratios differently.
Fig. 4 Treatment effects on market outcomes without market entry and exit of firms and heterogeneous general skills of workers. Notes: Panels show the long run effects of the four automation experiments, compared to a baseline before the automation shock. Box plots summarize the simulation results of 50 replications: upper hinge of box refers to 75th percentile, lower hinge of box refers to 25th percentile, line in box is median. There is no market entry or exit, firms are homogeneous, and workers differ with respect to general skills. Treatments are: 0 – baseline, 1 – deepening of automation, 2 – labor augmenting technological advances, 3 – deepening of automation and labor augmenting technological advances, 4 – shift in tasks Full size image
Automation deepening, i.e. an increase in machine productivity decreases the wage ratio of the high skill workers relative to the low skill workers, while increasing the relative employment rate. This is because the reallocation of tasks to machines triggered by automation deepening mainly affects relatively low tasks. While employment decreases for both skill groups, the employment effect is stronger for the low skill workers for whom fewer tasks exist to apply for. Workers with high general skills are also less employed, but the effect on their employment level is substantially smaller since only a fraction of high skill workers is allocated to low level tasks. Due to a composition effect, the average wage of the employed low skill workers increases relative to that of the high skill workers, where, like in the baseline, all wages decrease in real terms.
Higher labor productivity, due to labor augmenting technological advances, triggers exactly the opposite effects, since here tasks are reallocated from machines to workers. Both types of workers gain from higher labor productivity which translates into higher wages. For the low skill workers, however, as more tasks are done by workers, average wages do not increase by so much. This composition effect is almost absent for the high skill workers as they are still running the higher tasks. Hence the ratio of average wages of high skill and low skill workers increases. For a better understanding of the effect on the relative employment rate, we can disentangle the high skill workers’ employment into employment on high tasks and low tasks (not shown in figures). What we observe is that high skill workers’ employment on high tasks stays fairly constant. High skill workers’ and low skill workers’ employment on lower tasks increases with higher labor productivity. So both groups gain in terms of employment, but low skill workers gain more so that the relative employment rate decreases.
For automation relating to higher labor and machine productivity, i.e. Experiment (3), the two effects almost cancel out. For a shift in tasks, the mechanisms described in relation to the experiment on a higher labor productivity are prevailing. There are more tasks at the upper part of the task range which are done by high skill workers. Some of these high skill workers were assigned to lower tasks before. Thus employment of the high skill workers hardly changes, and employment of the low skill workers increases as there is less competition from high skill workers for the lower tasks.
4.2.2 Market entry and exit
Homogeneous workers
We turn to a discussion of our results for the scenario with entry and exit of firms and homogeneous agents now. These results are summarized in Fig. 5. Compared to an industry with high barriers to entry for new firms, see the results in Fig. 2, we observe that in the baseline (without technology shocks) the labor share is higher, fewer tasks are allocated to workers, total output is higher, real wages are higher, and wage inequality is reduced.
Fig. 5 Treatment effects on market outcomes with entry and exit of firms. Notes: Panels show the long run effects of the four automation experiments, compared to a baseline before the automation shock. Box plots summarize the simulation results of 50 replications: upper hinge of box refers to 75th percentile, lower hinge of box refers to 25th percentile, line in box is median. There is market entry and exit, firms and workers are homogeneous. Treatments are: 0 – baseline, 1 – deepening of automation, 2 – labor augmenting technological advances, 3 – deepening of automation and labor augmenting technological advances, 4 – shift in tasks Full size image
Without entry barriers the positive firm profits, which arise under the initial (and fixed) number of firms (this is the same number of firms present in the runs underlying Fig. 2) triggers entry by additional firms. This increases the production capacity and total output in the industry compared to the scenario without entry and exit. The new firms demand labor and thereby push up total employment and at the same time the consumption good price decreases due to higher total output. Together this drives up real wages. Since labor becomes relatively more expensive the average number of tasks in a firm carried out by workers goes down. The wage effect dominates leading to a higher labor share in the scenario with exit and entry. As expected, firms’ profits go down to zero (not shown in Fig. 5). Since firms operate with fewer workers on their task range, wage inequality declines because of the resulting composition effect.
Considering the effects of our four automation experiments, we observe that interestingly the pattern of changes of total output and average real wages across experiments is different compared to the no entry and exit case. Under automation deepening average real wages are higher compared to the baseline now, whereas the opposite effect occurs without entry and exit. Furthermore, again in contrast to the case without exit and entry, total real output is higher with an increase in machine productivity compared to the total real output after an increase in labor productivity.
Both of these observations are driven by the different changes in the number of firms induced under automation deepening compared to labor augmenting technological change. Whereas the former leads to the entry of additional firms, the latter results in a smaller long-run number of firms compared to the baseline without a technology shock. The mechanism underlying this difference is essentially the same that induces an increase (decrease) of profits due to high capital (labor) productivity in the case without exit and entry. The fact that there is a larger long-run number of firms in the industry with automation deepening than with labor augmenting technological change is the dominant factor in explaining why total output in the industry now is larger in the scenario with a capital productivity increase compared to a labor productivity increase. The higher output results in a lower price of the consumption good and this price decrease is sufficiently strong to induce higher real wages under automation deepening compared to the baseline although nominal wages (not shown here) are actually lower. The change in nominal wages is the result of the interplay of two effects: a composition effect that increases wages because fewer (and mainly high level) tasks are done by workers and a demand effect. This second effect is negative, the effect of substitution of labor with capital on the firm level outweighs the additional labor demand due to firm entry. Under labor augmenting technological change both the composition effect and the demand effect have opposite signs compared to automation deepening. Again the demand effect dominates such that already nominal wages are larger compared to the baseline. The fact that the consumption good price is lower, due to higher output, compared to the baseline, induces that the positive effect of an increase in labor productivity on real wages is even stronger.
As mentioned above, the level of wage inequality in this scenario with entry and exit is substantially lower compared to the case with high barriers to entry. This is mainly driven by a composition effect since in the scenario with entry and exit firms on average fill a smaller set of tasks with workers. The fact that the reduction in inequality is mainly due to a reduction in within firm inequality becomes apparent from the observation that the fraction of the Theil index stemming from within firm inequality is lower in Fig. 5 compared to Fig. 2. Considering the effect of the different types of automation on wage inequality and its distribution on within and between firm components, the qualitative findings obtained without exit and entry, however, carry over also to the scenario with no entry barriers.
Heterogeneous general skills of workers
Finally, we turn to the case of a competitive economy where firms can enter and exit the market, and workers’ general skills are heterogeneous.Footnote 7 Again, we compare the effects of the automation shocks with those observed in the no entry and exit scenario with heterogeneous skills analyzed earlier in Section 4.2.1.
As we have already observed for the case with free entry and homogeneous general skills, also in the case with heterogeneous skills total real output and the labor share increase as firms enter the market. Wage inequality is lower for all types of automation. Furthermore, allowing for entry and exit of firms changes the effects that automation deepening or labor augmenting technological change have on the average real wages and total output. The qualitative pattern, however, is the same as for the case of homogeneous general skills of workers, and so are the underlying mechanisms. To save space we do not illustrate these observations with separate figures.
What qualitatively differs is the effect of a higher labor or machine productivity on employment of the high skill workers. Figure 6 summarizes these findings. Without entry and exit of firms, high skill employment decreases with more productive machines. With entry of firms, high skill employment is slightly increasing with more productive machines. The underlying mechanisms explaining the observation is related to the entry of new firms. These new firms employ additional high skill workers on their high tasks. This overcompensates the fewer employment opportunities of the high skill workers on lower tasks, which is associated with a higher machine productivity. In fact, we can check (not shown in the figures) that the employment rate of the high skill workers and the low skill workers on lower tasks falls. While employment of the high skill workers increases with higher labor productivity in the scenario without entry and exit, it hardly changes in the scenario with an endogenous number of firms. Here firms exit the market after an increase in labor productivity, which worsens the employment opportunities of the workers. This counteracts the positive effect on employment chances that comes with the firms’ decision to run more tasks with workers (who have become more productive).
| 2023-01-14T00:00:00 |
2023/01/14
|
https://link.springer.com/article/10.1007/s00191-022-00803-5
|
[
{
"date": "2022/12/01",
"position": 8,
"query": "automation job displacement"
}
] |
What Happened To Amazon's Employees After AI Automated ...
|
What Happened To Amazon’s Employees After AI Automated Their Work
|
https://kantrowitz.medium.com
|
[
"Alex Kantrowitz"
] |
When Amazon automated its retail employees' tasks, it didn't eliminate their jobs, but it did fundamentally change them. Vendor managers are now more auditors ...
|
What Happened To Amazon’s Employees After AI Automated Their Work
Instead of mass firings, Amazon assigned workers whose tasks it automated to inventive new roles. Alex Kantrowitz 5 min read · Jan 1, 2023 -- Listen Share
Last week, I published an excerpt from my book, Always Day One, about how Amazon automated work in its corporate offices. This week, let’s look at what happened to the employees whose tasks it automated.
Thank you so much to those who picked up Always Day One last week. If you’re interested in checking it out — or just jumping into your first book of 2023 — you can find it on Amazon, Apple Books, Barnes and Noble, and Bookshop.org.
I’d also like to thank you all for reading Big Technology. I’m about to reach 150,000 subscribers on Medium and am so thrilled you’ve opted into this journey. My promise to you is to dedicate myself to even deeper journalism next year. I have some big stories in the works, stories that embody what Big Technology aims for: nuanced, reported, substantive, and off the beaten path. I can’t wait to share them with you. And please, make sure you’re on my Substack newsletter list to get them first. You can sign up here.
Stay tuned for what comes next here, and have a great new year. See you in 2023!
What Happened To Amazon’s Employees After Its AI Automated Their Work
After Amazon automated its vendor managers’ forecasting, purchasing, and negotiation tasks, I expected to find them sullen. The narrative typically dives into mass unemployment, the end of work, and end times. So I was a bit surprised when they instead seemed matter-of-fact about what happened, unworried about what this new wave of AI portends.
“When we heard ordering was going to be automated by algorithms, on the one hand, it’s like, ‘Okay, what’s happening to my job?’” said Elaine Kwon, a former Amazon vendor manager. “On the other hand you’re also not surprised, you’re like, ‘Okay, as a business this makes sense, and this is in line with what a tech company should be trying to do.’ ”
Another current employee told me that at Amazon, “you’re constantly trying to work yourself out of a job. You should not be doing the same thing day to day. Once you’ve done something consistently, you need to find mechanisms to invent and simplify.”
From a business standpoint, it’s easy to see why Amazon employees (who receive a chunk of their compensation in stock) feel this way. Amazon’s business is a flywheel — a system that gets better and stronger as each component improves. By offering a wide selection of products at low prices, Amazon generates traffic from people looking to buy. The traffic makes Amazon a more enticing place for sellers, who sell more products at better prices to reach Amazon’s customers, generating more demand.
In Amazon’s early days, with fewer seller relationships to manage, the company could hire humans to manage its vendor relationships. But as Amazon scaled to twenty million products, the labor cost to manage every relationship with a human was prohibitive, causing prices to rise and sticking a wrench in the flywheel.
“You could start the Amazon business in an un-technical way. But you couldn’t scale it,” said Ralf Herbrich, Amazon’s first director of machine learning. “Each of those processes in our flywheel, pretty much each of them, will really only scale up if we are automating some of those decisions that people are doing, particularly the ones that are based on repeated patterns that we observe, and that’s where AI comes in.”
Twenty years ago, an Amazon vendor manager could handle a few hundred products, Herbrich said. Today, they’re working with anywhere from ten thousand to one hundred thousand. (An Amazon spokesperson said Herbrich was using these numbers as an example and they shouldn’t be taken at face value.)
When Amazon automated its retail employees’ tasks, it didn’t eliminate their jobs, but it did fundamentally change them. Vendor managers are now more auditors than doers. “They go from typing to selecting,” Herbrich said. “When there are mistakes, often what we find is that they now need to have the skills to diagnose what inputs to the algorithm may be wrong. It shifts from making the outputs, how many units to buy, to changing the inputs.”
Here’s an example of how that plays out: Amazon’s inventory forecasting system was once missing predictions on some basic fashion products. Herbrich was incredulous; white socks should not be hard to forecast. So, he ordered a review of the inputs going into the prediction tool and found that Amazon had fifty-eight thousand different color categories in total. Spelling mistakes and nonstandard spellings had thrown the system off, and when they standardized color, things went back to normal.
Thanks to Hands off the Wheel, Amazon’s retail division now operates more leanly and efficiently. The concept has also enabled Amazon’s third-party marketplace and fulfillment operation — where vendors list directly on Amazon, instead of relying on Amazon as a middleman — to thrive.
The prestige of the vendor manager job has worn off a bit too, and many vendor managers have moved to new roles within Amazon. When I browsed LinkedIn to find out where they went, I found many ended up in two specific job categories: program manager and product manager. Program and product managers are professional inventors at Amazon. They dream up new things and steward them along as they get built. Product managers typically focus on getting individual products built, and program managers focus on multiple, interrelated projects.
According to LinkedIn’s data, these are the fastest-growing job functions inside Amazon today. “That was a thing that a lot of people really looked for,” Kwon told me. “They were also looking for other cool teams that valued innovation.”
Tim, another ex-vendor manager, noticed a similar migration. “I have friends in categories where two years ago there were twelve people in that category, now there’s three,” he told me. “Almost every person that I knew in retail at this point has a job now that is product manager, program manager. Nobody is really in a core retail function anymore. If you’re a non-engineer, you become a program manager or a product manager.”
By automating work inside its retail division, Amazon is opening up new opportunities for inventing, which was the plan all along, according to Jeff Wilke, its ex-CEO of Worldwide Consumer. “People that were doing these mundane, repeated tasks are now being freed up to do tasks that are about invention,” he said. “The things that are harder for machines to do.”
| 2023-01-01T00:00:00 |
2023/01/01
|
https://kantrowitz.medium.com/what-happened-to-amazons-employees-after-ai-automated-their-work-45711efa33bf
|
[
{
"date": "2022/12/01",
"position": 9,
"query": "automation job displacement"
},
{
"date": "2022/12/01",
"position": 2,
"query": "AI job losses"
},
{
"date": "2022/12/01",
"position": 14,
"query": "AI replacing workers"
},
{
"date": "2022/12/01",
"position": 1,
"query": "AI workers"
},
{
"date": "2022/12/01",
"position": 55,
"query": "AI layoffs"
},
{
"date": "2023/01/01",
"position": 23,
"query": "automation job displacement"
},
{
"date": "2023/01/01",
"position": 19,
"query": "AI replacing workers"
},
{
"date": "2023/01/01",
"position": 16,
"query": "AI job creation vs elimination"
}
] |
Will AI will replace Humans in the workforce?
|
Will AI will replace Humans in the workforce?
|
https://www.linkedin.com
|
[
"G L Consulting",
"Amit Gupta",
"Mudit Agarwal",
"Head Of Business Technology",
"Automation Engineering At Bill",
"Monikaben Lala",
"Chief Marketing Officer",
"Product Mvp Expert",
"Cyber Security Enthusiast",
"Gisec Dubai In May"
] |
While it is true that AI has the potential to automate many tasks that are currently performed by humans, it is unlikely that it will completely replace human ...
|
In recent years, there has been a growing concern that the rapid advancement of artificial intelligence (AI) could lead to widespread job displacement, with machines and algorithms replacing human workers in many industries. While it is true that AI has the potential to automate many tasks that are currently performed by humans, it is unlikely that it will completely replace human workers in the near future.
One of the reasons that AI is not likely to replace human workers is because it is still limited in its abilities. While AI algorithms can be trained to perform specific tasks with a high degree of accuracy, they are not capable of the kind of creative thinking and problem-solving that humans excel at. This means that there will always be a need for human workers in fields that require these skills, such as research, design, and strategic planning.
Another reason why AI is not likely to replace human workers is because many jobs require a human touch. For example, jobs in the service industry, such as waiters, bartenders, and customer service representatives, rely heavily on interpersonal skills and the ability to empathize with customers. These are skills that AI algorithms cannot replicate, and so there will always be a need for human workers in these fields.
Furthermore, the development and deployment of AI technology is not happening in a vacuum. As AI becomes more advanced, it is likely that new industries and job opportunities will be created. For example, the growth of the AI industry has already led to the creation of new jobs in fields such as data science and machine learning. As AI continues to advance, it is likely that even more job opportunities will be created, offsetting any potential job losses.
In conclusion, while it is true that AI has the potential to automate many tasks currently performed by human workers, it is unlikely that it will completely replace humans in the workforce. AI is still limited in its abilities, and many jobs require a human touch that cannot be replicated by machines. Additionally, the growth of the AI industry is creating new job opportunities that offset any potential job losses. Therefore, it is unlikely that AI will replace human workers in the near future.
Source: Auto-generated using CHAT GPT3 https://chat.openai.com
| 2022-12-01T00:00:00 |
https://www.linkedin.com/pulse/ai-replace-humans-workforce-sidharth-mukherjee
|
[
{
"date": "2022/12/01",
"position": 10,
"query": "automation job displacement"
},
{
"date": "2022/12/01",
"position": 4,
"query": "AI job losses"
},
{
"date": "2022/12/01",
"position": 1,
"query": "AI replacing workers"
},
{
"date": "2022/12/01",
"position": 7,
"query": "AI workforce transformation"
},
{
"date": "2022/12/01",
"position": 17,
"query": "future of work AI"
},
{
"date": "2022/12/01",
"position": 12,
"query": "machine learning workforce"
},
{
"date": "2022/12/01",
"position": 9,
"query": "AI workers"
}
] |
|
Does robotization affect job quality? Evidence from ...
|
Does robotization affect job quality? Evidence from European regional labor markets
|
https://pmc.ncbi.nlm.nih.gov
|
[
"José Ignacio Antón",
"University Of Salamanca",
"Instituto Universitario General Gutiérrez Mellado",
"Universidad Nacional De Educación A Distancia",
"Madrid",
"Enrique Fernández Macías",
"Joint Research Centre",
"European Commission",
"Brussels",
"Rudolf Winter Ebmer"
] |
by JI Antón · 2022 · Cited by 45 — Our results indicate that robotization has a negative impact on the quality of work in the dimension of work intensity and no relevant impact on the domains of ...
|
Abstract Whereas there are recent papers on the effect of robot adoption on employment and wages, there is no evidence on how robots affect non‐monetary working conditions. We explore the impact of robot adoption on several domains of non‐monetary working conditions in Europe over the period 1995–2005 combining information from the World Robotics Survey and the European Working Conditions Survey. In order to deal with the possible endogeneity of robot deployment, we employ an instrumental variables strategy, using the robot exposure by sector in other developed countries as an instrument. Our results indicate that robotization has a negative impact on the quality of work in the dimension of work intensity and no relevant impact on the domains of physical environment or skills and discretion.
INTRODUCTION Automation represents a major shaping force of today's labor markets, contributing to rising living standards (Atack et al., 2019; Autor, 2015; Autor & Salomons, 2018), but also being considered a relevant source of anxiety for citizens: 75% of Europeans see technological progress as a phenomenon threatening their job prospects (European Commission, 2017a). While there is increasing empirical evidence showing positive effects of robot adoption on productivity (Dauth et al., 2021; Graetz & Michaels, 2018; Jungmittag & Pesole, 2019; Kromann et al., 2020), research on the impact of this technology on the labor market is mainly limited to employment and wages (Acemoglu & Restrepo, 2020a; Antón et al., 2022; Borjas & Freeman, 2019; Chiacchio et al., 2018; Dahlin, 2019; Dauth et al., 2021; De Vries et al., 2020; Graetz & Michaels, 2018; Jäger et al., 2016; Klenert et al., 2022; Koch et al., 2021). The aim of this paper is to explore whether the increase in density of industrial robots in Europe affects working conditions, in general. This is relevant for two reasons. In the first place, workers do care for working conditions. Workers are willing to trade money for improvements in other domains in the sense of compensating differentials (Clark, 2015; Maestas et al., 2018; Muñoz de Bustillo et al., 2011), even if labor market imperfections and job rationing do not guarantee that the market compensates such attributes according to workers' preferences (Bonhomme & Jolivet, 2009). Working conditions are one of two most important concerns for European citizens (European Commission, 2017b): more than a half of them reported that the quality of work has worsened during the last years. Moreover, other than business cycle fluctuations or changes in bargaining conditions, the introduction of (industrial) robots should in principle modify working conditions directly, but it is unclear in which direction. The main applications of this technology, whose adoption took off in the late 80s and early 90s, are handling operations and machine tending, assembling and disassembling and the sequential addition of standardized interchangeable parts to a complex product (e.g., a car; International Federation of Robotics [IFR], 2017a). There are several channels through which they could impact on different areas of working conditions, essentially, by modifying the tasks performed by employees. First, in relation to work intensity, one should bear in mind that robots are centrally controlled machines with which workers have to interact. They can therefore intensify work rhythms and labor effort if they make monitoring workers' performance easier—as suggested by Brown et al. (2010) or Weil (2014) for certain technologies—and make employees' tasks more dependent on the pace of work of machines. Second, regarding the physical environment of the workplace, robots replace certain tasks, which tend to be the most repetitive and heavy, thus contributing positively to job quality in terms of health and safety. The introduction of technology can prevent occupational accidents and diseases, but may also mean the appearance of new risks associated with malfunctions (which result in incidents like collisions and unexpected movements) (Vautrin & Dei Svaldi, 1986). Third, robots require standardization of processes, which can affect workers by reducing their autonomy, making employees' tasks more dependent on them. Furthermore, robot adoption might also result in workers' relocation within the same firm. Employees whose tasks this technology assumes can climb the occupational ladder and take new tasks that require higher skill levels than the previous one (Dauth et al., 2021); these tasks could, therefore, be more meaningful and fulfilling. Furthermore, the raise in productivity due to robot adoption might also result in a wider space for improving not only wages but also other conditions at the workplace, even if they are costly for employers (Clark, 2015); gains which, in turn, are shaped by the possibility of monitoring workers' performance (Bartling et al., 2012). Therefore, the expected impact of robotization on job quality is quite ambiguous. It might alter the tasks performed by the workforce and, in principle, this effect can be more direct on those workers complementary to robots than those who are potentially replaceable. Nevertheless, the presence of robots might also create pressure on the work carried out by other workers, who might have to take up or modify the way they perform their (new) tasks due to the introduction of the technology, particularly, if it enlarges the possibilities of employee surveillance. We combine sector‐ and industry‐level data on robots with several European‐level surveys on working conditions that allow us to analyze how the increase in robot density affects working conditions at the local labor market. We use composite indices of job quality previously employed in the literature and use ordinary least squares (OLS) in changes over the period 1995–2005. As there might be reverse causality or a problem with missing variables, we resort to instrumental variables (IV) techniques based on sector‐level trends in robot adoption in other leading countries, such as South Korea, Sweden, Switzerland, or Australia. Whatever method we use, we find that the increase in robot adoption across Europe had a negative effect on job quality in work intensity but does not have any effect on other aspects of job quality, like physical job environment and skills and discretion of workers on the job. Robots applied in the industrial production process are not so widespread as computers and their applications have little to do with the ones of artificial intelligence. Therefore, it is debatable to which extent the deepening in the adoption of this technology represents a qualitative change (as one could more easily argue, for instance, regarding artificial intelligence (Acemoglu & Restrepo, 2020b; Fernández‐Macías et al., 2021)).1 As a consequence, the impact of robots might not be the same as in the case of other technologies previous literature has analyzed thoroughly (see, Acemoglu & Autor, 2011; Autor, 2015; Autor & Salomons, 2018; Barbieri et al., 2019; Fernández‐Macías & Hurley, 2016; Jerbashian, 2019). With our research, we contribute to several recent discussions. There is a growing literature on the impact of robotization on employment and wages, which is far from having reached a consensus. While several studies for the US suggest a negative impact on employment and wages (Acemoglu & Restrepo, 2020b; Borjas & Freeman, 2019; Dahlin, 2019), the effects are not so clear for other economies. Those negative effects only seem to apply to manufacturing in Germany (Dauth et al., 2021), while the work of Chiacchio et al. (2018) for six EU countries only shows a detrimental impact on employment, but not for wages. The pioneering study of Graetz and Michaels (2018), including a wide set of developed countries, identifies a positive effect on wages and a neutral effect on employment, whereas Klenert et al. (2022), which extend the period of analysis and limit their exploration to manufacturing, even point out a positive contribution of robots to employment growth.2 Similarly, the cross‐country studies of Carbonero et al. (2018) and De Backer et al. (2018) draw mixed conclusions on the impact of robots on job creation. Firm‐level studies, on the other hand, find a positive association between the introduction of robots and employment growth in France (Domini et al., 2021) and Spain (Koch et al., 2021). So far, there is only limited research on other non‐monetary aspects of job quality. Closer to our topic are recent papers by Gihleb et al. (2020); Gunadi and Ryu (2021) and Lerch (2020). While Lerch (2020) is mostly concerned with labor market effects of robots, he also presents some evidence that robot adoption might be correlated with the prevalence of health problems or admissions to hospitals for displaced workers. Gihleb et al. (2020) and Gunadi and Ryu (2021) use data on industrial robot penetration and find that increased robot use is reducing work‐related injuries and improving health in the US, finding a negative relationship. The work of Gihleb et al. (2020), which is contemporaneous to ours also explores the effect on work intensity in Germany, without identifying any impact. Nevertheless, it is worth mentioning that their data and setup only allow them to explore between‐occupation change in the latter variable (while a large part of the variation in what workers do takes place within occupations) (Freeman et al., 2020; Maier, 2021). Our study is therefore the first to explore the impact of robots on different areas of working conditions and working conditions in general. Moreover, we can differentiate between different aspects of working conditions and with a focus on the European labor market. We also contribute to the discussion about changes in working conditions. Fernández‐Macías et al. (2015) show that job quality in the EU was remarkably stable before and after the financial crises with some increase in job quality in the European periphery. Green et al. (2013) look at different components of working conditions and find the component of work intensity and—to some extent—working time quality to improve in Europe. Moreover, they study the dispersion of these measures across groups and across time. Bryson et al. (2013) investigate the impact of organizational changes and trade unions on working conditions, whereas Cottini and Lucifora (2013) explore the consequences of working conditions on mental health. Closer to our topic are studies relating changes in computer use with working conditions: Menon et al. (2020) report that computerization has no large effects on working conditions in general, there is even a mild positive effect on job discretion. Green and McIntosh (2001) in an earlier study show that computer use leads to an intensification of the workplace.3 In our study, we extend this analysis with a closer look at the impact of robotization on working conditions in general. Following this introduction, the rest of the paper unfolds as follows. Data and methods describes the databases employed in the analysis and outlines the identification strategy used in the econometric analysis. We present the main results of the paper in Results and the Conclusions and last section summarizes and discusses the main conclusions of the paper.
DATA AND METHODS Data Robots In order to assess the effect of robotization on the working conditions of European workers, we combine several databases that contain information on robot adoption and job attributes. Our first source is the World Robotics 2017 edition, a dataset administered by the International Federation of Robotics (IFR, 2017a), the main association of manufacturers of robots worldwide. It comprises information on industrial robot stocks and deliveries by country and sector of activity all over the world from 1993 to 2016. As mentioned above, the robots included in the IFR (2017a) consist of industrial machinery, digitally controlled, mainly aimed at handling operations and machine tending, welding and soldering and assembling and disassembling. In terms of accounting, these robots are part of non‐information and communication technology capital, with the exception of their associated software needed to manage them.4 The IFR basically constructs a series of robot stocks on the basis of deliveries, using a perpetual‐inventory approach and a 12‐year depreciation. This is a more reliable approach—as compared to using stocks—since the association of robot producers controls those inflows directly. As the distribution of robots is missing in some years and countries, we impute initial unspecified stocks or deliveries on the basis of the distribution by industry in the three closest years to the period of interest with specified information.5 Working conditions We use the 2nd, 3th and 4th waves of the European Working Conditions Survey (EWCS), carried out in 5‐year intervals by the European Foundation for the Improvement of Living Conditions (Eurofound, 2018), 1995, 2000 and 2005.6 There are two additional waves (2010 and 2015), but we focus on the period 1995–2005 in order to avoid confounding effects of the Great Recession, which had a markedly different impact across countries and regions.7 For our sample countries, the number of robots per thousand workers rose from 0.800 in 1995 to almost 2.200 in 2005, with only a slight increase to 2.750 in 2015. Finally, as there is less variation in the latter period, our proposed IV strategy does not produce a strong first stage. In any case, we present the results for this second period in the annex and comment on them in the next section. The EWCS represents the most comprehensive database for the analysis of non‐monetary working conditions across Europe on a comparative perspective, covering the European Union (EU) members, several accession countries and other states like Norway and Switzerland. We focus on the 12 EU countries with the highest ratio of robots per worker during the analyzed period.8 Each wave includes a minimum of 1000 interviewees in each country and year. As robot technology is mainly used in manufacturing, we focus on privately salaried workers employed in mining and quarrying and the secondary sector (manufacturing, electricity, gas water supply and construction), which concentrates more than 90% of these types of robots. Unfortunately, there is no further disaggregation of these industries. This leaves us a sample of 7764 workers that we collapse in order to obtain the region‐level outcomes and covariates. The EWCS contains a rich set of variables covering different dimensions of working conditions; we describe that in the next subsection. Control variables We use the European Union Labor Force Survey (EU‐LFS; Eurostat, 2018) and due to missing regional information for Germany the European Community Household Panel (ECHP) for the years 1995 and 2000 (Eurostat, 2003).9 Changes in information, communication and technology (ICT) capital stock per worker are from the EU KLEMS (Stehrer et al., 2019), data for Chinese imports come from the United Nations International Trade Statistics Database, to which we access through the World Integrated Trade Solution (WITS; World Bank, 2020) following Autor et al. (2013).10 We construct instrumental variables from the Korean Industrial Productivity Database (KIP), provided by the Korea Productivity Center (KPC, 2015), labor force statistics from Australia and information from Eurostat for Switzerland. Our database on working conditions, the EWCS, does not contain detailed information on sectors of activity (only some large industry groups are available), but it is representative by region. In the fashion of previous literature (Acemoglu & Restrepo, 2020a; Dauth et al., 2021), we perform the analysis at such a level. In order to calculate the change in robot exposure by region, assuming that the distribution of the change in robot stocks by region over a certain period of interest depends on the distribution of employment at the beginning of the period, we combine detailed sector‐level data by country on robots and region‐level employment data by industry obtained from several ad hoc requests to the Eurostat User Support (Eurostat, 2020).11 We provide further details on the construction of the variation in the robot exposure by region in the next subsection. Methodology As mentioned above, our identification strategy exploits the regional variation in the increase in the adoption of robots. Following the strategy proposed by Acemoglu and Restrepo (2020a), we compute the change in the exposure to robotization by region assuming that the robot inflows during a certain interval of time follows the distribution of employment in the initial period. Our geographical units of analysis mainly correspond to the Nomenclature of Territorial Units for Statistics at the second level (NUTS 2), although in some cases, because of the existence of administrative changes in the boundaries of NUTS we cannot trace over time, we make use of larger geographical units. As a result, we are able to trace 80 regions over the period 1995–2005. Given the very low mobility across NUTS 2 in Europe (Gákova & Dijkstra, 2008; Janiak & Wasmer, 2008), we can consider our regions as reasonably closed labor markets, in the sense that it is not likely that robot adoption results in relevant outflows from regions with high deployment to others with low adoption of the technology.12 Another problem that might arise is a sort of sample selection bias. We concentrate our analysis on workers in these regions in the years 1995, 2000 and 2005. If the exposure to robots would change or reduce the workforce considerably, we would be in trouble, comparing working conditions of those in the region before the advent of robots with the working conditions of those still employed in the region after the exposure to robot adoption. While Acemoglu and Restrepo (2020a) do find a negative impact of robots on employment in the US, studies for Europe do not find such effects (Antón et al., 2022; Dauth et al., 2021). In addition, we use additional variables to control for changes in the composition of the workforce. The main variable of interest in our analysis is the increase in robot exposure (RE), which we define as the change in the number of robots during a certain period t in a region r divided by the number of workers in the region at the beginning of the period, that is, Δ RE rt = 1 L rt ∑ j L rjt L jt Δ R jt (1) where R jt represents the change in robot stocks in sector j in the country where the region is located over period t; L rt , the initial number of workers in the region at the beginning of the period of interest (i.e., 1995 for the first difference, 1995–2005, and 2000 for the second time lapse, 2000–2005); L jt denotes the employment figures in industry j in region r in the initial year and L rjt , the number of workers in region r in industry j at the same moment of time. In this fashion, we attribute to each region a change in the stock of robots according to the share of employment in this sector in the initial period.13 In order to explore the impact of robot adoption on working conditions, we estimate the following equation: Δ Y rt = β 0 + Δ RE rt β 1 + Z rt ′ β 2 + ε rt (2) ΔY rt denotes the change in the average job quality indicator of region r over the period t. Z rt ′ is a set of start‐of‐the‐period regional control variables, very similar to those considered by Acemoglu and Restrepo (2020a) and Autor et al. (2013), including the share of employment in mining and quarrying and the secondary sector in the region (to which we refer as the share of industry for brevity), population (in logs), share of females, age structure of the workforce, the share of population with middle or high education, the average routine task intensity (RTI; Autor & Dorn, 2013; Goos et al., 2014; Mahutga et al., 2018; Schmidpeter & Winter‐Ebmer, 2021) and the average offshorability risk (Blinder & Krueger, 2013; Mahutga et al., 2018).14 Note that using an econometric specification with both outcome and the treatment variable in changes, we control for regional time‐constant heterogeneity. Given that we pool two 5‐year differences, we include time fixed effects covering each of those periods. Second, we add a geographical dummy for core‐periphery countries to capture group‐of‐countries‐specific time trends.15 Finally, it is possible that some changes in working conditions have to do with changes in the labor force composition. In order to mitigate this selection effect, we control for the changes in the share of female workers, the proportion of workers with medium education, the proportion of workers with high education and the share of workers aged less than 30 years old and aged 50 years old or more employed in the region in the industries considered in the analysis. Similar to previous analyses of the impact of robotization on employment or wages (Acemoglu & Restrepo, 2020a; Dauth et al., 2021), there is the possibility of reverse causation. In these studies, it may well be that robot adoption is caused by developments on the labor market, like the availability of suitable workers or a fast‐rising wage in the respective sector. In our case, reverse causation could occur for similar considerations: Since working conditions can also be indirect cost components (slower work pace or costs for accident avoidance) or have an impact on labor supply with respect to a specific industry, reverse causation could apply. Given our use of non‐monetary working conditions, the argument for reverse causation is less strong as in the case of wages. Still, we use the same strategy as Acemoglu and Restrepo (2020a) and Dauth et al. (2021), who instrument the adoption of robots by the trends in other developed countries.16 Given our focus on European Union countries, we look at the patterns of robotization by sector in South Korea, one of the world leaders in the adoption of this technology (IFR, 2017b; United Nations Conference on Trade and Development, 2017), in order to build our IV. Considering the size of this economy and its limited integration with EU countries (compared to other member states), it is not likely that Korean industry‐level developments trigger any relevant general‐equilibrium effects. The exclusion restriction of the IV strategy requires that the instrument (robotization in Korean industries) has no impact on European working conditions over and above its indirect impact via robotization in Europe. We strongly believe that this is, indeed, the case. We also build on data from Sweden (the pioneer in robot adoption in Europe) and Australia and Switzerland (two developed economies outside the European Union) in order to check the robustness of our results using alternative instruments. We can express the increase in robot exposure as a function of the importance of each industry in the region and the average increase in robot density per worker at the national level. In order to build our IV, we consider the increase in robot exposure per worker in each of our third countries instead of the variable corresponding to each European Union country, obtaining the following expression: Δ RE rt k = 1 L rt ∑ j L rjt Δ R jt k L jt k (3) where the superindex k denotes the third country used for building the IV (South Korea, Sweden, or Australia and Switzerland). Our IV is relevant, with an F‐statistic between 40 and 80 in different econometric specifications, using clustered standard errors at the regional level.17 In the construction of our variable “change in robot exposure,” we proceed as follows. The increase in robot density in a certain region draws on the change in robot stock and the employment at the beginning of the period of interest at the regional level. Therefore, it requires the number of robots and workers in each sector at the two‐digit level. The former variable comes from the IFR data, whereas the latter comes from the population estimates of the EU‐LFS (which can be obtained through the sampling weights). We obtain the number of workers (or any other variable) in each industry at the required level from the EU‐LFS through the Eurostat user support. As a result, we are able to construct a variable capturing the change in exposure to robot adoption at the regional level. In order to build changes in ICT capital stock per worker and in the exposure to Chinese imports, we depart from sector‐level data and follow a similar procedure to the one applied to robots based on the initial distribution of employment, considering roughly the same industry classification as in the case of robots and even a more detailed one regarding Chinese imports. Our measures of working conditions, based on the EWCS and developed by Eurofound and their collaborators (see, e.g., Eurofound, 2012, 2015, 2019; Fernández‐Macías et al., 2015; Green et al., 2013; Menon et al., 2020; Muñoz de Bustillo et al., 2011a), comprise three dimensions: work intensity, physical environment and skills and discretion. We reformulate these indicators in order to ensure that the variables of interest are available in the three waves of the survey. It is relevant to highlight that this set of indicators privileges the inclusion of “objective” rather than “subjective” variables when possible in order to minimize the effect of adaptation, adjustment or cognitive dissonance (Bhave & Glomb, 2013; Bowling et al., 2005; Muñoz de Bustillo et al., 2011b; Pugh et al., 2011; Ritter et al., 2016).18 The index of work intensity comprises two sub‐dimensions, quantitative demands and pace determinants and interdependency. The first sub‐dimension builds on three variables, pace of work (high speed), tight deadlines and time pressure, while our indicator of pace determinants and interdependency considers how interviewee's work depends on colleagues, customer demands, production targets, machine speed and bosses. Job quality in physical environment considers three domains: ambient risks (vibrations, noise, high temperatures and low temperatures), biological and chemical risks (exposition to fumes and vapors and chemicals) and posture‐related risks (tiring positions, heavy loads and repetitive movements). Finally, the quality of work in terms of skills and discretion comprises three sub‐dimensions: cognitive tasks (carrying out complex tasks and working with computers, smartphones, laptops, etc.), decision latitude (control the order of tasks, speed of work, methods of work and timing of breaks) and training (receiving training provided by the employer and the possibility of learning new things). Following the previous literature (see, e.g., Eurofound, 2019), we combine these variables, most of them of an ordinal nature, in order to define indicators of job quality in each of the dimensions and sub‐dimensions in a positive sense—that is, the higher the measure, the higher the well‐being—and using a 0–100 scale. For instance, the attribute vibrations receives the highest score when the workers are never exposed to this sort of workplace risk. Each variable receives the same weight within each sub‐dimension and we compute the arithmetic average of these sub‐domains in order to again obtain a score between 0 and 100 for our index of job quality in work intensity.19 Although, as argued above, our indicators of job quality draw on objective working conditions when possible (e.g., temperature level instead of satisfaction with the workplace temperature), in order to assess the robustness of our findings we also look at the effects of robot adoption on three subjective binary variables: the workers' self‐awareness of the impact of work on their health and self‐perceived work‐related stress and anxiety.20 Our left hand side variable is a regional average of the indicator of interest using sampling weights (that the EWCS calculates from the EU‐LFS estimates) with the aim of making it representative of the corresponding population.
RESULTS Table 1 displays descriptive statistics of the dependent variables and covariates of our database, containing 80 European regions. We present the figures for the three mentioned dimensions (work intensity, physical environment and skills and discretion) and the two sub‐domains composing work intensity. The evolution of these variables over time does not seem to follow a clear pattern. The number of robots per worker by region multiplies by more than 2.5 from 1995 to 2005. Figure 1 plots the correlation between 5‐year changes in robot exposure and changes in job quality by dimension over the period 1995–2005. The graphs suggest a negative correlation in the case of work intensity, a somewhat weaker negative one with physical environment and a slightly positive one with respect to skills and discretion. TABLE 1. Descriptive statistics Means (standard deviations) 1995 2000 2005 Robots per thousand workers 0.798 1.486 2.126 (0.585) (1.192) (1.672) Work intensity (0–100) 55.459 55.374 55.710 (6.969) (6.428) (7.153) Quantitative demands (0–100) 59.305 59.878 63.741 (9.991) (7.996) (7.500) Pace and determinants (0–100) 51.574 50.859 47.661 (7.830) (9.353) (9.648) Physical environment (0–100) 72.602 70.092 72.431 (6.441) (6.889) (5.916) Skills and discretion (0–100) 55.595 53.409 53.570 (9.659) (9.290) (11.255) Share of workers with health affected by work 0.618 0.639 0.349 (0.149) (0.144) (0.181) Share of workers with health‐related stress 0.290 0.228 0.200 (0.131) (0.117) (0.138) Share of workers with health‐related anxiety 0.045 0.050 0.063 (0.062) (0.062) (0.089) Share of pop. employed in industry 0.301 0.297 0.283 (0.060) (0.067) (0.064) Population (thousand people) 7061.838 7939.785 8014.270 (4949.596) (5232.782) (5111.177) Share of females 0.498 0.498 0.499 (0.009) (0.009) (0.007) Share of pop. above 64 6.654 6.151 5.855 (0.941) (0.909) (0.870) Share of pop. with medium education 0.412 0.403 0.420 (0.127) (0.123) (0.120) Share with high education 0.168 0.176 0.206 (0.065) (0.063) (0.067) Average RTI index 0.108 0.090 0.031 (0.090) (0.106) (0.071) Average offshorability index 0.022 0.012 −0.052 (0.109) (0.122) (0.098) Share of female workers 0.215 0.221 0.236 (0.117) (0.106) (0.051) Share of workers below 30 0.224 0.212 0.238 (0.123) (0.121) (0.038) Share of workers with 50 or more 0.193 0.236 0.201 (0.127) (0.154) (0.040) Share of medium‐educated workers 0.404 0.429 0.473 (0.178) (0.189) (0.162) Share of highly educated workers 0.273 0.233 0.173 (0.263) (0.206) (0.078) ICT capital stock (thousand US$ per worker) 7.720 6.311 7.198 (2.513) (1.556) (1.450) Chinese imports (US$ per worker) 1464.923 3068.354 8001.657 (630.298) (1695.555) (4977.016) No. of observations 80 80 80 Open in a new tab FIGURE 1. Open in a new tab Job quality index of work intensity and robot exposure (5‐year differences, 1995–2005). Observations weighted by the number of workers in the region at the beginning of the period. Source: Authors' analysis from EWCS, EU‐LFS and IFR. We present the main results of our analysis of the effects of robot adoption on work intensity, physical environment and skills and discretion in Tables 2 and 3, respectively.21 In these tables, we display both OLS and IV estimates, without and with controls for the change in the share of workers of different characteristics in the working population in the region. The relevant F‐statistic of the first stage is well above 50, pointing out to the relevance of our IV. We present the complete details on the first stage in Table A1 in the Appendix. TABLE 2. Effect of robot adoption on job quality: Work intensity (I) (II) (III) (IV) OLS OLS IV IV ΔRobot exposure −4.473*** −4.321*** −5.635*** −5.165*** (1.248) (1.226) (1.935) (1.574) Share of employment in industry 30.968* 25.506 32.935* 26.351 (17.183) (18.085) (17.273) (17.983) Population (log) 2.103** 1.693** 2.068** 1.678** (0.891) (0.816) (0.881) (0.808) Share of females 3.153 −58.390 −8.109 −68.156 (68.478) (88.488) (70.635) (91.812) Share of pop. above 64 0.640 0.657 0.607 0.613 (0.556) (0.718) (0.552) (0.718) Share of pop. with medium education 19.641** 14.156* 20.439*** 14.670* (7.671) (7.948) (7.714) (7.985) Share of pop. with high education 76.305*** 56.076** 75.722*** 55.436** (20.201) (21.979) (19.954) (22.377) RTI 24.923** 10.745 23.343** 9.578 (10.379) (12.848) (10.308) (13.153) OFF −20.473 −10.734 −17.765 −8.776 (13.231) (14.249) (13.431) (14.578) R 2 0.180 0.267 No. of observations 160 160 160 160 Mean of dependent variable 0.015 0.015 0.015 0.015 Mean of independent variable 0.607 0.607 0.607 0.607 First‐stage Wald F‐statistic 95.055 65.245 Compositional changes ✓ ✓ Open in a new tab TABLE 3. Effect of robot adoption on job quality: Physical environment and skills and discretion (I) (II) (III) (IV) OLS OLS IV IV Panel A. Physical environment ΔRobot exposure −0.407 −0.187 −2.081 −1.627 (1.577) (1.395) (2.096) (1.820) R 2 0.142 0.182 No. of observations 160 160 160 160 Mean of dependent variable 0.463 0.463 0.463 0.463 Mean of independent variable 0.607 0.607 0.607 0.607 First‐stage Wald F‐statistic 95.055 65.245 Panel B. Skills and discretion ΔRobot exposure 0.413 1.269 −1.862 −0.936 (1.863) (1.811) (2.883) (2.606) R 2 0.057 0.067 No. of observations 160 160 160 160 Mean of dependent variable 0.463 0.463 0.463 0.463 Mean of independent variable 0.607 0.607 0.607 0.607 First‐stage Wald F‐statistic 95.055 65.245 Start‐of‐period‐controls ✓ ✓ ✓ ✓ Compositional changes ✓ ✓ Open in a new tab Table 2 shows that the adoption of robots reduces job quality with respect to work intensity. All four estimates are very consistent, columns (1) and (2) excluding or including variables for compositional change in the workforce show an effect of −4.5, whereas the IV results are somewhat higher at −5.2 to −5.6; the statistical indistinguishability between OLS and IV results indicates no big relevance for endogeneity. The quantitative result means that an increase in robot adoption of one unit (which is around one standard deviation in 2000) increases work intensity by 4–5 units (60–80 percent of a standard deviation in 2000). In other words, the increase in robots between 1995 and 2005 from 0.8 to −2.1 per thousand workers led to an increase in work intensity of 5.6–7.3 points (87–114 percent of a standard deviation in 2000). These effects are rather large, but comparable to those of Menon et al. (2020) in size: They calculate the effect of computers on working conditions in the European Union, finding negative but insignificant coefficients for the impact of computer use on work intensity, but a positive impact of computer use on work quality in terms of work discretion. Table 3 reports similar estimations for working conditions in terms of physical environment and skills and discretion. Panel A of the table refers to physical environment and Panel B to skills and discretion. Here, the effects are smaller, mostly negative (i.e., reducing job quality) and insignificant. This refers to both OLS and IV results: physical environment and skills and discretion are not impacted by the adoption of robots. We have seen that there is a negative effect of robotization on job quality, but only in the dimension of work intensity, not in physical environment and skills and discretion. In Table 4 we further proceed by looking at the sub‐domains of work intensity, quantitative demands and pace and interdependence. Again, we present OLS and IV coefficients, which are fairly consistent. Both dimensions of work intensity are negatively related with robotization. The impact on the sub‐dimension of quantitative demands is considerably stronger than in the whole job quality dimension, while the effect in the case of pace and interdependency is somewhat weaker, but still statistically significant. TABLE 4. Effect of robot adoption on the sub‐dimensions of work intensity Quantitative demands Pace and interdependency (I) (II) (III) (IV) OLS IV OLS IV ΔRobot exposure −6.296 *** −6.890*** −2.328** −3.453* (1.670) (1.938) (1.161) (1.780) No. of observations 160 160 160 160 R 2 0.319 0.320 Mean of dependent variable 2.254 2.254 −2.215 −2.215 Mean of independent variable 0.607 0.607 0.607 0.607 First‐stage Wald F‐statistic 65.245 65.245 Start‐of‐period controls ✓ ✓ ✓ ✓ Compositional changes ✓ ✓ ✓ ✓ Open in a new tab So far, we can say that work intensity increased for workers that were employed in mining and quarrying and the manufacturing sector that adopted robots most intensively during this period. The results for industry might not have implications on working conditions in the whole economy if robot adoption there results in a displacement of workers from these economic activities to the rest of the economy. Drawing on EU‐LFS data (which includes the reference population for employment rates and provides the framework for the sampling weights of the EWCS) and using the same identification strategy and taxonomy of regions as with working conditions, we test whether robot adoption affects the share of employed working‐age people in industry, the rest of sectors and the whole economy. The results of this exercise, shown in Table 5, rule out this possibility. TABLE 5. Effect of robot adoption on employment (1995–2005) Employment rate in industry Employment rate in other sectors Overall employment rate (I) (II) (III) (IV) (V) (VI) OLS IV OLS IV OLS IV ΔRobot exposure −0.002 −0.007 0.008 0.011 0.006 0.004 (0.004) (0.006) (0.008) (0.009) (0.006) (0.007) R 2 0.360 0.266 0.421 No. of observations 160 160 160 160 160 160 Mean of dependent variable −0.001 −0.001 0.028 0.028 0.027 0.027 Mean of independent variable 0.557 0.557 0.557 0.557 0.557 0.557 1st stage Wald F statistic 90.593 90.593 90.593 Start‐of‐period controls ✓ ✓ ✓ ✓ ✓ ✓ Open in a new tab The effect of robotization on wages should also be interesting for interpreting the relevance of our results. Higher monetary compensation might compensate for worse working conditions. Unfortunately, our database does not allow testing for that mechanism in a similar fashion as in the domains considered here (this information is only available in a proper way since the fifth wave of the EWCS). Nevertheless, the work of Chiacchio et al. (2018), which considers a quite similar sample of countries, employs the approach based on robot adoption and follows a specification analogous to ours, finds no effect on wages in industry and a non‐robust impact on remuneration of total workers (the estimated coefficient is significant and negative in some cases and not statistically different from zero in others) for the period 1995–2007. Therefore, the absence of evidence of wage increases due to robot adoption in Europe over the period of interest suggests that the potential compensation mentioned above does not apply here. In order to check the robustness of our main results, we perform several additional estimations whose results are presented in Table 6. In the first two columns, we test whether our results hold under the use of other instruments: in column (I) we use two countries outside the European Union not included in our sample of regions, Australia and Switzerland. Under this specification, the effect of robot adoption remains negative and significant. In column (II), we use the increase in robot penetration by sector in Sweden (one of the leaders in the adoption of this technology in Europe) in order to build our IV. In this case, we have to exclude Sweden from the countries considered in the analysis. Our results are pretty similar to the ones reported under our original instrument based on South Korea. TABLE 6. Robustness checks: Work intensity (IV estimates) Variable Alternative IVs (AUS, CH) Alternative IV (SE) Additional controls Unweighted Falsification test (I) (II) (III) (IV) (V) ΔRobot exposure −3.903** −4.631*** −6.567*** −3.387* 1.067 (1.561) (1.626) (1.802) (1.870) (1.281) No. of observations 160 158 150 160 160 Mean of dependent variable 0.015 −0.078 0.103 0.563 0.324 Mean of independent variable 0.607 0.610 0.626 0.483 0.533 First‐stage Wald F‐statistic 64.607 50.272 57.702 70.723 96.571 Hansen J p‐value 0.401 Start‐of‐period controls ✓ ✓ ✓ ✓ ✓ Compositional changes ✓ ✓ ✓ ✓ ✓ Chinese import exposure ✓ ΔICT capital ✓ Open in a new tab In the third column, we include two additional controls that, though being potentially endogenous, have been shown to influence labor market outcomes: the increase in exposure to Chinese imports and the increase in the ICT capital per worker. Moreover, they could correlate with the adoption of robots. Results have to be taken with care, therefore. The estimates in column (III) show that the baseline results do not qualitatively change when adding these additional covariates, corroborating our main results. The fourth one displays the results when our regressions are not weighted by initial regional employment. Although we believe that weighting is desirable given the difference sizes of the geographical units (some of which we have to merge), the main message of our analysis holds. In order to test whether the presence of certain influential regions might be the main driver of our results, in the same fashion as Gunadi and Ryu (2021), we compute the parameter estimates of the effect of robotization on job quality leaving out one region at a time (Figure 2). These results confirm the outcome of our main analysis.22 FIGURE 2. Open in a new tab Robustness checks: Leave‐one out test (5‐year differences, 1995–2005)The figure presents the parameter estimates of the effect of robotization on job quality leaving out a region each time. The econometric specifications include an intercept, a dummy for the period 2000–2005, a dummy for core‐periphery European countries, start‐of‐period controls and controls for compositional changes. Standard errors clustered at the regional level in dashed lines. Observations weighted by the number of workers in the region at the beginning of the period. Source: Authors' analysis from EWCS, ECHP, EU‐LFS, IFR and KIP. The final robustness check is a rough “falsification” test, where we look at the effects of the change in robot exposure per worker in the region on workers in agriculture, forestry and fishing and the services sector. Given that most of the robots are concentrated in manufacturing, we should expect a null or, at least, a much lower impact of robots on the job quality of workers employed there. If our results were based on other concurrent events—correlated with robot adoption—such a placebo might catch these concurrent events. As expected, and in contrast to such a hypothesis, there is no effect of robotization in this falsification exercise (column [V]). We present similar robustness checks for the impact of robotization on job quality in the dimensions, physical environment and skills and discretion in the Appendix (Tables A2 and A3). These results are very similar to those presented in Table 3 and do not show any effect of robotization on either the physical environment of the job or skills and discretion in the job. Given the impact of the work intensity identified above, it is worth exploring whether the effects on this domain translate into negative health consequences (Table 7). Our results suggest that robot adoption raises the share of employees reporting that their work affects their health, with a one‐unit shift in robot density elevating this magnitude by 6.6 percentage points. Specifically, this effect seems to confirm the positive impact of robotization on the proportion of workers declaring work‐related stress and anxiety problems: an increase of one robot per thousand workers raises the probability of reporting those conditions by 9.2 and 3.2 percentage points, respectively. These outcomes are not at odds with the ones reported by the works of Gihleb et al. (2020), which associates a more intensive use of this technology with higher rates of mentally unhealthy days among the workforce, and Lerch (2020), which suggest a rise in hospital admissions and take‐up rates of disability benefits because of robot adoption. TABLE 7. Effect of robot adoption on stress, anxiety and the share of workers reporting that work affects their health (IV estimates) Work affects health Stress Anxiety (I) (II) (III) ΔRobot exposure 0.066* 0.092*** 0.032** (0.034) (0.035) (0.014) No. of observations 160 160 160 Mean of dependent variable −0.028 −0.040 0.004 Mean of independent variable 0.607 0.607 0.607 First‐stage Wald F‐statistic 65.245 65.245 65.245 Start‐of‐period controls ✓ ✓ ✓ Compositional changes ✓ ✓ ✓ Open in a new tab Last, we refer to the results for the period 2005–2015 (Table A4), which, as mentioned in Data, is subject to certain threats to identification and, therefore, its interpretation requires caution. In this time interval, robot adoption does not exert any effect on any of the job quality dimensions. Apart from the limitations commented above, we outline several explanations. First, it is possible that the higher intensity of robot adoption in the first period implies that the main effects of this technology in terms of workplace reorganization might have been observed during the first one. For instance, it is possible that the main changes in terms of task reorganization might have already taken place.23 Secondly, this result, which is coherent with the more negative impact of robots on employment identified by some works before the Great Recession (Antón et al., 2022; Bekhtiar et al., 2021; Chiacchio et al., 2018), aligns with the larger impact of robot adoption on productivity in Europe after 2007 compared to the pre‐crisis period (Jungmittag & Pesole, 2019). Given the link between productivity and remuneration packages (which includes non‐pecuniary working conditions), the absence of a negative impact of robots on job quality in the second period would be consistent with those larger productivity increases.
CONCLUSIONS The impact of technology on the workplace, workers, and their work environment attracts a lot of concern among citizens and researchers in Social Sciences, alike. The adoption of industrial robots, even if not new, is one of the more visible realizations of such technological changes. While there are a relevant number of studies concerned with the impact of this technology on employment and wages, ours is the first comprehensive study on the impact of robotization on working conditions in Europe. We employ data from the European Working Conditions Survey and instrumental variables techniques in order to explore how a more intense adoption of this technology shapes job quality in regional labor markets. Over the period 1995 to 2005 an increase in robots used in industry led to worse working conditions with respect to tougher work intensity, but there are no effects on other working conditions, like physical environment of the job or skills and discretion in the job. Negative results for anxiety and stress on the job confirm our analysis. While robots are substituting for arduous—repetitive, heavy or fatiguing—tasks, their precision and predictability and standardization may lead to an increase in work intensity. While work intensity, indeed, increased for workers in the manufacturing sector, which was instrumental in the adoption of these robots, structural change—out of these sectors—could make a final assessment on total working conditions impossible. Additional evidence shows that, first, there is no displacement of workers out of manufacturing, and, second, there are no changes in working conditions in the service sector. While we do find a consistently negative effect of robotization on working conditions in the period 1995–2005, where robot introduction exploded, there is no such effect in the period after the Great Recession: the reasons behind may be a previous adaptation to a new situation as well as generally more volatile employment and working conditions across European regions after the economic crisis.
FUNDING INFORMATION Antón acknowledges funding from the Ramón Areces Foundation (17th National Competition for Social Sciences Research Grants) and Winter‐Ebmer, from the Linz Institute for Technology (LIT) and the Austrian Science Fund (FWF).
CONFLICT OF INTEREST No conflict of interest.
ACKNOWLEDGMENTS The authors thank comments from participants at the 32nd Annual Conference Society for the Advancement of Socio‐Economics (July 2020, Amsterdam) and the 32nd Annual European Association for Evolutionary Political Economy (September 2020, Bilbao) and two anonymous referees.
TABLE A1. First‐stage regression of the change in robot exposure on the change in robot exposure using the changes in sectoral robot density in South Korea (I) (II) ΔRobot exposure (South Korea) 1.057*** 1.073*** (0.104) (0.125) Share of industry −0.540 −0.939 (0.744) (0.819) Population (log) −0.028 −0.031 (0.026) (0.029) Share of females 2.606 2.701 (2.585) (2.691) Share of pop. above 64 −0.052*** −0.052*** (0.018) (0.017) Share of pop. with medium education 1.445*** 1.547*** (0.310) (0.332) Share of pop. with high education 0.186 −0.462 (0.468) (0.605) RTI −1.265*** −1.666*** (0.295) (0.346) OFF 0.779** 1.120*** (0.335) (0.392) R 2 0.849 0.856 No. of observations 160 160 Mean of dependent variable 0.607 0.607 Mean of the instrument 0.793 0.793 First‐stage Wald F‐statistic 95.055 65.245 Partial R 2 of instrument 0.727 0.708 Compositional changes ✓ Open in a new tab TABLE A2. Robustness checks: Physical environment (IV estimates) Variable Alternative IVs (AUS, CH) Alternative IV (SE) Additional controls Unweighted Falsification test (I) (II) (III) (IV) (V) ΔRobot exposure −1.052 −0.810 −0.589 −0.591 −0.810 (0.803) (0.855) (0.946) (1.086) (0.842) No. of observations 160 158 150 160 160 Mean of dependent variable 1.129 1.180 1.150 1.020 1.129 Mean of independent variable 0.533 0.535 0.545 0.533 0.533 First‐stage Wald F‐statistic 70.187 84.057 85.491 88.115 96.571 Hansen J p‐value 0.943 Start‐of‐period controls ✓ ✓ ✓ ✓ ✓ Compositional changes ✓ ✓ ✓ ✓ ✓ Chinese import exposure ✓ ΔICT capital ✓ Open in a new tab TABLE A3. Robustness checks: Skills and discretion (IV estimates) Alternative IVs (AUS, CH) Alternative IV (SE) Additional controls Unweighted Falsification test (I) (II) (III) (IV) (V) ΔRobot exposure 0.347 −0.282 1.063 2.159 −0.100 (1.408) (1.472) (1.536) (1.992) (1.496) No. of observations 160 158 150 160 160 Mean of dependent variable −0.140 −0.247 −0.211 0.840 −0.140 Mean of independent variable 0.533 0.535 0.545 0.533 0.533 First‐stage Wald F‐statistic 70.187 84.057 85.491 88.115 96.571 Hansen J p‐value 0.868 Start‐of‐period controls ✓ ✓ ✓ ✓ ✓ Compositional changes ✓ ✓ ✓ ✓ ✓ Chinese import exposure ✓ ΔICT capital ✓ Open in a new tab TABLE A4. Effect of robot adoption on job quality (2005–2015) (I) (II) (III) (IV) OLS OLS IV IV Panel A. Job intensity ΔRobot exposure 2.467 2.494 7.064 7.109 (2.427) (2.634) (7.004) (6.529) R 2 0.062 0.094 No. of observations 160 160 160 160 Mean of dependent variable 1.626 1.626 1.626 1.626 Mean of independent variable 0.334 0.334 0.334 0.334 First‐stage Wald F statistic 11.628 14.814 Panel B. Physical environment ΔRobot exposure 0.290 0.292 2.869 1.079 (2.199) (2.051) (7.162) (6.185) R 2 0.019 0.117 No. of observations 160 160 160 160 Mean of dependent variable 0.386 0.386 0.386 0.386 Mean of independent variable 0.334 0.334 0.334 0.334 First‐stage Wald F statistic 11.628 14.814 Panel C. Skills and discretion ΔRobot exposure −3.424 −3.288 −5.828 −4.801 (2.258) (2.285) (4.640) (4.031) R 2 0.081 0.110 No. of observations 160 160 160 160 Mean of dependent variable 3.832 3.832 3.832 3.832 Mean of independent variable 0.334 0.334 0.334 0.334 First‐stage Wald F statistic 11.628 14.814 Start‐of‐period controls ✓ ✓ ✓ ✓ Compositional changes ✓ ✓ Open in a new tab
Antón, José‐Ignacio Fernández‐Macías Enrique and Winter‐Ebmer Rudolf. 2023. “Does robotization affect job quality? Evidence from European regional labor markets.” Industrial Relations: A Journal of Economy and Society 62: 233–256. 10.1111/irel.12324
Footnotes 1 These robots carry out physical tasks involving the moving and precise manipulation of objects within standardized industrial processes. These robots are not anthropomorphic, many of them resemble arms. They typically end in an effector (which might look like a human hand) that often carries out a precise manipulation task. Usually, they remain within a predefined and limited space and are constrained to a very particular task. The applications of these robots, mentioned above, suggest that they do not represent a radical departure from the long‐term process of industrial automation but its latest iteration. Most of these industrial robots perform essentially the same type of operations as previous mechanization and automation technologies, replacing labor input in routine tasks that involve physical strength and dexterity. 2 See also Bekhtiar et al. (2021) and Fernández‐Macías et al. (2021) on problems with industry‐level data. 3 There is a large literature on job satisfaction or happiness as general indicators of working conditions (see Clark and Oswald (1994) or Clark (2005) for early references). These indicators may lack comparability as they may also comprise differences in expectations (Osterman, 2013); they are general indicators and there is no research linking these indicators to robots. 4 IFR (2017a) provides robot figures by industry according to the International Standard Industrial Classification of All Economic Activities, Revision 4, which is largely compatible with the Statistical Classification of Economic Activities in the European Community, Revision 2 (NACE Rev. 2). 5 This process is very similar to the one followed by Graetz and Michaels (2018). They use the total number of specified deliveries for imputation (instead of the three closest years). Our series are virtually identical. For more details on the imputation procedure, see the supplementary material of Fernández‐Macías et al. (2021). 6 We do not include the first wave of the EWCS, because of limited coverage of countries, the substantial smaller number of variables due to job quality, and the absence of robot data prior to 1993 in the IFR database. 7 The Great Recession implied large asymmetric shocks across European countries and even regions within the same country. For instance, the length of the crisis in Germany or Sweden is roughly a year, while Spain, Portugal or Greece were still much below the pre‐crisis GDP per capita and employment levels in 2015. Previous literature highlights the relevance of this contraction for working conditions, so its consideration could be problematic, particularly, in terms of work intensity in manufacturing. For example, in Spain, many indicators improved because of the lack of demand (Muñoz de Bustillo & Antón, 2011), and there were declines in some dimensions in other countries (Eurofound, 2021; Fernández‐Macías et al., 2015; Vaughan‐Whitehead, 2011) 8 The list of countries includes Austria, Belgium, Denmark, Finland, France, Germany, Italy, the Netherlands, Portugal, Spain, Sweden and the United Kingdom. 9 Detailed information by region and industry sometimes was only available through an ad hoc request to the Eurostat User Support (Eurostat, 2020). Moreover, sectors have to be reclassified from NACE Rev. 1.1 to NACE Rev. 2. 10 Previous literature finds relevant negative effects of exposure to Chinese imports not only on employment and wages (Autor et al., 2016), but also on health outcomes (Lang et al., 2019). 11 We consider 20 sectors of activity (in the nomenclature of NACE Rev. 2) we are able to match in our robot and employment data: agriculture, hunting and forestry; fishing (A); mining and quarrying (B); food products and beverages; tobacco products (C10–12); textiles, leather, wearing apparel (C13–15); wood and wood products (including furniture) (C16); paper and paper products; publishing and printing (C17–18); chemical products, pharmaceuticals, cosmetics, unspecified chemical, petroleum products (C19–21); rubber and plastical products (C22); glass, ceramics, stone, mineral products not elsewhere classified (C23); basic metals (C24); metal products (except machinery and equipment) (C25); electrical/electronics (C26–27), industrial machinery (C28); automotive (C29); other transport equipment (C30), other manufacturing branches (C32); electricity, gas and water supply (D, E), construction (F), education, research and development (P) and others. The questionnaires of the EU‐LFS effectively collect this detailed information on the distribution of the labor force by region and industry, but the anonymized microdata does not disclose it because of confidentiality reasons and we access it to several tailored petitions. 12 The units considered in the analysis correspond to NUTS 2, NUTS 1, or even larger territories (e.g., combinations of several ones because of the changes in the nomenclature over time). The eventual different size of the regions is not problematic as long as we consider enough large units where out‐migration is no issue. 13 Regrettably, the EU‐LFS is not available for most of the countries before 1995, which prevents us from using lagged employment shares. Under this circumstance, one cannot compute the so‐called Rotemberg weights in this shift‐share design (Goldsmith‐Pinkham et al., 2020). Within this framework, the lagged share of employment would work as an instrument for the change in robot density and the Rotemberg weight would capture how much bias the overall estimate would have if a given instrument (industry) were biased by certain percentage. Nevertheless, in our setup, the procedure should be less problematic than in the canonical applications of Goldsmith‐Pinkham et al. (2020), because we know with certainty the robot exposure for all our countries separately and, then, for each country, we make use of the shift‐share approach in order to compute the regional exposure. Furthermore, following Dottori (2021), who finds very large Rotemberg weights for some industries, we will show below that our results are robust to the use of instruments based on the robotization in countries with different relevance of the automotive sector. 14 The aim of controlling for the initial values of RTI and offshorability is to rule out that other sources of labor market changes due to technological changes different from robot adoption might conflate with the latter. The RTI intends to capture to which extent an occupation is routine‐task intensive. The logic behind the RTI measure is that automation is more likely to affect routine, manual, non‐interactive job tasks. Likewise, the offshorability risk index tries to measure the degree to which a certain occupation might be outsourced to a remote location. 15 We are unable to include country dummies given that some states only contain one traceable region because of their size or changes in the boundaries of NUTS2. 16 It is worth mentioning that previous studies using robot data from the IFR to explore the impact of this technology on labor market outcomes find very close OLS and IV estimates (Acemoglu & Restrepo, 2020a; Dauth et al., 2021; Graetz & Michaels, 2018), thus suggesting little evidence of endogeneity in the first place. 17 In the case of our third IV specification, in order to get a strong enough first stage, we include the change in robot exposure per worker based on Australian data and the same variable based on Swiss data at the same time. 18 There are other dimensions of job quality proposed by the Eurofound: working‐time quality, social environment, and prospects. We do not consider them in the analysis because most of the key variables that integrate them are not available in all waves. The interested reader can find details on all the methodological issues related to the operationalization of the job quality indicators in the literature (Eurofound, 2012, 2015, 2019; Fernández‐Macías et al., 2015; Green et al., 2013; Menon et al., 2020; Muñoz de Bustillo et al., 2011a). The procedures to construct them unfold as follows. We employ the raw variables (i.e., the questions available in the survey) to define all the dimensions of job quality, whereby a higher value of the indicator means a better job, transforming all the items using a min‐max normalization between 0 and 100. For instance, if a variable runs from 1 (best value) to 5 (worst value), we compute 100 ⋅ 5 − value of the variable / 5 − 1 . Each subdimension provides an average score of its component variables and, in turn, each dimension is the result of the arithmetic mean of the scores of the sub‐dimensions. In the same fashion as the bulk of the recent research using these kinds of measures (see, e.g., Eurostat, 2019), each variable receives the same weight within each sub‐dimension and we assign the same importance to each sub‐dimension when computing the score for each dimension. Sensitivity analyses in Muñoz de Bustillo et al. (2011a) suggest that these composite measures are robust to the use of different weighting schemes because there is a high positive correlation between the outcomes in different dimensions. 19 The sensitivity analyses presented by Muñoz de Bustillo et al. (2011a) suggest that the composite measures of these dimensions are quite robust to the use of different weighting schemes because there is a high positive correlation between the outcomes in different domains. 20 The exact wording of the questions is “Does your work affect your health, or not?” and “How does it affect your health?”, respectively. In the latter question, the interviewer offers several possible answers, among them, stress and anxiety. It allows multiple answers. 21 Note that the number of observation is 180, instead of 240, because we estimate models in changes. 22 Similarly, if we leave out one country each time, the results do not change. 23 In their analysis on Germany, Dauth et al. (2021) find that robotization encourages firms to relocate incumbent employees to other tasks and reduce the hiring of young workers. It is reasonable to think that, after this more disruptive impact in the beginning, companies do not have to introduce strong changes in their functioning to the same extent when robot adoption reaches a certain threshold.
DATA AVAILABILITY STATEMENT This paper makes use of different data sources, whose access is subject to different policies. European Working Conditions Survey, EU KLEMS, and Korean and Australian data are available at the websites listed in the reference section. European Union Labour Force Survey and European Community Household Panel data are available from Eurostat upon request for specific research projects (https://ec.europa.eu/eurostat/web/microdata). World Robotics 2017. Industrial Robots data are available upon a fee from International Federation of Robotics. We accessed detailed statistics on employment by economic activity and region from the European Labour Force Survey through Eurostat User Support (https://ec.europa.eu/eurostat/help/support). The code for replicating the results is available from J.‐I. Antón upon request.
| 2022-12-08T00:00:00 |
2022/12/08
|
https://pmc.ncbi.nlm.nih.gov/articles/PMC10946908/
|
[
{
"date": "2022/12/01",
"position": 18,
"query": "automation job displacement"
}
] |
The Future of Work: How Artificial Intelligence is Changing ...
|
The Future of Work: How Artificial Intelligence is Changing the Job Market
|
https://medium.com
|
[
"Cj Crisp"
] |
However, there are also concerns about the potential for job displacement and the ethical implications of using AI to make decisions. The future of work ...
|
The Future of Work: How Artificial Intelligence is Changing the Job Market CJ Crisp 2 min read · Dec 21, 2022 -- Listen Share
Artificial intelligence (AI) has the potential to revolutionize the way we work, from automating tasks to enhancing productivity. As AI technology continues to advance, it is increasingly being adopted by businesses in a wide range of industries. In this article, we will explore how AI is changing the job market and what the future of work might look like.
Artificial intelligence refers to the development of computer systems that can perform tasks that would normally require human intelligence, such as learning, problem-solving, and decision-making. AI technology is being used in a variety of applications, including image recognition, natural language processing, and autonomous vehicles.
The concept of artificial intelligence has been around for decades, but it was not until the 21st century that AI technology began to mature. In the past few years, there has been a significant increase in the adoption of AI by businesses, leading to the creation of new job roles and the automation of certain tasks.
AI is being used in a variety of industries, including finance, healthcare, and manufacturing. Many companies are using AI to automate tasks, such as data entry and customer service, while others are using it to enhance productivity and decision-making. Some experts predict that AI will eventually replace certain jobs, while others believe it will create new job opportunities.
There are several advantages to the use of AI in the workplace, including increased efficiency and accuracy, as well as the ability to handle tasks that are repetitive or dangerous for humans. However, there are also concerns about the potential for job displacement and the ethical implications of using AI to make decisions.
The future of work with AI is still uncertain, but it is likely that it will continue to play a significant role in the job market. Some experts predict that AI will eventually replace certain jobs, while others believe it will create new job opportunities in areas such as data science and AI development. It is important for businesses and individuals to stay informed about the latest developments in AI and to consider how they may impact their work in the future.
Artificial intelligence is changing the way we work, and it is likely to continue to do so in the future. While there are both benefits and challenges associated with AI in the job market, it is important for businesses and individuals to stay informed about the latest developments and to consider how they may impact their work. As AI technology continues to advance, it is likely that it will play an increasingly important role in the future of work.
| 2022-12-21T00:00:00 |
2022/12/21
|
https://medium.com/@crispcormac/the-future-of-work-how-artificial-intelligence-is-changing-the-job-market-7de991fab609
|
[
{
"date": "2022/12/01",
"position": 22,
"query": "automation job displacement"
},
{
"date": "2022/12/01",
"position": 9,
"query": "workplace AI adoption"
}
] |
How Automation Affects the Freight Industry and What to ...
|
Jobs and Automated Freight Transportation: How Automation Affects the Freight Industry and What to Do About It
|
https://escholarship.org
|
[
"Jaller",
"Otero-Palencia",
"D Agostino",
"Mollie C."
] |
by M Jaller · 2022 · Cited by 4 — Other subsectors will see declining job quality and/or job losses that require workers to transition to new roles or sectors entirely, when possible. Changes in ...
|
The expansion of automation in the U.S. economy is increasingly tangible and will presumably entail positive and negative impacts that are not yet well understood. In the freight sector, there is uncertainty about how and when automation will impact labor. Beyond this, there are further unknowns about what the impacts will be on such freight subsectors as warehousing, long- and short-haul. It is expected that penetration rates of freight automation will vary across subsectors. In some subsectors, new jobs will be created and/or working conditions will improve. Other subsectors will see declining job quality and/or job losses that require workers to transition to new roles or sectors entirely, when possible. Changes in job opportunities and quality will vary within sectors and subsectors, by region, and/or by firm. This study offers an overview and recommendations in three directions. First, despite the uncertainties and based on past and present examples of automation, it provides some insights about strategies that may help impacted workers within and outside of the heavy freight sector transition. Second, it discusses examples of existing public policies that can support a transition for automation-impacted workers. And third, it provides insights on how different freight subsectors are likely to be impacted by automation.
View the NCST Project Webpage
| 2022-12-01T00:00:00 |
2022/12/01
|
https://escholarship.org/uc/item/0vk5t0rw
|
[
{
"date": "2022/12/01",
"position": 24,
"query": "automation job displacement"
}
] |
Future of work in 2050: thinking beyond the COVID-19 pandemic
|
Future of work in 2050: thinking beyond the COVID-19 pandemic - European Journal of Futures Research
|
https://eujournalfuturesresearch.springeropen.com
|
[
"Barbosa",
"Carlos Eduardo",
"Program Of Systems",
"Computer Engineering",
"Universidade Federal Do Rio De Janeiro",
"Rio De Janeiro",
"Centro De Análises De Sistemas Navais",
"Casnav",
"De Lima",
"Yuri Oliveira"
] |
by CE Barbosa · 2022 · Cited by 36 — On the other hand, the displacement theory of work affirms that automation will provoke the end of certain careers and the creation of new ones, thus ...
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This section details the methodology used in this work. First, we present the study dynamics, detailing who participate in the study, how the study was placed in time (number and types of sessions), the foresight methods used, specific goals for each method, and how each method contributed to achieving our main goal, which is to provide scenarios for the future of work in the 2050 horizon. Second, we introduce each method used and explain how they were used in the context of this work.
Foresight studies follow no specific methodology, each study tailor the methodology according to its goals. However, the literature indicates that Foresight becomes more reliable when different and complementary methods are combined [13], once they provide multiple perspectives for the analysis, reducing the probability of a biased result. Therefore, we used a Foresight framework that generalizes Foresights as workflows [14] to structure this study.
In this study, we decided to present our results as scenarios, which is a method to develop consistent evolutions of the future based on a set of assumptions [15] and present the results efficiently to third parties, such as decision-makers. We use the following methods to build the scenarios: Bibliometrics, Brainstorming, and Futures Wheel, respectively. These methods are mostly qualitative; thus, the participants were oriented to base their conclusions on the data gathered in the Bibliometrics method.
The study dynamic
The study was performed by thirteen researchers (including the moderators) with different backgrounds, for example, in computer engineering, production engineering, public management, architecture and urbanism, and design. These researchers were divided into five groups, taking into consideration their expertise and interests, corresponding to different topics regarding the future of work: computerization/automation (2 researchers), employment (2 researchers), education (3 researchers), social welfare (2 researchers), and economy (2 researchers). The topics were defined by a literature review [16] that was previously done by researchers of the future of work. The other moderator is an expert in future studies. These two moderators participated in all groups, guiding the participants to follow the methodology and providing suggestions as necessary. It is also worth noting that the moderators presented and discussed the results of each step of the methodology in meetings with all the participants of the study. Furthermore, the guidance provided by the moderators and the participation of, at least, two participants for each topic helped to reduce biases and ensured that even though each topic had a relatively small number of researchers. Methods such as Brainstorming and Futures Wheel were performed as group activities, integrating the entire interdisciplinary group of thirteen people in the collaboration efforts to ensure the quality of the results.
The study was conducted during 8 sessions, once per week. Each session had an approximate duration of 2 hours. Since the duration and format of the sessions were defined before the execution of the study, there were few absences from the participants, and most of them were communicated previously. In absences, the moderators explained the work to be done by e-mail and were available to answer any question. Since most of the work was done in the week between the sessions, each group could perform meetings to produce their contributions. However, the Brainstorming for all groups was performed in a single 5-h session with mandatory attendance.
This study was performed with the aid of software, named Tiamat [17]. Tiamat software is a modular collaborative Foresight Support System, designed to support on-site and remote (through the Internet) studies using the concept of Foresight method workflow [17]. The software was used in several studies [12, 18,19,20,21] before and allowed the moderators to orchestrate the study, all participants to communicate asynchronously, and serve as a repository that offers traceability to the intermediary results. The Tiamat framework offers a process that can be followed independently from the software; therefore, although we used the computer system to support the study, no special software is required to perform our methodology.
In the first session, the moderators presented the methodology and the Tiamat software and also divided the researchers into the aforementioned five groups. Between the first and the second session, the participants were responsible to search the literature and gather relevant material to be analyzed. The use of bibliometrics is useful to level the knowledge among the interdisciplinary participants, gather and store the state of the art of the topics of the study and select primary citations for further writing future scenarios. Details of the results are presented in the next section.
In the second session, each group presented its findings and the participants had the opportunity to recommend papers to other groups. Each participant would then have two weeks to fully read the papers considered important for their topics.
The third session was focused on discussing their findings until the moment. Each participant would share how the documents that were read up until that session contributed to understanding the future of the topic assigned to their group. Also, the participants were encouraged to share any trends found in the reading that was related to the topic studied by another group, thus stimulating the collaboration between groups.
In the fourth session, the moderators performed five Brainstorming sessions (one for each group) in which the researchers presented the main events they found. For example, the employment group found the event “more flexibility in the employment contract.” In the Brainstorming, we discussed the impacts of each event and proposed new events from the discussion. The Brainstorming ended with voting on which events should be included in the next steps. The moderators lead the brainstorming sessions according to Osborn’s brainstorming guidelines for the generation of ideas [22]. The participants from the other topics were allowed to participate since many papers that they read also discuss other participants’ topics and their different perspectives may contribute to the topic in discussion. The output of the brainstorming is a list of possible future events regarding the topic of the group. These possible future events were used as input for the next method, Futures Wheel. Between the fourth and fifth sessions, the groups were invited to develop a Futures Wheel [23], a method that stimulates participants to discover events that are consequences of other events. The Futures Weel also establishes cause-consequence relationships among events which are highlighted in a graph format. We started the Futures Wheel of each group with the events discovered in the brainstorming as initial events, allowing the participants to include, remove, or modify events while they indicate cause-consequence relationships between events, i.e., discovering primary and secondary consequences of events.
In the fifth session, each group presented and discussed their Futures Wheel. At the end of the fifth session, the moderators asked the groups to use the list of expanded events, developed during the Brainstorming and Futures Wheel to identify and develop the main trends for each topic using the scenarios technique and the literature already gathered to support their research—new literature could be added further. We call trend a set of possible future events that told a cohesive matter, heavily supported by the literature—not only the literature gathered in the bibliometrics step. Each group developed trends related to the topic of their study.
In the sixth and seventh sessions, the participants presented the trends for each topic. After the seventh session, the moderators dismissed the topic division, joining all participants to develop 3 scenarios: one which considers the best outcome for each event, one that considers the worst outcome for each event, and one that considers the most likely outcome for each event. The participants were asked to check the consistency of each scenario produced.
Therefore, the eighth session was focused to check the produced scenarios. The moderators provided one extra week for the participants to fix all spelling and formatting errors and analyze the proposed suggestions from the eighth session. The final version of the scenarios was delivered using the Tiamat software. This study encounters dynamic is presented in Fig. 1.
Fig. 1 Study dynamic diagram Full size image
With the COVID-19 pandemic, we knew that our study had to be updated to consider its impact. The update was done in 2 months, from June to August 2020, by gathering seven of the researchers involved in the first part of the study and assigning at least one of them to each of the five topics of the research. The methodologies applied to explore the impact of COVID-19 on the future of work and update the trend scenarios were Bibliometrics and Brainstorming. The Bibliometrics was based on 42 papers about the impact of COVID-19 on the different topics that were being studied with several reports serving as a support to grasp the scenario that was unfolding during the pandemic. Then, the group reviewed the likely scenario to consider how the combination of these individual impacts would change the future of work in 2050. We performed the update without face-to-face sessions, as expected during the COVID restrictions.
The resulting trend scenarios presented in Section 3, and the likely scenario presented in Section 4 already consider the COVID-19 pandemic impact on the future of work.
The methodology in practice
In this section, we formalize the study dynamics: first, we provide a global view of the study, in the form of a workflow; second, we explain how each method work and provide results for methods from one topic as an example of the methodology. The topic of Employment will be used to illustrate the methodology in this section. Since trends and scenarios are the main findings presented in detail in this work, we present in this section only example results from Bibliometrics, Brainstorming, and Futures Wheel.
We highlight that the participants were trained and guided during the study to produce high-quality data and guarantee its uniformity and consistency. In Fig. 2, we present our methodology in the form of a workflow following the Foresight framework proposed by Barbosa [14].
The five topics were analyzed individually using four methods: Bibliometrics, Brainstorming, Futures Wheel, and Scenarios.
Bibliometrics
Bibliometric analysis is the analysis of large numbers of scientific documents. Bibliometric analysis is usually taken from patent and scientific publication databases [24] and should be combined with other measures or expert opinions to be balanced [25]. The bibliometric analysis summarizes document characteristics for statistical analysis and infers linkage among documents, where it may be used to find indirect links among concepts [26]. For inferring the linkage among documents, there are a few approaches: co-citation analysis, co-word analysis, and mapping. Co-citation increases the linkage between documents as they cite the same references. Co-word increases the linkage between documents as they use the same relevant words. Mapping presents the bibliometric data and findings, facilitating its interpretation by humans.
In this study context, we used bibliometrics to perform a simple literature review, using both scientific databases and documents available on the Internet, such as governmental reports. Therefore, the researchers gathered data in the literature to build a knowledge base, used to identify trends and support scenarios. Moderators did not enforce the use of systematic reviews of the literature; therefore, we infer the use of random search on several bases and Google to include gray literature. Snowballing was also allowed. We expected to reach beyond the academic literature, including data from technical reports from governmental organizations, non-governmental organizations, think tanks, and companies to capture early signals of change and enrich the study by providing plural views about the future of work. Due to the time restriction between the sessions, we back down from performing a mapping of the literature. The summary of the Bibliometrics results is presented in Table 1.
Table 1 General results for the Bibliometrics method Full size table
As an example of an output of the Bibliometrics method, Table 2 presents the results from the employment group.
Table 2 Results for the bibliometrics method for the employment group Full size table
Brainstorming
Brainstorming [22] is a group technique focused on idea generation that frees its participants from criticism [27]. Osborn’s brainstorming guidelines for the generation of ideas: no immediate concern for quality or evaluation, in a set time frame, encourage building on the ideas of others, and recorded by a non-idea-contributing facilitator/scribe [28]. Although brainstorming is a very old concept, it is still widely used. Putman and Paulus [29] proposed a set of rules based on the original Osborn’s rules but extended for interactive groups. Putman and Paulus [29] proposed rules to avoid criticism, stimulate freewheeling in other participants’ ideas, stimulate quantity over quality of ideas, stimulate the combination and improvement of ideas, avoid losing focus on the task, avoid moments of silence, and stimulate review previously ideas and categories.
In this study context, we used Brainstorming to raise possible future events of each group research topic—computerization/automation, employment, education, social welfare, and economy—on work, based on the literature analyzed in the previous step (Bibliometrics). Moderators place these events are the starting point for the further analysis performed by each research group, following the Putman and Paulus rules. Participants were stimulated to use the literature to develop the events, which were not limited to the Bibliometrics results—i.e, the participants could perform snowballing for example to gather more information. However, the main source of possible future events comes from their understanding of the complex scenario and their further reasoning into ideas. Such ideas—even if they exist—are not easily findable in the literature. Finally, we voted on the list of proposed ideas, developing basic trends to be further analyzed. We present the selected brainstorming events from the Employment group in Table 3.
Table 3 Results for the brainstorming method for the employment group Full size table
Futures wheel
The Futures Wheel [23] is a method to identify the consequences of trends and events. For the sake of simplicity, we will refer to trends or events only as events. Starting initial events, the participants define a set of primary consequences. The participants should ask themselves three questions to discover the consequences: “If this event occurs, then what happens next?”, “What necessarily goes with this event?”, and “What are the impacts or consequences?”. The Futures Wheel analysis continues recursively, i.e., each primary consequence is analyzed to generate a set of secondary consequences. Although the Futures Wheel may go on indefinitely, rarely does it go further than the tertiary consequences, mostly because the complexity of the analysis grows exponentially. Contradictory consequences may also occur and the participants must consider them.
The participants of the Futures Wheel map the event to its consequences, producing concentric graphs, which highlight the potential complexity of interactions, showing that the consequences do not happen all at once, but in an evolutionary, interactive sequence [23].
In this study context, we used Futures Wheel to further discussed the events listed in the Brainstorms. Therefore, the Futures Wheel mapped the events to their consequences, producing concentric graphs of primary, secondary, and tertiary consequences. New events were included as a result of this analysis. The Futures Wheel from the Employment group is shown in Fig. 3.
Fig. 3 Futures wheel from the employment group Full size image
Scenarios
Scenarios are possible evolutions of the future consistent with some set of assumptions [15]. Scenarios have been termed the “archetypal product of futures studies” [30]. They can be achieved through creative thinking about future possibilities (explorative scenarios) as well as through active working towards the production of a desirable future or set of futures (normative scenarios) [31].
Scenarios represent the combination of a set of extrapolated current trends or projections, and these must be internally consistent, i.e., not contradict each other. For example, when analyzing possible futures related to ATM usage, a scenario where an increase in cashless money transfer and an increase in the usage of ATMs by the general population should be pruned, as these events are mutually exclusive, therefore making the scenario inconsistent [32]. Indeed, Shoemaker [33] suggests that three tests of internal consistency are especially useful. Firstly, remove scenarios with trends whose time frames do not match. Secondly, remove scenarios in which predicted outcomes are inconsistent with each other. Lastly, remove scenarios in which major players are placed in unlikely positions.
In this study context, we used scenarios to analyze the events and developed the trend scenarios that are presented in detail in Section 3, using each group Futures Wheel and literature gathered. Therefore, the trend scenarios discuss trends for each topic of this study, and they are heavily based on the literature.
Finally, we also use scenarios to develop three scenarios for work in 2050: an optimistic/positive scenario, a pessimist/negative scenario, and a likely scenario. To develop such scenarios, we dismissed the division of groups into topics, since the scenarios must consider all topics. The Scenarios for work in 2050 were built on all the knowledge gathered in all previous steps. Therefore, the scenarios are based on the joint analysis of the trend scenarios to understand how they interact. We also classify the trends as more or less likely to happen and if a trend can be considered good or bad for society. Due to space limitations, we present the likely scenario in Section 4, which considers the combination of the trends for the future of work that the participants considered as most probable.
Trend scenarios for future work
This section will present the future trends for the areas analyzed in this study: computerization/automation, employment, education, social welfare, and economics.
Computerization/automation
The last century started a transition in industrial automation as machines are increasingly better to make decisions, not only performing manual activities but allowing more activities to be automated. The most cited paper concerning the topic estimated that 47% of the US workforce was under a high probability of computerization (automation by computer technologies, mainly AI and Robotics) in the next decades [34]. Later studies that applied the same methodology showed that the number of workers in occupations that are likely to suffer computerization varies from country to country. In developing economies such as Brazil, the percentage reaches 60% [35] while in advanced economies such as the UK, the number drops to 35% [36].
Areas such as the retail market, archiving, data collection and processing, and line assembly operations will be highly impacted. Still, even for workers at higher risk, adopting automation is not simple: it requires analysis of some key points, such as technical feasibility; development and implementation costs; labor market dynamics, considering its demand, costs, and social characteristics; economic benefits, such as governmental policies; and social acceptance [37].
As automation increases, it will require policies to protect unprepared and vulnerable workers, allowing them to migrate to the new model of production [38]. Underdeveloped nations face higher risks since they are rarely part of the discussion about this topic and are outside of the focus of studies. Erroneous interventions also leave underdeveloped nations incapable to compete against developed nations, producing economic, social, and political inequalities along with technological advancement [38]. It is important to note, however, that unemployment levels have remained stable in the long run, despite disruptions caused by industrial revolutions, as workers migrated to new jobs sometimes enabled by new technologies or the number of jobs was increased because of a higher consumption [39, 40].
The increasing adoption of automation technologies results in ever-lower costs of hardware, sensors, network, processing, and storage; a more refined and accurate set of data allowing tests and studies even without human supervision; and a great expansion and absorption of knowledge unprecedented [41].
The last 20 years have brought remarkable progress in AI, one of the most important technologies in the current wave of automation, and now, we can build machines capable of learning even when humans are unable to teach them, producing new knowledge faster than humans [41]. Due to these advances, it will be possible to have AI working with humans as assistants, from reading e-mails to driving cars. However, it will also raise privacy, security, and ethical issues, with unintended consequences if we cannot identify these challenges promptly [42].
The Internet of Things (IoT) is another important automation technology that has been experiencing considerable expansion, especially in areas such as medical and health care, smart building, intelligent transportation, industry, and logistics [43]. IoT includes low-cost and high-performance processors attached to low-cost sensors; they usually include some form of analytical software, many of them in highly distributed architectures, i.e., cloud computing [44]. IoT is a central element of Industry 4.0 where the integration between humans and machines can speed up the production systems by 30%, raising their efficiency by 25%, and allowing a new scale of product customization [45].
IoT evolved into the development of smart medical devices creating the concept of the Internet of Medical Things (IoMT). IoMT allows real-time monitoring of the health condition of a person using smart sensors and connected devices and can also help the medical staff at the hospitals by remote monitoring chronic-conditions patients at home. Thus, IoT reduces the workload on medical staff and becomes a necessity instead of a luxury [46].
The COVID-19 pandemic affects several automation-related segments such as telemedicine, IoMT, manufacturing, and supply networks, AI, and smart payments. In telemedicine, the increased adoption of telemedicine to keep patients and medical staff safe can be highlighted. One example is Tele-Critical Care (TCC), a tool related to telemedicine that enables intensivists in traditional intensive care units (ICU) to speed up critically ill triage, thus improving ICU bed management; in hospitals without ICU, TCC enables remote care for critically ill patients, preventing transferring these patients. Another telemedicine tool is Telementoring, in which experts from low-demand areas help their high-demanded peers. During the COVID-19 crisis, intensivists used tiered telementoring to provide consultation on patients with a higher risk from experiencing respiratory and organ failure. One intensivist was capable to oversee 100–250 patients through telementoring [47].
Manufacturing is also being adapted for the post-COVID era, as workplace standard practices are adapted to the physical-distancing policy, thus stimulating concepts such as Smart Manufacturing and Industry 4.0 [48, 49]. AI will be the backbone of automated transportation systems, both on the streets (driverless trucks and cars), and in factories and warehouses (Automated Guided Vehicles) [49]. AI is related to the development of cleaning and disinfecting robots as well [46]. Such innovations make manufacturing and supply chains more resilient to human-related interruptions.
Finally, smart payment (also known as contactless payment) technologies minimize human contact during cash payments; their demand tends to continue high in the post-COVID era [46].
The COVID-19 pandemic accelerated the adoption of several technologies—such as Big Data, robotics, AI, and IoT—as they help companies and society in general to mitigate the impacts of the pandemic. In some cases, the adoption of technologies was necessary to maintain the operation of businesses, making digital literacy an essential skill, and allowing workers to see technologies more as a tool than a replacement [50], a movement that started even before the pandemic as digital skills were already becoming an important determinant of employability in the digital age [51, 52]. The adoption of automation and digitalization tends to intensify during and after the pandemic as essential activities will use more automation to safely attend to their customers and activities that can be moved online such as retail, entertainment, and recreation will be digitalized [53].
Employment
The world is entering its 4th Industrial Revolution where technologies such as AI, nanotechnology, 3D printing, robotics, and biotechnology are being used in combination and creating new possibilities for production [54]. Technological unemployment is once again a preoccupation in this new industrial revolution and it can be defined as “non-employment due to our discovery of ways of saving the use of labor, exceeding the pace at which we can find new uses for work” [55]. On the other hand, the displacement theory of work affirms that automation will provoke the end of certain careers and the creation of new ones, thus causing little or no harm to employment.
Globalization, another major force in the future of employment, has created two trends in the markets: outsourcing and immigration. Remote work is already a reality, even in traditional enterprises such as IBM, where only 42% of employees work in IBM’s location [56]. Remote work was only recently adopted by large companies, but in startups, it is already common. The distribution of offices in different places or even spaces for co-work will promote a reduction of expenses for the companies, becoming an alternative to the central offices in costly commercial locations [57]. However, illegal immigration from underdeveloped nations will be motivated by the combination of unemployment, food scarcity, wars, and other extreme situations [58].
There is also a trend for greater flexibility for workers, making it possible for them to mix different part-time jobs. In this scenario, virtual reality (VR) and augmented reality (AR) may be used to amplify immersion and collaboration, allowing workers to be “where” they are needed [59].
The return of the elderly to the workforce will be motivated by the increased difficulty in fulfilling their retirement plans, in general, and also by the sense of helping society with their experience [60, 61]. This trend produces a significant impact on society since organizations can continue to be competitive by having access to a larger pool of qualified professionals, reducing the scarcity of specialists, and the impact on social security systems [60]. Considering advances in the automation of health care, increased use of continuous health tracking devices, reduced health costs, and human errors being reduced due to automation, life expectancy, and the time that a person will be able to perform work and actively participate in society tend to increase [62, 63].
According to the International Labor Organization (ILO) [64], “non-standard forms of employment have become a contemporary feature of labor markets around the world.” In South America, 6 of the 10 young people working in the informal economy today [65]. This trend is not exclusive to developing countries [66].
Recognizing the inevitable growth of non-standard forms of employment, a policy proposed by Harris and Krueger [67] introduces a new “self-employed” designation that is not eligible for overtime payment and unemployment insurance but protects workers by antidiscrimination statutes and gives them the right to organize and withhold taxes [67]. Their employers, be they online or offline, would make tax contributions to the payroll [67].
The labor movement suffered recent changes influenced by globalization and technological change but has managed to remain relevant as new forms of work and challenges for workers appeared [68]. Some examples of how the labor movement is being organized by digital platforms’ workers are the App-Based Driver Association, a group from Seattle-US of app-based (e.g., Lyft, and Uber) drivers, and Turkopticon, an initiative by the University of California San Diego that gives the possibility of Amazon Mechanical Turk workers to evaluate their Human Intelligent Tasks [69]. Another way that new labor movements can be created and empowered is by seeking support from traditional unions and other social actors. An example is the FairCrowdWork Watch, a platform developed by the IG Metall (dominant metalworkers’ union in Germany)—that allows workers to rate platforms, compare their payments with others, and receive legal advisory [70]. The diversity of new workers’ movements and the importance of their agenda show that these organizations are likely to continue existing in the future by adapting themselves to each new challenge with the support of technology. Nevertheless, this trend does not mean that traditional unions will become more relevant in the future as digital platform workers tend to feel a certain apathy towards unions partly explained by their identification with entrepreneurship [68].
The job losses caused by the current pandemic are expected to be worse than the 2008 crisis because around 38% of the global workforce is in economic sectors that are suffering a collapse in demand such as manufacturing, hospitality, tourism, and transportation. The crisis is also expected to increase unemployment rates around the world to two-digit numbers even in places that had very low rates before the pandemic as the USA and developing countries can experience even worse outcomes [53].
In Italy, an analysis of the impact of the coronavirus on 7800 companies shows that the aggregate shock on a 3-month horizon is −21% and −16% in twelve months with companies canceling 44% of the preexisting scheduled R&D plans. When it comes to employment, the expected aggregate drop is −6.5% [71]. In the USA, a survey of 10,000 households shows the impact of the virus from January to April of this year. The employment rate fell by 5%; overall spending dropped by US$1000 per month (a 31% drop), especially with mortgages, student, and auto loans which indicates the possibility of a wave of defaults soon; 42% of employed respondents lost earnings due to the virus (an average of over US$5000) [72].
Another challenge brought by the pandemic is the asymmetry of the impact on jobs as it will disproportionately affect entire social categories as low-skilled, low-wage jobs usually held by minorities, immigrants, women, and other disadvantaged groups will suffer the effects of the crisis in the long term [50, 53].
COVID-19 showed the importance of the low-wage workforce which comprises a considerable portion of the essential sectors. Immigration systems in advanced economies tend to be more open to high-skilled workers and more restrictive when it comes to low-skilled workers. In the UK, 16.1% of essential workers are foreign-born. Specifically in the health industry, 18.6% of the workforce is foreign and 13.4% of the workforce is not from the European Union. In a “sharp” crisis like the one caused by COVID-19, immigration systems cannot change quickly enough to supply immigrant workers to needed areas. Forty-six percent of foreign-born essential workers in the UK do not meet the post-Brexit immigration rules that stipulate minimum thresholds for immigrants’ jobs’ skills and wages [73].
Many unemployed people will seek jobs in other cities and countries, leading to migration. Massive migrations to other countries may cause two impacts: anti-immigration laws in the countries receiving immigrants and a lack of young workers to develop the countries losing their workforce [74]. According to Goniewicz et al. [75], future policy should incorporate lessons learned from the COVID-19 pandemic. Granting refugee status to immigrants is controversial and pro-immigration policies can cause confusion and conflicts [74]. A policy for long-term social distancing and the gradual personal interactions of low-risk individuals should be implemented. Workplaces should be adapted to facilitate physical distancing.
At the micro-level, as the measures to control the coronavirus spread involve social distancing, society is experiencing a surge in remote work, specifically working from home [76]. This change brings new challenges to workers as unplugging from work demands is one of the new work-life conflicts [50, 76,77,78].
Education
The changes in the world of work will force education to be adapted and advancements in technology may help teachers to achieve this goal. The education system needs to train increasingly specialized workers, due to the end of some careers and the emergency of new ones [79]. This cycle will be more active and impactful in the future, bringing the need for lifelong learning to adapt workers to different jobs. However, the reactive characteristic of changes in education (that trains workers for an almost obsolete job to bring them to the current market) needs to be adapted, continuously updating their curricula on new job trends, and providing relevant competencies for job opportunities [79]. Governments and education institutions have a key role in keeping education updated for new workers. Governments are also responsible for stimulating the creation of new jobs. In this way, they create initiatives such as short-term higher education courses focused on a faster insertion into the labor market [80] and Massive Open Online Courses (MOOC) capable of teaching and training thousands of workers at the same time, complementing traditional educational methods to provide faster adaptation of education in the future [81].
As work will need less time to be performed due to automation [79], workers will have more free time, which may be used to learn, rest, or work on a second job. Information becomes cheaper (in many cases free), brought by the expansion of knowledge through the Internet, and this trend boosts the use of MOOCs by workers. Thus, we will see teachers becoming advisors, directing students through the knowledge freely available [79]. Education will be personalized, with tailored learning plans to fulfill the worker’s needs, interests, and preferences stimulating students to spend more time learning the skills related to their interests [82]. Besides, projects such as the open access initiative will help the sharing of research facilitating free access to knowledge [83]. MOOCs and other educational online environments have also the capability to train people looking for self-employment, either to supplement their monthly income or as the only existing employment option.
New jobs will require highly skilled, knowledge-intensive workers [84]. Science, Technology, Engineering, and Mathematics (STEM) skills play a key role in any country’s economic success and require years of investment in education [85]. This higher demand for highly skilled workers will also affect low-skill jobs, mainly those in services [85]. Fundamental skills such as literacy, numeracy, communication, and team working are required for most jobs [85, 86].
In addition, the worker of the future will need to learn the following skills: critical thinking and problem-solving (cognitive skills), presentation and conflict resolution (interpersonal skills), and adaptability and self-development (intrapersonal skills) [87]. Jobs with a lower risk of automation rely on social and creative skills [84]. Therefore, the most important skills needed for these jobs are collaboration, self-regulation, knowledge construction, communication, real-world problem-solving, and the use of technology for learning [88].
During the COVID-19 pandemic, millions of students were unable to go to school and received general recommendations to use digital tools such as online study platforms [89,90,91].
Before the COVID-19 pandemic, we assessed that as free information becomes more prevalent in society, we may see teachers replaced by MOOCs if people became more self-taught. From mid-March to mid-May 2020, Coursera, one of the most used MOOC platforms, saw a growth of ten million new users [92].
Regarding the impact on schools in the USA, Dutta [93] discusses several consequences of prolonged stay-at-home and school closures for children. Prolonged isolation from their grandparents, teachers, classmates, and friends is likely to cause sadness and stress. The unexpected transition to distance learning makes students struggle to learn the required knowledge for their respective grades, while schools face difficulties with the standardized COVID-19 test requirements. Schools also play an integral role in promoting healthy eating and maintaining an active and healthy lifestyle—which may lead to sedentary behaviors and, thus, increased rates of child obesity. Inequality is also an issue: some students will have problems accessing the distance learning web modules, and children in need will lose access to school meals, facing starvation. Kneale et al. [94] list other consequences: children losing access to school-based health care, increased injury risks due to self-care or inadequate care, higher risk of child abuse or violence, and even increased child labor and marriage rates.
The World Bank produced a report on the impact of the pandemic on education financing. They projected declines in government revenue due to slower economic activity. On the other hand, countries are overspending their budgets on health and social protection. Such a combination will deteriorate the fiscal balances in most countries during 2020. Therefore, governments will tend to reprioritize their budgets, reducing the education budget, reducing the per capita education spending in almost all country income groups and regions, and impacting future education outcomes. Even in a scenario of economic growth for 2021, the education budget tends to stay stagnate or fall in most countries [95].
Social welfare
Technological progress will affect how we work. In healthcare, technology will increase the quality of diagnoses and improve people’s quality of life. The population is proportionately aging [96], and, as a result, people’s productive ages will increase, raising the economically active population. Besides, a bigger population implies changing pension systems as well as workers who exceed the minimum/normal retirement age for a better pension.
In general, eligibility rules for retiring are complex and the pension benefit varies according to the objectives of each government. In 2014, the normal age of the normal pension was 64.0 years (men)/63.1 years (women), assuming entry into the labor market at age 20 [97]. The forecast for 2054 is a rise in the normal retirement age, with more countries raising the normal retirement age to above 65 years while reducing the gender disparities in retirement age [97].
Figure 4 shows a forecast that economic inequality tends to grow in the future, bringing negative consequences for the distribution of wealth, since previously accumulated wealth grows faster than production and wages. Thus, the current wealthy people tend to become the dominant rentiers over those who do not own properties but only their work [98].
Fig. 4 After-tax rate of return on capital vs. growth rate at the world level, until 2100 [98] Full size image
Gender equality influences the return on capital rate and the growth of income and output. An increase in women’s participation in the economy results in more political power. If the trend toward increasing gender equality is sustained in the coming decades, a slower increase in economic inequality can be expected [99].
Gender equality has improved in the last 50 years, but several countries still fail to provide even fundamental rights to women, especially in North Africa and the Middle East. The gender gap has slowly reduced in the past decade. Health and education subindexes reached values close to 1 (equality), while economic and political show much lower values. At the current rate, the gender gap will only be closed by the year 2100 [100].
Some researchers argue that growing inequality is a result of the exponential shift in technology. The new technology provides the economic reward for the winners of our modern economy, while the losers become increasingly expendable and less resourceful [101]. Racial inequality should be also considered. For example, Native Americans, Africans, and Latin Americans present a lower Human Development Index (IDH) than Asian and white Americans [102]. Sexual orientation is also a taboo subject in many countries, making it difficult to develop evidence-based policies. LGBTI rights are also necessary for an equal society. Anti-discrimination laws are necessary to make people more tolerant of the LGBTI community.
For McGahey [103], technology or computerization job losses, demographic changes, and rising costs of social benefits are challenges for social welfare states. Thus, social welfare states should offer new types of social benefits or build a new way out of this problem. McGahey suggests that there is a way for introducing Universal Basic Income (UBI) as a floor income, providing basic subsistence, complementing the existing welfare state policies, or, in some cases, substituting it [103]. Universal Basic Assets (UBA), sometimes considered an evolution of UBI [104], is defined as a basic set of resources every person is entitled to have: housing, education, health, and financial security.
The COVID-19 pandemic affected social welfare in several ways because it causes an economic crisis and global recession. Although each country tries to stimulate its economy, methods vary due to financial and political constraints. US unemployment raised to its highest level since the great depression. Most households have insufficient savings to live through this type of adversity. Governments provided liquidity to the most vulnerable households through penalty-free withdrawals from retirement savings accounts and stimulus checks, among others. The consequences of such actions include large government debts [105].
Massive unemployment led many families with young children to become food insecure. Household Food Insecurity (HFI) increases the risk of chronic undernutrition and infectious diseases in children, maternal anemia, obesity, and type 2 diabetes [106]. According to Pérez-Escamilla et al. [106], COVID-19 HFI will affect more vulnerable groups such as young children, pregnant, and lactating women. Many disadvantaged students lost their access to free school meals [74]. Half of them are in low- and lower-middle-income countries; losing this meal also reduces the most vulnerable families’ income [106]. Under such circumstances, children in some nations are at higher risk of child marriage and child labor [74]. COVID-19-related stockpiling and panic buying have also affected food security. The just-in-time supply chains are vulnerable to disruptions; this sudden rise in demand caused empty shelves and higher prices for some products. Poor availability of food in supermarkets forced households to access food from food banks that already suffering from the sudden increase in demand and reduced volunteer numbers. Some independent food banks achieved their “breaking point” and others closed entirely [107]. According to Power et al. [107], the food aid system seems unable to face health and economic emergencies simultaneously. According to Pérez-Escamilla et al. [106], COVID-19 has shown “how unprepared the world is to protect populations against hunger, food, nutrition, and health insecurity during global emergency situations.”
Economy
The current demographic changes will highly impact the economy in 2050. The first demographic change will be caused by world population growth, which will rise from the current 7.6 billion to about 10 billion people by 2050 [108]. This worldwide increase in population is a challenge as it shows that hundreds of millions (or a few billion) jobs must be created [108].
A second demographic change that will impact economies is population aging. By 2050, the population of developing countries will still be younger than those of developed countries [109]. As the worker live longer, they must also have to work for a longer period to support their pension schemes. The extension of working years will also impact youth employment as more experienced workers will dispute a few job opportunities with them.
A third demographic change is an increase in urbanization. Besides the population growth, people in rural areas are migrating to medium and large cities. According to Schwettmann [108], 64% of the population of developing countries and 86% of the population of developed countries will be urbanized by 2050. This trend has mixed impacts, as urbanization may cause unplanned city growth, pollution-related health hazards, and unemployment. However, urbanization may reduce the costs of transport and education, and create cultural diversity [108].
The 2050 economy will be based on knowledge-intensive work. Knowledge-intensive services now include business services such as finances, accounting and software, medical services, and engineering [110]. Knowledge-intensive careers have fewer jobs; mostly because they require very high skills and advanced degrees in the fields of science and engineering. Thus, the unskilled and less educated, which represent most of the population of many countries, are excluded from most of the opportunities in knowledge-intensive production [110].
Technology is only a factor to determine economic results such as growth, inequality, or employment—however, technology is the main driver of Gross Domestic Product (GPD) growth per capita. Leading economies have access to similar technologies which resulted in different economic results throughout history, mostly because they have different policies and institutions [111].
According to the McKinsey Global Institute [37], in the USA, 46% of the time spent on work activities is technically automatable, using the current technologies. They estimate that the current automation technologies could replace 50% of working hours on a global scale. Increasing computerization will affect almost every occupation, not limited to factory workers. This automation potential represents 1.2 billion workers, which wages about US$ 14.6 trillion [37].
Rises in labor productivity usually translate into increased average wages, providing the opportunity for workers to reduce their working hours and increase the offer of goods and services [111].
Another important trend related to the economy is a phenomenon named “Rise of The Rest,” which describes the shift of the GDP from developed countries to developing countries. Nowadays, global economic activity is already shifting from the G7 to the G20 [109, 112]. As a consequence, developing countries have a faster increase in their technology, capital, and people [109, 112]. Figure 5 presents the projected average GDP growth from 2016 to 2050. We highlight that COVID-19 and the recent Russian invasion of Ukraine [113, 114] may cause an impact on these long-term GDP projections, especially for Russia.
Fig. 5 Projected average real GDP growth 2016–2050 [109] Full size image
The COVID-19 pandemic produced a health and economic crisis with unprecedented scale and magnitude producing an unforeseen combination of supply and demand shocks for the global economy that will affect it for a long time even after the coronavirus control policies [71]. Almost 90% of the world economy went under some sort of lockdown measures by mid-April and an economic crisis unparalleled to none since the great depression has taken place. Blockades of national frontiers imposed by governments have paralyzed economic activities in general, laying off millions of workers worldwide and having a major impact on the world economy. Global GDP is forecast to decline by 3.2%, reaching a drop of 5.0% among developed countries; and production losses projected for 2020 and 2021—almost US$ 8.5 trillion—will eliminate almost all production gains of the previous 4 years [53, 115, 116].
Among developing countries, large fiscal deficits and high levels of public debt will pose significant challenges, particularly for economies dependent on commodities and tourism. The severity of the economic impact depends mainly on two factors: the duration of the restrictions (economy, circulation, and transport) and the size and effectiveness of fiscal responses to the crisis [117].
Households are strongly affected due to lockdown restrictions, causing job losses. This situation reflects in a decreased consumer power, being perceived as more accentuated in sectors such as tourism/hospitality and clothing [72]. The greatest concern on financial health at the family level is due to the uncertainty as to whether financial reserves will cope with an extended lockdown period [117]. For this reason, governments have introduced financial support programs for groups of people who are economically vulnerable to the effects of the pandemic; however, not all have established norms regarding credit scores, which also have an influence on consumption and the granting of future credit [118].
The global production chain was also impacted: mainly affected by the closure of countries’ borders, this network of international economic relations proved to be highly dependent on a small number of countries, such as China—causing the absence of industrial inputs and unbalancing the trade balance of the countries under its influence [117].
The COVID-19 pandemic also impacts the financial sector. Risk management models built over the past few decades have not been able to guarantee global financial stability and contain the effects of this financial crisis [119]. Stock exchanges are experiencing a period of high volatility around the world, mainly in Asia—investor uncertainty reflects the pandemic’s effect on the economy [120].
The governmental response involves a set of strategic actions, learning lessons from this event to build resilience for possible future crises. At the household level, governmental actions include proposals for financial support for those who had income losses and for stimulating family cost savings to build emergency finance reserves. At the business level, governmental actions include preventing corporate bankruptcy and mass layoffs by identifying companies that are in the most critical stage to support loans and investments so that they can rebuild. At the local economy level, governmental actions include identifying interventions to improve business recovery after COVID-19 and prioritizing investment in critical economic sectors and businesses, based on the added value to the local community [121].
Stronger development cooperation, supporting efforts to contain the pandemic, and extending economic and financial assistance to countries most affected by the crisis will be of utmost importance to accelerate the recovery and put the world back on the path of sustainable development [115].
Likely scenario for work in 2050
In this section, we use the trends and trend scenarios presented in Section 3 to build a most-likely scenario of how working will be in the year 2050. First, a short story of this scenario is told, followed by a discussion of the actions that the social actors (government, companies, and workers) would take to lead us into this future.
Working in 2050
The advancement of computerization will make some jobs obsolete while new ones will be created, as happened in previous industrial revolutions. The benefits generated by automation are important and induce widespread improvements in society as society has taken the right actions to guarantee that technology adoption does so, at least in most cases.
Occupations that involve social and creative intelligence, and/or advanced perception and manipulation tasks will be less affected by computerization due to the technology limitation to emulate these behaviors.
Society will see a job shift from low-skill to knowledge-intensive occupations. Many people will face unemployment and companies will be stimulated by governments to help reduce the transition impact by training workers in new skills before completely automating their jobs. Those that cannot be helped by these measures will receive a basic income from the state.
Communication will be globally improved due to the reduction of costs in Internet access in most countries. Better communication will further improve the integration between national and international markets, allowing more people to offer their services on the internet. The COVID-19 pandemic and the measures adopted to contain it still have an impact on work because by 2050 employers will be aware that they may be forced to move their production to remote work at any time. Thus, we expect some jobs to have at least one “remote day.”
Workers will not be associated with traditional trade unions as new forms of worker organization movements will be recognized by governments and employers. These organizations will show that new technologies can be used to innovate not only the way people work but also how they defend their rights as workers.
As populations age, the minimum retirement age will increase. This will not represent a big impact on companies for, as jobs become more knowledge-intensive, it will be increasingly easier and even profitable to keep senior workers in their jobs.
Gender equality will increase, but even in 2050, many countries will still be far from the ideal equality between men and women. Another group that will see advancements in their rights, despite some resistance from far-right groups, will be the LGBTQIA+ as more initiatives such as anti-discrimination laws are created. Racial discrimination will also reduce.
The income distribution will be another factor for social inequality reduction. Several UBI and UBA trials will happen worldwide, but few societies are going to adopt these social welfare policies permanently.
The world population will be close to 10 billion, and governments will face the challenge of creating jobs for hundreds of millions or, at least, providing means for their survival. As some countries will fail to do so, workers will either migrate or use the internet to offer their services globally. Both developing and developed countries will see a reduction in their rural population as urbanization grows.
In 2050, the economy will be more knowledge-intensive as low-skilled jobs are reduced and new ones, based on more creative and social activities, are created. Rises in labor productivity will provide increased average wages, allowing workers to reduce their working hours while the offer of goods and services is increased.
The COVID-19 crisis will affect the global production chain in the long term. Many governments will consider the production of some fundamental industrial inputs as strategic, demanding local production by law, even with higher costs. This will reduce their dependence on other countries.
Several countries will face large fiscal deficits and high levels of public debt, aggravated by the COVID-19 pandemic. The severity of the economic impact will be related to how strongly each government acted to break the spreading of the SARS-CoV-2 virus. Another effect to be seen is the increased migration in the search for better economic opportunities, which will lead to an increase in nationalist and far-right movements in Europe and the USA.
Actions leading to the most-likely scenario
In this section, we present a compilation of a set of actions that could be taken to make the most-likely scenario come to fruition. Most of these actions are related to public policy and have been mapped during the different steps of the research.
For the most-likely scenario to become true, governments will have to make sure that technology meets not only the capital demands but also the population’s needs. As such, governments that promote regulatory policies to control the advancement of computerization, avoiding mass unemployment without stopping innovation, will perceive better results—both in economic growth and welfare. These regulatory policies include the ones aimed to protect vulnerable people, those with few resources and less educated, who must be prepared for this transformation. Governments will be able to respond to the job shift by investing in broader and better education and teaching new skills for the new occupations that will arise while providing the displaced workers with, at least, the minimum living conditions during this period of transition [122].
As companies perceive the consumer market reduction risk due to unemployment, they will promote reducing the working hours, thus maintaining a stable employment rate, and allowing economic growth. This type of action benefits not only the consumer market but can also relief the pressure on the social safety net by reducing the unemployed [123]. More than 90 decades ago, Keynes made a famous prediction that the duration of the workweek would be of only 15h by the time his grandchildren came to age but more modern predictions consider that a reduction limited to 20–40% of the workweek would be more realistic [55, 123].
In many countries, non-standard employment workers will have minimum rights granted through government regulation after a series of disputes with their employers. This will improve the quality of services provided and reduce the insecurity of workers in this category. Disputes surrounding the employment contract of platform workers are already a reality around the world with mixed results and different proposals are already being put forward and can be expected to increase in the future to create at least some intermediary set of rights that place NSE workers in a situation of less insecurity than the current one [67].
By progressively increasing taxes for larger corporations, and taxing great fortunes and heritages, the government will distribute wealth to the poorer sections of society, including the promotion of basic income efforts. Wealth distribution efforts would allow the reduction of poverty and the stabilization of inequality. The implementation of international tax cooperation could also help with the reduction of inequality between developed and developing countries with the payment of universal basic income as a way of transferring resources from the first group to the second [124]. This is certainly a challenge that can be seen as a stretch considering the current reality of global cooperation, but even if no international circulation of financial resources is not possible in the future, at least some cooperation in terms of knowledge regarding actions to deal with the negative consequences of technological change can be considered in the future [124].
| 2022-12-14T00:00:00 |
2022/12/14
|
https://eujournalfuturesresearch.springeropen.com/articles/10.1186/s40309-022-00210-w
|
[
{
"date": "2022/12/01",
"position": 26,
"query": "automation job displacement"
},
{
"date": "2022/12/01",
"position": 29,
"query": "future of work AI"
},
{
"date": "2022/12/01",
"position": 21,
"query": "universal basic income AI"
}
] |
"The Dark Side of Technology: Ethical Considerations"
|
"The Dark Side of Technology: Ethical Considerations"
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https://www.linkedin.com
|
[
"Mohit Sewak",
"Matthew Rosenquist",
"Jules Polonetsky",
"Vote Frenzy"
] |
The loss of jobs due to automation can have broader economic and social implications, including income inequality and social unrest. It is important for ...
|
Introduction:
Technology has become an integral part of our daily lives, and it has the power to transform society in both positive and negative ways. From smartphones and social media to artificial intelligence and autonomous vehicles, technology has the potential to revolutionize the way we live, work, and communicate.
However, as technology continues to advance, it is important to consider the ethical implications of these innovations. From data privacy and surveillance to social media and mental health to artificial intelligence and job displacement, there are many potential ethical concerns associated with technology. In this blog, we will explore some of these issues and the importance of addressing them in order to create a more responsible and sustainable future with technology.
Data privacy and surveillance:
One of the major ethical concerns surrounding technology is the issue of data privacy and surveillance. With the proliferation of smartphones, social media, and the internet of things (IoT), technology has made it easier than ever for companies and governments to collect and store personal data. This data can include everything from your browsing history and search queries to your location, social media posts, and credit card transactions.
While the collection of personal data can be convenient and beneficial in some cases (such as personalized product recommendations or targeted advertising), there is also the potential for data misuse or abuse by companies or governments. This can include the sale of personal data to third parties without your consent or the use of data for nefarious purposes such as identity theft or targeted surveillance.
The impact on individual privacy and civil liberties is a major concern in this context. The loss of privacy can have serious consequences for individuals, including the potential for discrimination or reprisal based on personal data. It is important for companies and governments to be transparent about their data collection practices and to give individuals the ability to opt-out or control their data. It is also important for individuals to be aware of their data privacy rights and to take steps to protect their personal information.
Social media and mental health:
Social media has become an integral part of modern life, with billions of people around the world using platforms like Facebook, Twitter, and Instagram to connect with friends, family, and colleagues. However, the power of social media to shape public opinion and influence behavior raises important ethical considerations.
One concern is the potential negative effects on mental health. Studies have shown that excessive use of social media can lead to increased anxiety, depression, and other mental health issues. This is due in part to the constant stream of information and the pressure to present a perfect image online. It is also due to the nature of social media algorithms, which often prioritize content that elicits strong emotional reactions, including anger and fear.
The ethical responsibility of social media platforms to protect user well-being is an important consideration in this context. Social media companies have a duty to create a safe and healthy environment for their users, and this includes addressing the potential negative impacts on mental health. This may involve providing resources and support for users, implementing policies to reduce harmful content, and being transparent about their algorithms and data collection practices. It is also important for individuals to use social media mindfully and to be aware of the potential effects on their mental health.
Artificial intelligence and job displacement:
As artificial intelligence (AI) continues to advance, there is the potential for it to replace human jobs in various industries. From manufacturing and customer service to transportation and healthcare, AI has the potential to automate tasks and processes that were previously done by humans.
While the use of AI can bring many benefits, such as increased efficiency and accuracy, it also raises important ethical considerations around job displacement. The loss of jobs due to automation can have serious consequences for individuals and society as a whole. It is important to consider the impact on workers and to ensure a fair transition for those who may be affected. This may involve providing support for retraining and finding new employment, as well as addressing issues such as unemployment benefits and pension plans.
The potential consequences of job displacement extend beyond the individual level. The loss of jobs due to automation can have broader economic and social implications, including income inequality and social unrest. It is important for governments and businesses to consider these potential consequences and to work together to create a more sustainable and equitable future with AI.
Conclusion:
| 2022-12-01T00:00:00 |
https://www.linkedin.com/pulse/dark-side-technology-ethical-considerations-mr-a-banerjee
|
[
{
"date": "2022/12/01",
"position": 27,
"query": "automation job displacement"
}
] |
|
Routinization, within-occupation task changes and long- ...
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Routinization, within-occupation task changes and long-run employment dynamics ☆
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https://pmc.ncbi.nlm.nih.gov
|
[
"Davide Consoli",
"Ingenio",
"Csic-Universitat Politecnica De Valencia",
"Giovanni Marin",
"Department Of Economics",
"Society",
"Politics",
"University Of Urbino 'Carlo Bo'",
"Italy",
"Seeds"
] |
by D Consoli · 2023 · Cited by 20 — The present study adds to the literature on routinization and employment by capturing within-occupation task changes over the period 1980–2010.
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The present study adds to the literature on routinization and employment by capturing within-occupation task changes over the period 1980–2010. The main contributions are the measurement of such changes and the combination of two data sources on occupational task content for the United States: the Dictionary of Occupational Titles (DOT) and the Occupational Information Network (O*NET). We show that within-occupation reorientation away from routine tasks: i) accounts for 1/3 of the decline in routine-task use; ii) accelerated in the 1990s, decelerated in the 2000s but with significant convergence across occupations; and iii) allowed workers to escape the employment and wage decline, conditional on the initial level of routine-task intensity. The latter finding suggests that task reorientation is a key channel through which labour markets adapt to various forms of labour-saving technological change.
The remainder of the paper is organized as follows. Section 2 details the main sources and the procedures for the construction of our DOT-ONET database followed in Section 3 by descriptive evidence on the evolution of within-occupation task changes over the period 1980–2010. Section 4 presents exploratory analysis on the association between computer use at work and task configuration; on changes in education supply associated with shifts in occupational task content; and on employment outcomes by occupation and by macro-sectors. Section 5 summarizes and concludes.
Furthermore, the present study differs from closely related works which use job ad data from job vacancies ( Atalay et al., 2020 ; Deming and Kahn, 2018 ). Although such an approach is promising in terms of accuracy in the construction of new task measures at both firm- and occupation-level, there are concerns regarding both the lack of adequate sample representativeness in building the task measures, especially for low-skilled workers, and the relatively short ( Deming and Kahn, 2018 ) time spans available. The present study complements Atalay et al. (2020) in that, while their measure based on job vacancies better captures task change among abstract occupations, ours better captures task change among clerical and blue-collar jobs. In both Atalay et al. (2020) and our study, the main contribution is to propose new approaches to measure within-occupation task reorientation. Finally, our work adds to recent theoretical literature on task directed technical change. Acemoglu and Retrepo (2018) argue that the displacement effect of routine labour-replacing technology is counterbalanced by the emergence of new, more complex, high-skilled work activities. Our study offers a more nuanced view by showing that, while occupations that de-routinize the most exhibit positive employment dynamics, the bulk of within-occupation reorientation occurred among occupations with higher exposure to routine-replacing technological change, namely: middle-skill clerical and manual jobs. In other words, we show that adaptation through task reorientation occurs across the board. This is relevant for on-going debates on the aggregate effects of automation. To illustrate, Arntz et al. (2017) show that, even in a broad occupational category at high risk of automation a large fraction of jobs specializes in tasks that are complementary to the new capital equipment. Combined with our findings, this is again indicative that task reconfiguration is an important channel through which workers can cope with the labour market effects of technological change.
Our analysis contributes to various streams of research based on the task approach ( ALM, 2003 ; Acemoglu and Autor, 2011 ). To begin with, the proposed approach affords the opportunity to analyse a time period that is both longer and more recent relative to prior studies on within-occupation task changes ( Spitz-Oener, 2006 ; Ross, 2017 , Ross, 2021 ). The most comprehensive study by Spitz-Oener (2006) focuses on Germany, but only until 1999 and not on long-term employment growth. We also add to prior work on new occupations and technology diffusion. While Lin (2011) tracks new job titles in US cities using the census classification based on the DOT over 1980–2000, we study the evolution of the task content of both existing and new occupations over 1980 and 2010 and find that task reorientation in existing occupations is more relevant in explaining labour market dynamics than that of new jobs.
Section 4 presents an exploratory analysis of the main correlates of within-occupation task changes. Therein, computer use at work in the 1990s exhibits a positive association with within-occupation changes in analytical and interactive tasks, and a negative association with changes in routine-task intensity. Further, within-occupation changes in RTI are associated with decennial employment growth over the entire timespan and especially in the 1990s even after controlling for, inter alia, initial routine task intensity. Finally, over the three decades under analysis, both employment and wages grow relatively faster in occupations that de-routine the most conditional on the initial level of routine task intensity. We interpret these regularities as empirical support to our proposed measure.
Section 3 presents descriptive evidence of changes in the task content of occupations based on the decennial Census and American Community Survey (ACS). Therein we show that within-occupation task changes account for 37 % of the overall decline in routine task use between 1980 and 2010. The within-occupation component is especially important in the 1990s (67 % of the decadal change), while it declines in the 2000s. Further, beneath the decrease of aggregate RTI we observe substantial change in the distribution of work tasks, in particular a catching-up of routine-task intensity during the last decade. Lastly, we find that changes in the task content of occupations are heterogeneous across sectors and broad occupational groups. The shift away from routine work of the 1990s was mostly in abstract occupations, and in non-manufacturing sectors, while the 2000s saw a reversal of this trend, with de-routinization being more prominent among blue collars and clerical occupations, and stronger in manufacturing.
Section 2 puts forth a procedure to identify matching items in DOT and O*NET and construct a time-varying measure of job tasks for 322 occupations. The guiding criterion is the similarity between the task title and the task description. Based on this, we build an index of routine task intensity (RTI) that also accounts for within occupation task changes. We show that the proposed procedure reliably matches the moments of the distributions of the underlying task measures for each of the two data sources over time.
Within-occupation task change is a long-term phenomenon that requires the mutual adaptation of demand and supply of skills. The paucity of suitable data sources offers a cue to the first and main contribution of the present paper. A thorough analysis of how job content evolves requires data that cover a long-time span. The most common resource to this end are the DOT, which was updated until 1991 upon the release of O*NET. Despite being designed for a similar purpose, however, matching these two data sources for extended time series analysis presents some challenges. Primarily, the complexity of the data has increased significantly with the inception of O*NET, so that using task items from these two sources, while maintaining consistency, requires a high degree of discretion on the part of researchers ( Autor, 2013 ).
Although the seminal study by Autor, Levy and Murnane (2003; henceforth ALM) calls attention to variations at the ‘intensive’ margin (i.e., changes in job tasks within an occupation), this dimension has remained relatively under-explored, also due to data limitations. The present study fills this gap by creating a time-varying measure of routine-task orientation for 322 occupations based on two main data sources for the United States (US), namely, the Dictionary of Occupational Titles (DOT) and its successor, the Occupational Information Network (O*NET). Such a measure allows us to build a consistent time series of within-occupation task changes over a thirty-year period, from 1980 to 2010, and to assess long-term structural changes in the US labour market over various phases of technological change.
Our matching procedure carries the major limitations due to differences between DOT and O*NET; the different versions within O*NET (particularly early job-analyst- vs survey-based versions); the choice of matched tasks between DOT and O*NET. So, results from our empirical exercise may well be biased due to the above, and should be taken with caution. Despite this, we believe that the three robustness exercises presented here are encouraging and mark a first step in an unexplored but arguably promising trajectory.
Third, we compute the cross-sectional relationship between computer use at work and single items composing our task measures (Table A4). 8 In line with expectations, computer use is significantly and positively correlated with abstract tasks (MATH and LANGUAGE) and routine cognitive tasks (CLERIC), while it is significantly and negatively correlated with (routine and non-routine) manual tasks (MANUAL and NRM) and with the RTI. Importantly, the magnitude of the estimated coefficients is similar across decades. Further analyses of the relationship between technology adoption and within-occupation task shifts in Section 4.1 reinforce this result.
Second, the result above is further corroborated by bootstrap-based tests on the first, second, third and fourth moments of DOT (1990) and O*NET (2000) distributions for our task measures (Table A3). Notably, we only find a significant difference between the averages for clerical between 1990 and 2000 and not for math, language and manual and the RTI index. A more variegated pattern emerges for other moments of the distributions (standard deviation, skewness and kurtosis). However, when statistically significant differences are detected between 1990 and 2000, the same is found for the following decade (2000−2010), meaning that changes in the distribution of task measures reflects a long-term pattern rather than a change that is artificially induced by our match.
First, we search for marked differences between DOT and O*NET that may be attributable to our matching procedure. We did not find any systematic differences in average task scores between 1990 and 2000 (when O*NET was first introduced) compared to previous or subsequent periods (1980–1990 and 2000–2010). This is to say that, if systematic differences in the value of our task measures exist in blending DOT and O*NET, they are not necessarily due to our matching procedure. The quantile-to-quantile plots of Appendix A3 showing the distribution (by quantile) in the two selected years provide support to this. 7 Even when some differences exist (e.g., Cleric and Manual in Figs. A3 and A4), they cancel each other out when we aggregate information for the four task measures into our routinisation index (Fig. A5).
The index captures the relative routine task requirements and, thus, the exposure to routine-replacing technical change of an occupation. Following the rationale of ALM (2003, p. 1287) we focus only on routine cognitive and routine manual tasks, and non-routine analytic and non-routine interactive tasks. In contrast to recent literature on the variation within the task content of occupations ( Atalay et al., 2020 ; Ross, 2017 , Ross, 2021 ), we employ an index of routine intensity. We prefer this to a single measure because the index can smoothen movements in task measures due to changes in scales and classification between DOT and O*NET and, thus, it is better suited to the analysis of long-term changes. Moreover, the index captures the relative importance of routine tasks relative to non-routine tasks, which is the key to assess the exposure of an occupation to routine-replacing technical change.
Table 1 shows the DOT-O*NET matching items and reports the scale of each in the two data sources. When more than one candidate item was found in O*NET, we took the average value. To illustrate, for two measures the scale is similar (MANUAL and CLERIC, 1–5 level in DOT and 1–5 importance in O*NET) while it is different for MATH and LANGUAGE. 5 The discrepancy is due to the different range between levels in DOT and levels in O*NET (DOT scale of 1–6 vs O*NET 0–7). Since the distribution of O*NET “level” is bounded, in most cases between 1 and 6, we truncate extreme values to 1 (bottom) and 6 (top). 6 Consequently, we end up with 4 DOT variables linked to their corresponding O*NET match on similar scales.
Following these general rules, we identify suitable DOT items that correspond to the four dimensions of occupational task requirements identified by ALM (2003) as those that are particularly affected by automation and Information and Communication Technologies (ICTs): non-routine cognitive tasks (analytical and interactive), routine cognitive tasks and routine manual activity. Our first search yielded 16 different items, four for each of the dimensions of occupational task requirements that can be meaningfully associated between DOT and O*NET. In a second iteration we further reduced the selection to four items (one per dimension) following the aforementioned three criteria. These four items have been subsequently used to build an occupational task intensity measure.
The third general rule is maintaining similar scales for task scores in the two databases. Notably, we picked task measures with ordinal (Likert-type) scales. The problem here is that, while all O*NET task scores are defined on an ordinal scale, DOT assigns task scores using either an ordinal scale or dichotomous value. An example is “Direction Control and Planning” (DCP), which can either be present (equal to 1) or absent (equal to 0) in the DOT. Our choice to select items with similar scales avoids loss of information due to the transformation of ordinal variables into dichotomous ones and avoids manipulations that could alter the pattern of task changes through time.
Our proposed matching procedure follows three general rules. The first two concern the similarity in the task title and task description. Because O*NET was designed as the natural successor of DOT ( Truthan and Karman, 2003 ), our main reference for the matching exercise is the summary of the DOT variables (occupations and work content) that have been converted to fit the relational model of O*NET as detailed in the first O*NET Data Dictionary (1998). Subsequent versions of the latter do not contain explicit references to DOT. Accordingly, we thoroughly examined variable descriptions in both sources to search for suitable matches. To illustrate, we consider that the DOT variable Clerical Perception (“the ability to perceive pertinent detail in verbal or tabular material. Ability to observe differences in copy, to proofread words and numbers, and to avoid perceptual errors in arithmetic computation”) bears a very similar title and description to the O*NET item Clerical (“knowledge of administrative and clerical procedures and systems such as word processing, managing files and records, stenography and transcription, designing forms, and other office procedures and terminology”).
As Ross, 2017 , Ross, 2021 notes, although the rationale of O*NET was to generate a database with survey data collected solely from incumbent workers, the first release (version 4.0, June 2002) contained scores that were assigned by job analysts who used prior DOT versions as a reference. As a consequence, O*NET 4.0 blends the new rating system and the old methods. To check for measurement errors due to this that may affect the matching, we also tried O*NET 11.0 (December 2006) instead of O*NET 4.0. In O*NET 11.0 up to 647 occupations out of 798 (96.6 %) are assessed by means of survey data collected solely from incumbent workers. 3 Since our main results are confirmed in the ancillary regressions based on O*NET 11.0, we maintain O*NET 4.0 to estimate decadal changes that are central to our analysis. 4
The main critical issue is that O*NET has a comparatively higher number of task-related variables (approximately 400) compared to DOT (44). Moreover, O*NET measures have different scales: the ordinal ‘level’ scale (0–7) and the ordinal ‘importance’ scale (1–5). 2 This is also recognised by Autor (2013, p. 192) : “When the DOT was replaced by the O*NET in 1998, the complexity of the database increased by an order of magnitude. Version 14.0 of the O*NET database, released in June of 2009, contained 400 separate rating scales, which is almost half as many scales as the number of occupations coded by O*NET […] In practice, this means that researchers who wish to use these databases as sources for task measures are essentially required to pick and choose among the plethora of scales available, a problem that is much more severe for O*NET than for DOT.” [emphasis is our own]. Consequently, the task selection originally proposed by ALM (2003) is not suited to our purpose and, due to the constraints highlighted above (high dimensionality and plurality of scales in O*NET), researchers' discretion in the choice of task measures is critical.
The key variables for our analysis are measures of occupational skill requirements and task intensity. Previous studies have relied on one of the two sources available for the US, namely, the 1977 and 1991 editions of DOT (e.g., ALM, 2003 ) or O*NET (e.g., Acemoglu and Autor, 2011 ). One of the main contributions of the present paper is the elaboration of a novel matching procedure to merge DOT and O*NET for the purpose of extending the time span of the analysis.
Combining these data sources, we build a balanced panel of 322 occupations based on the harmonized OCC1990 occupational classification from IPUMS. This raises the issue of how to construct the task measures for the panel of 322 occupations aggregating information from DOT and O*NET, which are available at a much finer level of aggregation. 1 ALM (2003) use weights of the April 1971 CPS Monthly File ( National Academy of Sciences, 1981 ) and retrieve the employment shares of fine-grained job titles in the DOT for a single year (1971). This procedure, however, automatically eliminates the variation in within-task intensity associated with the emergence of new jobs with tasks that suit the demands of new technology ( Lin, 2011 ). Since new jobs are important drivers of employment growth ( Acemoglu and Restrepo, 2018 ), we follow Lin (2011) and use uniform weights to aggregate the task content of detailed occupational titles from DOT and O*NET to the level of the 322 occupations under analysis. In so doing, within-occupation task change also captures the emergence of new job titles and changes in the task content of the occupation.
We combine information from different data sources to develop a consistent picture in the change of skill/task inputs over a thirty-year time period. In particular, we rely on the 1977 and 1991 editions (‘Fourth’ and ‘Revised Fourth’, respectively) of the DOT and the 2002 (version 4.0) and 2012 (version 18.0) editions of O*NET. Information on employment and educational attainment is retrieved from Census-based microdata, following recent literature (e.g., Autor and Dorn, 2013 ). We also use Integrated Public Use Micro Samples (IPUMS, Ruggles et al., 2018 ): for years 1980, 1990 and 2000 we use the 5 % sample of the decennial censuses, while for 2010 we combine three waves (2010, 2011, 2012) of the American Community Survey (ACS), which covers a representative sample of 1 % of the US population.
Taken together, these stylized facts indicate that accounting for within-occupation changes in task content yields a complex picture of long-term structural changes in US labour markets. These issues will be explored more in detail in the next section with the aid of multivariate regressions to shed light on the conditional correlation between de-routinization and employment growth.
The second diagram of Fig. 2 (top, right-hand side) shows changes in employment by quintiles of within-occupational changes in RTI. Here we observe that occupations that de-routinize the most (Q1 and Q2) have worse employment performance throughout the period. This, however, does not account for the initial level of RTI. In the third diagram of Fig. 2 (bottom left-hand side), we unpack the trends of the sub-group of occupations that de-routinize the most – i.e., the first and second quantile in terms of change in routine task intensity – controlling for the initial level of RTI. 14 We find that occupations that de-routinize the most are highly polarized in terms of long-term employment changes. Among those that are highly routine intensive (Q5 and Q4) we observe a large employment decline, while we observe large employment increases among those with low initial routine intensity (Q2 15 ). That is, among the occupations that substantially de-routinize, only those that are initially less routine escape the employment decline.
A key objective of the present study is to assess the relationship between qualitative change in the task content of occupations and changes in labour demand. To this end, we unpack aggregate trends of full-time US employees over the period 1980–2010 by partitioning the labour force into quintiles of initial RTI. In Fig. 2 , the employment share of all groups is set to 1 in 1980 so that subsequent points in the diagram depict the mean employment of each group of occupations over time, net of overall employment growth. The first diagram of Fig. 2 (top, left-hand side) shows changes in employment by quintiles of initial values of RTI. Here, a divide emerges between occupations that were less intensive in routine tasks in the 1980 - which saw substantial increases in labour demand - and those with a stronger bias towards routine activities. This confirms a standard result of the existing literature: employment opportunities polarise depending on the initial level of exposure to routine-replacing technical change ( ALM, 2003 ; Spitz-Oener, 2006 ; Deming and Kahn, 2018 ).
When considering different industries ( Table 5 ), the decline in the RTI is larger in manufacturing than in non-manufacturing ones. Moreover, within-occupation change contributes to more than half (51 %) of the total decline in RTI in manufacturing, while it only accounts for 38 % of the decline in RTI in non-manufacturing sectors. Importantly, the within-occupation component is relatively more important in non-manufacturing sectors (the 1990s) than in manufacturing sectors (the 2000s). This is consistent with the fact that the first wave of ICTs in the 1990s replaced clerical tasks in service sectors while the second wave in the 2000s affected the automation of manual tasks in industry ( Brynjolfsson and McAfee, 2014 ). Together with the differential decadal patterns across occupations, we interpret this as a confirmation of the reliability of our proposed measure of within-task occupational changes to closely mimic well-established facts on labour market changes due to automation. 13
A recurrent pattern in our data is that during the first wave of ICTs in the 1990s the strongest change was the decline of RTI among Abstract occupations. Conversely, in the 2000s high-skill Abstract occupations became more routine intensive over time, again in line with the Great Reversal hypothesis ( Beaudry et al., 2016 ). Re-routinization of Abstract occupations may reveal the greater capacity of machines in performing tasks such as translating complex documents, writing reports and legal briefs, as well as diagnosing diseases ( Brynjolfsson and McAfee, 2014 ; Frey and Osborne, 2017 ), or simply a limitation of our measures for these occupations. 12
Table 4 , Table 5 replicate the decomposition of routinization index by, respectively, macro occupational groups (Abstract, Clerical, Blue-collar and Service occupations) and the two macro-sectors (non-manufacturing and manufacturing). Consistent with this, the de-routinization observed in Table 2 is driven by a task reorientation primarily among Clerical and Blue-collar occupations. For Clerical occupations, the decline in the RTI is constant over time, while for Blue-collar occupations, it is more pronounced in the last decade.
The scatter diagram in Fig. 1 illustrates the extent to which the routine task input of each occupation changes (vertical axis) relative to each occupation's initial RTI. Consistent with the above, we observe significant differences across decades. The flat, if slightly increasing, trends of the first two decades (top panels) contrast with the convergence signalled by pattern of the 2000s (bottom, left-hand panel). Therein, the decrease in routine task intensity is larger among jobs that had a higher RTI at the beginning of the period. On the whole, the pattern of the 2000s clearly dominates the overall change (bottom, right-hand side panel). Compared to the 1990s, where the distribution of routine task intensity to new technologies is slightly more dispersed, the 2000s are characterized by substantial redistribution of non-routine intensive tasks towards low- and medium-skilled occupations.
Our finding on the prominence of the within-occupation component is consistent with the study by Spitz-Oener (2006) on the German labour market over the period 1979–1999. A recent paper by Atalay et al. (2020) also investigates changes in the task content of occupations in the US using textual data extracted from job ads published in major national newspapers. Remarkably, both studies find an acceleration in the within-occupation component in 1990s compared to the 1980s. Our analysis extends those studies by also including the 2000–2010 decade, where the significant deceleration of the within component closely matches that of de-routinization, which Beaudry et al. (2016) refer to as the ‘Great Reversal’.
Table 3 summarizes changes in job task input by intensive (within) and extensive (between) margins. The main takeaway is that the within-occupation component explains 37 % of overall decline in RTI over 1980–2010, while the between-occupation accounts for 40 % and the between-industry accounts for the remaining 23 %. Note that the within component closely tracks the overall evolution of the RTI index as its effect is concentrated in the 1990s, explaining 2/3 of the overall change in this decade. This contrasts with the weakening in the contributions to both the within-industry, between-occupation and the between-industry components in the 1990s.
The trends shown in Table 2 pool together changes in the task content within each occupation as well as changes in the occupational composition. To gain a more precise understanding of the importance of within- vs between-occupation forces that have driven de-routinization, we decompose the overall change in RTI into three components:
Changes to Abstract jobs in the third decade reveal the main limitation of our measure of routine task intensity compared to that used in related research by Atalay et al. (2020) , namely that each component of the RTI index is bounded. Thereby, if an occupation had minimal level of routine intensity in 1980, a further decrease in the routine intensity cannot occur by construction. This is relevant for Abstract jobs that are near the minimum of routine task intensity.
The significant task change of the 1990s is consistent with the historical acceleration in the diffusion of ICTs ( Autor et al., 1998 ). Looking at heterogeneous patterns across occupations, Abstract ones are the first to de-routinize in the first two decades, followed by Blue Collar and Clerical jobs in the last decade. 9 This sequence of task reconfigurations is not only consistent with models of technological revolutions in which new technologies are adopted first by high-skilled workers and then by the least skilled ones (e.g., Zeira, 1998 ; Caselli, 1999 ; Beaudry and Green, 2005 ), but it also suggests that high-skilled workers have to learn new tasks that complement new technologies.
Before presenting some regularities based on our time-varying index of task change, Table 2 shows the trends in the use of human routine input in the US economy between 1980 and 2010. Therein, the evolution of routine task intensity captures both the within- and the between-component forces. In line with previous studies, the more general index of RTI used here shows that the overall level of routinization in 2010 is substantially smaller than that of 1980 (Column 1). The decline in RTI is very limited between 1980 and 1990 (only −2.2 %), accelerates remarkably in the 1990s (−10.7 %) and then, consistently with Beaudry et al. (2016) , slows down again in the 2000s.
4. Drivers and implications of within-occupation task changes
Tasks are the key dimension of interest in the race between technology and education (Goldin and Katz, 2010; Acemoglu and Autor, 2011). On the one hand, new technologies have the potential to trigger radical reallocation in the share and type of tasks performed by humans, and this can occur along both the intensive (within-occupation) and the extensive (between-occupation) margin. On the other hand, where the intensive margin is relevant, educational programs are expected to keep pace with changes in the demand for specific tasks (Vona and Consoli, 2015). Since clerical jobs, such as clerks and assistants, have experienced substantial task reconfiguration towards organizational skills and non-routine tasks (∆RTI = −0.19 over the three decades), a concurrent change in training is necessary if workers are to be equipped with skills that match the incumbent technological paradigm. We expect that the diffusion of technology, such as ICTs, is the primary driver of within-occupation task shifts, and that these shifts are correlated with changes in the educational requirements. By testing these two predictions, the first two parts of this section closely follow related papers on changes in within-occupation task (ALM, 2003; Spitz-Oener, 2006).
The third and part of this section explores the correlation between within-occupation task shifts and labour market outcomes, namely employment and wages growth. Notice that task changes can be interpreted as a proxy of the degree of adaptation to structural transformations. Theory on routine-replacing technological change, such as the Ricardian model of Acemoglu and Autor (2011) and especially the recent extension with endogenous technical change of Acemoglu and Restrepo (2018), clearly advocates that successful adaptation should entail a reorientation away from routine tasks. Accordingly, occupations that de-routinize faster are expected to experience faster growth in wages and employment shares.
4.1. Technological change and within-occupation task changes We examine the association between within-occupation task shifts and a proxy of technological change in the workplace: the change in the share of workers using computers. Although we are aware of the limitations of this, it is the only occupation-level measure for which data are available and that has been used in previous studies (e.g., Autor et al., 1998). As information on computer use at work by occupations from CPS (Current Population Survey) is only available for few selected samples, we focus our analysis on the 1990–2000 decade.16 This is critical for the present study given the data compatibility issues due to the matching of DOT and O*NET. Similarly, within-occupation task changes occurred mostly in this decade, which further reinforces our choice.17 We use a long-difference estimator to retrieve the associations between the change in the task content of occupation and the change in computer use, controlling for the initial levels of task input and computer use18: ∆ Tas k o 1990 − 2000 = α + βTas k o 1990 + γComputer us e o 1989 + δ ∆ Computer us e o 1989 − 1997 + ε o (3) Also, this analysis represents a further robustness check of choice of task items for the match between DOT and O*NET. For this reason, and in contrast to subsequent analyses where we focus on the aggregate routine-task intensity index, we present the correlations for the four component task items of the RTI index, the non-routine manual task measure and the RTI index itself. Table 6 reports the results of this analysis. In line with the existing literature (ALM, 2003; Spitz-Oener, 2006), we find a positive contribution of the change in computer use to the within-occupation change in analytical (math) and interactive (language) tasks, a negative contribution to the change in routine (manual and clerical) and no clear effect on non-routine manual tasks (NRM). By combining these results, we find a negative association between the change in computer use and the change in routine-task intensity. Table 6. Technological change and within-occupation task changes. (1) (2) (3) (4) (6) (5) Growth 1990–2000 Math Language Cleric Manual NRM RTI Task intensity in 1990 −0.436⁎⁎⁎ −0.389⁎⁎⁎ 0.022 −0.238⁎⁎ −0.646⁎⁎⁎ −0.050 (0.070) (0.063) (0.149) (0.102) (0.136) (0.128) Computer use in 1989 0.162⁎⁎ 0.096 −0.013 −0.263⁎⁎⁎ −0.108 −0.068 (0.081) (0.086) (0.122) (0.061) (0.068) (0.255) Growth in computer use (1989–1997) 0.146⁎ 0.384⁎⁎⁎ −0.335 −0.429⁎⁎⁎ −0.101 −0.654⁎⁎ (0.078) (0.128) (0.219) (0.117) (0.103) (0.292) R squared 0.327 0.209 0.0327 0.290 0.320 0.0392 Open in a new tab Note that the increase in the share of workers using computers at work was 14.3 % during this decade. This implies an average change in RTI that is 1.6 times the actual change (−0.094 vs. −0.058). Although this may appear surprisingly large, it is in line with the estimates by ALM (2003; for the period 1984–1997 computerization more than fully accounts for the observed changes in single task measure) and Spitz-Oener (2006); effects ranging between 47 % (non-routine interactive) and 90 % (routine cognitive) that combined together in a RTI index will deliver an association of a similar size). Overall, our data-set built on the match of DOT and O*NET confirms the strong association between computer use and task reconfiguration within an occupation. In light of this, we can safely attribute the bulk of this reconfiguration to routine-replacing technological change.
| 2023-01-14T00:00:00 |
2023/01/14
|
https://pmc.ncbi.nlm.nih.gov/articles/PMC9746329/
|
[
{
"date": "2022/12/01",
"position": 38,
"query": "automation job displacement"
}
] |
Using Data Analytics upskilling to accelerate career ...
|
Using Data Analytics upskilling to accelerate career advancement
|
https://programs.pathstream.com
|
[] |
The potential for artificial intelligence to automate repetitive and mundane tasks means a huge shift in the workforce. While there's concern about workforce ...
|
Common trends driving the need to upskill Data Analytics skills
With the advancements in digital transformation and technology being at the forefront of strategic initiatives, there is increasing demand for tech-savvy, data-specific job roles for most employees within an organization. The global pandemic has further sped this transformation.
Additionally, with technology heavily driving the way companies are run, the need to upskill data science competence is rapidly increasing. Now and in the future, professionals need to be aware of these trends to help them future-proof their skills to be more attractive and competitive.
Below are some of the trends that drive the need for upskilling and reskilling in data analytics:
1. Data analytics is universal
Over the last decade, data was largely considered a by-product of digital platforms and applications. Organizations focused primarily on cleaning, storing, and managing big data securely.
Today, companies focus on data as an enterprise-wide asset that must be broken down and refined using data science and analytics for profits. However, the volume of available data is rapidly increasing, so organizations will need more employees with data skills to profit continuously from the pervasive volumes of data.
2. Artificial Intelligence (AI)
Artificial intelligence is a significant factor driving the need to upskill data analytics skills. AI is replacing a large part of the workforce and has become an integral part of the corporate business units as well as functional strategy and operations.
With AI and machine learning, systems are becoming smarter and achieving more daily. The potential for artificial intelligence to automate repetitive and mundane tasks means a huge shift in the workforce. While there’s concern about workforce displacement, several opportunities are created for employees to move to higher-level roles.
3. Data-driven performance
Organizations and employees continue to fall behind on performance as the skills needed to understand big data and the positive impact that data contributes need to catch up. High-performing organizations dedicate at least 20% of their EBITA to data analytics. In addition, the gap in performance between organizations with data-driven teams and those lacking data skills grows wider each year. Data analytics upskilling helps employees understand and interpret data leading to a sustainable future.
4. Skills gap and talent shortage
A McKinsey survey reports that data analytics is the greatest skill gap noted through organizations. 40% of organizations confirm that it is a significant priority and crucial for overall business success to upskill these gaps.
Furthermore, while companies are trying to become more data-driven, data science and analytics efforts are being held back by a data talent shortage. A combination of factors impacts the deficit: high salary demands, a hyper-competitive market, or a prolonged period to fill data-related roles. The result is that demand for data analytics talent outruns supply. Upskilling and reskilling can help organizations build an internal pool of necessary talent and make them less dependent on costly, hard-to-hire talent.
5. The bottom-line cost
The WEF reports that the average cost of hiring and onboarding new employees is roughly $4,425. On the other hand, data from the Association for Talent Development reports that the average cost of upskilling existing talent is just $1,300. Even if the figure is doubled to cover the technical complexity of data skills, organizations find upskilling existing employees much more cost-effective than hiring new talent. And with data skillsets come better salaries.
6. Cross-functional teams
Data science and analytics are now considered a team sport. To achieve data-driven objectives and incorporate the diversity of skills that teams require, these data project teams must be cross-functional. This combines teams working toward similar goals.
| 2022-12-01T00:00:00 |
2022/12/01
|
https://programs.pathstream.com/2022/12/01/data-analytics-upskilling/
|
[
{
"date": "2022/12/01",
"position": 50,
"query": "automation job displacement"
},
{
"date": "2022/12/01",
"position": 79,
"query": "machine learning workforce"
}
] |
AI fear-mongering is irrational panic, and it's getting old ...
|
The heart of the internet
|
https://www.reddit.com
|
[] |
One potential risk of AI is that it may lead to job displacement, as ... To support those whose jobs are no longer necessary due to automation, you bolster ...
|
AI is expensive and difficult to run, and is decades from widespread implementation the way people are talking about it. We have huge leaps in computing to accomplish before that happens, and even then AI is a tool like any other.
Are we on Mars? How’s the environment doing? Does everyone on earth have clean water?
The future will not be utopia or dystopia.
| 2022-12-01T00:00:00 |
https://www.reddit.com/r/Futurology/comments/zo2ugk/ai_fearmongering_is_irrational_panic_and_its/
|
[
{
"date": "2022/12/01",
"position": 57,
"query": "automation job displacement"
}
] |
|
Distributive and displacement effects of a coordinated wage
|
Distributive and displacement effects of a coordinated wage bargaining scheme
|
https://ideas.repec.org
|
[
"Pablo Blanchard",
"Paula Carrasco",
"Rodrigo Ceni",
"Cecilia Parada",
"Sofía Santín",
"Universidad De La República",
"Uruguay . Facultad De Ciencias Económicas Y De Administración. Instituto De Economía",
"Author",
"Listed"
] |
by P Blanchard · 2021 · Cited by 8 — We estimate the impact on wage distribution, job displacement, and employment ... "The Fall of the Labor Share and the Rise of Superstar Firms [“Automation ...
|
The rise in inequality in developed countries returns to the political and economic spotlight wage policies and their implications for labor markets. In developing countries, however, wage policies are one of the main instruments chosen by governments to deal with inequality and poverty. This paper aims to assess the distributive and displacement effects of a wage policy featuring a coordinated collective wage bargaining scheme and a national minimum wage. We estimate the impact on wage distribution, job displacement, and employment of this wage policy, which consists of more than two hundred sectoral minimum wages and a national minimum wage. We find that the wage policy reduces inequality in the lower tail of the wage distribution for all formal workers and affects the right bottom for male workers. This distributive effect does not align with the significant deployment effect in the bottom sectoral distribution, and this small effect fades out when we consider the entrance of new workers. Finally, when we analyze the impact on the whole distribution, we observe that for those sectors with the more left wage distribution, we find a bigger displacement effect, but again if we assess the performance of the total employment, we find null impacts.
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| 2021-11-14T00:00:00 |
2021/11/14
|
https://ideas.repec.org/p/ulr/wpaper/dt-26-21.html
|
[
{
"date": "2022/12/01",
"position": 59,
"query": "automation job displacement"
}
] |
Back to Cheap Labour? Increasing Employment and Wage ...
|
Back to Cheap Labour? Increasing Employment and Wage Disparities in Contemporary China
|
https://www.cambridge.org
|
[
"Yiran Xia",
"Dimitris Friesen",
"Nourya Cohen",
"Caijie Lu",
"Scott Rozelle"
] |
by Y Xia · 2023 · Cited by 2 — Such economic forces (for example, globalization, automation) mean that the economy could possibly begin to experience shifts in the nature of employment for ...
|
After nearly two decades of rising wages for those in the unskilled sectors of China's economy, in the mid-2010s employment and wages in China began to experience new polarizing trends. Using data from the National Bureau of Statistics of China, this paper examines trends in multiple sectors and subeconomies of China, revealing the substantial rise of employment in informal, low-skilled services as well as the steady decline of wage growth in the informal subeconomy. At the same time, we find that although employment growth in the formal subeconomy is relatively moderate, wage growth in high-skilled services is steadily rising. These two trends pose a challenge for China, presenting a new and uncertain period of economic change.
| 2023-03-14T00:00:00 |
2023/03/14
|
https://www.cambridge.org/core/journals/china-quarterly/article/back-to-cheap-labour-increasing-employment-and-wage-disparities-in-contemporary-china/01570B39EB3944F18C79B34B5D3A66DF
|
[
{
"date": "2022/12/01",
"position": 68,
"query": "automation job displacement"
}
] |
Adaptive Immersive Learning Environments for Teaching ...
|
Adaptive Immersive Learning Environments for Teaching Industrial Robotics (Conference Paper)
|
https://par.nsf.gov
|
[
"Vassigh",
"Corrigan",
"Bogosian",
"Peterson",
"Narula",
"Bhavleen Kaur",
"Perez",
"Lor",
"Mohammadreza Akbari",
"Vodinepally"
] |
by S Vassigh · 2023 · Cited by 4 — However, it is also expected that these technologies will lead to job displacement, alter skill profiles for existing jobs, and change how people work.
|
AI, robotics, and automation are reshaping many industries, including the Architecture, Engineering, and Construction (AEC) industries. For students aiming to enter these evolving fields, comprehensive and accessible training in high-tech roles is becoming increasingly important. Traditional robotics education, while often effective, usually necessitates small class sizes and specialized equipment. On-the-job training introduces safety risks, particularly for inexperienced individuals. The integration of advanced technologies for training presents an alternative that reduces the need for extensive physical resources and minimizes safety concerns. This paper introduces the Intelligent Learning Platform for Robotics Operations (IL-PRO), an innovative project that integrates the use of Artificial Intelligence (AI), Virtual Reality (VR), and game-assisted learning for teaching robotic arms operations. The goal of this project is to address the limitations of traditional training through the implementation of personalized learning strategies supported by Adaptive Learning Systems (ALS). These systems hold the potential to transform education by customizing content to cater to various levels of understanding, preferred learning styles, past experiences, and diverse linguistic and socio-cultural backgrounds.Central to IL-PRO is the development of its ALS, which uses student progress variables and multimodal machine learning to infer students’ level of understanding and automate task and feedback delivery. The curriculum is organized into modules, starting with fundamental robotic concepts, and advancing to complex motion planning and programming. The curriculum is guided by a learner model that is continuously refined through data collection. Furthermore, the project incorporates gaming elements into its VR learning approach to create an engaging educational environment. Thus, the learning content is designed to engage students with simulated robots and input devices to solve sequences of game-based challenges. The challenge sequences are designed similarly to levels in a game, each with increasing complexity, in order to systematically incrementally build students' knowledge, skills, and confidence in robotic operations. The project is conducted by a team of interdisciplinary faculty from Florida International University (FIU), the University of California Irvine (UCI), the University of Hawaii (UH) and the University of Kansas-Missouri (UKM). The collaboration between these institutions enables the sharing of resources and expertise that are essential for the development of this comprehensive learning platform.
Award ID(s): 2315647
PAR ID: 10514334
Corporate Creator(s): AHFE
Publisher / Repository: AHFE International
Date Published: 2023-01-01
Journal Name: Emerging Technologies and Future of Work
Edition / Version: 1
Volume: 117
ISBN: 978-1-958651-93-3
Subject(s) / Keyword(s): Adaptive Learning Systems, Robotics Training, Virtual Reality Learning, Personalized Learning, Game Assisted learning
Format(s): Medium: X Size: 1.2 Other: online
Size(s): 1.2
Location: Honolulu, Hawaii
| 2023-01-01T00:00:00 |
2023/01/01
|
https://par.nsf.gov/biblio/10514334-adaptive-immersive-learning-environments-teaching-industrial-robotics
|
[
{
"date": "2022/12/01",
"position": 76,
"query": "automation job displacement"
},
{
"date": "2023/01/01",
"position": 100,
"query": "automation job displacement"
}
] |
Machine Learning-Enabled Smart Industrial Automation ...
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Machine Learning-Enabled Smart Industrial Automation Systems Using Internet of Things
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https://www.mdpi.com
|
[
"Al Shahrani",
"Ali M.",
"Alomar",
"Madani Abdu",
"Alqahtani",
"Khaled N.",
"Basingab",
"Mohammed Salem",
"Sharma",
"Rizwan"
] |
by AM Al Shahrani · 2022 · Cited by 49 — ... Automated Guided Vehicle (AGV) could optimally integrate charging stations into a tour of job locations. Modern industrial AGV systems employ algorithms to ...
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4.1. Elaborative Stepwise Stacked Artificial Neural Networks (ESSANN)
k indicates k th instance to be analyzed by the model. ESSANN model is a group of ‘ n ’ MultiLayer Perceptron (MLP) models stacked over each other. Each MLP layer consists of an input layer, multiple hidden layers, and an output layer. The process involved in each MLP layer is explained below. The industrial data to be analyzed is provided as input to the first MLP layer. The first MLP layer processes and analyzes the industrial data to output the industrial decision. The output of the first MLP layer is sent as input to the second MLP layer. In this way, data are propagated from the previous MLP layer to the next MLP layer in the ESSANN model. As the data reaches the n th MLP layer, the output of the n th MLP layer is presented as the final output of the ESSANN model. In a very short period, artificial neural networks can analyze massive amounts of data with complicated characteristics and isolate various patterns. Consequently, they are helpful for a wide range of commercial functions, including industrial automation, spotting data abnormalities or mistakes, and picking up certain sights, noises, or visuals. With limitless supplied inputs, they may employ identity to deliver the best results. A collection with the ESSANN for all conceivable variables measured was proposed to provide sufficient guidance. First, in a StackWise neural network, particular combination strategies of MLPs based on a simple average, the least-squares approach, and a nonlinear combination strategy based on a cascade of neural networks were considered. The performance of the different models was compared using the entire set of patterns. A schematic of the proposed structure is shown in Figure 2 . The stacking strategy was implemented on a subset of the available suboptimal models after they were sorted based on their modeling performance. Thus, better-working MLPs were considered first. The input fed to the proposed ESSANN model was partitioned into subsets. In Figure 2 indicatesth instance to be analyzed by the model. ESSANN model is a group of ‘’ MultiLayer Perceptron (MLP) models stacked over each other. Each MLP layer consists of an input layer, multiple hidden layers, and an output layer. The process involved in each MLP layer is explained below. The industrial data to be analyzed is provided as input to the first MLP layer. The first MLP layer processes and analyzes the industrial data to output the industrial decision. The output of the first MLP layer is sent as input to the second MLP layer. In this way, data are propagated from the previous MLP layer to the next MLP layer in the ESSANN model. As the data reaches theth MLP layer, the output of theth MLP layer is presented as the final output of the ESSANN model.
Algorithm 3: ESSANN 1: ESSANN approach (Input, Neurons, Iteration)
Generate a source database 2: Input ← a database that includes every variation of variables that might exist
Equip ESSANNs 3: for Input = 1 to n do 4: for Neurons = 1 to n do 5: for iterate = 1 to n do 6: Equip ESSANN 7: ESSANN-Storage ← saves the highest value 8: end for 9: end for 10: ESSANN-store ← based on inputs, preserve the most accurate forecasting ESSANN 11: end for 12: Return ESSANN-Storage ESSANN for all variable combination 13: end approach In an MLP neural network, it is particularly important to choose the number of hidden layers and the number of neurons in each layer. In a small dataset, too many hidden layers will not only make the model more complicated, but also lead to overfitting of the model and poor model generalization ability. Therefore, in small datasets, one or two hidden layers of MLP neural networks are generally used for modeling. We established one hidden layer and two hidden layers of MLP models and chose the model with the least error as the final prediction model of the pollutants. To solve this issue, a stepwise model was established through the influence of various factors on industrial automation systems, and it was used to provide the fitted value of the automation system at the corresponding moment. Then, the stepwise MLP neural network model was established by taking the fitted value and other data and time measured by the self-built point as input values and the national control point data as output values. An algorithm was employed to determine the elaborative stepwise stacked artificial neural network parameters for each pairing. The algorithm for ESSANN is depicted below (Algorithm 3).
ESSANNs are non-parametric machine learning techniques that comprise a matrix of linked neurons. Based on an input signal, the neuron weights and the inputs are given. In each MLP layer, the input layer has data, random weights, and bias term. These then pass through the hidden layer, and it then outputs the result. After this, the model learning error is determined, and finally, based on error, the model weights are updated. This weight updating is done continuously until we get a satisfactory error rate. This iterative weight updating process is applied to each MLP layer in the ESSANN model. The generation of the output signal depends on the signal. This signal is then sent and may activate more neurons depending on the structure of the network. In contrast, machine learning (ML) is related to the learning representation of data. The ESSANN, as these are often called, is motivated by the organization and function of the brain. The method uses a pyramid of ideas in a subject area to help the machine learn from experience. Because this information is obtained automatically, this method does not require manual interaction to offer machine expertise. A pyramid of ideas makes it easier to divide complicated ideas into several levels of easier things. When there are several processing levels, ML approaches learn through several different layers. This method has been used in industries such as robotics, genetics, and pharmaceuticals. ESSANN in industrial automation contributes to increased efficiency, reliability, and efficiency, all of which reduce operating costs. However, a decrease in production costs is the main benefit of an industrial automation system. This can be achieved by employing the proposed method. We must first understand the structure of a neural network to understand the structure of an elaborative stepwise stacked artificial neural network. A massive amount of elaborate artificial neurons, also known as modules, are assembled in a hierarchy of levels to construct a neural network.
| 2023-01-14T00:00:00 |
2023/01/14
|
https://www.mdpi.com/1424-8220/23/1/324
|
[
{
"date": "2022/12/01",
"position": 88,
"query": "automation job displacement"
}
] |
What jobs in IT are the most safe from being replaced by AI?
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The heart of the internet
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https://www.reddit.com
|
[] |
The only jobs safe from AI and Automation are the positions that decide which jobs get replaced by AI or Automation.
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Just curious about people’s thoughts on what jobs in IT are the most safe from being replaced by AI in the future or will take the longest time to be replaced. Since future is a broad term I will say the next 10-15yrs.
I know we never know what the future holds, just wanted to hear your thoughts and/or predictions.
| 2022-12-01T00:00:00 |
https://www.reddit.com/r/sysadmin/comments/zl4h2o/what_jobs_in_it_are_the_most_safe_from_being/
|
[
{
"date": "2022/12/01",
"position": 3,
"query": "AI job losses"
},
{
"date": "2022/12/01",
"position": 2,
"query": "AI replacing workers"
},
{
"date": "2022/12/01",
"position": 5,
"query": "AI unemployment rate"
}
] |
|
Workplace automation and political replacement: a valid ...
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Workplace automation and political replacement: a valid analogy?
|
https://link.springer.com
|
[
"Burley",
"Philosophy Department",
"Applied Ethics Center",
"University Of Massachusetts Boston",
"Boston",
"Eisikovits",
"Nir.Eisikovits Umb.Edu",
"Ma",
"Jake Burley",
"Nir Eisikovits"
] |
by J Burley · 2023 · Cited by 3 — AI-driven occupational replacement is notably piecemeal and incremental. It is very rare for a profession to be entirely replaced by technological advances, and ...
|
A great deal of theorizing has emerged about the economic ramifications of increased automation. However, significantly less attention has been paid to the potential effects of AI-driven occupational replacement on less measurable metrics—in particular, what it feels like to be replaced. In politics, we see examples of nation-states and extremist groups invoking the concept of replacement as a motivator for political action, unrest, and, at times, violence. In the realm of workplace automation, and in particular, in the case of AI-driven workplace automation, the replacement of human labor with artificial labor is an explicit goal. In this paper, we suggest that, given the effects that the experience of a sense of replacement has in political contexts and the potential for that sense of replacement to motivate unrest and violence, we should be concerned about the widely predicted replacement of workers over the coming decades beyond the potential economic challenges which may arise.
| 2023-11-14T00:00:00 |
2023/11/14
|
https://link.springer.com/article/10.1007/s43681-022-00245-6
|
[
{
"date": "2022/12/01",
"position": 17,
"query": "AI replacing workers"
}
] |
The medical profession transformed by artificial intelligence
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The medical profession transformed by artificial intelligence: Qualitative study
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https://pmc.ncbi.nlm.nih.gov
|
[
"Lina Mosch",
"Charité",
"Universitätsmedizin Berlin",
"Corporate Member Of Freie Universität Berlin",
"Humboldt-Universität Zu Berlin",
"Institute Of Medical Informatics",
"Berlin",
"Department Of Anesthesiology",
"Intensive Care Medicine",
"Daniel Fürstenau"
] |
by L Mosch · 2022 · Cited by 19 — According to the World Health Organization (WHO), however, AI may not fully replace clinical decision-making, but it could improve decisions made by clinicians.
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The study highlights changes in job profiles of physicians and outlines demands for new categories of medical professionals considering AI-induced changes of work. Physicians should redefine their self-image and assume more responsibility in the age of AI-supported medicine. There is a need for the development of scenarios and concepts for future job profiles in the health professions as well as their education and training.
Specialized tasks currently performed by physicians in all areas of medicine would likely be taken over by AI, including bureaucratic tasks, clinical decision support, and research. However, the concern that physicians will be replaced by an AI system is unfounded, according to experts; AI systems today would be designed only for a specific use case and could not replace the human factor in the patient–physician relationship. Nevertheless, the job profile and professional role of physicians would be transformed as a result of new forms of human–AI collaboration and shifts to higher-value activities. AI could spur novel, more interprofessional teams in medical practice and research and, eventually, democratization and de-hierarchization.
Healthcaare delivery will change through the increasing use of artificial intelligence (AI). Physicians are likely to be among the professions most affected, though to what extent is not yet clear.
With this interview study, we aim to investigate the impact of AI implementations on the future of the medical profession, ways of working in healthcare, and the demand for (new) profession(al)s. We address the following research questions (RQs):
With the aim to improve both efficiency and patient care in a healthcare system threatened by staff shortage and demographic change, AI systems will impact clinical decision-making and diagnostics, transforming the work of physicians in the next decade. 14 According to Topol et al., “with modern AI, a mix of human and artificial intelligences can be deployed across discipline boundaries to generate a greater collective intelligence”. 15 Various medical professions might be affected by this to differing extents. 5 , 11 , 16 – 22 Physicians' workflows and activities will be transformed by AI/intelligence mix, but what exactly are the implications for the profession's work practices, team compositions, and collaboration patterns in the future digitized healthcare system 14 , 23 , 24 ?
AI refers to systems that display intelligent behavior by analyzing their environment and taking actions—with some degree of autonomy—to achieve specific goals. 4 In medicine, specific goals achieved by AI systems are, for instance, the classification of clinically important abnormalities in chest radiographs or detection of melanoma in images of skin lesions at a performance level comparable to practicing physicians. 5 , 6 Technologies such as image recognition, computer vision, robotics, and natural language processing can support physicians and/or take over medical tasks in the sense of automatization. 7 AI can help to overcome human limitations in collecting, processing, and analyzing data sets (e.g. limited capacity to absorb and process data, fatigability, human bias). 8 , 9 AI-supported clinical decision support can strengthen efficiency and effectiveness at all levels of healthcare delivery (i.e. screening and prevention, diagnosis, treatment planning, and rehabilitation). However, little research has been performed to analyze the automation capability of different medical tasks with respect to the implications for the physician's job. 10 AI systems always operate at the same level of precision, do not get tired, and have a large memory capacity—characteristics that are in high demand in times of staff shortages, stress, and time pressure in healthcare. Due to the rapid progress in AI research and development, some observers have asked whether AI might replace physicians in the future. 11 , 12 According to the World Health Organization (WHO), however, AI may not fully replace clinical decision-making, but it could improve decisions made by clinicians. 13 Which medical tasks could be automated by AI systems, and which tasks should be performed by a physician—possibly assisted by AI?
Evidence-based medicine is a data-driven field. 1 , 2 In recent years, the rapid progress in medical research and the digitalization of healthcare have led to an ever-growing amount of data. 3 Artificial intelligence (AI) is being touted as the means to unlock the potential of this treasure trove of data.
Overview of thematic categorization framework with theories (dark grey), categories (light grey), and sub-categories (white). The numbers in parentheses indicate the number of experts who were assigned one or more statements that aligned with the corresponding topic.
Analysis of the interview transcripts was performed by LM, DF, JB, and JW, whereas coding and categorization were reviewed by all participating researchers. A thematic analysis approach 25 was followed, and the categorization framework developed by Grodal et al. 33 was used for guidance. In a first step, we actively categorized the data into thematic units (generating initial categories 33 ). Codes emerged for RQs 1–3 (see Figure 2 ). In a second step, codes were sub-specialized, refined, and stabilized by identifying arguments supporting the overarching theory (see Figure 2 ). In a final step, codes were summarized and consolidated.
Furthermore, two transcripts of expert interviews conducted in German by JB as part of the Higher Education Forum on Digitization were included in the qualitative content analysis. Two members of the “AI Campus Expert Lab Medicine” were interviewed by JB in the context of a theme week held by the Higher Education Forum on Digitization on the topic of AI. 31 , 32 The content of the questions clearly corresponded to the questionnaire of the mentioned study.
Interviews were conducted with 24 experts in the field of AI in medicine and medical education and training. Their backgrounds were medicine (15/24), computer science/informatics (9/24), medical education (3/24), and other (8/24; see Appendix 1 ). Of the 24 experts, 10 had more than one field of expertise (e.g. medicine and computer science) and 17 had more than 10 years of work experience in their respective field. Twenty-one interviews were conducted in German, while interview #7 was conducted in English. Quotes used in this publication were translated and modified for better readability, if necessary. The median length of the interviews was 28 min and 47 s, with a minimum length of 16 min and 17 s and a maximum length of 47 min and 17 s. The interviews were recorded and transcribed verbatim.
The interview guide was created based on the research questions, a scoping literature review, and a questionnaire developed for a previous study on teaching in digital medicine. 30 The guiding questions were discussed and selected in several meetings within the research group with the participation of two experienced AI researchers—a computational linguist and a computer scientist—and tested in a pilot interview. The pilot interview, conducted on 22 April 2021, did not result in any changes to the interview guide. The final interview guide consisted of a catalog of 17 items (see Appendix 2 ). Based on the professional background and position of the interviewee, the research group selected approximately 10 questions for the interview. The selection was always made by the interviewing author and confirmed by the remaining authors. The interviewee was emailed the interview guide tailored to him/her in advance of the interview. Twenty-two interviews were conducted by LM, JB, and JW.
Participants in the study were selected based on their experience at the interface of AI and medicine, with a focus on education. Benchmarks were membership in the “AI Campus Expert Lab Medicine,” 28 pioneering work and research contributions (i.e. scientific publications, policy papers, work experience in major companies) in this field, as well as holding leadership positions in bodies representing the interests of relevant stakeholders (e.g. students, physicians)—for more details, see Appendix 1 . Sample size was discussed in the context of the Information Power in Qualitative Interview Studies Model by Malterud et al., 29 considering the relatively broad aim and the cross-case analysis approach as factors for a larger sample size. On the other hand, high sample specificity, high quality of dialogue between researchers and participants, and theoretical saturation in the analysis led us to limit the number of participants to 22 interviews and the number of included interview transcripts to two (see below, Data collection).
This process also goes hand in hand with the paradigm that is increasingly coming to the fore: patient-centered healthcare and putting the patients back at the center. Unfortunately, this sometimes is neglected in our not yet so digitized medicine. (#21, physician in leading positions in representative bodies of the medical profession and with responsibility for digitization-related topics)
Finally, some experts (21%, 5/24) predicted that, as patient-centered and user-oriented healthcare is becoming increasingly important for healthcare providers, physicians should prepare and adjust to more empowered patients with different needs and expectations, especially so as to not leave the market to private companies.
It is important to open people's eyes because they are definitely going to work with some kind of AI in ten years. It is important to develop competence and be critical: What can AI do, what can AI not do? How can I use AI for my own benefit? […] This is very important in order to keep medicine effective and not to frustrate people. (#12, physician and founder of an AI-based health start-up)
Some experts interviewed in this study (8%, 2/24) foresee a transformation in medical education. Much of the knowledge taught in medical school today could become worthless in the future, as AI can make knowledge available to everyone in a consolidated and processed form. Physicians and medical students will spend less time acquiring factual knowledge, because AI can make this available much faster, more comprehensively, and in a more evidence-based way.
Hierarchical patterns will change due to AI, and we need concepts for this. AI could lead to democratization and de-hierarchization in medicine, as complex knowledge and insights become accessible to everyone, some experts proclaimed (8%, 2/24). They anticipate that there will be a shift in eminence as the required skills and activities of physicians change and citizens are empowered to participate in their own healthcare.
It is important to create expertise for AI in the medical community as well as to establish “Digital Medicine Experts” as a new profession with deeper knowledge and skills at the interface of medicine and AI, the experts said.
More and more professions would enter the work environment of physicians: data scientists, mathematicians, physicists, biologists, engineers, etc. Furthermore, existing health professions such as psychologists, physiotherapists, and nurses will work together with the physician and the AI system. In medical research, computer scientists and physicians should work closely together; physicians should be involved in programming AI algorithms, some experts stated. In addition, social scientists should be involved in medical research to explore social determinants of health, as socioeconomic data could soon be integrated by AI and used for research. Some experts suggest recruiting computer scientists abroad as well as prioritizing and incentivizing interdisciplinary research to drive innovation in AI in Germany.
More interprofessional collaboration, new professions, and qualifications will evolve. Interprofessional collaboration would become even more important in the era of AI in medicine, half of the experts stated (50%, 12/24), as a multitude of perspectives would be accumulated and discussed in clinical everyday life. This could lead to better solutions and a more holistic understanding of the underlying problem.
There is no permanent certainty in healthcare. And as doctors, we don't like that. […] We are very uncomfortable with uncertainty. […] Being able to learn how to learn means you are perfectly aware and acknowledging that there are so many uncertainties in what we know. (#7, physician and principal research scientist in computational physiology)
Digitization and AI would challenge the medical profession and the skills it requires in entirely new ways. More and more knowledge and evidence is being accumulated in less and less time, which requires an adaptation of medical training concepts as well as new solutions for knowledge translation from research to practice and teaching, many experts stated (54%, 13/24). Moving away from the one-size-fits-all paradigm and toward precision medicine, physicians should learn to better deal with uncertainties in medical practice.
Additionally, physicians would also need to take responsibility for their own qualification and education in AI. According to the majority of experts (54%, 13/24), physicians should acquire AI-related competencies to be able to handle AI systems, to assess their applicability in a specific case, to detect sources of error, and to evaluate the output of the AI system. According to the experts interviewed, basic AI competencies encompass competencies related to the ethical, legal, and social implications of AI (ELSI competencies), a general understanding of technical functionalities, and the ability to practically apply AI systems. ELSI competencies include, but are not limited to, competency in critical reflection and impact assessment of the application of AI systems—related to both individual patient care and societal or ethical issues. A general understanding of the functionality of AI systems would require basic knowledge in, for instance, data science and machine learning models, the interviewed experts agreed. In addition, basic AI definitions would need to be clarified in the public domain, as skepticism and doubt about AI often stem from an unclear public narrative, some experts said.
To fulfill their responsibility and not relinquish it to other stakeholders, physicians should see themselves as an important part of the societal discourse on medical responsibility, medical role conception, and self-image, as well as on ethical, social, and legal issues regarding AI. Experts see medical professional associations as having a responsibility to develop positions and concepts for the introduction of AI in medical practice, education, and research. Physicians should participate in and drive the research and development of medical AI systems. In medical practice, they should take responsibility for advancing personalized medicine and initiate research-based treatment, the experts suggested. Some experts assume that the professional role of physicians could change completely—if physicians do not participate in the process and take over responsibilities.
Physicians should redefine their self-image and assume more responsibility in the age of AI-supported medicine. AI is perceived differently by physicians than other technological innovations; this is, according to experts, because of certain narratives and the “overblown” AI hype, which make it seem like AI can potentially replace physicians. The medical profession would need to recognize that AI will undoubtedly be integrated into routine clinical practice in the coming years and that it can support clinical practice and improve outcomes. An understanding and vision of both the role of AI and physicians should be developed within the medical profession. The vision for the physician's role and self-image should include critical reflection, consideration, and evaluation of AI decision proposals, the experts suggested.
The role of physicians is closely linked to the role of AI, as physicians define the areas of application and issues for AI, the experts stated. According to most experts (75%, 18/24), the job profile of physicians might undergo significant changes due to the increasing implementation of AI. However, the core competencies and activities of physicians would remain the same.
Some experts (21%, 5/24) see AI as an additional tool for physicians for specific tasks in predefined contexts, but in perspective, they suggest that assessment by AI will gain in importance and that AI could even carry equal weight with human doctors in medical decision making.
Although AI would perform specific tasks in predefined contexts, physicians would increasingly take on the role of generalists, according to the majority of experts (63%, 15/24); the experts believe the physicians would synthesize and contextualize information from multiple sources, which would lead to a more holistic approach to a disease or patient case. AI systems could be considered as a consultant—a further external opinion the physician should integrate into the decision-making process. Therefore, risk analysis will become more important. A physician would still weigh various possible hypotheses and make the decision in the end, being the “filter behind the AI.”
The experts interviewed for this study state that AI could solve major problems in medical research and augment medical practice by complementing the limits of human intelligence. Therefore, according to some experts (41%, 10/24), concepts for combining artificial and human intelligence in research settings and clinical workflows should be developed. AI should be critically monitored; the danger would be to trust unreflectively in the output of the AI systems (automation bias). Clinical trials and registration studies for AI-based medical devices should be conducted using local databases, and the evidence gathered should be used to inform legislations and guidelines for human–AI interaction in healthcare.
In conclusion, the majority of the experts were concerned about AI dividing the medical profession. AI would not replace physicians, but the “AI-naïve” might be replaced by “AI-literate” physicians, according to some experts. Physicians’ fear of being replaced by AI would suggest a lack of awareness of the evolution of the physician's professional role. With no general AI yet in existence, physicians' fear of AI today is largely due to a lack of basic knowledge and awareness of AI (in medicine), the experts stated.
[…] AI will not lead to better medicine and more satisfied patients if physicians do not communicate it well. You have to make this clear to physicians: You must also be able to communicate the knowledge. Having it for yourselves is not enough. (#23, physician with a research focus on AI-augmented wearables and mobile health devices)
AI will not take over the interaction between physician and patient in the coming decades, according to some experts (23%, 5/24). Contrarily, AI would create more time for human attention, anamnesis, and physical examination—the “real” craft of medicine. Thus, the communication skills of physicians will become more important. Thereby, physicians help patients to participate in their own treatment process by enabling them to make informed decisions or take an active part in the decision-making process.
AI can only be applied and trained if it has access to large representative data sets. Many important data formats are not yet accessible to AI systems, and there is a lack of representative, freely accessible, and annotated data sets that can be used as a basis for training AI systems. Experts call for the establishment of and adherence to quality standards in data collection and data documentation. Quality standards would also be needed for reporting the results of studies using medical AI systems to ensure transparent disclosure of their benefits and performance. Many medical AI systems have been trained with retrospective data and need to be validated prospectively, though some experts have criticized that this is usually associated with declining performance. Additionally, algorithms trained on publicly available data sets would have limited clinical applicability because real-world data are much more heterogeneous.
For example, a system that has been trained to recognize certain diseases will only look for those diseases and will not be able to recognize other things. […] So there are major limitations for such AI systems because of this specialization. (#20, computer scientist in a leading position in an association for digitalization, responsible for topics in the field of AI)
Most experts (63%, 15/24) explained that the role and potential of AI is often still overestimated. AI systems would always be designed for specific use cases. However, the variety of a physician's tasks would be too heterogeneous, complex, and unpredictable for AI systems to handle.
With regard to the role model, you have to ask yourself what image a doctor has of himself or herself if he or she believes that the profession is threatened by an AI that […] can do nothing else but volumetrize and measure tumor sizes. […] That is not the image I have of myself as a physician. That would be sad. So I donot feel threatened by these tools at all; on the contrary. I look forward to them. (#1, physician and computer scientist conducting research on AI-based applications for diagnostic support)
AI models are created by humans to respond to a certain task, which is in turn also defined by humans. According to most experts in this study (72%, 16/24), physicians would be needed to define the questions that AI algorithms should solve. At a higher level, they need to oversee the course of treatment and, in that context, link and classify the outputs of AI systems. AI would act as an additional tool for physicians; according to some experts, there may even be parity between medical and AI assessment. However, the experts agree that the indication for the use of AI should be determined by physicians.
Although AI has great potential to completely automate away certain specialized or routine tasks, numerous tasks will continue to require human supervision, problem definition, or human interaction. The experts interviewed for this study gave three reasons why AI will not replace physicians entirely, which are detailed below.
The interviewed experts see great potential for AI applications for research purposes. In drug development, therapies can be personalized with regard to dosage, selection, timing, and route of administration with the help of AI. New vaccines and precision therapies for cancer could be brought to market through further research into pharmacogenetics. Personalized medicine can also be enabled through research with patient data (e.g. smartphone data, social/demographic data, data from wearables). AI could enable analysis of large amounts of (big) data that was previously inaccessible to researchers.
AI will be able to play a role in all disciplines. In what form, that remains to be seen. But that doesn't just apply to the medical field. It also applies across all sectors and branches of medicine […]. And of course, that also means that the users are different. It's the doctors, patients, health insurers and researchers. All of them will see the opportunities to exploit potential to improve their work. (#21, physician in leading positions in representative bodies of the medical profession and with responsibility for digitization-related topics)
Although interviewees agree that physicians will still be responsible for clinical decision-making, the experts believe that AI-based decision support will change clinical reasoning. Beyond this, AI-based clinical decision support systems in all areas of healthcare (prevention, diagnosis, therapy, and rehabilitation) could enable improved efficiency, increased patient safety, and conservation of resources. Some experts suggest making the use of AI decision support mandatory in complex patient cases and medication regimens (pharmacogenetics and drug–drug interactions) and including it in medical guidelines.
[…] Artificial intelligence is boosting our computing power, boosting our ability to extract patterns from all the signals that we are capturing with all the tools that we have in medicine. (#7, physician and principal research scientist in computational physiology)
In addition, AI systems could outperform human interpretation and mathematical abilities in terms of integrable variables and dimensions. AI can contextualize many dimensions of variables in a specific use case and assemble them into a set of rules that is not based as much on simplification as human thinking. It specializes in quickly and completely detecting changes and recognizing patterns in complex data sets.
Most experts in our study (75%, 18/24) stated that AI systems are specialists—with broad capabilities. Given the rapid increase in complexity in medicine, humans are reaching their limits when it comes to rapidly assessing, consolidating, interpreting, and summarizing large and multidimensional data sets (medical knowledge, scientific findings, patient history data, etc.). AI, however, could do this, enabling precision medicine, where data is processed in a patient-, diagnosis-, and treatment-specific manner.
All experts interviewed expect to see a steady increase in the use of AI in medicine. AI could outperform humans in specific tasks in all medical specialties, from prevention to diagnosis, therapy to rehabilitation, and for research purposes.
Discussion
This qualitative interview study gives an overview of the potential impact of AI on medical tasks, physicians’ job profile, and the ways of working in a future AI-augmented healthcare system. The study builds upon previous research on this topic and re-examines assumptions and theories that have been made about the effects of AI implementation into clinical routine, following the problematization approach, as described by Chatterjee and Davison.34
Repetitive administrative tasks and activities involving the analysis of large, multidimensional but homogeneous data sets in a given context can be taken over by AI systems. Ultimately, AI will not replace physicians in the near future, but it has the potential to significantly change activities and the professional role of physicians and other professions in the healthcare sector. The interdisciplinary expert panel outlines a demand for new professionals through AI and highlights the responsibility of the medical profession for shaping the AI-induced transformation of healthcare. Physicians and their professional associations are challenged by a changing range of tasks and a change in their job profile and professional role. Hierarchies both within a professional healthcare team and in the patient–physician relationship could be broken down by ubiquitously available up-to-date knowledge and consolidated scientific evidence. Reforms in medical education are needed to prepare future physicians for the AI-augmented healthcare system. There is a need for the development of scenarios and concepts of future health professions.
Changing job profiles—the AI-augmented physician In 2017, Forbes Magazine published an article titled “Prepare Yourselves, Robots Will Soon Replace Doctors In Healthcare.”35 The question of whether AI will replace physicians has been evaluated in numerous publications (mainly position papers or reviews) in the last five years, focused either on specific medical specialties or on medicine in general.16,17,19,21,36–45 The results of this study support the position that AI will not replace physicians but will significantly transform the job profile of health professionals and bring about a responsibility to prepare for this transformation.41 Even though AI can be used for diagnosis, it will remain the task of physicians to validate it. Experts argue that clinical decision support systems should be thought of more as clinical reasoning systems, supporting physicians’ way of reasoning by including different sources and types of data. Critical interpretation, contextualization, and integration of outputs from such systems would remain part of the physicians’ role.46,47 With AI performing routine tasks or automating operational and clinical tasks, physicians’ workload will be reduced. This also leads to a shift in workforce skills toward social and emotional skills, bonding between humans, and technological and information management skills.14,43,48 AI systems being available for patients would facilitate access to health information. Although AI might lead to more egalitarian medicine, fostering shared decision processes between patients and physicians, physicians will still need to address patients’ questions and expectations. Furthermore, direction toward appropriate tools and sources and guidance in the interpretation of health information will remain a task of physicians.49,50 Our finding that “AI cannot imitate the human connection” is also confirmed by several publications. Most AI systems currently do not embrace empathy or emotional contact. A study examining compliance with medical recommendations depending on the use of AI in the context of a hypothetical scenario of skin cancer diagnosis and treatment showed that patients consider AI tools innovative, but would be more likely to follow recommendations when the assessment is performed by a physician or a physician using AI. The concept of social presence can explain this result.51 Experts believe that technological singularity—a future point in time at which computers will exhibit superhuman intelligence—cannot be achieved without empathy in healthcare, leading to the development of concepts like artificial empathy or empathy-driven digital tools.47,52,53 Personalized healthcare is only possible if it is responsive to patient needs, with an emphasis on the physician's perception of (past) medical history and physical examination. In this respect, human judgment will still surpass AI, and critical human skills must be increasingly taught and assessed to continue to ensure safety and efficiency in healthcare.43,54 Slow adoption of AI in healthcare can be explained by the traditional risk aversion in this domain, the anxiety stemming from uncertainty, and the fear of being replaced at work.55 To ensure safe and beneficial solutions, all stakeholders of the healthcare system should actively shape the development and integration of AI solutions in this sector.48 Palumbo et al. argue that “targeted initiatives are required to make value co-creation possible in a cyber-physical health-care setting.”56 Further challenges related to the use of AI but not identified in our study include patient autonomy protection matters, lack of transparency in the use of algorithms, or potential misappropriation of AI to replace services currently offered.57,58
The future of jobs in healthcare—new occupational profiles arising in the era of AI The results of the present study indicate the looming of new occupational profiles with the implementation of AI in healthcare. Already now, the “data-driven physician”59 emerges, meaning that existing task profiles of doctors will be enriched with data-rich tasks such as collecting, managing, analyzing, and interpreting data, and justifying decisions based thereupon.48,60 We will see the “bionic radiologist,”61 dermatologist, pathologist, anesthesiologist, gastroenterologist, and so forth. Their tasks will be unbundled and freed from some routine tasks and those where prediction by AI is more cost-effective (also taking into account the ethical and social costs of bad decisions), then re-bundled to take on new tasks related to data visualization, interpretation, problem definition, and monitoring of algorithmic decisions. Digital clinical scientists will be an addition to that and will be able to carry out AI-related research studies as well as clinical evaluations of AI systems.62,63 Moreover, existing clinical scientists focusing on the molecular basis of medicine are already enhancing their task profile with numerous digital practices. Although this shift may not replace all of a physician's tasks, it calls for physicians to take responsibility for their own qualification and education in AI. Not all tasks will be performed by doctors themselves in the future. Other occupational profiles have already entered the physician's turf, as emphasized by the experts in this study. These include data scientists, data engineers, and “analytics translator” roles.64 The ways of working together in such multi-professional teams are yet to be defined, the structures and positions yet to be established. These include, for example, reimbursement and financing models for incorporating multiprofessionalism into the health care system. Feng et al. advocate for the creation of “AI-quality improvement (QI) units,” in which clinicians, data scientists, model developers, hospital administrators, and regulatory agencies collaborate to monitor and improve the quality of AI systems deployed in a hospital setting.65 The numerous ethical, social, and legal challenges require the augmentation of inter- and transdisciplinary teams. These teams will and already include sociologists, lawyers, ethicists, psychologists, human factor engineers, and other roles. Moreover, evaluation also requires economics experts. This calls not only for physicians to take responsibility for their own AI competencies but also for other professions to be taught the medical competencies necessary for meaningful collaboration in interprofessional teams—and with AI.
Learn how to learn—the need to reform medical education The impact of AI on the medical profession and the healthcare system in general will require reforms to the education system. The focus is on raising awareness among students, physicians in medical training, and specialists who are occupied with the implications of the changes that the healthcare system is experiencing as a result of the digital transformation.14,15,66 Experts interviewed for this study agree that AI-related competencies should be prioritized in medical education and (national) concepts for upskilling future physicians should be put in place.67,68 The need for qualification regarding AI within the medical profession is immense and enormously heterogeneous. Therefore, new master's programs and micro-credentials or micro-degrees can make a meaningful contribution, since rapid technological developments in AI induce special challenges to keep teaching contents in accordance with the state of the art, requiring more adaptive curricula and possibly new formats such as digital learning opportunities. The same applies to continuing medical education and training, where the need for qualification in AI is even greater than in medical studies.27,69 The application-oriented teaching approach of AI competencies should be particularly emphasized. The focus is on the indication for the use of AI systems, their evaluation and interpretation, and the outcome in the context of clinical decision-making. In addition, practical application must be mastered, including the ability to communicate the content to patients and colleagues in the clinical setting. This requires innovative new approaches to teaching that move away from the passive accumulation of knowledge and place a greater focus on “flipped classroom” models, hands-on experiences, and skills orientation, and an emphasis on interprofessional collaboration.27,70–72 The ability to reflect is of particular importance and can also be described as “learning how to learn.” This means reflecting on one's medical skills, for instance, but also critically questioning AI outputs, as physicians act as “filters behind the AI.” The “learn how to learn” approach implies new ways of thinking, including embracing medical uncertainties, and provides problem-solving strategies, which are also part of the so-called “Village Mentoring” concept.73,74 The focus is on promoting collaborative and interdisciplinary scientific work and non-hierarchical communication. Any member of a research group can become both a mentor and a mentee. The “village” has no permanent members, but is based on projects, so teams can regroup according to current needs and research questions. This allows for better transfer from research to teaching and student involvement in current research projects.73 In addition, enhanced integration of research and teaching facilitates better adaptation to the rapidly changing work environment in the healthcare sector.
| 2022-12-13T00:00:00 |
2022/12/13
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https://pmc.ncbi.nlm.nih.gov/articles/PMC9756357/
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"date": "2022/12/01",
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A LABOR MODEL OF CORPORATE LIABILITY FOR AI
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EMPLOYED ALGORITHMS: A LABOR MODEL OF CORPORATE LIABILITY FOR AI.
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https://search.ebscohost.com
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"Diamantis",
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by ME DIAMANTIS · 2023 · Cited by 36 — 34 Decades before corporations started replacing employees with algorithms, they turned to contract workers.35. Contractors do the same jobs as employees and ...
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Connecting you to content on EBSCOhost
| 2023-01-01T00:00:00 |
2023/01/01
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https://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=00127086&AN=160979530&h=7KiSjY3BdGbCsY0hTETxns0GLFul2%2BQiUz3OmprAxIwDQFguaOYloKuDWpneu157Hc5tTfgpQpMpkW22g3Dd1w%3D%3D&crl=f
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Group Discussion: 1. Artificial Intelligence Pros and Cons?
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Group Discussion: 1. Artificial Intelligence Pros and Cons?
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https://www.scribd.com
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It notes that AI can be used to complete dangerous tasks, replace humans in repetitive work, and work endlessly without breaks. However, it also notes that AI ...
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Document Information click to expand document information
The document discusses several topics related to artificial intelligence, including pros and cons. It notes that AI can be used to complete dangerous tasks, replace humans in repetitive work, and work endlessly without breaks. However, it also notes that AI cannot think creatively outside the box, may increase unemployment, and could make humans lazy. The document also discusses cashless economies, women as managers, privatization, balancing professional and family life, and whether globalization is necessary.
| 2022-12-01T00:00:00 |
https://www.scribd.com/document/515904290/GROUP-D
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"query": "universal basic income AI"
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|
A survey of AI ethics in business literature: Maps and ...
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A survey of AI ethics in business literature: Maps and trends between 2000 and 2021
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https://www.frontiersin.org
|
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"Daza",
"Marco Tulio",
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by MT Daza · 2022 · Cited by 34 — According to a University of Oxford study, 47% of jobs will be lost due to automation in the next 25 years (Frey and Osborne, 2013). However, Beerbaum and Otto ...
|
Artificial intelligence is spreading rapidly in business products and processes, with innovations that bring great benefits to society; however, significant risks also arise. AI-enabled systems make decisions autonomously and influence users and the environment, presenting multiple ethical issues. This work focuses on the ethics of AI use in business. We conduct a survey of business journal articles published between 2000 and mid-2021 to identify the most influential journals, articles, and authors, the most influential ethical schools, and the main ethical issues of AI in business. It describes the state-of-the-art in the field and identifies trends in ethical issues arising from AI. Thus, we present maps and trends of the ethics in AI in business literature.
Introduction
The availability of massive datasets and new machine learning techniques has triggered rapid advances in AI in the past decade (Acemoglu et al., 2022). This technology-driven transformation is reshaping business, economy, and society (Loureiro et al., 2021). Innovations bringing great benefits and new challenges herald the arrival of a new industrial revolution (Marsh, 2012). Therefore, significant risks arise, and with them, the need for ethical assessment.
The fourth industrial revolution is causing a dramatic transformation of the world economy (Schwab, 2017). Companies as diverse as Google, Spotify, Under Armor, and so forth enhance their performance through the adoption of AI (Vlačić et al., 2021). Corporations that provide these platforms, such as Microsoft, Amazon, Alphabet (Google), and Apple, form part of a group whose market capitalization has exceeded one trillion dollars. Worldwide spending on cognitive and AI systems has grown from $24.0 billion in 2018 (Loureiro et al., 2021) to $93.5 billion in 2021 (Zhang et al., 2022).
The impact of AI is not limited to business and the economy; it prompts a profound transformation of work (Rodriguez-Lluesma et al., 2020). Like previous industrial revolutions, the fourth raises concerns that automation will wipe out jobs (Autor, 2015). AI-driven robots are replacing blue-collar workers in factories (Belanche et al., 2020), while Robotic Process Automation (RPA) systems are taking white-collar jobs. AI-based platforms are writing essays (Knibbs, 2022), computer code (Thompson, 2022), and creating art (Johnson, 2022a).
According to a University of Oxford study, 47% of jobs will be lost due to automation in the next 25 years (Frey and Osborne, 2013). However, Beerbaum and Otto (2021) suggests that these jobs will soon be replaced by new ones. Nevertheless, it is unclear how quickly they can be recovered or if newly created jobs will be of quality. Companies in the On-Demand Economy fuel the proliferation of precarious jobs; for Cherry (2016), this has devalued work, driving wages below the legal minimum and providing an excuse to avoid paying social security benefits.
AI transformation of work has a broad social impact. AI-enabled systems determine whether someone is hired, promoted, or approved for a loan, as well as which ads and news articles consumers see (Martin, 2019). These algorithmic decisions can have unfair negative consequences or even violate human rights (Kriebitz and Lütge, 2020).
There are other harms originating from AI's development and deployment. Training data for machine learning is obtained and used in ways that often violate people's privacy (Thiebes et al., 2020). AI-enabled systems can be used for surveillance (Stahl et al., 2021). Social media platforms wield enormous influence on users. They can undermine public health (Bhargava and Velasquez, 2020), polarize social groups, affect democratic participation, foster the spread of fake news and conspiracy theories (Zuboff, 2018), and even aid in terrorist attacks (Rauf, 2021).
We must consider that the ability of humans to cause harm to others has increased with new technologies; now, machines themselves could cause damage (Letheren et al., 2020). Consequently, ethical assessment is required to understand AI-associated issues, support better decisions, and establish standards to develop and implement AI systems. Thus, AI could also serve to promote flourishing.
However, it is not enough to have an evaluation that sheds light on our actions (or that of the machines). It is also necessary to justify and convince the organization's leadership why we opt for specific behavior. This acquires relevance in business, even more so where ethical choices are not usually the most lucrative. Furthermore, problems may arise when there is no theoretical support in the face of complex ethical problems, such as the lack of supporting arguments, weak justifications, or erroneous decisions. Therefore, we believe that discussing ethical theories is essential.
The first motivation of our work is to understand the state of AI ethics in business publications from a perspective that recognizes its intrinsic moral value. We note a lack of research with a holistic perspective in the literature, which is essential to study this topic. We highlight three key aspects. First, we conducted a bibliometric analysis of the literature on the subject, identifying the most influential journals, articles, and authors, which allowed us to situate ourselves in the field. Second, we categorize the main ethical issues of AI in business and identify the schools of ethical thought that are being used to address them. This perspective is necessary to recognize the value of ethics as an inquiry tool to evaluate competing tech policy strategies and practices, which have been downplayed by the industry as a communication strategy or a facade to cover up unethical behaviors (Bietti, 2020).
Our second motivation is to provide a survey of AI ethics literature with a comprehensive approach focused on the field of business, including more than specific areas, functions, or principles. We intend to find gaps in the literature, identify under-researched areas, and map the state-of-the-art in the field.
Although current literature presents valuable insights into specific domains, no research article focuses on the issues of AI in the business field comprehensively using an ethical perspective. None of the eleven Systematic Literature Reviews (SLRs) published between 2000 and 2021 had AI ethics as the primary focus across all business areas and functions (see Table 1). Most SLRs are centered on AI topics in business or business ethics separately. Only two reviews have an ethical approach to AI in business. Although they address specific domains, Bhatta (2021) studies the digitalization of leadership, and Hermann (2021) explores AI in marketing. Ryan and Stahl (2021) carried out the only SLR focused on AI ethics. However, their work does not focus on the business domain and has a limited approach to ethics since its scope is limited only to principles and guidelines.
TABLE 1
Table 1. Systematic literature reviews that address AI, business, and ethics between 2000 and 2021.
As a third motivation, we attempt to establish the connection between ethical schools of thought and the main AI issues in business. Thus, we classified papers into three leading ethical schools to measure their influence. Few authors study this phenomenon from the perspective of ethical theories, whether deontological, consequentialist, virtue ethics or a combination. Hermann (2021) carried out the only SLR that adopted an ethical theory standpoint, complementing deontological considerations with a utilitarian perspective. The other SLRs do not endorse an ethical theory. Most authors do not anchor their proposals in a foundational ethical theory. Some merely acknowledge that ethical problems exist and that future research is needed. Furthermore, we did not find an SLR or a research article that addresses the influence of ethical theories on the topic of AI in business.
Unlike most articles, which analyze AI ethics in an isolated context, this paper offers a survey of business journal articles focused comprehensively on AI ethics (not just guidance documents, Ryan and Stahl, 2021) across business domains and topics (not just leadership, Bhatta, 2021; marketing, Hermann, 2021; strategy, Caner and Bhatti, 2020) that connects the issues to specific ethical schools or theories. In this way, AI ethics connects not only to business ethics but also to socioeconomic and political ethics in general through major ethical traditions.
We organized this article into four sections. This introduction presents an outline of the impact of AI and our motivations. Section two continues with the methodology, the setting up of our database, and our research questions with the metrics and techniques used. Section three discusses our findings regarding the most influential articles, journals, and authors, presents a classification of the articles according to the ethical school used (if any), and proposes a categorization for the most recurring issues. We then proceed to analyze the evolution of these issues. Finally, in the fourth section, we present the maps and trends identified as conclusions and suggest areas for future research.
Methodology
We built our dataset by performing a structured search for scientific articles that study the ethics of AI in business and management between 2000 and mid-2021. We used five major academic databases: Web of Science, Scopus, Emerald, Business Source Ultimate, and Google Scholar, from which we retrieved 349 articles using the search strategy shown in Table 2.
TABLE 2
Table 2. Search strategy.
After a screening process, we discarded duplicates, book chapters, and other irrelevant documents. The remaining articles were filtered to leave 95 articles in our primary dataset.
We gathered all the groups from different databases (SCOPUS, WOS, Google Scholar, EBSCO, and Emerald Insight), each with a different file format, into a single file and standardized its set-up. We used the CSV (comma-separated values) structured table format required by the VOSviewer software to build and visualize bibliometric networks. To complete our database, we then conducted an online search on authors' profiles, institutions, and countries. We also verified the citations of each article and those of each author with a cutoff date of May 11th, 2022.
We cleaned up the “keywords” column of our database file. This process was necessary to gain clarity and prevent the same concept from appearing under different names. We replaced all keyword occurrences of “AI,” “artificial intelligence (ai),” and “artificial intelligence” with “Artificial Intelligence”; additionally, we abbreviated all keywords that included “artificial intelligence” + “another word” (e.g., “artificial intelligence ethics,” “artificial intelligence safety,” “artificial intelligence guidelines”) to use “ai” + “another word.”
Finally, we proceeded with the formulation of research questions that would guide our work.
Research questions
This study comprises five main research questions (hereafter referred to as RQ):
• RQ1: What are the most influential journals?
• RQ2: What are the most influential articles?
• RQ3: Which are the most influential authors?
• RQ4: What are the major schools of thought on the ethics of AI in business?
• RQ5: What are the main ethical issues of AI in business?
We carefully reviewed the 95 papers and applied bibliometric analysis techniques. Scholars use bibliometric analysis to uncover emerging trends in author, article, and journal performance, collaboration patterns, and research constituents and to explore the intellectual structure of a specific domain in literature (Donthu et al., 2021). This method encapsulates the application of several quantitative techniques to bibliometric data, such as using performance analysis indicators and science mapping techniques.
Most influential articles (RQ1), journals (RQ2), and authors (RQ3): A performance analysis
For RQ1, RQ2, and RQ3, we used performance analysis techniques that examine the contributions of different research constituents using publication-related and citation-related metrics (Donthu et al., 2021). Using citations as a metric to identify the most influential publications allowed us to understand the intellectual dynamics of this research field (Donthu et al., 2021) and measure their impact and influence.
For RQ1, besides the journal's total citations, we contrasted the number of publications in the timeframe of this review to assess productivity.
For RQ2, we built graphics using the number of articles and total citations over time to analyze their evolution. We could not perform a co-citation analysis with the information gathered from multiple databases. The reason was that some did not provide complete metadata; information regarding references was also missing from some papers. Furthermore, the total number of authors, the institutions, and countries of origin of 5 articles were not identifiable with the articles, nor were they found in the searches carried out in academic databases.
For RQ3, we used the total number of citations, h-index, institution, and country to deepen the analysis of author influence. It is important to mention that citation does not necessarily mean agreement with an author; however, it could indicate the author's relevance to the discussion.
Of many performance indicators, we chose the h-index because it assumes that the number of citations received by a researcher is a better indicator of the relevance of their work than the number of papers they publish or journals where they published. It considers the number of papers published and the citations to those papers in a balanced way. Thus, it is helpful in making comparisons between scientists (Hirsch and Buela-Casal, 2014).
We finally examined the countries with most publications. Since articles are often published by multiple authors from different institutions, we considered each author's institution.
Major ethical schools of thought: Screening literature (RQ4)
For RQ4, we turned to the Stanford Encyclopedia of Philosophy (SEP) for major ethical schools of thought, and we found that consequentialist, deontological, and virtue ethics are preferred by most authors in different domains (Mathieson, 2007; Moriarty, 2008; Hursthouse and Pettigrove, 2018; Norman, 2022). Therefore, we used them as a starting point in the field of AI in business.
To associate articles and authors with one or more ethical theories, we used SEP entries on Deontological Ethics (Larry and Moore, 2021), Consequentialism (Sinnott-Armstrong, 2021), and Virtue Ethics (Hursthouse and Pettigrove, 2018), yielding the following questions as criteria: (a) Are the solutions given to the ethical issues raised in the article derived from duty or a rule-based approach?, for deontologist approaches; (b) Are there references to outcomes, utility, or the primacy of consequential methods for establishing ethical principles? Does the argument involve calculating utility or benefits? for consequentialists; and (c) does the author suggest the approach of AI ethics from the standpoint of eudaimonia/flourishing? While tackling different ethical issues, are there references to virtues or virtuous agents? for virtue ethics.
We then proceeded to review the arguments in the publications and classify each into these categories. Some articles could have more than one ethical school perspective or not have any. After classifying the papers, we used the information collected and unified the databases to associate ethical theories with authors and publication dates.
Main ethical issues (RQ5): Science mapping and inductive approach
We used science mapping techniques to identify the main issues in our topic and answer RQ5. These techniques examine how research constituents are connected and identify intellectual interactions and structural connections (Donthu et al., 2021).
The co-word analysis belongs to the science mapping toolbox. It is a technique that examines the actual content of the publication. This method assumes that words that frequently appear together have a thematic relationship with one another (Donthu et al., 2021). So, we applied this technique to identify the main thematic clusters in our dataset using the co-occurrence of keywords feature of the VOSviewer software.
The software identified that from 404 keywords set, there were 303 connected and forming a network, along with five thematic clusters. Some keywords can have a very general connotation (e.g., artificial intelligence, ethics), so it could be challenging to assign them to a thematic cluster (Donthu et al., 2021). Hence, we only used the most important concepts in this map as a supplementing resource.
Subsequently, through the review of our bibliographic set, we found ethical issues that repeatedly appear. Whether in developing or deploying AI-enabled systems, those issues arise across different business functions and industries. We took into account the article by Hermann (2021), in which he identifies transparency, justice and fairness, non-maleficence, responsibility, and privacy, as the most mentioned principles in the scientific literature on AI ethics.
We also looked at other sources for the most relevant ethical issues and concerns about AI in a general context (not just business). The “gray” literature, as opposed to “white” literature, is non-peer-reviewed scientific information that is not available using commercial information sources (Yasin et al., 2020). One fundamental feature of gray literature material is that it is readily published and often posted as soon as written (Vaska et al., 2010). Hence, we refer to the gray literature to contrast our findings (see Table 3).
TABLE 3
Table 3. Main debates, principles, and concerns over AI ethics in gray literature.
We reviewed 14 documents and organized them into four levels according to their publishing instance. On the first level, we review international organizations; the second level governments; the third level academic institutions; and the fourth level private companies. This review identifies the same issues, central debates, and concerns as in scientific literature.
Finally, we use an inductive approach to identify the main debatable issues, concerns, and values. For example, transparency and confidentiality, along with concerns about privacy violations, surveillance, data minimization, and purpose limitation, formed one category. In the same way, the categories were grouped around bias, employment, and social media. Finally, a broader group gathered foundational issues that cut across all other categories and included discussions of AI safety, security, algorithm accountability, artificial moral agents, and the capabilities of the technology.
Thus, we propose five categories: (1) Foundational issues of AI in business; (2) Transparency, privacy, and trust; (3) Bias, preferences, and justice; (4) Employment and automation; (5) Social media, participation, and democracy. We proceeded then to classify each article within one of these.
Discussion and findings
Most influential journals (RQ1)
Our group comprises 95 articles published in 54 journals. The Journal of Business Ethics (JBE) is the most cited with 1,072 citations; it is also the most productive, with 22 publications. Only five articles were published in JBE between 2000 and 2018 and 17 through 2019 and 2021.
JBE is the only journal that addresses all three major schools of ethical thought. The influence of the journal and its broad reach is related to the journal's productivity; between 2000 and mid-2021. JBE published 148 volumes with at least four issues each, while the next most cited journal had only 84 volumes. Figure 1 shows the ten most influential journals by their citations, and the number of publications reflects their productivity.
FIGURE 1
Figure 1. Influence and productivity of academic journals.
Finding 1: JBE is the most influential and productive journal. It covers a broad range of AI ethics topics and is the only one to address the three major ethical schools.
The Journal of Service Management (JSM) followed with 926 citations. However, JSM productivity is far behind with only four articles. The influence of JSM can be explained by one outlier article by Wirtz et al. (2018), which is the most cited in our study, with 734 citations.
Next, the Journal of the Academy of Marketing Science (JAMS) is third with 540 citations; its sole publication by Davenport et al. (2020) is the second most-cited article. The fact that a journal with a single publication holds the third position is remarkable. The same case occurs with SSRN with 333 citations in fourth place and Comparative Labor Law and Policy Journal with 308 citations in fifth place, both with only one article. Finally, the rest of the journals obtained less than 300 citations.
Finding 2: The most influential journals are specialized in business ethics, management, and marketing.
The Journal of Business Research (165 citations) published four articles, all of them in 2021. Business Horizons (256 citations) and Business Ethics Quarterly (45 citations) had three publications each, one from 2004 and two from 2020. Most journals have only one publication (44 out of 54); however, in some cases, that was enough to position them in the top ten journals, which concentrates 3,908 out of 4,743 total citations.
Finding 3: There is an uneven distribution of citations; the top ten concentrates 80%; six journals with only one article are in that list.
Most influential articles (RQ2)
Our dataset contains 237 authors; the total number of citations is 4,743, with a mean of 50 citations per article. There is a high concentration in the top five papers, which received 2,199 citations, and only 24 papers have citations above the mean. Table 4 lists the ten most-cited articles with their authors, year of publication, and journal.
TABLE 4
Table 4. List of 10 most cited articles.
Wirtz et al. (2018) published the most influential article with 734 citations, focusing on the impact of service robots in the industry. The most-influential articles focus almost equally on foundational issues and AI's impact on business functions across different industries.
Marketing occupies the top slot of 22 articles and 1,875 citations, almost double that of human resources in second place (see Table 5). The most relevant topics in marketing are customer behavior and sales (Belanche et al., 2020; Davenport et al., 2020; Reshma and Sam Tharakan, 2021; Vlačić et al., 2021), the attention economy, and social media (Bhargava and Velasquez, 2020; Dossena et al., 2020), digital surveillance (Loi et al., 2020), and service robots and chatbots (Wirtz et al., 2018; Henkel et al., 2020; Odekerken-Schröder et al., 2020; Syvänen and Valentini, 2020; Borau et al., 2021; Söderlund and Oikarinen, 2021).
TABLE 5
Table 5. Business functions addressed in articles.
Why is marketing the most discussed topic? One reason may be that advertising was the first beneficiary of AI's capabilities. Google applied it to present personalized ads to its users (Zuboff, 2018). Furthermore, McKinsey and Co. considers marketing and sales the area with the most significant potential to benefit from AI, predicting that AI can create $1.4 trillion to $2.6 trillion worth of business value (Chui et al., 2018).
Human resources (HR) followed with 12 articles totaling 933 citations. The discussions on technological unemployment and automation (Sutton et al., 2018; Kim and Scheller-Wolf, 2019; Holford, 2020; Beerbaum and Otto, 2021), digital transformation, and the devaluation of work (Cherry, 2016; Rodriguez-Lluesma et al., 2020), new competencies and future skills (Moldenhauer and Londt, 2019; Leitner-Hanetseder et al., 2021) and algorithm-based HR decisions (Leicht-Deobald et al., 2019; Terblanche, 2020), are relevant to this topic.
Third was production with eight articles and 248 citations, and finance was fourth with seven articles and 135 citations. Here, the supply chain (Garay-Rondero et al., 2020), technology design and development (Neubert and Montañez, 2020; North-Samardzic, 2020; Ryan and Stahl, 2021), auditing (Munoko et al., 2020), accounting (Losbichler and Lehner, 2021), and taxes (Berger et al., 2020; LaMothe and Bobek, 2020), among other issues, are addressed.
Robotics and RPA have optimized many processes in finance and production with substantial effects on cost reduction, though it may have caused job losses and devaluation of human work. Despite existing dilemmas, the study of ethical issues in both seems to be a research area under development.
Finding 4: Marketing dominates among business functions, followed by human resources, production, and finance.
The foundational issues cut through many domains. These articles address AI's current and future capabilities (Kaplan and Haenlein, 2020), machines' autonomy to make decisions (Johnson, 2015), reliability and accountability of algorithms (Martin, 2019), and how to develop safe and trustworthy AI (Yampolskiy and Fox, 2013; Thiebes et al., 2020). Other issues of concern include employment and the devaluation of work, privacy violation, algorithmic bias, and the effects of social media on society.
An explosive increase in interest in the ethics of AI in business
Between 2000 and 2017, there were only 11 publications on the ethics of AI in Business (see Figure 2). An explosive increase in publications followed; 84 articles were published between 2018 and 2021. Twenty years ago, there was less research production, digital publications were less frequent, and open access was less extensive.
FIGURE 2
Figure 2. Number of publications between 2000 and mid-2021.
Most citations belong to papers published from 2018 onwards, coinciding with the increase in scientific publications. Thus, it is consistent with the increase in Google searches on the term “ethics of AI” (see Figure 3).
FIGURE 3
Figure 3. Interest over time in “Ethics of artificial intelligence.” Source: Google trends.
Although generally, the older an article, the greater the chances of being cited; in this case, the most cited articles were published in the last four years, as shown in Figure 4. There is one exception, “Beyond Misclassification: The Digital Transformation of Work,” with 308 citations by Cherry (2016). This article is the first to address one of the ethical issues in a factual and not merely conceptual way, referring to the impact of this technology on the labor market.
FIGURE 4
Figure 4. Citations by year of publication.
Cherry (2016) analyzes the transformation of work through different labor court cases in the on-demand economy. Crowdwork has promoted the proliferation of precarious work, which includes automatic management and workers' deskilling, offering a disturbing image of future work.
One possible reason for this article's influence is that it is the first to present evidence of the harm that AI could cause in labor. Before Cherry (2016), issues addressed were more hypothetical than factual. Concerns revolved around what might happen if the technology gained new capabilities. Subsequent publications deal with real issues and situations affecting people.
A change in conversation: From objects to subjects
Early publications focused not on AI but on moral issues related to technology's impact on companies. Those publications addressed tensions between proprietary and open-source software (Schmidt, 2004), the misuse of IT resources within the workplace (Chu et al., 2015), and whether computers can help make better ethical decisions (Mathieson, 2007).
The common denominator is an older conception of AI, resembling “good old-fashioned artificial intelligence” or GOFAI (Grim and Singer, 2020), developed using linear programming. Thus the resulting software was perceived as a tool used for specific purposes with clearly defined rules and limits.
Later publications opened the door to a new conception of AI as a subject. These propose a moral Turing test to establish whether corporations (Henriques, 2005) or machines have moral agency (Guarini, 2007) and at which level of intelligence it should be granted (Yampolskiy and Fox, 2013). Johnson (2015) wonders if it is possible that in the future, artificial agents will acquire the capacity for autonomous behavior with no human being responsible for them. As AI became widespread, ethical issues and questions appeared in the scientific literature. Should AI be regarded as natural persons, legal persons, animals, or objects? (Beerbaum and Otto, 2021).
After the period of stagnation between 1975 to 1995, known as the “AI winter” (Müller, 2021), the great availability of data, cheaper storage, and new machine learning techniques expanded the applications and capacity of AI. It became more affordable and higher performing entering new spheres. AI ceased to be exclusive to technicians, experts, and scholars and ventured into the market of consumer products and services (see Figure 5).
FIGURE 5
Figure 5. AI in consumer products and services timeline.
Finding 5: With the incursion of AI into consumer products and services comes an increased interest in the ethics of AI in business in 2018 and a boom in scientific publications.
AI devices were perceived as valuable tools that served people's purposes. Yet there are concerns about how firms handle our data and deal with privacy.
Situations occurred in which machines competed with humans; automation replaced workers and stoked fears that millions of jobs will be lost (Carter, 2018). In 2011, IBM's Watson defeated human champions on Jeopardy (Kaplan and Haenlein, 2020); in 2017, Google's AlphaGo defeated Chinese player Ke Jie in the game “Go.” A machine that learned the game by playing against itself thousands of times proved to be better than the world champion. Later in 2022, a Google engineer was fired after claiming that LaMDA, a company's chatbot, was sentient and even demanded legal representation for it (Johnson, 2022b).
Kurzweil (2005) claimed that AI would eventually surpass human intelligence, awakening concerns that it will render humans obsolete and useless and, in the worst-case scenario, destroy humanity (Du and Xie, 2021). For Yampolskiy and Fox (2013), “an intelligence that improves itself to levels so much beyond ours that we become not just an ‘inferior race' but destroyed as a side-effect of the entity's activities in pursuit of its goals.”
We believe the increase in publications could be because machines are now perceived as ethical subjects or agents. This technology is capable of mimicking humans (Vlačić et al., 2021), making decisions autonomously, and influencing people and their environment. Concerns arise that AI might pose a threat, and ethics become essential to the conversation.
Finding 6: With AI's increased capacity, a change in perception occurs, from AI as an object to AI as a subject or agent; Cherry's (2016) article marks a milestone between scientific publications with hypothetical perspectives and those that address real issues.
Most influential authors (RQ3)
Based on total citations, we constructed the list of the ten most influential authors (Table 6). In addition, we include the h-index to have a second element of comparison to measure the author's influence. This score allows us to measure authors' productivity and impact compared to their total citations; it is calculated using the author's number of publications with at least the same number of citations. Thus, an author with an h-index of 50 has published 50 articles that have been cited at least 50 times. Using the h-index, we can eliminate outlier publications that might present a distorted view of an author's impact
TABLE 6
Table 6. Ten most influential authors by their total citations.
Furthermore, some authors published most of their work and received most of their citations from previous publications, for instance, in business ethics or management. Therefore, using total citations will measure the author's influence in a broader sense and is not limited to the ethics of AI in business.
The list is dominated by two scholars from Babson College in the US. Davenport has almost twice the number of citations as his colleague Grewal. However, only 10 points separate them in their h-index. They co-authored the article “How artificial intelligence will change the future of marketing” (Davenport et al., 2020), which is the second most cited.
Grewal, with 75,942 citations, almost doubles those of the Haenlein. Yet, in this case, the difference between their h-index score is 60. The difference in the number of citations between the top two authors and the rest is noteworthy. From the third position, the differences between the number of citations are not so significant and gradually decrease. However, the h-index scores do not follow the same logic. For example, in the sixth position by its citations, Wirtz has an h-index of 75, the third highest.
Finding 7: Davenport from Babson College is the most influential author by its citations. The top ten could change using the h-index parameter; Flavian and Roper would substitute Haenlein and Kaplan.
Among the ten most-cited authors, half are marketing professors; two come from management, two from information technologies and computer science, and one from business administration. The predominance of marketing professors corresponds to the findings of RQ2, where we observed that marketing is the most studied domain.
Finding 8: Half of the most influential authors are marketing professors.
The most cited works of Davenport, Grewal, O.C. Ferrell, Chau, and Capelli were published before the rise of AI ethics, around 2000, related to management, marketing, and IT. Since then, the first three began the study of AI in business, although only O.C. Ferrell used a specific ethical perspective founded on the deontological and utilitarian schools.
Although they also have relevant works before 2000, Paterson and Jansen published their most influential works around 2010 in marketing and social media. Both continued to research AI in business. Paterson co-authored with Wirtz the most cited article in our dataset in 2018 about AI's foray into the service sector.
The most influential works of Wirtz, Kaplan, and Haenlein were published after 2010. After the arrival of machine learning and deep learning techniques. Their publications' topics are marketing, ethics, and foundational aspects of AI.
The most influential female author, Gaby Odekerken, from Maastricht University, occupies the 12th position with an h-index of 34 and 14,242 citations. Men dominate the field; only eleven women are among the 50 most influential authors.
A recent study shows that not the top, but the second and third-tier universities, contributed most to research advances (Fassin, 2022). Our findings bear this out. Only one institution from Shanghai top ten Academic Rankings of World Universities (ARWU) appears in our dataset. The University of Oxford, 7th in ARWU, has Calzada in position 55. Cappelli from the University of Pennsylvania, ranked 15th in ARWU, occupies the tenth position on our list.
Our most influential authors belong to lower-ranked universities, such as Auburn University (ARWU:501-600), National University of Singapore (ARWU:71), and Babson College, ESCP, University of Nottingham Ningbo, Qatar Computing Research Institute, which are not ranked.
Finding 9: The most influential authors are affiliated with second and third tier universities and research institutions.
The influence of the US and Europe
The US leads our group of countries; 57 of 237 authors belong to an American research institution, followed by the UK with 20, Switzerland with 13, and Australia and Germany with 12, respectively. Figure 6 presents a map showing which countries have published the most.
FIGURE 6
Figure 6. Authors by the country of their institution.
If we consider Europe as a single entity, it would be the most productive, with 126 authors, slightly more than double the US. The high productivity of American and European scholars can relate to the funding available for research and development (R&D). In 2020, it was USD 664 billion for the US (3.4% of its GDP) and 385 for the EU (2.2% of its GDP) (OECD., 2022). However, the US budget is 279 billion higher than the EU, which reflects that the availability of resources is necessary but not decisive; there are other factors.
The world's first legal framework for AI was presented in April 2021 by the European Commission: The Artificial Intelligence Act (AIA). This norm will have a de facto effect outside European borders. It is due to the so-called “Brussels effect,” a kind of unilateral regulatory globalization in which EU guidelines become the global market standard (Bradford, 2020).
The construction of legal frameworks closely relates to ethics since it must serve as its foundation. The EU has been more involved in regulating AI than the US and China. Both countries have opted for less regulation, assuming that too much can inhibit innovation and reduce competitiveness (Lee, 2018). This difference could drive or inhibit research in the field.
Asia occupies third place with 19 authors. India contributes with nine; China with six; and Pakistan, Qatar, Singapore, and Taiwan with one each. The small number of Chinese authors is remarkable for a country that in 2020 invested 563 billion in R&D, surpassing the EU. China also surpassed the US in venture capital investment in AI startups in 2017. The Chinese percentage was 48%, almost half of the world's total (Vincent, 2018).
There is a strong push from the Chinese government to encourage the development of AI. Their goal is to make their country the center of global innovation in AI by 2030 (Lee, 2018). For this, they have tried to take advantage of their large population, data wealth, and rapid scalability. The small number of Chinese authors could be because the ethical issues of AI have not raised enough interest due to the lack of political incentives. Also, bear in mind that we included only English publications.
Some events discourage research on the subject. In September 2021, the Chinese government published the country's first AI ethics guidelines (Shen, 2021). This “New Generation of Artificial Intelligence Code of Ethics” was not exempt from criticism. Angshuman Kaushik wrote: “It is quite mystifying to see a country as infamous as China globally for its AI ethics violations, come up with an Ethics Code for the world to sit up and take notice. Its violations list is endless, ranging from the use of Uighur-tracking facial recognition technology and the use of emotion detection software against them in its Xinjiang province to its flouting of human rights norms and draconian manner of application of the social credit system” (Montreal AI Ethics Institute., 2022).
The almost null participation of African and Latin American authors is remarkable. Only three countries are represented: South Africa, Mexico, and Chile. We believe that less-developed technology and lack of funding and policies encouraging research and development are among the possible causes.
Finding 10: The US and Europe lead in the publication of AI ethics in business articles. However, the productivity of scientific publications on this topic seems to depend not only on funding but the political agenda could also be a factor.
Major schools of thought for ethics of AI in business (RQ4)
We need ethical theories to better support decision-making and to provide well-founded justifications to act in a determined way. However, there are important incompatibilities among ethical theories. Each has a different approach, and decision processes will not always achieve an ideal; there will be trade-offs (Mathieson, 2007).
The classification of the articles into different ethical theories, or schools of thought, represents a turning point. Only 24 articles use a theoretical approach, and 71 papers do not advocate a specific ethical theory. In the same way, we observe that only six philosophers appear in the list of the 50 most influential authors (by their number of citations); this could be the cause of the few articles that use a specific ethical theory to support their arguments.
We found that publications use three major ethical schools: consequentialist, deontology, and virtue ethics, as shown in Table 7.
TABLE 7
Table 7. Number of papers and citations according to their ethical theory.
Finding 11: Most AI business ethics authors do not use an ethical theory approach; they lack a philosophical perspective.
Five articles have an eclectic approach. Leicht-Deobald et al. (2019) and Ferrell and Ferrell (2021) observe the differences between deontological and consequentialist perspectives and propose a combination to address AI problems in business.
Letheren et al. (2020) suggest that all three schools should be applied as a lens to decide where ethical dilemmas lie. Mathieson (2007) proposes designing an ethical decision support system using all of them. However, there are often conflicts they do not recognize. Seele et al. (2019) assert that depending on which school of thought is adopted, a given position could lead to contrary assessments.
Personalized pricing can provide an example. Seele et al. (2019) point out that this technology tends to be perceived as unfair, asymmetric, or even inhumane. For instance, Uber taxis charging exorbitant fares during terrorist attacks. It may be appropriate from a deontological perspective since it adheres to its established rules, which seek to attract drivers by increasing prices in places where demand rises. However, from a consequentialist standpoint, it would be questionable, and utterly reprehensible from the view of virtue ethics. Since increasing profit, taking advantage of a dangerous situation does not serve the common good or human flourishing.
Finding 12: The preferred ethical theory is consequentialist, followed by virtue ethics, deontology, and eclectic approaches.
Consequentialist approaches dominate our list with eight papers. It is also the most cited, with 793, almost four times as deontological and virtue ethics. This theory states that moral rectitude depends only on the consequences of an act. Consequentialist theories embody the basic intuition that what is best or right is whatever makes the world best in the future (Sinnott-Armstrong, 2021). In this group, we include the utilitarian and behavioral approaches.
One possible reason for this theory's dominance is that most organizations focus on calculating utility or profits. In business and neoclassical economics, the result is usually privileged over the means. Beerbaum and Otto (2021) uncovers this issue. Using the Uber-Waymo trial as an example, he exposed the culture of agile software development, which prioritizes software release over testing and verification, and encourages shortcuts to diminish costs. Most companies prioritize maximizing quick profits, which is an old issue for business ethics.
The virtue ethics approach is just one article behind consequentialism with seven articles; however, it is third by number of citations. Etymologically, “virtue” comes from the Latin word “virtus,” which stands for “what is best” or “excellence” in human beings. “Virtue,” then, means “what is best in human beings” or “human excellence” (Sison, 2015). Virtue as a framework for ethics differs from rights, duties, and calculations of consequences, and has its focus on good character (Neubert and Montañez, 2020).
Authors who use the virtue ethics approach highlight AI's importance in producing improvements at a societal level and not only to increase profits. Let us examine the effects of addictive algorithms in social media and marketing. Virtue ethics might propose to use practical wisdom such that each person in the design process decides on the extent of user engagement (Thorpe and Roper, 2019). However, this could be problematic as leaving sensitive decisions to people's discretion could lead to inconsistencies or abuse, endangering human flourishing.
Only four articles use an exclusively deontological perspective; however, it is the second most cited. Deontology is a normative duty-based theory that guides and assesses our choices of what we ought to do, in contrast to those that assess what kind of person we are and should be (Alexander and Moore, 2021), such as virtue ethics. Deontologists focus on the action itself and oppose consequentialists who measure the morality of an action based on its consequences. In other words, ethical behavior is based on a predetermined set of norms or rules that must always be followed.
Still, most high-level interventions in the AI ethics discussion are principle-based, such as the guidelines produced by the European High-Level Expert Group on AI (Stahl et al., 2021), IBM's Principles for Trust and Transparency (IBM., 2018), or the Asilomar AI Principles (Future of Life Institute., 2017).
Let us analyze the evolution of ethical theories in the literature. Of all 24 articles in this group, 21 were published between 2019 and 2021, and only three before (see Table 8).
TABLE 8
Table 8. Articles with an ethical theory perspective.
The first three articles were published between 2004 and 2015. Schmidt (2004) alludes to the natural law theory approach associated with virtue ethics. He examines the conflicts that arise over intellectual property and software licenses. Mathieson (2007) studies the use of a support system for ethical decision-making. And Chu et al. (2015) use behavioral theory, related to the consequentialist approach, to explain the reasons for information systems resources misuse in the workplace.
The topics covered in these first three articles bear little relation to the current conception of AI perceived as a subject. At this stage, most machines are objects with no autonomy and limited capacity. The ethical responsibility for ethical issues rests solely with the users of the technology, just as it would with a knife which can be used both as a tool or a weapon.
As of 2019, AI-driven machines capable of autonomous learning with predicting and decision-making capacity have become widespread. As most papers were published in the last three years, it is hard to establish any trend.
An assessment of the benefits and harms caused by AI marks later publications. Tradeoffs will have to be made, evidencing the need for ethical judgment. Moral questions appeared; are the algorithms unbiased, impartial, and efficient? (Leicht-Deobald et al., 2019); who will be responsible for the ethical consequences of decisions made by algorithms? (Martin, 2019).
Amazon discovered that its AI hiring algorithm discriminated against women and had to drop its use. Even when the sex of applicants was not being used as a criterion, attributes associated with women candidates caused them to be ruled out (Cappelli et al., 2019). The reason was that the training datasets were based on previous applicants, predominantly men (Davenport et al., 2020). Martin et al. (2019) propose that if a design team creates an impenetrable AI decision, then the firm should be responsible for those decisions.
In later publications, not just the user of the technology could be held accountable but also organizations, firms, and developers (Belanche et al., 2020) who sometimes try to hide behind the opacity of algorithms (Martin, 2019; Carroll and Olegario, 2020).
Finding 13: Initially, accountability was attributed exclusively to the user; later, it was extended to developers and firms.
Main ethical issues of AI in business (RQ5)
Multiple ethical issues appeared as AI acquired greater power and complexity. These issues cover a broad spectrum, from privacy violations to world domination by sentient machines. However, we will not focus on the dangers of AI acquiring consciousness and will of its own since we consider this more fictional than factual.
This section describes our findings regarding the main issues in the business AI ethics literature. It is organized according to the five categories we built from analyzing the problems, concerns, and values we identified around the main debates. Figure 7 shows this classification exercise.
FIGURE 7
Figure 7. Main ethical issues of AI in business.
Finding 14: Five categories can group the main ethical issues of AI in business: 1) foundational issues of AI in business; 2) transparency, privacy, and trust; 3) bias, preferences, and justice; 4) employment and automation; and 5) social media, participation, and democracy.
Foundational issues of AI
These articles focus on the comprehensive characteristics of the technology, its capacities, possibilities, and technical aspects. This category intersects with the other four identified. We find references to the three levels of intelligence that AI can possess. The first two are Artificial Narrow Intelligence (ANI) and Artificial General Intelligence (AGI); both can equal or outperform human performance. Though ANI is focused on a specific domain and AGI can extend into new domains (Davenport et al., 2020).
There is currently no functional AGI. However, once an AI with that ability is created (if at all), it could improve its ability using machine learning. At some point, it could surpass human levels and increase its intelligence exponentially without stopping. This intelligence explosion is known as singularity and would result in Artificial Super Intelligence (ASI). ASI is a hypothetical group of self-aware systems capable of scientific creativity, social skills, and general wisdom (Kaplan and Haenlein, 2020).
A significant challenge to the claim that only human beings can be responsible comes from those for whom agents can learn as they operate (Johnson, 2015). However, all existing AIs are below human levels of intelligence, and we generally do not ascribe moral agency to infrahuman agents such as non-human animals or even children (Yampolskiy and Fox, 2013). Therefore, humans should be held accountable for AI's negative impacts or harms.
Some authors propose principles, guidelines, and frameworks to avoid risks and mitigate possible damages (Cole and Banerjee, 2013; Yampolskiy and Fox, 2013; Clarke, 2019; Kriebitz and Lütge, 2020; Neubert and Montañez, 2020; Ferrell and Ferrell, 2021). Others explore specific problems and propose solutions, like Fischer et al. (2021), who suggests using this technology to combat climate change. Thus, the discussion about the responsible development and deployment of AI appears.
Another foundational debate is that of ethical decision-making with the help of AI. Unethical behavior in business can harm companies and make their employees personally liable (Mathieson, 2007), with economic, legal, and social consequences. AI-enabled decision support systems have sought to deliver timely and reliable information to decision-makers. However, these systems' biases have caused discrimination and unfairness. Additionally, the perception that these systems are more efficient and free of bias has led to excessive confidence and, in some cases, to delegate full responsibility to them.
Transparency, privacy, and trust
AI needs large amounts of data to perform tasks and expand capabilities. However, collecting this data could conflict with the right to privacy (Kriebitz and Lütge, 2020), as it is often obtained without user consent. Furthermore, AI-enabled systems can perform sophisticated tasks like biometric and facial recognition or natural language processing, enabling unprecedented surveillance techniques.
Privacy and transparency are recurrent issues in business functions, such as marketing and sales (Thorpe and Roper, 2019; Hermann, 2021). Companies like Google, Amazon, and Facebook use people's personal information for targeted advertising (Kaplan and Haenlein, 2020). The tension between privacy and transparency presents a dilemma for users of digital platforms. When browsing the Internet or using a smartphone, we generate information about our habits and preferences, which are then stored and later (or immediately) used to predict or influence our behavior (Guha et al., 2021).
However, they are not the only domains where privacy is relevant. Algorithmic HR decision-making requires employee monitoring, often without their knowledge (Leicht-Deobald et al., 2019). Furthermore, companies that use algorithmic pricing, such as insurers, ridesharing, or airlines, require access to personal data (Seele et al., 2019), which could lead to discrimination. Another example is the application of AI in the interrogation tools of judicial systems, such as facial sentiment analysis, where the legal principle of nemo tenetur se ipsum accusare, no one can be forced to accuse himself, would be violated (Kriebitz and Lütge, 2020).
Additionally, AI-powered devices such as drones, doorbells, or surveillance cameras in shops store information in the cloud. Customers become concerned if companies have access to data they could use or sell. Neighbors might protest if cameras record their front yard activities without permission. Also, the data could be subpoenaed by law enforcement agencies or obtained illegally by hackers (Davenport et al., 2020).
Data breaches and theft of sensitive information are troubling, but the possibility of being used by an autocratic government against its people represents a more significant concern. The Chinese government uses facial recognition technology to monitor its citizens within its social credit system (Calzada and Almirall, 2020), which has been used to oppress Uyghur Muslims in Xinjiang province (Kriebitz and Lütge, 2020).
Furthermore, AI-driven devices can classify people based on age, gender, race, or sexual orientation (North-Samardzic, 2020). Researchers from Cambridge University and Microsoft were able to predict sexual orientation with only a few Facebook likes, with an 88% accuracy in men and 75% in women (Rosen, 2013). The ease of obtaining these predictions could raise concerns when considering that there are still eleven countries that criminalize LGBT people and can impose the death penalty
Bias, preferences, and justice
The criteria used by machines for decision-making are not always clear and constitute a black box (Kaplan and Haenlein, 2020). On many occasions, this information is protected by business secrecy; at other times, it is impossible or too expensive to isolate which exact factors these algorithms consider (Davenport et al., 2020).
Google's AI language translation algorithm produced gender-biased results in the Turkish language. In translating a gender-neutral pronoun, the algorithm decided that men would be described as entrepreneurial while women were described as lazy (Neubert and Montañez, 2020).
Another emblematic case is Tay, Microsoft's AI-enabled chatbot (see Figure 5), which learned by screening Twitter feeds and took less than 24 hours to publish politically incorrect messages full of misogyny, racism, pro-Nazi, and anti-Semitic (Kriebitz and Lütge, 2020). Indeed, the machine itself was not racist but learned racism from our previous behavior. This gives us a disturbing picture of how other AI-enabled systems might operate now or in the future.
AI-system biases have the veneer of objectivity, yet the algorithm created by machine learning can be just as biased and unjust as one written by humans (Martin, 2019). Worse, given their rapid proliferation in businesses and organizations, AI systems can reproduce and amplify these biases exponentially and cause serious harm.
In 2016, a ProPublica investigation found that software used in some US courts to assess the potential risk of recidivism discriminated against racial minorities. This program called Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) returned scores in which blacks were almost twice as likely to be labeled as higher risk but not actually re-offend (Angwin et al., 2016). Decisions made under the influence of this algorithm can have severe repercussions. Not only is it a matter of getting out on parole, but a criminal record can make it challenging to get a job in the future.
The damage caused by algorithm discrimination may not be deliberate. However, this does not mean that the company and the developers of the biased technology should not be held accountable. Acknowledging bias has led to calls for algorithms to be “explainable” or “interpretable” (Martin et al., 2019).
Employment and automation
The deployment of AI in all business areas came with a paradigm shift in the labor market. It is the second most frequent topic in our study and emerges as one of the biggest concerns, with 31 articles addressing it. Three main topics appear, the proliferation of precarious jobs in the On-Demand Economy (the gig economy), the replacement of humans in work, and the loss of jobs due to automation.
Let us review the case of platforms such as Uber, Lyft, Crowdflower, TaskRabbit, and other On-Demand Economy companies that built their business model by putting people in contact for micro-tasks. This model is also known as “crowdwork,” and contrary to what is happening with robots and RPA, it has fueled the proliferation of new jobs. However, this trend is associated with transient and non-linear careers and has devalued work, promoting wages below the legal minimum and becoming an excuse to avoid paying social security benefits (Cherry, 2016; Rodriguez-Lluesma et al., 2020).
Furthermore, RPA has become a significant trend (Beerbaum and Otto, 2021) due to its ability to operate uninterruptedly, with high scalability and low operating costs. It is the software equivalent in offices to mechanical robots in factories and has rapidly replaced humans in different fields. This phenomenon accelerated during the COVID-19 pandemic due to confinement measures. A consequence is that many jobs have been lost, albeit in subtle ways. Although most robots are not physically replacing workers by taking over their desks, many of these job losses are positions that were handled by individuals or those of companies that went bankrupt. For instance, the explosive growth of streaming video platforms like Netflix caused companies like Blockbuster to close; many small bookstores and retailers closed, and their jobs were taken over by Amazon's 200,000 robots (Roose, 2021; Koetsier, 2022).
Notwithstanding, automation sometimes does not constitute an innovation or an improvement for efficiency; it simply mimics what a human does, for example, in self-checkout kiosks. This phenomenon, referred to as “so-so automation” (Acemoglu et al., 2022), does not lead to value and wealth creation but only to job losses and the devaluation of work.
Nevertheless, some authors believe that fears of AI leading to mass unemployment are unlikely. They argue that new industries will emerge, creating more jobs than lost (Autor, 2015; Kaplan and Haenlein, 2020; Malone et al., 2020; Rodriguez-Lluesma et al., 2020; Beerbaum and Otto, 2021). Yet, nobody knows if newly created jobs will be enough or when it will happen.
We observe that the impact of AI on the labor market has ambivalent implications. These changes represent a challenge that, if not addressed correctly, could accentuate income inequality between individuals and social classes. Part of this discussion revolves around ensuring that the new wealth is distributed fairly and equitably, including those who will be left jobless. While some authors propose that machines and humans should collaborate instead of competing, we agree that AI would be more effective if focused on increasing the capabilities of humans instead of replacing them (Sutton et al., 2018; Davenport et al., 2020; Guha et al., 2021; Brynjolfsson, 2022).
Social media, participation, and democracy
For some, AI-enabled social media is a support tool for business functions, for example, in sales (Reshma and Sam Tharakan, 2021), marketing (Dossena et al., 2020), customer service (Murtarelli et al., 2021), management (Delanoy, 2020), and public relations (Rantanen et al., 2020). However, we will focus on the societal repercussions of social media.
Unlike most businesses where the product is the source of income, on social media platforms, the users' attention is sold as a product to advertising companies (Bhargava and Velasquez, 2020). In a model called the attention economy, the services of, for example, Google, TikTok, or Facebook are designed to keep users engaged as long as possible. The longer users stay, the more the companies earn by offering relevant, user-targeted ads based on their habits, mood, or purchase intentions.
According to Bhargava and Velasquez (2020), these companies use “adaptive algorithms” to personalize the content and ads appearing in an endless user feed, causing an addiction already recognized as a public health problem in some countries. Kaplan and Haenlein (2020) observe that excessive use of social platforms may be associated with increased anxiety and depression. They observe other problems of social media, such as the dissemination of fake news, cyberbullying, and harassment.
Finally, some authors remark that social media platforms are used by hate activists to propagate messages that produce strong emotions against victims. Rauf (2021) considers getting caught in the debate easily, even for critics of such hate. It leads to a vicious cycle that provides data for social media companies, garners more publicity for the topic, and attracts others to it. In his article, Rauf depicts social media as an enabler of terror before, during, and after the 2019 Christchurch terrorist attacks in New Zealand.
Finding 15: Initial papers addressed foundational issues only. After 2016, issues around privacy, bias, employment, and social media's effect on society appeared.
Conclusion
This work presents an overview of the most influential journals, articles, and authors in literature. It allows us to understand the current state of publications on AI ethics in the field of business broadly and comprehensively; our first and second motivations are thus satisfied. However, the small number of articles that frame arguments from some of the main ethical schools of thought has made it challenging to connect the main issues with the main ethical theories.
In this work, a map describes how the conceptual space is distributed in terms of a journal, article, or author influence and the prominence of an ethical issue or school. A trend describes how the distribution of that conceptual space varies over time. Our findings allowed us to draw maps and trends formulated through the following propositions.
Proposition 1 (map): JBE is the most influential (by number of citations), productive (by number of articles), and comprehensive (by breadth of topics and schools) journal; although other journals published the top three most cited articles.
Proposition 2 (trend): JBE is the most consistent journal publishing articles from 2000 to 2021.
Proposition 3 (map): The most influential articles (by number of citations) are distributed almost equally among business functions and foundational issues. Among the business functions, the top slot belongs to marketing, followed by human resources, and production and finance afterward. The foundational issues discuss AI's current and future capabilities, accountability, and trustworthiness.
Proposition 4 (trend): Hardly any articles were published until 2018, when there was an explosion. Possible causes are a) the beginning of the widespread use of consumer AI (enabled by greater availability of data, cheaper data storage, and machine learning techniques) and b) the shift in perception from AI as object or tool to AI as subject or agent that can compete or even supplant humans. What before was a mere hypothesis now becomes an imminent possibility.
Proposition 5 (map): Davenport and Grewal from Babson College in the US are the most influential authors on the ethics of AI in business. The ten most influential authors are male, and half are marketing professors. We observe a dominance of authors affiliated with US and EU institutions, and China's absence is notable given its government's manifest interest in taking a leading role in AI development.
Proposition 6 (trend): Most influential authors had a solid research record even before the AI ethics in business boom in 2018. Their research on AI ethics is an extension of their previous works.
Proposition 7 (map): Most authors (71) do not use an ethical theory to support their positions on the ethics of AI in business. However, among those who do use a school of thought, consequentialists (8) dominate, closely followed by virtue ethics (7) and deontology (4), and there are five that use a combination of them. The small number of articles with an ethical theory approach makes the connection between AI ethics and other, more comprehensive ethical domains more difficult.
Proposition 8 (trend): Almost all articles using an ethical theory were published after 2019; only three are previous. The first articles placed the responsibility for the outputs of the technology exclusively on the user. After the adoption of consumer AI and the shift to understanding AI as a subject or agent, articles deal with AI, and the firms and developers are added as accountable instances.
Proposition 9 (map): Foundational issues are the dominant category; they cut across different domains and are usually combined with other topics. Next is employment and automation, perhaps where the harms and benefits caused by AI are most immediate. However, privacy violations, algorithmic bias, and social media's effects follow closely, where harms are probably perceived as less severe.
Proposition 10 (trend): Work of Cherry (2016) marks a turning point between the hypothetical and the factual approaches in articles. And although the distribution of foundational issues papers covers the entire range of years, all works published before 2016 were within its domain. Subsequent works deal with issues such as privacy, bias, employment, and social media's effects on social participation.
AI ethics in business is a growing research field. We propose a future research agenda to deepen our findings and verify some of our hypotheses.
• First, we think further research is needed to verify if the results obtained in this current study apply to domains of AI ethics other than business, for example, political science, computer science, or medicine.
• Furthermore, we believe further studies are needed to measure the impact of the political agenda on the productivity of scientific articles in Europe, the US, and China. In the same way, researchers could verify the hypothetical reasons we offer to explain the 2018 AI ethics in business publications boom.
• This work found that few articles explored AI ethics from a philosophical perspective; this represents an opportunity, particularly in production and finance, which are currently under-researched areas. Our findings suggest that authors with more profound philosophical training tend to use ethical theories as a foundation in their articles; further research is needed to verify this hypothesis.
• The small number of articles using an ethical school of thought in their arguments made it hard to establish connections between schools and specific issues. Future research is needed to close this gap. Additionally, a contrast with the findings of this work can be established from the study of the most influential issues and ethical schools in Chinese publications.
The study of the ethics of AI could contribute to developing technology at the service of humans and aspire to create value, provide well-being for society, and promote the supreme good and final end of human life: happiness (Sison, 2015).
Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
Author contributions
MD analyzed the dataset and classified articles according to the ethical schools of thought and main issues, prepared the graphs and tables of the study, and carried out the analysis of the results and the identification of findings and final propositions. UI designed and executed the search strategy, the dataset analysis, and classified articles according to the ethical schools of thought and the main issues and participated in the drafting of the document. Both the authors contributed to the article and approved the submitted version.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Footnotes
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Zhang, D., Maslej, N., Brynjolfsson, E., Etchemendy, J., Lyons, T., Manyika, J., et al (2022). The AI Index 2022 Annual Report. Available online at: https://aiindex.stanford.edu/wp-content/uploads/2022/03/2022-AI-Index-Report_Master.pdf (accessed January 28, 2022). Google Scholar
| 2022-12-19T00:00:00 |
2022/12/19
|
https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2022.1042661/full
|
[
{
"date": "2022/12/01",
"position": 67,
"query": "AI replacing workers"
}
] |
Artificial intelligence
|
Artificial intelligence
|
https://www.linkedin.com
|
[] |
... AI tools often complete jobs quickly and with relatively few errors. This ... And it could change where and how students learn, perhaps even replacing some ...
|
Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision How does AI work? As the hype around AI has accelerated, vendors have been scrambling to promote how their products and services use AI. Often what they refer to as AI is simply one component of AI, such as machine learning. AI requires a foundation of specialized hardware and software for writing and training machine learning algorithms. No one programming language is synonymous with AI, but a few, including Python, R and Java, are popular. In general, AI systems work by ingesting large amounts of labeled training data, analyzing the data for correlations and patterns, and using these patterns to make predictions about future states. In this way, a chatbot that is fed examples of text chats can learn to produce lifelike exchanges with people, or an image recognition tool can learn to identify and describe objects in images by reviewing millions of examples. AI programming focuses on three cognitive skills: learning, reasoning and self-correction. Learning processes. This aspect of AI programming focuses on acquiring data and creating rules for how to turn the data into actionable information. The rules, which are called algorithms, provide computing devices with step-by-step instructions for how to complete a specific task. THIS ARTICLE IS PART OF A guide to artificial intelligence in the enterprise Which also includes: 4 main types of artificial intelligence: Explained 7 key benefits of AI for business 10 steps to achieve AI implementation in your business DOWNLOAD 1 Download this entire guide for FREE now! Reasoning processes. This aspect of AI programming focuses on choosing the right algorithm to reach a desired outcome. Self-correction processes. This aspect of AI programming is designed to continually fine-tune algorithms and ensure they provide the most accurate results possible. Why is artificial intelligence important? AI is important because it can give enterprises insights into their operations that they may not have been aware of previously and because, in some cases, AI can perform tasks better than humans. Particularly when it comes to repetitive, detail-oriented tasks like analyzing large numbers of legal documents to ensure relevant fields are filled in properly, AI tools often complete jobs quickly and with relatively few errors. This has helped fuel an explosion in efficiency and opened the door to entirely new business opportunities for some larger enterprises. Prior to the current wave of AI, it would have been hard to imagine using computer software to connect riders to taxis, but today Uber has become one of the largest companies in the world by doing just that. It utilizes sophisticated machine learning algorithms to predict when people are likely to need rides in certain areas, which helps proactively get drivers on the road before they're needed. As another example, Google has become one of the largest players for a range of online services by using machine learning to understand how people use their services and then improving them. In 2017, the company's CEO, Sundar Pichai, pronounced that Google would operate as an "AI first" company. Today's largest and most successful enterprises have used AI to improve their operations and gain advantage on their competitors. What are the advantages and disadvantages of artificial intelligence? Artificial neural networks and deep learning artificial intelligence technologies are quickly evolving, primarily because AI processes large amounts of data much faster and makes predictions more accurately than humanly possible. While the huge volume of data being created on a daily basis would bury a human researcher, AI applications that use machine learning can take that data and quickly turn it into actionable information. As of this writing, the primary disadvantage of using AI is that it is expensive to process the large amounts of data that AI programming requires. Advantages Good at detail-oriented jobs; Reduced time for data-heavy tasks; Delivers consistent results; and AI-powered virtual agents are always available. Disadvantages Expensive; Requires deep technical expertise; Limited supply of qualified workers to build AI tools; Only knows what it's been shown; and Lack of ability to generalize from one task to another. Strong AI vs. weak AI AI can be categorized as either weak or strong. Weak AI, also known as narrow AI, is an AI system that is designed and trained to complete a specific task. Industrial robots and virtual personal assistants, such as Apple's Siri, use weak AI. Strong AI, also known as artificial general intelligence (AGI), describes programming that can replicate the cognitive abilities of the human brain. When presented with an unfamiliar task, a strong AI system can use fuzzy logic to apply knowledge from one domain to another and find a solution autonomously. In theory, a strong AI program should be able to pass both a Turing Test and the Chinese room test. What are the 4 types of artificial intelligence? Arend Hintze, an assistant professor of integrative biology and computer science and engineering at Michigan State University, explained in a 2016 article that AI can be categorized into four types, beginning with the task-specific intelligent systems in wide use today and progressing to sentient systems, which do not yet exist. The categories are as follows: Type 1: Reactive machines. These AI systems have no memory and are task specific. An example is Deep Blue, the IBM chess program that beat Garry Kasparov in the 1990s. Deep Blue can identify pieces on the chessboard and make predictions, but because it has no memory, it cannot use past experiences to inform future ones. Type 2: Limited memory. These AI systems have memory, so they can use past experiences to inform future decisions. Some of the decision-making functions in self-driving cars are designed this way. Type 3: Theory of mind. Theory of mind is a psychology term. When applied to AI, it means that the system would have the social intelligence to understand emotions. This type of AI will be able to infer human intentions and predict behavior, a necessary skill for AI systems to become integral members of human teams. Type 4: Self-awareness. In this category, AI systems have a sense of self, which gives them consciousness. Machines with self-awareness understand their own current state. This type of AI does not yet exist. What are examples of AI technology and how is it used today? AI is incorporated into a variety of different types of technology. Here are six examples: Automation. When paired with AI technologies, automation tools can expand the volume and types of tasks performed. An example is robotic process automation (RPA), a type of software that automates repetitive, rules-based data processing tasks traditionally done by humans. When combined with machine learning and emerging AI tools, RPA can automate bigger portions of enterprise jobs, enabling RPA's tactical bots to pass along intelligence from AI and respond to process changes. Machine learning. This is the science of getting a computer to act without programming. Deep learning is a subset of machine learning that, in very simple terms, can be thought of as the automation of predictive analytics. There are three types of machine learning algorithms: Supervised learning. Data sets are labeled so that patterns can be detected and used to label new data sets. Unsupervised learning. Data sets aren't labeled and are sorted according to similarities or differences. Reinforcement learning. Data sets aren't labeled but, after performing an action or several actions, the AI system is given feedback. Machine vision. This technology gives a machine the ability to see. Machine vision captures and analyzes visual information using a camera, analog-to-digital conversion and digital signal processing. It is often compared to human eyesight, but machine vision isn't bound by biology and can be programmed to see through walls, for example. It is used in a range of applications from signature identification to medical image analysis. Computer vision, which is focused on machine-based image processing, is often conflated with machine vision. Natural language processing (NLP). This is the processing of human language by a computer program. One of the older and best-known examples of NLP is spam detection, which looks at the subject line and text of an email and decides if it's junk. Current approaches to NLP are based on machine learning. NLP tasks include text translation, sentiment analysis and speech recognition. Robotics. This field of engineering focuses on the design and manufacturing of robots. Robots are often used to perform tasks that are difficult for humans to perform or perform consistently. For example, robots are used in assembly lines for car production or by NASA to move large objects in space. Researchers are also using machine learning to build robots that can interact in social settings. Self-driving cars. Autonomous vehicles use a combination of computer vision, image recognition and deep learning to build automated skill at piloting a vehicle while staying in a given lane and avoiding unexpected obstructions, such as pedestrians. AI is not just one technology. What are the applications of AI? Artificial intelligence has made its way into a wide variety of markets. Here are nine examples. AI in healthcare. The biggest bets are on improving patient outcomes and reducing costs. Companies are applying machine learning to make better and faster diagnoses than humans. One of the best-known healthcare technologies is IBM Watson. It understands natural language and can respond to questions asked of it. The system mines patient data and other available data sources to form a hypothesis, which it then presents with a confidence scoring schema. Other AI applications include using online virtual health assistants and chatbots to help patients and healthcare customers find medical information, schedule appointments, understand the billing process and complete other administrative processes. An array of AI technologies is also being used to predict, fight and understand pandemics such as COVID-19. AI in business. Machine learning algorithms are being integrated into analytics and customer relationship management (CRM) platforms to uncover information on how to better serve customers. Chatbots have been incorporated into websites to provide immediate service to customers. Automation of job positions has also become a talking point among academics and IT analysts. AI in education. AI can automate grading, giving educators more time. It can assess students and adapt to their needs, helping them work at their own pace. AI tutors can provide additional support to students, ensuring they stay on track. And it could change where and how students learn, perhaps even replacing some teachers. AI in finance. AI in personal finance applications, such as Intuit Mint or TurboTax, is disrupting financial institutions. Applications such as these collect personal data and provide financial advice. Other programs, such as IBM Watson, have been applied to the process of buying a home. Today, artificial intelligence software performs much of the trading on Wall Street. AI in law. The discovery process -- sifting through documents -- in law is often overwhelming for humans. Using AI to help automate the legal industry's labor-intensive processes is saving time and improving client service. Law firms are using machine learning to describe data and predict outcomes, computer vision to classify and extract information from documents and natural language processing to interpret requests for information. AI in manufacturing. Manufacturing has been at the forefront of incorporating robots into the workflow. For example, the industrial robots that were at one time programmed to perform single tasks and separated from human workers, increasingly function as cobots: Smaller, multitasking robots that collaborate with humans and take on responsibility for more parts of the job in warehouses, factory floors and other workspaces. AI in banking. Banks are successfully employing chatbots to make their customers aware of services and offerings and to handle transactions that don't require human intervention. AI virtual assistants are being used to improve and cut the costs of compliance with banking regulations. Banking organizations are also using AI to improve their decision-making for loans, and to set credit limits and identify investment opportunities. AI in transportation. In addition to AI's fundamental role in operating autonomous vehicles, AI technologies are used in transportation to manage traffic, predict flight delays, and make ocean shipping safer and more efficient. Security. AI and machine learning are at the top of the buzzword list security vendors use today to differentiate their offerings. Those terms also represent truly viable technologies. Organizations use machine learning in security information and event management (SIEM) software and related areas to detect anomalies and identify suspicious activities that indicate threats. By analyzing data and using logic to identify similarities to known malicious code, AI can provide alerts to new and emerging attacks much sooner than human employees and previous technology iterations. The maturing technology is playing a big role in helping organizations fight off cyber attacks. Augmented intelligence vs. artificial intelligence Some industry experts believe the term artificial intelligence is too closely linked to popular culture, and this has caused the general public to have improbable expectations about how AI will change the workplace and life in general. Augmented intelligence. Some researchers and marketers hope the label augmented intelligence, which has a more neutral connotation, will help people understand that most implementations of AI will be weak and simply improve products and services. Examples include automatically surfacing important information in business intelligence reports or highlighting important information in legal filings. Artificial intelligence. True AI, or artificial general intelligence, is closely associated with the concept of the technological singularity -- a future ruled by an artificial superintelligence that far surpasses the human brain's ability to understand it or how it is shaping our reality. This remains within the realm of science fiction, though some developers are working on the problem. Many believe that technologies such as quantum computing could play an important role in making AGI a reality and that we should reserve the use of the term AI for this kind of general intelligence. Ethical use of artificial intelligence While AI tools present a range of new functionality for businesses, the use of artificial intelligence also raises ethical questions because, for better or worse, an AI system will reinforce what it has already learned. This can be problematic because machine learning algorithms, which underpin many of the most advanced AI tools, are only as smart as the data they are given in training. Because a human being selects what data is used to train an AI program, the potential for machine learning bias is inherent and must be monitored closely. Anyone looking to use machine learning as part of real-world, in-production systems needs to factor ethics into their AI training processes and strive to avoid bias. This is especially true when using AI algorithms that are inherently unexplainable in deep learning and generative adversarial network (GAN) applications. Explainability is a potential stumbling block to using AI in industries that operate under strict regulatory compliance requirements. For example, financial institutions in the United States operate under regulations that require them to explain their credit-issuing decisions. When a decision to refuse credit is made by AI programming, however, it can be difficult to explain how the decision was arrived at because the AI tools used to make such decisions operate by teasing out subtle correlations between thousands of variables. When the decision-making process cannot be explained, the program may be referred to as black box AI. These components make up responsible AI use. Despite potential risks, there are currently few regulations governing the use of AI tools, and where laws do exist, they typically pertain to AI indirectly. For example, as previously mentioned, United States Fair Lending regulations require financial institutions to explain credit decisions to potential customers. This limits the extent to which lenders can use deep learning algorithms, which by their nature are opaque and lack explainability. The European Union's General Data Protection Regulation (GDPR) puts strict limits on how enterprises can use consumer data, which impedes the training and functionality of many consumer-facing AI applications. In October 2016, the National Science and Technology Council issued a report examining the potential role governmental regulation might play in AI development, but it did not recommend specific legislation be considered. Crafting laws to regulate AI will not be easy, in part because AI comprises a variety of technologies that companies use for different ends, and partly because regulations can come at the cost of AI progress and development. The rapid evolution of AI technologies is another obstacle to forming meaningful regulation of AI. Technology breakthroughs and novel applications can make existing laws instantly obsolete. For example, existing laws regulating the privacy of conversations and recorded conversations do not cover the challenge posed by voice assistants like Amazon's Alexa and Apple's Siri that gather but do not distribute conversation -- except to the companies' technology teams which use it to improve machine learning algorithms. And, of course, the laws that governments do manage to craft to regulate AI don't stop criminals from using the technology with malicious intent. Cognitive computing and AI The terms AI and cognitive computing are sometimes used interchangeably, but, generally speaking, the label AI is used in reference to machines that replace human intelligence by simulating how we sense, learn, process and react to information in the environment. The label cognitive computing is used in reference to products and services that mimic and augment human thought processes. What is the history of AI? The concept of inanimate objects endowed with intelligence has been around since ancient times. The Greek god Hephaestus was depicted in myths as forging robot-like servants out of gold. Engineers in ancient Egypt built statues of gods animated by priests. Throughout the centuries, thinkers from Aristotle to the 13th century Spanish theologian Ramon Llull to René Descartes and Thomas Bayes used the tools and logic of their times to describe human thought processes as symbols, laying the foundation for AI concepts such as general knowledge representation..
| 2022-12-01T00:00:00 |
https://www.linkedin.com/pulse/artificial-intelligence-sejal-baweja
|
[
{
"date": "2022/12/01",
"position": 71,
"query": "AI replacing workers"
}
] |
|
A survey of AI ethics in business literature: Maps and ...
|
A survey of AI ethics in business literature: Maps and trends between 2000 and 2021
|
https://pmc.ncbi.nlm.nih.gov
|
[
"Marco Tulio Daza",
"Institute Of Data Science",
"Artificial Intelligence",
"Datai",
"School Of Economics",
"Business",
"University Of Navarra",
"Pamplona",
"Information Systems Department",
"University Center For Economic"
] |
by MT Daza · 2022 · Cited by 34 — According to a University of Oxford study, 47% of jobs will be lost due to automation in the next 25 years (Frey and Osborne, 2013). However, Beerbaum and Otto ...
|
Artificial intelligence is spreading rapidly in business products and processes, with innovations that bring great benefits to society; however, significant risks also arise. AI-enabled systems make decisions autonomously and influence users and the environment, presenting multiple ethical issues. This work focuses on the ethics of AI use in business. We conduct a survey of business journal articles published between 2000 and mid-2021 to identify the most influential journals, articles, and authors, the most influential ethical schools, and the main ethical issues of AI in business. It describes the state-of-the-art in the field and identifies trends in ethical issues arising from AI. Thus, we present maps and trends of the ethics in AI in business literature.
Introduction
The availability of massive datasets and new machine learning techniques has triggered rapid advances in AI in the past decade (Acemoglu et al., 2022). This technology-driven transformation is reshaping business, economy, and society (Loureiro et al., 2021). Innovations bringing great benefits and new challenges herald the arrival of a new industrial revolution (Marsh, 2012). Therefore, significant risks arise, and with them, the need for ethical assessment.
The fourth industrial revolution is causing a dramatic transformation of the world economy (Schwab, 2017). Companies as diverse as Google, Spotify, Under Armor, and so forth enhance their performance through the adoption of AI (Vlačić et al., 2021). Corporations that provide these platforms, such as Microsoft, Amazon, Alphabet (Google), and Apple, form part of a group whose market capitalization has exceeded one trillion dollars.1 Worldwide spending on cognitive and AI systems has grown from $24.0 billion in 2018 (Loureiro et al., 2021) to $93.5 billion in 2021 (Zhang et al., 2022).
The impact of AI is not limited to business and the economy; it prompts a profound transformation of work (Rodriguez-Lluesma et al., 2020). Like previous industrial revolutions, the fourth raises concerns that automation will wipe out jobs (Autor, 2015). AI-driven robots are replacing blue-collar workers in factories (Belanche et al., 2020), while Robotic Process Automation (RPA) systems are taking white-collar jobs. AI-based platforms are writing essays (Knibbs, 2022), computer code (Thompson, 2022), and creating art (Johnson, 2022a).
According to a University of Oxford study, 47% of jobs will be lost due to automation in the next 25 years (Frey and Osborne, 2013). However, Beerbaum and Otto (2021) suggests that these jobs will soon be replaced by new ones. Nevertheless, it is unclear how quickly they can be recovered or if newly created jobs will be of quality. Companies in the On-Demand Economy fuel the proliferation of precarious jobs; for Cherry (2016), this has devalued work, driving wages below the legal minimum and providing an excuse to avoid paying social security benefits.
AI transformation of work has a broad social impact. AI-enabled systems determine whether someone is hired, promoted, or approved for a loan, as well as which ads and news articles consumers see (Martin, 2019). These algorithmic decisions can have unfair negative consequences or even violate human rights (Kriebitz and Lütge, 2020).
There are other harms originating from AI's development and deployment. Training data for machine learning is obtained and used in ways that often violate people's privacy (Thiebes et al., 2020). AI-enabled systems can be used for surveillance (Stahl et al., 2021). Social media platforms wield enormous influence on users. They can undermine public health (Bhargava and Velasquez, 2020), polarize social groups, affect democratic participation, foster the spread of fake news and conspiracy theories (Zuboff, 2018), and even aid in terrorist attacks (Rauf, 2021).
We must consider that the ability of humans to cause harm to others has increased with new technologies; now, machines themselves could cause damage (Letheren et al., 2020). Consequently, ethical assessment is required to understand AI-associated issues, support better decisions, and establish standards to develop and implement AI systems. Thus, AI could also serve to promote flourishing.
However, it is not enough to have an evaluation that sheds light on our actions (or that of the machines). It is also necessary to justify and convince the organization's leadership why we opt for specific behavior. This acquires relevance in business, even more so where ethical choices are not usually the most lucrative. Furthermore, problems may arise when there is no theoretical support in the face of complex ethical problems, such as the lack of supporting arguments, weak justifications, or erroneous decisions. Therefore, we believe that discussing ethical theories is essential.
The first motivation of our work is to understand the state of AI ethics in business publications from a perspective that recognizes its intrinsic moral value. We note a lack of research with a holistic perspective in the literature, which is essential to study this topic. We highlight three key aspects. First, we conducted a bibliometric analysis of the literature on the subject, identifying the most influential journals, articles, and authors, which allowed us to situate ourselves in the field. Second, we categorize the main ethical issues of AI in business and identify the schools of ethical thought that are being used to address them. This perspective is necessary to recognize the value of ethics as an inquiry tool to evaluate competing tech policy strategies and practices, which have been downplayed by the industry as a communication strategy or a facade to cover up unethical behaviors (Bietti, 2020).
Our second motivation is to provide a survey of AI ethics literature with a comprehensive approach focused on the field of business, including more than specific areas, functions, or principles. We intend to find gaps in the literature, identify under-researched areas, and map the state-of-the-art in the field.
Although current literature presents valuable insights into specific domains, no research article focuses on the issues of AI in the business field comprehensively using an ethical perspective. None of the eleven Systematic Literature Reviews (SLRs) published between 2000 and 2021 had AI ethics as the primary focus across all business areas and functions (see Table 1). Most SLRs are centered on AI topics in business or business ethics separately. Only two reviews have an ethical approach to AI in business. Although they address specific domains, Bhatta (2021) studies the digitalization of leadership, and Hermann (2021) explores AI in marketing. Ryan and Stahl (2021) carried out the only SLR focused on AI ethics. However, their work does not focus on the business domain and has a limited approach to ethics since its scope is limited only to principles and guidelines.
Table 1. Systematic literature reviews that address AI, business, and ethics between 2000 and 2021. Author(s) Title Year Source Scope Main topic Focus Bhatta et al. Emerging ethical challenges of leadership in the digital era: A multi-vocal literature review 2021 Electronic Journal of Business Ethics and Organization Studies 1985–2020 Ethical challenges for leadership Ethics of AI in business Caner and Bhatti A conceptual framework on defining business strategy for artificial intelligence 2020 Contemporary Management Research 2015–2019 AI business strategy AI in business Hermann et al. Leveraging artificial intelligence in marketing for social good - an ethical perspective 2021 Journal of Business Ethics No time constraints Ethics of AI in marketing Ethics of AI in business Liu et al. A big-data approach to understanding the thematic landscape of the field of business ethics, 1982–2016 2019 Journal of Business Ethics 1982–2016 Business ethics Business ethics Losbichler and Lehner Limits of artificial intelligence in controlling and the ways forwards: A call for future accounting research 2021 Journal of Applied Accounting Research No time constraints AI in management accounting and monitoring AI in business Loureiro et al. Artificial intelligence in business: State of the art and future research agenda 2021 Journal of Business Research 1970–2019 AI in business AI in business North-Samardzic Biometric technology and ethics: Beyond security applications 2020 Journal of Business Ethics No time constraints Biometric technology and privacy in business Business ethics Ryan and Stahl Artificial intelligence ethics guidelines for developers and users: Clarifying their content and normative implications 2021 Journal of Information, Communication and Ethics in Society No time constraints Ethic guidelines for AI Ethics of AI Schinagl and Shahim What do we know about information security governance? “from the basement to the boardroom”: Toward digital security governance 2020 Information and Computer Security No time constraints Information security governance Business Syvänen and Valentini Conversational agents in online organization-stakeholder interactions: A state-of-the-art analysis and implications for further research 2020 Journal of Communication Management No time constraints Chatbots in business AI in business Vlačić et al. The evolving role of artificial intelligence in marketing: A review and research agenda 2021 Journal of Business Research 1987–2020 AI in marketing AI in business Open in a new tab
As a third motivation, we attempt to establish the connection between ethical schools of thought and the main AI issues in business. Thus, we classified papers into three leading ethical schools to measure their influence. Few authors study this phenomenon from the perspective of ethical theories, whether deontological, consequentialist, virtue ethics or a combination. Hermann (2021) carried out the only SLR that adopted an ethical theory standpoint, complementing deontological considerations with a utilitarian perspective. The other SLRs do not endorse an ethical theory. Most authors do not anchor their proposals in a foundational ethical theory. Some merely acknowledge that ethical problems exist and that future research is needed. Furthermore, we did not find an SLR or a research article that addresses the influence of ethical theories on the topic of AI in business.
Unlike most articles, which analyze AI ethics in an isolated context, this paper offers a survey of business journal articles focused comprehensively on AI ethics (not just guidance documents, Ryan and Stahl, 2021) across business domains and topics (not just leadership, Bhatta, 2021; marketing, Hermann, 2021; strategy, Caner and Bhatti, 2020) that connects the issues to specific ethical schools or theories. In this way, AI ethics connects not only to business ethics but also to socioeconomic and political ethics in general through major ethical traditions.
We organized this article into four sections. This introduction presents an outline of the impact of AI and our motivations. Section two continues with the methodology, the setting up of our database, and our research questions with the metrics and techniques used. Section three discusses our findings regarding the most influential articles, journals, and authors, presents a classification of the articles according to the ethical school used (if any), and proposes a categorization for the most recurring issues. We then proceed to analyze the evolution of these issues. Finally, in the fourth section, we present the maps and trends identified as conclusions and suggest areas for future research.
| 2022-12-19T00:00:00 |
2022/12/19
|
https://pmc.ncbi.nlm.nih.gov/articles/PMC9806431/
|
[
{
"date": "2022/12/01",
"position": 93,
"query": "AI replacing workers"
},
{
"date": "2022/12/01",
"position": 42,
"query": "artificial intelligence business leaders"
}
] |
Artificial intelligence and unemployment:An international ...
|
Artificial intelligence and unemployment:An international evidence
|
https://ideas.repec.org
|
[
"Nguyen",
"Quoc Phu",
"Vo",
"Duc Hong",
"Author",
"Listed"
] |
by QP Nguyen · 2022 · Cited by 57 — In general, artificial intelligence increases unemployment until a certain inflation threshold is attained, and then the effect reduces afterwards. Second, the ...
|
This paper examines the possible effect of artificial intelligence (AI) on unemployment using a broad database of AI-related patents in 40 developed and developing markets from 2000 to 2019. The study employs a panel smooth transition regression (PSTR) model to analyse the relationship between artificial intelligence and unemployment under various inflation levels. The study contributes to the existing literature with several findings. First, results from our analysis confirm the non-linear relationship between artificial intelligence and unemployment depending on the threshold of inflation. In general, artificial intelligence increases unemployment until a certain inflation threshold is attained, and then the effect reduces afterwards. Second, the smooth mechanism employed in this analysis can capture individual estimates varying amongst countries over time.
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| 2022-07-14T00:00:00 |
2022/07/14
|
https://ideas.repec.org/a/eee/streco/v63y2022icp40-55.html
|
[
{
"date": "2022/12/01",
"position": 4,
"query": "AI unemployment rate"
}
] |
Growth trends for selected occupations considered at risk from ...
|
Growth trends for selected occupations considered at risk from automation : Monthly Labor Review : U.S. Bureau of Labor Statistics
|
https://www.bls.gov
|
[
"Michael J. Handel"
] |
One widely cited and emulated study claimed 47 percent of U.S. jobs were at risk of automation between 2010 and 2030. This and similar highly publicized claims ...
|
Technological change has always attracted attention because of its potential effects on employment. Recent advances in robotics and artificial intelligence (AI) have attracted even more interest than usual, and the breadth and speed of these advances have raised the possibility of widespread job displacement in the near future. Many observers consider these new technologies fundamentally different from previous waves of computing technology. New computing capacities—in areas such as image recognition, robotic manipulation, text processing, natural-language processing, and pattern recognition, and, more generally, the ability to learn and improve rapidly in relatively autonomous ways—represent a break from the hand-coded, rules-based programs of the past. In this view, newer robots and AI represent a clear departure from previous waves of computing, one that accelerates the pace of technological change and job displacement.
Recent writings on automation and employment often refer to certain occupations in passing as examples of jobs that are currently affected by advanced robots and AI or are likely to be affected in the near future. These occupations may be intended as representative examples of general processes or the leading edge of trends that will eventually affect other occupations. Because the occupations are not examined in depth, a natural question is whether the expectations expressed in the automation literature are consistent with U.S. Bureau of Labor Statistics (BLS) projections and recent employment trends for these occupations. There are also potential problems with representativeness in using individual occupations as exemplars. Because part of the workforce is being used as a stand-in for the whole, it is natural to ask whether these particular occupations are actually shrinking rapidly, as well as whether the general practice of citing individual examples has some inherent limitations that are worth bearing in mind.
This article assembles the individual occupations that widely cited recent works on automation consider highly vulnerable to substitution by robots and AI and examines their projected growth from 2019 to 2029. For context, the growth of these occupations over 2008–18 is compared with both the 2019–29 projections and the 2008–18 projections published a decade earlier. Finally, actual and projected growth in these occupations is compared with their growth over 1999–2009. In addition to providing trend data for the past 20 years and projections for the following 10 years, these data permit some consideration of the accuracy of the 2029 projections against the performance of the 2008–18 projections. This article does not examine overall trends by major occupational group, which is covered in a companion paper.[1] Rather, it examines how well recent discussions of automation describe developments in the specific occupations that have been selected to illustrate general trends. The article also examines limitations surrounding any use of occupational examples to understand general employment trends.
Current automation discourse
Since the emergence of information and communications technology (ICT) in the late 1940s, the development of this technology has been uniquely dynamic. Both the power and breadth of applications of ICTs have grown rapidly. Milestones include mainframe computing, early manufacturing automation, minicomputers, microcomputers, the internet, and mobile computing, among others. From their inception, ICTs were understood as a watershed in the development of technology, with great potential for both abundance and displacement. Repeating periodic concerns raised earlier regarding mechanical technology, some observers in the 1950s and 1960s argued computers and industrial automation could lead to massive job losses.[2] Responding to these concerns, congressional hearings, a series of studies by the U.S. Bureau of Labor Statistics, and a multivolume report by a special presidential commission investigated the employment implications of computers and automation.[3] However, when economic growth picked up in the late 1960s and unemployment fell to 3.5 percent, the issue faded into the background.
Concern regarding the impact of computers on jobs reemerged in the late 1980s. Economists became aware of a trend toward greater earnings inequality, which included increasing wage differentials between education groups. The dominant explanation was that new technology increasingly favored more educated workers. This concept of skill-biased technological change (SBTC) pointed to the spread of microcomputers as reducing the demand for less-skilled workers.[4] When wage gaps between the middle and lower percentiles of the wage distribution stopped widening in the late 1990s, attention turned toward the possibility that declining labor demand affected middle-skilled jobs more than low-skilled jobs because their tasks were more codifiable and programmable. It was argued this made middle-skilled jobs more susceptible to automation than low-skill service jobs because service jobs require sensory perception, common sense, and other tacit skills that are difficult to replicate with algorithmic, if–then programming logic.[5]
A new wave of computing has led to further revision regarding the effects of automation. Beginning in 2005 with the successful demonstration of self-driving vehicles, views of the capabilities of ICTs have expanded. A series of often unexpected and dramatic breakthroughs in AI and robotics have expanded the kinds of tasks that information technology can perform. Now robots clean floors, deliver packages, perform warehouse work, check inventory on supermarket shelves, patrol malls, and assist surgery. Other robots lay bricks and sew garments. After 2010, corporate research and development on self-driving vehicles and other AI rose significantly. Breakthroughs in complex game-playing raised the possibility of more general automated intelligence that might replace numerous cognitively complex jobs.[6] By 2015, image recognition, speech recognition, natural-language processing and speech synthesis, machine translation, automated captioning and subtitling, automated text generation, and business analytics made remarkable advances powered by improved hardware, big data, and advances in neural networks and other machine learning techniques. Even experts in the field of AI were often surprised by the rapid rate of improvement and the milestones reached in such a compressed timeframe.[7]
This sudden burst of progress led some researchers and commentators to change their views regarding the kinds of jobs that might be subject to widespread automation in the near future. Complex perceptual and cognitive tasks, like driving vehicles, composing news articles, extracting information from legal documents, and reading radiological images, as well as all kinds of manual and service tasks previously performed by humans, came to be seen as vulnerable to large-scale substitution by machines. Widely cited writings on automation have argued strongly that the new wave of computing technology is fundamentally different from previous waves in terms of its capabilities and speed, and that it may produce massive job displacement in the near future.[8] One widely cited and emulated study claimed 47 percent of U.S. jobs were at risk of automation between 2010 and 2030.[9] This and similar highly publicized claims generated their own congressional hearings and government reports.[10]
One common feature of recent writing on automation is the use of particular occupations as illustrations of the rapid encroachment of robots and AI on jobs previously restricted to human workers. BLS projects 10-year occupational employment trends on a regular basis, so it is natural to ask whether the BLS projections also consider these particular occupations highly vulnerable to automation and whether recent employment trends also suggest reasons for concern. Given the overall size of the U.S. workforce, there are also questions regarding the total number of jobs that are in these potentially vulnerable occupations. If the projections and recent trends suggest these occupations are vulnerable and the occupations are large, either individually or collectively, then recent automation concerns receive support. By contrast, if their growth follows relatively consistent or moderate trends over time or the number of jobs involved is not outsized relative to the overall labor force, then the experience of these occupations would not support current levels of concern regarding automation.
How AI can affect employment
Several considerations are worth noting in investigating this question because they potentially qualify the view that AI and robots will have outsize employment impacts, some of which apply to technological change more generally.
Technological vintage
The current literature on automation considers recent robots and AI much more powerful and consequential for employment than previous ICTs, representing a break with previous computing technology. Therefore, the employment effects of the current wave of computing should be distinguished from the effects of previous waves of computing, such as older automation technologies, conventional office software, the internet, and mobile technology. It is the new functions of leading technologies, their wider applicability, and faster development that has generated especial concern for the future of work. If the new technology simply represents a “handoff” of productivity leadership from maturing ICTs to a new generation of ICTs, without a break or discontinuity in employment trends, then one would not expect to see the current, heightened levels of concern. Therefore, the specific employment effects of newer robots and AI need to be distinguished from the effects of well-established computing technologies, such as digitization, ICT-enabled self-service, ICT-enabled offshoring, e-commerce, and office automation, whose employment effects began earlier and have not necessarily been exhausted. None of these older technologies uses or is dependent on machine learning or advanced robotics. This article focuses on the potential effects of robotics and AI, which have generated the greatest attention and concern recently, but, as will be seen, it is not always easy to maintain the distinction between newer and older computing technologies in practice when examining occupational growth trends.
Scale effects
In addition, the automation literature tends to focus on job destruction due to technological substitution without much consideration of the possibility that innovations could increase demand for a good or service and thus require more workers to meet that demand (a scale effect). The most common scale effect mentioned in discussions of automation generally refers to firm or industry growth resulting from automation-related price decreases and accompanying growth in product demand. It is possible, for example, that partial automation of warehouses may lower retail prices and expand the use of e-commerce so that the total number of warehouse workers might grow even as staffing patterns within each warehouse become leaner. However, if e-commerce demand eventually stops growing and automation continues to progress, employment levels will no longer be buoyed by growth in the size of the product market.
Perhaps more importantly, embedded in the BLS projection models is the understanding that a growing population generally increases total demand for goods and services, which usually increases the total demand for labor, even when productivity is rising.[11] Likewise, a growing labor force provides the means for satisfying growing demand for final goods and services and is typically associated with job growth. The automation literature tends to focus on the jobs that technology eliminates or may eliminate, usually omitting effects of population growth and growth in the total size of the economy, which are reflected in both employment growth and BLS projections.
Task and job variation within occupations
Firms have often responded to the introduction of new labor-saving technology by redefining the tasks involved in existing jobs. Economists often point to the “lump of labor” fallacy in discussing previous waves of technological innovation.[12] According to this view, there is no fixed amount of work to be done in an economy, as an economy not only grows over time but becomes more diversified. Even though machines may reduce the number of jobs in some occupations, historically the workforce has shifted to other, often new occupations. This principle can be extended to the tasks within occupations, as well. Automation of some tasks, such as document review within legal occupations, may result in more time spent on other, perhaps new, tasks without necessarily a reduction in the number of jobs. Although this is certainly not inevitable, observers need to be aware that there is a potential lump of labor fallacy within occupations that may inhibit occupational decline, as well as the more familiar application of the concept across occupations. When technology substitutes for some tasks within an occupation, it is possible that workers will be shifted to other, sometimes new, tasks, rather than simply losing their jobs. The extent to which technology augments organizational capacities rather than simply saving labor is an empirical question that tends to be overlooked in discussions of automation, perhaps because it represents continuity rather than a break with existing practices. To the extent jobs are lost, firms may rely on attrition rather than layoffs, which will moderate the pace of occupational decline.
Another issue is that occupational titles in the Standard Occupational Classification (SOC) system may cover a wider range of jobs than may be immediately apparent, and not all of these jobs may be equally susceptible to automation. For example, some estimates of jobs at risk from automation mention the three million jobs in the broad occupation driver/sales workers and truck drivers (SOC 53-3030) as vulnerable to displacement by self-driving trucks, rather than the 1.7 million jobs in the detailed occupation heavy and tractor-trailer truck drivers (SOC 53-3032), some of which, like cement truck drivers, also do not drive long-haul highway routes considered most suitable for autonomous vehicles.[13] Likewise, software may replace accountants who mainly prepare tax filings, but accounting jobs are varied, and those focused on other tasks may remain completely unaffected. Even when jobs within an occupation correspond more closely to assumptions, those jobs may contain more tasks that are difficult to automate than is commonly recognized, which may slow the replacement of human workers by machines.[14] Underestimating the variety of jobs covered by an occupational title and their varying susceptibility to automation is distinct from situations in which the task content of jobs changes in response to the introduction of technology. However, in both cases there is a risk of overestimating the importance of a particular technology for a given occupation because of underestimating the variety of their task content, either in the cross-section or over time.
Omitting considerations like scale effects, job redefinition, and job variety, or mentioning them only in passing, assumes by default that the effects of technological substitution are stronger than the effects of economic growth and other offsetting variables that operate to maintain or increase jobs in affected occupations.[15] As will be seen, this is not necessarily a safe assumption. Nevertheless, it should be noted that nothing in this article should be interpreted as minimizing the number of jobs lost, the hardships experienced by workers affected by job loss, or the implications of the changing occupational composition for inequality and economic opportunity more generally.
Data
This article uses data from two BLS sources. Projections data are from the Employment Projections program and historical employment data are from the Occupational Employment and Wage Statistics (OEWS) program.
Employment Projections
BLS conducts research on job trends on an ongoing basis as part of its Employment Projections program. This research is used to make 10-year projections of occupational growth to help members of the public, such as jobseekers, counselors, and education planners, understand labor market trends. As part of the projections process, staff economists conduct detailed research on recent and emerging technologies used in the workplace, as well as prior trends in employment by occupation. This article uses projections files for 2008–18 and 2019–29. These projections cover a 20-year period frequently used as a projection interval in the recent automation literature.[16] The 2019–29 projections permit direct comparison between BLS research and recent work on automation, while avoiding distortions that would arise in the 2020–30 projections because of the effect of the coronavirus disease 2019 (COVID-19) pandemic on employment in 2020.[17] The 2019–29 projections file also includes brief explanations of the technological and other drivers of occupational trends that emerged from BLS research, which are cited at points below.[18]
The 2008–18 projections file contains actual occupational employment for 2008 and both projected and actual occupational employment for 2018. These data provide employment trends for the first decade of the current wave of ICTs and a benchmark for evaluating how well the projections methodology anticipated trends in occupational employment. Although there is no way to evaluate the projections for 2029, the performance of the 2008–18 projections provide a useful point of comparison when considering the likely performance of the 2019–29 projections.
Using the performance of the 2008–18 projections to provide perspective on the likely performance of the projections to 2029 is complicated by several factors. Projections aim to provide a general guide to the course of future occupational employment but recognize there will likely be some divergence between projected and actual figures in the target year, given previous experience and the intrinsic uncertainties involved. Therefore, modest differences between projected and actual values for 2018 should not be given great weight. However, the implications of any large divergences for projections to 2029 may be ambiguous because the later projection may take the earlier discrepancies into account and try to address the problems that caused them. Nevertheless, Carl Benedikt Frey and Michael Osborne argue the BLS projections lag technological developments, so it is important to note cases in which the projections underestimated change during the first decade of the AI era and continue to project gradual change in the next decade.[19]
Occupational Employment and Wage Statistics
Because some occupations identified as vulnerable to substitution by robotics and AI may have been affected by previous waves of computing technology, and may continue to experience these effects, this article also examines growth trends for the 1999–2009 period by using the Occupational Employment and Wage Statistics (OEWS) database. The OEWS database covers a somewhat narrower range of industries than the projections data. This difference will produce some discrepancies in the absolute levels of employment between the OEWS and projections files. However, the growth rates for 1999 to 2009 should be a useful benchmark for judging whether subsequent changes in occupational employment represent a qualitative break with prior rates of change, as suggested by recent work on automation. A discussion of occupational growth rates (in the next section) also uses the OEWS database for 1999 to 2018 to show typical rates of occupational growth and decline to provide context. Readers should bear in mind that two different data series, Employment Projections and OEWS database, are used to derive the main results for occupations considered highly susceptible to automation because the data series cover different years, which produces some noncomparabilities noted below.[20]
Both the projections and the OEWS database use the Standard Occupational Classification (SOC) system, which is a hierarchical system for coding occupations. For example, the 2000 SOC has 821 detailed occupations, which are nested within approximately 450 broad occupations, 96 minor occupation groups, and 23 major occupation groups. The writings consulted for this article generally use the equivalent of detailed occupations when discussing jobs at risk of automation, but those writings sometimes use the equivalent of broad or even minor occupations, which is reflected in the tables that follow. The relationship between automation and major occupation groups is covered in a companion paper.[21] The SOC system was revised significantly beginning in 2010 and, again, beginning with data collected in 2019, which can introduce some comparability issues across periods.
Occupational size and growth: general considerations
Because discussion of automation often uses particular occupations to illustrate general employment trends, it is useful to have some perspective on the varying sizes of occupations. BLS projections data report 163 million jobs in the United States in 2019, which are classified into 790 detailed occupations. Therefore, the average occupation had 206,072 jobs and accounted for about one-tenth of 1 percent (0.13 percent) of all jobs. However, the highly skewed nature of the distribution means that the share of the largest occupation accounted for more than 20 times the average (2.7 percent). Occupational size declines fairly rapidly thereafter, such that the share of the smallest occupation in the top decile of the occupational employment distribution accounted for about twice the average (0.29 percent). The median occupation had only about 54,000 jobs, accounting for just 0.03 percent of all jobs, and the nearly 400 occupations below the median each accounted for 0.01 percent of all jobs on average. Clearly, occupational size is an important consideration when focusing on particular cases given that most occupations are small in absolute terms and even large occupations account for relatively small shares of all jobs. (For further information on the occupational size distribution, see the appendix). Even very large percent declines imply limited overall job losses in the case of most occupations, and very large percent declines in occupational employment are themselves not very common.
Between 1999 and 2018, the total number of jobs grew 17 percent for the 669 occupations in the OEWS database that can be followed consistently. This represents average 10-year and 20-year growth rates of 8.6 percent and 18.0 percent, respectively. Given the general increase in jobs, which partly reflects growth in the population and labor force, one would expect most occupation-specific growth rates to be positive. However, while many occupations experienced below-average growth, others experienced absolute declines. An occupation must decline by 25 percent per decade to shrink by 44 percent over two decades, equivalent to 0.56 of its base-year level. This is comparable to the rate of change implied by Frey and Osborne’s widely cited forecast of a 47-percent decline in the total number of jobs over 20 years.[22]
Such steep declines are unusual. Table 1 shows the actual distribution of growth rates in the OEWS database for 1999–2018. The top data row shows that only about 15 percent of occupations (column 1), accounting for 9 percent of jobs in 1999 (column 2), declined by 25 percent or more per decade over the full period. Clearly, current views of automation imply a sharp break with the recent past in terms of the scale of occupational employment decline. Table 1 also shows that about half of all jobs in 1999 were in occupations that grew or shrank by up to 10 percent per decade over the next 19 years (row 2), two-thirds were in occupations that changed within 20 percent per decade (row 3), and 83 percent of jobs were in occupations that changed within 30 percent per decade (row 4).
A more detailed breakdown in panel B of table 1 shows that, as one moves away from the central, slowest growth category (0- to 10-percent growth) and toward categories representing larger shifts, the shares of occupations and jobs involved grow smaller. This is especially the case for declining occupations. Only 21 occupations, representing 1 percent of jobs in 1999, declined by 50 percent or more per decade from 1999 to 2018. By contrast, 55 occupations, representing 2.7 percent of jobs in 1999, grew by 50 percent or more per decade over the same period.
Table 1. Distribution of occupations’ 10-year growth rates, 1999–2018 10-year growth Percent of all occupations Percent of all jobs Number of occupations Percent of jobs, small occupations (N = 338) Percent of jobs, large occupations (N = 104) A. Select growth rates Declined by 25 percent or more 15.1 9.0 101 13.1 4.9 Changed ± 10 percent 30.9 51.2 207 23.8 59.9 Changed ± 20 percent 51.7 67.8 345 44.7 74.5 Changed ± 30 percent 69.3 83.0 463 64.8 87.5 B. All growth rates G ≤ 0.5 3.1 1.0 21 4.00 0.3 0.5 < G ≤ 0.7 8.7 6.3 58 11.30 5.5 0.7 < G ≤ 0.8 7.3 4.7 49 9.70 3.3 0.8 < G ≤ 0.9 9.3 5.3 62 9.80 2.7 0.9 < G < 1.0 13.8 16.0 92 11.60 16.9 1.0 ≤ G ≤ 1.1 17.2 35.2 115 12.20 43 1.1 < G ≤ 1.2 11.4 11.3 76 11.10 11.9 1.2 < G ≤ 1.3 10.3 10.5 69 10.40 9.7 1.3 < G < 1.5 10.8 7.1 72 11.30 5.2 1.5 ≤ G 8.2 2.7 55 8.70 1.4 Total 1 100.1 100.1 669 100.1 99.9
It would not be surprising to find that rates of change were larger for smaller occupations, because absolute changes imply larger percent changes. This is confirmed by comparing the employment-weighted distribution of occupational growth rates for occupations with less than 50,000 jobs in 1999 (see table 1, column 4) with the distribution for occupations with more than 250,000 jobs in 1999 (column 5), which together account for two-thirds of all occupations included in the table (442 of 669). The differences are even more apparent when the two distributions are compared graphically (see chart 1). In sum, very large declines in occupational employment were relatively uncommon between 1999 and 2018, and they tended to be concentrated among smaller occupations, limiting their effect on overall employment. The pattern of occupational decline implied by automation futurists would represent a dramatic departure from recent trends.
Chart 1. Distribution of 1999 jobs across growth categories, by occupation size Growth category Small occupations (in percent) Large occupations (in percent) 1 3.99 0.33 2 11.34 5.52 3 9.71 3.31 4 9.76 2.73 5 11.64 16.85 6 12.18 43.03 7 11.05 11.88 8 10.40 9.71 9 11.26 5.21 10 8.68 1.44
Occupations considered highly susceptible to automation
Tables 2 and 3 show recent and projected trends for 27 occupations that have been used as illustrations in widely cited works on the effects of robotics and AI on employment.[23] Most of the discussion will focus on the percent growth figures in the percent change columns of table 3, but the absolute number of jobs in table 2 and the changes in absolute levels in the absolute change columns of table 3 are relevant to the question of magnitudes and provided for reference. In both tables, occupations in the top panel are closer to “pure” cases in which technological drivers of employment are more likely to be exclusively robotics and AI. Occupations cited in the automation literature that appear in the bottom panel are more mixed and are likely to have been affected significantly by previous waves of computing technologies, in addition to any effects of more advanced information and communications technologies (ICTs). Nevertheless, the distinction is not clear, if only because the magnitudes of the various technological effects on different occupations are not well understood. All figures for 1999 to 2009 are derived from the OEWS database, while figures for 2008 to 2018 and 2019 to 2029 are derived from the Employment Projections database, which adds the self-employed and certain other groups to the OEWS totals.
Table 2. Job trends for detailed occupations considered high risk for automation, 1999–2029 (number of jobs in thousands) SOC code Title OEWS BLS projections database 1999 2009 2008 2018p 2018 2019 2029p 00-0000 All occupations 118,201 130,580 150,930 166,204 160,959 162,797 168,835 A. Potential AI-affected occupations 13-2052 Personal financial advisors 80.0 149.5 208.4 271.2 271.7 263.0 274.6 27-3091 Interpreters and translators 13.6 40.0 50.9 62.2 76.1 77.4 92.9 29-1067 Surgeons, except ophthalmologists 48.5 44.6 54.5 63.8 38.2 39.6 38.8 35-3021 Fast food and counter workers 2,358.9 3,186.7 3,227.1 3,670.4 4,184.4 4,047.7 4,508.6 37-2011 Janitors and cleaners 2,090.6 2,090.4 2,375.3 2,479.4 2,404.4 2,374.2 2,479.8 37-2012 Maids and housekeeping cleaners 913.5 887.9 1,498.2 1,583.7 1,494.4 1,474.9 1,470.7 37-3011 Landscaping and groundskeeping workers 739.5 860.0 1,205.8 1,422.9 1,205.2 1,188.0 1,307.9 53-3032 Heavy and tractor-trailer truck drivers 1,558.4 1,550.9 1,798.4 2,031.3 1,958.8 2,029.9 2,060.5 53-7051 Industrial truck and tractor operators 590.7 568.3 610.3 627.0 615.0 634.7 652.3 53-7062 Laborers and freight, stock, material moving 2,035.6 2,135.8 2,317.3 2,298.6 2,953.8 2,986.0 3,111.7 1 Subtotal 10,429.3 11,514.1 13,346.2 14,510.5 15,202.0 15,115.4 15,997.8 1 Percent of all jobs 8.8 8.8 8.8 8.7 9.4 9.3 9.5 B. Occupations with pre-AI potential effects 13-2072 Loan officers 200.2 298.2 327.8 360.9 316.2 316.9 327.0 13-2082 Tax preparers 58.1 61.1 95.8 98.6 86.6 88.4 87.9 15-1131 Computer programmers 528.6 367.9 426.7 414.4 250.3 213.9 193.8 23-1011 Lawyers 464.3 556.8 759.2 857.7 823.9 813.9 846.3 23-2011 Paralegals and legal assistants 175.9 246.8 301.5 380.8 342.9 337.8 373.1 27-3022 News analysts, reporters, journalists 64.6 52.0 69.3 64.9 49.7 52.0 46.2 27-3031 Public relations specialists 118.3 242.7 1 1 1 274.6 294.3 41-1011 First-line supervisors of retail sales workers 1,237.1 1,163.0 1,685.5 1,773.9 1,548.3 1,476.4 1,395.3 41-2011 Cashiers 3,162.1 3,439.4 3,550.0 3,675.5 3,648.5 3,600.9 3,335.5 41-2021 Counter and rental clerks 392.6 417.0 448.2 461.9 436.1 420.4 424.9 41-2031 Retail salespersons 3,729.0 4,209.5 4,489.2 4,863.9 4,510.9 4,371.4 4,346.3 43-4051 Customer service representatives 1,789.6 2,195.9 2,252.4 2,651.9 2,972.6 3,018.8 2,959.8 41-9041 Telemarketers 485.7 307.7 341.6 303.8 167.7 136.9 117.5 43-5081 Stockers and order fillers 1,800.8 1,864.4 1,858.8 1,993.3 2,056.6 2,135.8 2,151.3 43-4181 Reservation/transportation ticket agents 222.3 142.5 168.3 181.9 133.7 126.3 122.7 45-2091, 45-2092, 45-2093 Farmworkers and agricultural equipment operators 267.2 291.1 782.6 763.9 855.0 882.2 891.3 51-4120 2 Welders 478.7 399.3 466.4 455.9 462.4 476.1 487.2 1 Subtotal 15,123.0 16,197.9 17,852.1 19,128.5 18,338.7 18,427.0 18,097.0 1 Percent of all jobs 12.8 12.4 11.8 11.5 11.4 11.3 10.7 C. All potentially affected occupations 1 Total, all specific occupations 25,552.3 27,712.0 31,198.3 33,639.0 33,540.7 33,542.4 34,094.8 1 Percent of all jobs 21.6 21.2 20.7 20.2 20.8 20.6 20.2
Table 3. Job trends for detailed occupations considered high risk for automation 1999–2029, absolute and percent change SOC code Title Absolute change (in thousands) Percent change 1999–2009 2008–18p 2008–18 2019–29p 1999–2009 2008–18p 2008–18 2019–29p 00-0000 All occupations 12,378 15,274 10,028 6,038 10.5 10.1 6.6 3.7 A. Potential AI-affected occupations 13-2052 Personal financial advisors 69.5 62.8 63.3 11.6 86.9 30.1 30.4 4.4 27-3091 Interpreters and translators 26.4 11.3 25.2 15.5 193.3 22.2 49.4 20.0 29-1067 Surgeons, except ophthalmologists -3.9 9.3 -16.3 -0.8 -8.0 17.0 -30.0 -2.0 35-3021 Fast food and counter workers 827.8 443.3 957.3 460.9 35.1 13.7 29.7 11.4 37-2011 Janitors and cleaners -0.2 104.1 29.1 105.6 0.0 4.4 1.2 4.4 37-2012 Maids and housekeeping cleaners -25.6 85.5 -3.8 -4.2 -2.8 5.7 -0.3 -0.3 37-3011 Landscaping and groundskeeping workers 120.5 217.1 -0.6 119.9 16.3 18.0 0.0 10.1 53-3032 Heavy and tractor-trailer truck drivers -7.5 232.9 160.4 30.6 -0.5 12.9 8.9 1.5 53-7051 Industrial truck and tractor operators -22.4 16.7 4.6 17.6 -3.8 2.7 0.8 2.8 53-7062 Laborers and freight/stock/material moving 100.2 -18.7 636.5 125.7 4.9 -0.8 27.5 4.2 1 Subtotal or percent change 1,084.8 1,164.3 1,855.7 882.4 10.4 8.7 13.9 5.8 B. Occupations with pre-AI potential effects 13-2072 Loan officers 98.0 33.0 -11.6 10.1 49.0 10.1 -3.5 3.2 13-2082 Tax preparers 3.0 2.8 -9.3 -0.5 5.2 2.9 -9.7 -0.6 15-1131 Computer programmers -160.7 -12.3 -176.4 -20.1 -30.4 -2.9 -41.3 -9.4 23-1011 Lawyers 92.5 98.5 64.7 32.4 19.9 13.0 8.5 4.0 23-2011 Paralegals and legal assistants 70.9 79.3 41.4 35.3 40.3 26.3 13.7 10.4 27-3022 News analysts, reporters, journalists -12.6 -4.4 -19.6 -5.8 -19.6 -6.3 -28.3 -11.2 27-3031 Public relations specialists 124.4 1 1 19.7 105.2 1 1 7.2 41-1011 First-line supervisors, retail sales -74.0 88.4 -137.2 -81.1 -6.0 5.2 -8.1 -5.5 41-2011 Cashiers 277.3 125.5 98.5 -265.4 8.8 3.5 2.8 -7.4 41-2021 Counter and rental clerks 24.4 13.7 -12.1 4.5 6.2 3.1 -2.7 1.1 41-2031 Retail salespersons 480.5 374.7 21.7 -25.1 12.9 8.3 0.5 -0.6 43-4051 Customer service representatives 406.2 399.5 720.2 -59.0 22.7 17.7 32.0 -2.0 41-9041 Telemarketers -177.9 -37.8 -174.0 -19.4 -36.6 -11.1 -50.9 -14.2 43-5081 Stockers and order fillers 63.6 134.4 197.8 15.5 3.5 7.2 10.6 0.7 43-4181 Reservation/transportation ticket agents -79.8 13.6 -34.5 -3.6 -35.9 8.1 -20.5 -2.9 45-2091, 45-2092, 45-2093 Farmworkers and agricultural equipment operators 23.9 -18.7 72.4 9.1 8.9 -2.4 9.3 1.0 51-4120 2 Welders -79.4 -10.5 -4.1 11.1 -16.6 -2.3 -0.9 2.3 1 Subtotal or percent change 1,075.0 1,276.2 486.4 -330.0 7.1 7.1 2.7 -1.8 1 All specific occupations 2,159.8 2,440.5 2,342.1 552.4 8.5 7.8 7.5 1.6
At the most basic level, concerns regarding automation are supported if occupations in panel A of table 3 are projected to decline sharply in the 2019–29 period and, secondarily, if they also shrank in the 2008–18 period with the birth of the modern AI industry. Nevertheless, with continued population and labor force growth, one might also focus on occupations whose growth was far below average for a given period.
The AI literatures leads one to expect faster rates of employment decline for the 2019–29 period compared with previous periods. In table2, one would also expect rates of decline in panel A to be faster after 2008 than rates of decline for occupations in panel B for 1999 to 2009, when those occupations were affected only by prior waves of computing technology. Finally, if occupations in panel B are affected by both prior and new waves of computing technology after 2008, their rates of decline would be expected to be the most rapid of any group or period in table 3. Unfortunately, these kinds of comparisons across panels are complicated by the fact that job growth has been decelerating because of the decline in the growth of the working-age population over time, which exerts an independent drag on the growth of jobs overall.[24] For example, the total number of jobs grew 10.5 percent from 1999 to 2009 and then slowed to 6.5 percent from 2008 to 2018 according to the OEWS database, while the projections anticipate further deceleration to 3.7-percent growth from 2019 to 2029. Under these circumstances, one would expect slower job creation in more recent years regardless of technological advances.
Aggregate results
Panel A of table 2 shows occupations used in the literature as examples of occupations susceptible to job loss from AI and robots and classified here as unaffected by earlier ICTs. The number of jobs in this group rose from 13.3 million (2008) to 15.1 million (2019) and is projected to grow further, to nearly 16.0 million (2029). This group’s share of all jobs rose from 8.8 to 9.3 percent (2008–19) and is projected to rise more slowly to 9.5 percent by 2029. In other words, these occupations grew in both absolute and relative terms since 2008 and are expected to continue to do so. According to the OEWS database, these occupations also grew in absolute size from 1999 to 2009, but their relative share remained flat at 8.8 percent of jobs during this period. Table 3 shows this group grew somewhat faster than projected from 2008 to 2018 (13.9 percent real growth versus 8.7 percent expected growth) and is projected to continue to grow faster than average for 2019 to 2029. Therefore, neither recent data nor BLS projections suggest automation is a serious issue for this group overall, though individual occupations may face greater risks.
Occupations in panel B of table 2, potentially affected by both current and previous waves of computing, account for somewhat more jobs. Jobs in this group grew from 17.9 million (2008) to 18.4 million (2019) but are projected to fall to 18.1 million by 2029. This group’s share of all jobs fell from 11.8 percent (2008) to 11.3 percent (2019) and is projected to fall by a similar amount to 10.7 percent by 2029. It is worth noting that the 2008–18 projections anticipated these occupations would account for 11.5 percent of jobs in 2018, and their actual share was very close, 11.4 percent in 2018. These occupations grew more slowly than average over both the 1999–2009 and 2008–18 periods. (See table 3.) The projections anticipate their growth will turn negative for 2019 to 2029, declining by 1.8 percent. For this group, negative forces are expected to dominate offsetting forces like general growth in the population, labor force, and economy. However, the magnitude of the decline is expected to be much less than anticipated in the automation literature, even though growth in these occupations may be affected by both preexisting and newer varieties of computing technologies.
Panel C of table 2 shows jobs in the two groups of occupations, taken together, grew from 31.2 million (2008) to 33.5 million (2019) and are projected to grow to 34.1 million (2029). Their share of all jobs remained essentially flat at 20.7 percent for 2008 and 2019, and the share is projected to decline by 0.4 percentage points by 2029. The similarity between their projected and actual share of jobs in 2018 (20.2 percent versus 20.8 percent) increases one’s confidence in the general magnitudes of the projections for 2029 at the aggregate level. The bottom row of table 3 shows the combined group grew 7.5 percent over 2008–18, nearly identical to the projected rate (7.8 percent). This rate is comparable to the 8.5 percent recorded in the OEWS database for the 1999–2009 period and more rapid than the 1.6 percent projected for 2029, which would be somewhat below average relative to projected job growth overall. The next two sections discuss the 27 occupations in these two groups in more detail.
Detailed occupations newly affected by robotics and AI
Reading four key works on automation, one finds 11 occupations used as illustrations whose employment levels were likely to be relatively unaffected by previous waves of computing.[25] Therefore, one can have somewhat greater confidence that recent or projected employment declines for these occupations reflect newer technology, like robotics and AI, exclusively. In contrast to these “pure cases,” the next section (“Occupations also affected by previous waves of computing technology”) discusses occupations cited by the recent automation literature whose employment levels have likely been affected by prior waves of computing, as well as any effects of robots and AI. For none of the occupations in this section do the employment projections to 2029 or observed changes for the 2008–18 period fit the pattern of large-scale job loss suggested by the automation literature. The only occupation with actual or projected employment declines after 2008 is surgeons. The trend for this occupation–and for others growing at a below-average rate since 2008–is evident in the 1999–2009 period, as well, predating the AI breakthroughs of the early 2010s and therefore likely reflecting other forces (see table 3).
Personal financial advisors
Recent works on automation note that algorithms can provide personalized financial advice.[26] Indeed, BLS projections for 2029 anticipate greater use of “robo-advisors,” which have existed since the early 2000s.[27] However, the projections anticipate that the demand for personal financial advisors will continue to increase, more than offsetting the anticipated labor-saving effects of AI-driven advising systems. Indeed, AI may be widening the market by reducing cost of personal financial advice for people who may have been unable to afford human advisors. In this case, AI will augment rather than replace labor, as human workers would not have been hired to serve these clients in the absence of AI.[28] The projected 10-year growth will remain slightly above average (4.4 percent) but will not be close to the rapid 30-percent growth in both projections and reality for the 2008–18 period and the 87-percent growth recorded in the OEWS database for 1999 to 2009. Part of the growth in prior years reflected increases in the population engaging in retirement planning. Further research would be required to understand whether robo-advisors and other information technology account for a significant portion of the slowdown in growth since 2009.[29]
Interpreters and translators
The development of machine translation is one of the most remarkable achievements of AI in the past 20 years. Combined with speech recognition and speech generation capabilities, translation software may substitute for human translators of both written text and real-time conversations and presentations.[30] Nevertheless, projections to 2029 anticipate a 20-percent increase in jobs within this occupation as many businesses increasingly need translation services because of globalization. This would still represent deceleration compared with the 49.4-percent growth from 2008 to 2018, which itself was underestimated in the projections (22.2 percent), and the tripling recorded by the OEWS database in this small occupation from 1999 to 2009.
Surgeons, except ophthalmologists
Surgical robots have received significant attention.[31] Although some discuss them in the same context as industrial robots, suggesting they will substitute for nonroutine manual tasks performed by humans , others concede they are more likely to augment rather than replace human surgeons.[32] A recent systematic review did not find clear advantages of robotic over conventional surgery despite greater cost and length of operations, though the technology may improve in the future.[33] BLS projects the number of surgeon jobs will decline by 2 percent between 2019 and 2029, a significant moderation compared with the 30-percent decline from 2008 to 2018, which was probably not due to robot adoption, and a marked departure from the 17-percent increase that had been projected for the 2008–18 period. The OEWS also recorded a decline from 1999 to 2009 (−8.0 percent) despite the robust increase in jobs overall during this period.
Radiologists
Many consider radiology to be a clear case of a highly skilled white-collar occupation facing serious competition from deep-learning-based AI because of breakthroughs in image recognition after 2010 .[34] Numerous research papers show AI performance in automated image interpretation meets or exceeds those of radiologists and other medical professionals, generating considerable anxiety within these specialties.[35] Some believe automated medical image reading will change the task content of radiology jobs but not greatly reduce employment.[36] However, in 2016, Geoffrey Hinton, considered the father of deep learning and modern AI, said he thought the outlook for radiology jobs in the next 5 to 10 years was quite negative.[37] Likewise, a paper in a leading radiology publication agreed that “machine learning will become a powerful force in radiology in the next 5 to 10 years [2021–26], not in multiple decades…. Indeed, in a few years there may be no specialty called radiology.”[38]
BLS does not track the number of radiology jobs specifically, but the American Medical Association has tracked employment and major professional activity for all physicians and numerous medical specialties over time. According to these data, the total number of physicians whose main activity was patient care increased from 647,430 in 2000 to 870,264 in 2019, nearly 16.8-percent growth per decade. The number of radiologists whose main activity was patient care increased from 28,444 in 2000 to 37,068 in 2019, or by 15.0 percent per decade, as shown in chart 2. This makes radiologists a small occupation by the standards of the previous section. Radiologists as a share of all physicians fluctuated between 4.40 and 4.50 percent for almost all years between 2000 and 2014, then decreased to 4.26 percent by 2019, shown in chart 3. Understanding the reasons for the relative decline would require further study, but it is both rather gradual and offset by the overall expansion in healthcare such that the absolute number of radiologists has not declined appreciably.[39] Clearly, predictions of rapid labor substitution by AI are unlikely to be realized. Nevertheless, projections prepared for the Association of American Medical Colleges make passing reference to model predictions of a declining demand for radiologists between 2019 and 2034, without mentioning any estimated magnitude.[40] Those projections are based on demographic changes and current treatment rates by group and do not take technological change into account.
Chart 2. Total number of radiologists mainly providing patient care, 2000–19 Year Radiologists 2000 28,444 2001 29,588 2002 30,276 2003 30,998 2004 31,128 2005 31,806 2006 32,256 2007 32,496 2008 33,286 2009 33,758 2010 34,230 2011 34,608 2012 35,213 2013 36,610 2014 36,258 2015 36,341 2016 36,493 2017 36,822 2018 37,218 2019 37,068
Chart 3. Radiologists as a percent of all physicians, 2000–19 Year Radiologist share 2000 4.39 2001 4.42 2002 4.49 2003 4.48 2004 4.45 2005 4.43 2006 4.46 2007 4.46 2008 4.49 2009 4.50 2010 4.55 2011 4.51 2012 4.43 2013 4.44 2014 4.40 2015 4.37 2016 4.35 2017 4.32 2018 4.27 2019 4.26
It is important to recognize that trends in demand for radiologists are strongly affected by imaging reimbursement policies, population aging, and radiologists’ retirement rates.[41] In addition, the practice of radiology involves a wide variety of complex diagnostic and related tasks. How rapidly AI can scale to substitute for an appreciable number of those myriad tasks and any new ones that result from medical progress is an empirical question, but the process is likely to be gradual and far from realized by 2026.[42]
Fast food and counter workers
In 2012, a San Francisco startup demonstrated a fully automatic machine for making hamburgers that purportedly produced 350 complete burgers per hour and would pay for itself in under 1 year of operation. These and similar robotic food preparation machines prompted some to consider that fast food employment could be cut by 25–50 percent by 2030, or some similar date.[43] However, BLS projections to 2029 foresee 11.4-percent growth in this occupation, following nearly 30-percent increase for the 2008–18 period and 35-percent growth for the 1999–2009 period. The projections for the 2008–18 period (13.7 percent) underestimated actual growth considerably, so it will be interesting to see whether this occupation also grows faster than projected for the 2019–29 period.
Janitors and cleaners, and maids and housekeeping cleaners
The first robot vacuum cleaners were introduced in 2002, and their mapping and navigation software has grown much more sophisticated over time. Their early introduction provides a long timeframe to test the idea that even automation substituting for only some of an occupation’s component tasks will reduce the size of the occupation proportionately. More recently, robotic commercial floor cleaners have appeared, and such cleaning tasks are considered ripe for further substitution by robots.[44] Both janitors and maids and housekeepers are quite large occupations. (See table 2 and chart A-1 in the appendix.) However, janitor jobs are projected to increase by 4.4 percent from 2019 to 2029, roughly in line with their modest growth rates since 1999. The number of maids and housekeepers have contracted modestly over time. The OEWS series used for the 1999–2009 period includes only wage and salary workers, while projections and observed data for the 2008–18 period and the 2019–29 period also include the self-employed, which increases the number of maids and housekeepers by more than two-thirds when comparing figures for 2008 and 2009. As such, comparisons across data series should not be made for this occupation. However, the projections show the total number of maids and housekeepers declined by less than 0.3 percent between 2008 and 2018, despite 16 years of robot vacuum sales, and a similar decline is projected for the 2019–29 period. Although private households have long sought relief from the task of vacuuming, which accounts for a meaningful share of work time for maids and housekeepers, the availability of robot vacuums does not seem to have made large inroads into the number of jobs in this occupation and is not expected to do so by 2029.
Landscaping and groundskeeping workers
Robot lawn mowers that work without a human operator have attracted attention for years and have been cited as putting jobs at risk.[45] However, one leading roboticist, the cocreator of the robotic vacuum cleaner, is skeptical that they can be easily adapted for work on commercial properties.[46] BLS projects that jobs in this occupation will increase 10 percent from 2019 to 2029, though employment was flat from 2008 to 2018, when projections anticipated 18-percent growth, in contrast to the 16.3-percent growth recorded in the OEWS database for 1999 to 2009.
Heavy and tractor-trailer truck drivers
Driverless trucks have often been seen as the most likely near-term application of self-driving vehicles , though other writers on automation are more cautious about the ability of the technology to dispense with drivers entirely.[47] Jobs in this occupation are projected to increase 1.5 percent between 2019 and 2029, which, while representing a slowdown from 2008 to 2018 (8.9 percent), is more rapid than the 0.5 percent decrease recorded in the OEWS database for 1999 to 2009.
Industrial truck and tractor operators
Forklifts and other material-moving vehicles and carts represent another application of mobile robotic technology.[48] However, industrial truck and tractor operators jobs are projected to remain stable between 2019 and 2029, continuing a pattern observed at least as far back as 1999, perhaps partly reflecting the growth in e-commerce.
Laborers and freight, stock, and material movers, hand
Increased use of robots in warehouse operations might be expected to reduce the number of stock-handling jobs.[49] However, projections anticipate the occupation will grow 4.2 percent between 2019 and 2029, a slowdown from the remarkable 27.5-percent growth over the 2008–18 period that may have reflected the growth of e-commerce. However, the projections for the 2008–18 period proved much too pessimistic (−0.8 percent), so more robust growth than projected for the 2019–29 period is possible. In any case, the projected growth for 2029 is comparable to the growth recorded in the OEWS database for the 1999–2009 period (4.9 percent).
Occupations also affected by previous waves of computing technology
Seventeen occupations used as examples in the recent automation literature have been affected by prior waves of computing technology, in addition to any effects from innovative robots and AI (see table 3, bottom panel). Therefore, recent and projected trends in employment in these occupations may reflect the continuing effects of previous waves of computing whose ultimate impact is not yet fully realized. Fourteen of these occupations are projected to decline or grow by at least half a percentage point below average from 2019 to 2029. Eleven of these 17 occupations declined between 2008 and 2018, and 7 also declined between 1999 and 2009. Clearly, the occupations exposed to prior waves of computing have more consistently negative employment trends and projected trends than the occupations more plausibly affected only by recent advances in AI and robotics.
Loan officers
Recent research on automation notes that traditional algorithms threaten to automate the mortgage origination tasks of loan officers.[50] Projections anticipate modest growth (3.2 percent) to 2029, though the slight decline during the 2008–18 period (−3.2 percent) was not anticipated by the projections (10.1 percent), so it would not be surprising if future growth were modestly negative, as well. By contrast, the OEWS database registered robust growth (49.0 percent) from 1999 to 2009.
Tax preparers
Recent writing cites tax preparers as affected negatively by the ready availability of tax preparation software packages, as well as offshoring.[51] These software packages may use an earlier, rule-based form of AI called expert systems. The BLS projections anticipate this occupation will decline by 0.6 percent by 2029, though the decline for the 2008–18 period (−9.7 percent) was not anticipated by the projections for that period (2.9 percent). Nevertheless, this occupation grew 5.2 percent from 1999 to 2009, despite the general availability of tax software during that period.
One source credits the leading tax program with putting many jobs at risk by “allowing a machine to do the jobs of hundreds of thousands of human tax preparers.”[52] However, the total number of dedicated tax preparer jobs never reached 100,000 according to available occupation statistics. The occupation grew from 69,000 in 2000 to 96,000 in 2008, then declined by 9,300 jobs to 87,000 jobs in 2018.[53] (See tables 2 and 3.)
In fact, high rates of part-year and part-time work make determining the exact number of tax preparers rather complicated. Every year, jobs in the tax preparation services industry peak in February and decline 70–80 percent by July or August.[54] The OEWS data will miss the peak because the program gathers data in May and November. The Quarterly Census of Employment and Wages, which is the source of the figures just cited, collects data each month, but only captures jobs by industry, not by occupation. However, if the core occupation were at risk from expert systems software, it may be reasonable to consider much of the rest of the industry vulnerable as well. Chart 4 shows trends in the tax preparation industry’s employment for 2001 to 2019 using both February and annual average figures.[55] Both series show jobs growing from 2001 to 2009 before falling back to levels similar to those in 2001 by 2019, though the downward trend is much more modest for the annual average. Whether this decrease is merely a return to prior levels or the first stage of a long-run decline remains to be seen. Some fraction of accountants also engages in tax preparation, but job and task variety within that occupation would be expected to buffer the employment effects of tax software within the occupation.
Chart 4. Employment trends in tax preparation services (North American Industry Classification System 54-1213), February peak and annual average Year February peak Annual average 2001 162,999 75,198 2002 173,254 84,885 2003 188,842 88,905 2004 195,865 93,403 2005 207,070 96,042 2006 211,335 102,179 2007 222,737 107,175 2008 228,568 111,321 2009 237,675 113,083 2010 223,063 106,298 2011 209,042 105,614 2012 204,535 100,691 2013 193,813 98,370 2014 189,882 100,985 2015 196,500 103,685 2016 187,664 100,344 2017 173,931 97,330 2018 173,112 98,421 2019 166,502 96,719
Computer programmers
There have been previous efforts to automate aspects of software production, such as the development of computer-aided software engineering tools. Although some current writing on automation believes algorithms are likely to replace human programmers , others consider this to be a more distant prospect or even less likely.[56] Artificial intelligence has been used to create programs that can automatically write computer code in response to plain-language requests, but the code often fails or requires modifications by humans, limiting potential use.[57] Information technology occupations have tended to grow strongly despite the growth of automation and software tools, and current projections anticipate they will continue to do so. One exception is the computer programmer occupation, which is projected to decline by 9.4 percent from 2019 to 2029. However, BLS projections research attributes this mostly to offshoring. The projected magnitude may be an underestimate given the actual decline for the 2008–18 period (−41.3 percent) was far greater than projected (−3 percent), despite a similar decline registered in the OEWS database from 1999 to 2009 (−30.4 percent). Nevertheless, the importance of offshoring complicates any effort to attribute the declining number of jobs in this occupation to automation or AI.
Lawyers and paralegals and legal assistants
Lawyers and paralegals have been affected by a form of offshoring known as legal process outsourcing, as well as by cost-cutting pressures during and after the financial crisis of 2007–08.[58] However, the increased ability of AI software to process troves of electronic documents has led to suggestions that tasks performed by lawyers, paralegals, and related occupations may be under much greater threat from future automation.[59] By contrast, Professors Dana Remus and Frank Levy find lawyers’ tasks are much more diverse than recognized in these writings, and only about 4 percent of billed hours are spent on document review, which is the only task strongly susceptible to automation.[60] Consistent with this view of modest task substitution, BLS projects jobs will increase 4.0 percent for lawyers and 10.4 percent for paralegals between 2019 and 2029, though, again, this represents a slowdown for both groups relative to the 2008–18 period (8.5 percent and 13.7 percent) and even more so compared with rates recorded in the OEWS database for the 1999–2009 period (13.0 percent and 26.3 percent). Although the projections anticipate paralegals will take on more of the tasks previously assigned to legal secretaries and entry-level attorneys, BLS research economists do not consider technology to be a major driver of employment growth for these occupations.
News analysts, reporters, journalists and public relations specialists
Even more than legal occupations, journalist jobs have been changed by prior technology, most notably the internet and social media, which have led to declining readership and lower advertising revenue for many long-established news outlets. However, AI is now being used to compose simple news stories, corporate earnings reports, and similar reading matter, potentially reducing the need for journalists and public relations personnel.[61] The projections anticipate news-related jobs will decline by 11.2 percent between 2019 and 2029, although this decline would be more moderate than the declines for the 2008–18 period (−28.3 percent) and recorded in the OEWS database for the 1999–2009 period (−19.6 percent). However, the decline for 2008–18 was much greater than projected (−6.3 percent), so it is possible that the projected decline for 2029 is also an underestimate.
By contrast, the number of public relations specialists is projected to grow more than 7 percent between 2019 and 2029. Comparable figures are not available for 2008 to 2018, but the occupation grew much faster from 1999 to 2009, more than doubling according to the OEWS database. Nevertheless, this occupation is likely an example in which AI may automate certain tasks, like composing certain kinds of press releases, without substituting for enough tasks within the occupation to meaningfully decrease employment.
First-line supervisors of retail sales workers, retail salespersons, counter and rental clerks, and cashiers
Occupations in retail have been cited as vulnerable in recent work on automation.[62] The projections for 2019–29 agree that retail jobs are likely to decline. However, prior technologies, such as e-commerce and mobile apps, are likely to be more important than AI and robotics.[63] Projections research finds cashiers are also likely to decline because of greater use of self-checkout, mobile payments, and the bundling of checkout tasks into retail salespersons’ jobs, none of which rely heavily on AI. Cashiers are projected to decline by 7.4 percent between 2019 and 2029 after growing by 2.8 percent (2008–18) and 8.8 percent (1999–2009). Jobs for retail salespersons and counter and rental clerks are projected to be flat, continuing the trend from 2008–18, while the number of first-line retail supervisors is expected to decline modestly (−5.5 percent), continuing a trend visible since 1999.
Customer service representatives and telemarketers
Telemarketers and customer service representatives (CSR) have been replaced or supplemented with interactive voice response systems that can present callers or listeners with a preprogrammed set of options and, in some cases, interpret human speech and respond flexibly to queries in a limited fashion. Some see these jobs as vulnerable to replacement as artificial intelligence improves while others view customer service jobs as less amenable to automation because customer queries make tasks unpredictable .[64] In fact, CSR jobs are projected to remain stable, after growing by 32 percent (2008–18) and 23 percent (1999–2009), despite marked increases in the offshoring of CSR jobs over time. Projections research finds telemarketing jobs are more threatened by increased use of digital marketing than AI. Telemarketing jobs are anticipated to fall 14.2 percent over 2019–29, which may be optimistic given previous declines of 51 percent (2008–18) and 37 percent (1999–2009), which projections research attributed mostly to the increased use of “do not call” registries and caller ID functions on telephones, rather than automation.
Stockers and order fillers
As e-commerce has increased the need for efficient warehouses, efforts to automate warehouse operations have grown. Advances in warehouse robotics have received particular attention, but the focus on technological substitution usually does not account for the scale effects of e-commerce expansion.[65] In addition, only about 20 percent of stockers and order fillers are employed in the warehouse and storage industry, in which the introduction of robots is furthest along. As such, this may be another example in which job diversity within an occupation limits the potential for technological substitution.[66] BLS projects this occupation will grow 0.7 percent between 2019 and 2029, much less than the 10.6-percent growth between 2008 and 2018 (which was close to the projected value of 7.2 percent), and also less than the more modest 3.5-percent growth in the OEWS between 1999 and 2009. The projection of stable employment to 2029 reflects the combined effects of labor substitution due to various technologies (automated order-taking systems and automated storage and retrieval equipment based on sensors, bar codes, and radio frequency identification) and the growth of labor demand for tasks that technology cannot replace, a scale effect. Many of the computing technologies, as well as e-commerce itself, predate the recent wave of mobile robotics and machine learning, so there is some element of technological continuity in this case. Given the continued expansion of e-commerce, occupational employment trends in this sector may be a race between increasing technological capabilities and increasing demand for the industry’s output.
Reservation/transportation ticket agents
Many of the functions of this occupation have been replaced by websites.[67] The projections anticipate ticket agent jobs will drop nearly 3 percent from 2019 to 2029, which would represent significant moderation of the 20.5-percent decline from 2008 to 2018. However, the projections for 2008–18 anticipated job growth (8.1 percent), so there may be similar issues with the projections to 2029. Nevertheless, the decline from 2008 to 2018 was more moderate than the decline from 1999 to 2009 (−35.9 percent), so there is no evidence of accelerated job loss either.
Farmworkers and agricultural equipment operators
A number of agricultural robots are dexterous enough to milk cows, apply fertilizers and pesticides, prune grape vines, and harvest oranges, strawberries, and almonds.[68] However, farm jobs have been declining for many decades, so the extension of machines to fine motor tasks such as picking fruits and vegetables would represent a kind of handoff between older and newer technologies, rather than an entirely new area in which technology substitutes for labor. Nevertheless, this group of occupations is projected to grow by 1.0 percent between 2019 and 2029, after having grown more than 9.0 percent from 2008 to 2018, despite projections of 2.4-percent decline. According to the BLS projections research, this is because the effect of further mechanization, which reduces labor demand, is expected to be more than offset by increases in production, a scale effect. For table 2 and table 3, this group also includes agricultural equipment operators, who are projected to grow, in order to capture slightly offsetting job growth due to increased technology use in these work settings. The OEWS database recorded 8.9-percent growth for this occupation from 1999 to 2009, but the OEWS database excludes most agriculture-related industries so comparisons between the two series are inappropriate for this sector.
Welders
Welding was one of the earliest applications of industrial robotics and remains one of the most common uses of commercial robots.[69] However, after declining nearly 17 percent between 1999 and 2009, the number of welders remained relatively flat over 2008–18, consistent with projections. The projections for 2019–29 also anticipate little change.
Occupations losing the most jobs, 2008–18
The previous sections examined employment trends in occupations selected because of their presumed vulnerability to new automation technologies. By contrast, this section begins with the occupations that have experienced the largest job losses and discusses the technologies that may be responsible for their decline.
Table 4 is based on the same 669 occupations in the OEWS database as table 1. The top row shows that employment in these occupations increased 17 percent over the 19-year period, 1999 to 2018. Among 238 occupations that experienced large losses (greater than 5 percent) between 1999 and 2018, the total number of jobs declined by 8.5 million, or 31 percent, over 19 years (row 2). Relative to all jobs in 1999, this represents a loss of 7.4 percent. Because the number of jobs increased overall, retaining the jobs that were lost would have increased the total in 2018 by somewhat less (6.4 percent). Both figures are many times smaller than the widely cited 47-percent job loss envisioned for the 2010–30 period, despite reflecting other forces, such as trade and offshoring, in addition to technological change. In addition, the losses were more than offset by job gains elsewhere, as the total number of jobs increased by more than 19 million between 1999 and 2018, implying the other occupations gained more than 28 million jobs.
Table 4. Top 30 detailed occupations losing jobs, 1999–2018 SOC code Title 1999 2018 Change Percent change 1 All occupations 114,728,000 134,219,408 19,491,408 17.0 1 Occupations losing more than 5 percent of jobs 27,403,670 18,856,440 -8,547,230 -31.2 1 Occupations losing more than 45 percent of jobs 7,032,300 2,651,060 -4,381,240 -62.3 43-9021 Data entry keyers 520,220 174,930 -345,290 -66.4 41-9041 Telemarketers 485,650 164,160 -321,490 -66.2 15-1021 Computer programmers 528,600 230,470 -298,130 -56.4 51-6031 Sewing machine operators 403,770 136,450 -267,320 -66.2 43-9022 Word processors and typists 271,310 53,130 -218,180 -80.4 43-4151 Order clerks 376,430 159,210 -217,220 -57.7 43-2011 Switchboard operators 248,570 71,600 -176,970 -71.2 51-4031 Cutting, punching, press machine setters/operators 353,300 186,640 -166,660 -47.2 43-9011 Computer operators 198,500 34,700 -163,800 -82.5 43-4071 File clerks 266,890 110,020 -156,870 -58.8 43-5053 Postal service mail sorters, processors, operators 234,820 103,830 -130,990 -55.8 17-3023 Electrical and electronic engineering technicians 242,160 126,950 -115,210 -47.6 43-9051 Mail clerks and mail machine operators, except postal 198,440 86,150 -112,290 -56.6 53-7063 Machine feeders and offbearers 176,400 66,380 -110,020 -62.4 47-2211 Sheet metal workers 231,690 131,570 -100,120 -43.2 51-5022 Prepress technicians and workers 109,350 29,990 -79,360 -72.6 51-4032 Drilling and boring machine tool setters/operators 75,140 11,400 -63,740 -84.8 51-9151 Photographic process workers/machine operators 76,440 16,680 -59,760 -78.2 51-4111 Tool and die makers 132,350 72,700 -59,650 -45.1 43-5021 Couriers and messengers 134,370 75,720 -58,650 -43.6 51-6063 Textile knitting/weaving machine setters/operators/tenders 79,440 21,190 -58,250 -73.3 51-4033 Grinding/lapping/polishing/buffing machine tool setters/operators/tenders 127,920 71,870 -56,050 -43.8 51-6021 Pressers, textile, garment, and related materials 93,320 38,320 -55,000 -58.9 51-4034 Lathe and turning machine tool setters/operators/tenders 83,940 29,510 -54,430 -64.8 43-4041 Credit authorizers, checkers, and clerks 82,900 29,980 -52,920 -63.8 43-9071 Office machine operators, except computer 101,490 48,580 -52,910 -52.1 51-6064 Textile winding, twisting, and drawing out machine setters/operators/tenders 83,360 31,650 -51,710 -62.0 43-2021 Telephone operators 50,820 5,160 -45,660 -89.8 51-2021 Coil winders, tapers, and finishers 56,350 12,190 -44,160 -78.4 31-9094 Medical transcriptionists 97,260 53,730 -43,530 -44.8 Total 1 6,121,200 2,384,860 -3,736,340 1
There is a much smaller group of 81 occupations whose employment declined by more than 45 percent between 1999 and 2018, equivalent to a 47-percent drop over two decades. The total number of jobs in this group decreased by more than 62 percent (table 4, row 3). However, the 4.4 million jobs lost represent only 3.8 percent of all jobs in 1999 and would have added only 3.3 percent to the total in 2018 had they not been lost. Even though the percent losses within these occupations were similar to those foreseen in the recent automation literature, they were quite atypical, so their effect on overall employment was much smaller than the magnitude of their decline might suggest. This is not intended to minimize the number of jobs lost, the hardships experienced by workers affected by them, or the implications of the changing occupational composition for inequality and economic opportunity more generally. However, the incidence of these dramatic declines is more than an order of magnitude smaller than the 47-percent potential job losses commonly cited in the automation literature. One can find particular occupations that experienced dramatic job losses, but they do not generalize to the broader workforce, even during the first decade of the new wave of robotics and AI. The future would have to diverge dramatically from past patterns for job losses overall to reach 47 percent over 20 years.
Table 4 shows the 30 detailed occupations that lost the largest absolute number of jobs between 1999 and 2018, in descending order.[70] Except for four occupations that each declined nearly 45 percent, this group is a subset of the 81 occupations discussed above. This group accounts for 81 percent of jobs in occupations experiencing large percent drops. The total number of jobs in this group decreased 61 percent over 20 years. These occupations are notable for the apparent influence of previous waves of computing technologies (for example, those used for data entry keyers, word processors, file clerks, switchboard operators) and previous waves of mechanical technologies (for example, those used by sewing machine operators, mail sorters), as well as possible effects of trade and offshoring. The large decline in sewing machine operators reflects the continuation of a decades-long trend, while the large decline in word processors and data entry keyers reflects the ongoing effects of the microcomputer revolution beginning in the early 1980s and subsequent growth of digital data capture. Although the newest technologies attract the greatest attention, most of the major contractions appear to reflect long-run implications of more established technologies, whose full effects unfold over many decades. In terms of overall magnitude, even these reductions represent a 20-year loss of only about 2.5 percent of jobs, whether calculated with 1999 or 2018 totals as the base value.
High-technology occupations
Finally, consideration of the new technology raises the question of the jobs they create, as well as replace. Tables 5 and 6 show trends in science, technology, engineering, and mathematics-related (STEM) jobs. The projections anticipate that computer-related jobs will increase 11.5 percent between 2019 and 2029. However, this may be an underestimate, given growth rates around 30 percent between 1999 and 2018, which were somewhat underprojected for the 2008–18 period. Math-related occupations have grown even faster and are projected to continue to do so for the 2019–29 period, but this group is only about one-twentieth the size of the ICT occupational group. The most notable developments include the near doubling of statistician jobs from 2008 to 2018 and the emergence of data scientist as a recognized occupation. It seems likely that these trends reflect the increasing need for workers in fields like machine learning and data analytics. Some of these jobs produce the software whose potential to replace other kinds of labor have prompted recent concerns. Nevertheless, these two occupations together accounted for only 76,000 jobs in 2019 and were projected to grow to 101,000 jobs by 2029. Other STEM occupations experienced even weaker performance, lagging overall job growth between 2008 and 2018 and projected to continue to do so between 2019 and 2029.
Table 5. Trends in size of STEM occupations (in thousands of jobs) SOC code Title OEWS Projections database 1999 2009 2008 2018p 2018 2019 2029p ICT occupations 15-1251 Computer programmers 528.6 367.9 426.7 414.4 250.3 213.9 193.8 15-1232 Computer user support specialists 462.8 540.6 565.7 643.7 671.8 687.2 741.9 15-1221 Computer and information research scientists 26.3 26.1 28.9 35.9 31.7 32.7 37.7 15-1245 Database administrators and architects 101.5 108.1 120.4 144.7 116.9 132.5 145.3 15-1098 2 Combination (15-1051, 15-1071, 15-1081) 731.2 1,077.7 1,163.7 1,506.5 1,641.2 1 1 15-1211 Computer systems analysts 1 1 1 1 1 632.4 679.0 15-1212 Information security analysts 1 1 1 1 1 131.0 171.9 15-1231 Computer network support specialists 1 1 1 1 1 195.1 207.7 15-1241 Computer network architects 1 1 1 1 1 160.1 168.1 15-1244 Network and computer systems administrators 1 1 1 1 1 373.9 389.9 15-1031 2 Software engineers, applications 287.6 495.5 514.8 689.9 944.2 1 1 15-1032 2 Software engineers, systems software 209.0 385.2 394.8 515.0 421.3 1 1 15-1256 Software developers and quality assurance 1 1 1 1 1 1,469.2 1,785.2 15-1257 Web developers and digital interface designers 1 1 1 1 1 174.3 188.3 15-1299 Computer occupations, all other 1 1 209.3 236.8 412.8 431.1 455.8 1 Subtotal 2,347.0 3,001.0 3,424.3 4,187.0 4,490.2 4,633.4 5,164.6 Mathematics and statistical occupations 15-2011 Actuaries 12.6 17.9 19.7 23.9 25.0 27.7 32.6 15-2021 Mathematicians 3.5 2.8 2.9 3.6 2.9 2.9 3.0 15-2031 Operations research analysts 43.8 61.0 63.0 76.9 109.7 105.1 131.3 15-2041 Statisticians 14.6 21.4 22.6 25.5 44.4 42.7 57.5 15-2098 Data scientists and math occupations, all other 1 1 1 1 1 33.2 43.4 15-2090 Miscellaneous mathematical occupations 1 1 7.8 9.1 2.3 1 1 1 Subtotal 74.4 103.0 116.1 139.1 184.3 211.6 267.8 All other STEM (excluding social scientists, 19-3000) 17, 19 Architecture, engineering, life, physical sciences 2,515.1 2,868.4 3,526.5 3,953.8 3,667.9 3,822.5 3,949.1 1 Total 4,936.5 5,972.5 7,066.9 8,279.9 8,342.4 8,667.5 9,381.5
Table 6. Trends in size of STEM occupations, absolute and percent change SOC code Title Absolute change (in thousands) Percent change 1999–2009 2008–18p 2008–18 2019–29p 1999–2009 2008–18p 2008–18 2019–29p ICT occupations 15-1251 Computer programmers -160.7 -12.3 -176.4 -20.1 -30.4 -2.9 -41.3 -9.4 15-1232 Computer user support specialists 77.7 78.0 106.1 54.7 16.8 13.8 18.8 8.0 15-1221 Computer and information research scientists -0.2 7.0 2.8 5.0 -0.6 24.2 9.7 15.3 15-1245 Database administrators and architects 6.6 24.4 -3.5 12.8 6.5 20.3 -2.9 9.7 15-1098 2 Combination (15-1051, 15-1071, 15-1081) 346.5 342.8 477.5 1 47.4 29.5 41.0 1 15-1211 Computer systems analysts 1 1 1 46.6 1 1 1 7.4 15-1212 Information security analysts 1 1 1 40.9 1 1 1 31.2 15-1231 Computer network support specialists 1 1 1 12.6 1 1 1 6.5 15-1241 Computer network architects 1 1 1 8.0 1 1 1 5.0 15-1244 Network and computer systems administrators 1 1 1 16.0 1 1 1 4.3 15-1031 2 Software engineers, applications 207.9 175.1 429.4 1 72.3 34.0 83.4 1 15-1032 2 Software engineers, systems software 176.2 120.2 26.5 1 84.3 30.4 6.7 1 15-1256 Software developers and quality assurance 1 1 1 316.0 1 1 1 21.5 15-1257 Web developers and digital interface designers 1 1 1 14.0 1 1 1 8.0 15-1299 Computer occupations, all other 1 27.5 203.4 24.7 1 13.1 97.2 5.7 1 Subtotal 654.0 762.7 1,065.9 531.2 27.9 22.3 31.1 11.5 Mathematics and statistical occupations 15-2011 Actuaries 5.4 4.2 5.3 4.9 42.8 21.4 26.8 17.7 15-2021 Mathematicians -0.7 0.7 -0.1 0.1 -19.7 22.5 -1.9 3.4 15-2031 Operations research analysts 17.2 13.9 46.7 26.2 39.3 22.0 74.1 24.9 15-2041 Statisticians 6.8 2.9 21.8 14.8 46.2 13.1 96.7 34.7 15-2098 Data scientists, math occupations, all other 1 1 1 10.2 1 1 1 30.7 15-2090 Miscellaneous mathematical occupations 1 1.3 -5.5 1 1 16.2 -70.7 1 1 Subtotal 28.7 23.0 68.2 56.2 38.5 19.8 58.7 26.6 All other STEM (excluding social scientists, 19-3000) 17, 19 Architecture, engineer, life, physical sciences 353.4 427.3 141.4 126.6 14.0 12.1 4.0 3.3 1 Total 1,036.0 1,213.0 1,275.5 714.0 21.0 17.2 18.0 8.2
Conclusion
Numerous observers believe recent developments in robotics and AI may cause an unprecedented wave of automation-related job losses. In this view, the new technology is fundamentally different from earlier waves of computing technology because it improves at an accelerating rate and substitutes for a much wider range of job tasks. A widely cited scenario estimates that 47 percent of jobs are susceptible to automation between 2010 and 2030, implying a decisive break with previous rates of occupational growth and decline. As part of this argument, proponents have cited 28 detailed occupations as notably susceptible to job loss due to new technology. However, these occupations did not exhibit any general tendency toward notably rapid job loss in the first half of this period (2008–18) and are not projected to experience such losses in the second half (2019–29). Occupations that did decline were mostly those that were vulnerable to previous waves of computing technology and other trends, such as offshoring, rather than occupations newly susceptible to automation due to AI and advanced robotics. Actual job losses within these occupations for 2008–18 and the losses projected for 2019–29 often exhibit continuity with previous trends dating to 1999–2009 and are generally within the range of typical growth rates observed for all occupations for 1999–2018. This points to the importance of carefully distinguishing the effects of different generations of computing technology in any discussion of individual cases and overall trends. In general, most occupations are small, few decline by 30 percent or more over 10 years, and those that do so account for a small share of the total number of jobs. Although the performance of the 2008–18 projections with respect to these occupations taken individually was varied, the projections’ performance was strong for the group overall. The performance of the 2008–18 projections provides no reason to suppose that the projections to 2029, which also foresee gradual changes rather than a break with previous trends, systematically underestimate future job losses.
Even among the occupations cited in the automation literature, rates of actual and projected job loss rarely reached levels that the automation literature suggests are becoming common or soon will be. Although there are large occupations that experienced large percent declines over two decades, those occupations account for much less than 47 percent of jobs, and their losses are more than offset by growth in other occupations, which is to say those jobs are not representative of a broader trend.
Part of the reason new technology does not produce sharper changes in employment is the diversity of jobs within occupations and the diversity of tasks within jobs, not all of which are equally susceptible to technological substitution. Automation of some tasks may also alter the task composition of jobs, rather than simply reducing the number of jobs. The growth of product demand within industries implementing new technology can buffer the employment effects of technological change. On a broader scale, population growth and economic growth are associated with expanding employment. It is also possible that adoption rates for new production technology are more gradual than commonly assumed. The automation literature implicitly claims that technological substitution will be so great as to dominate any offsetting forces, producing unusually large job losses. However, the total number of jobs grew after the AI breakthroughs of the early 2010s, and BLS projects it will continue to do so, as will most of the specific occupations the automation literature considers to be on the leading edge of this wave of technological displacement.
Fears that automation will cause widespread job losses have been raised repeatedly in the past, which, in retrospect, usually greatly overestimated the scale of actual displacement. Recent experience and projections suggest a similar pattern may be occurring with recent developments in AI and robotics. For various reasons, technological change seems to be generally more gradual than commonly recognized. Prior waves of computing may be too familiar to receive much attention from observers of emerging trends, but their immediate effects are probably smaller than anticipated and their full impact unfolds gradually over a longer timeframe than recognized. None of this is to minimize the hardships experienced by displaced workers. However, rapid leaps in technology in the early 2010s prompted many to envision a future scenario of massive disruption. This article has examined specific occupations that are most favorable to the automation thesis and found little support for this view. It is entirely possible that robotics and AI are simply another in a long line of waves of innovation whose effects on employment will unfold at rates comparable to those in the past.
ACKNOWLEDGEMENT: I would like to thank Elizabeth Cross, Richard Freeman, Maury Gittleman, Lindsey Ice, Emily Krutsch, and Emily Rolen for very helpful comments on previous versions of this paper. I would like to thank Michael Wolf for very helpful comments, wide-ranging discussions, and assistance with the data used in this paper. I thank Stephen Pegula for providing data on radiology employment.
Appendix: The distribution of occupational sizes
The automation literature and discussions of changes in the nature of work more generally often use specific occupations as illustrations of what are believed to be general trends. It is useful to have a better understanding of the range of occupational sizes in both absolute and relative terms to have some yardstick for judging whether any given occupation of interest is large or small. The mean occupation, which has approximately 200,000 jobs, could serve as a dividing line between large and small occupations, while the median, which is much smaller (approximately 54,000 jobs), is probably much too small to serve as a useful benchmark, though it does indicate the highly skewed nature of the distribution. In fact, a closer examination shows that there are relatively few occupations that can be considered large by most reasonable standards and many occupations that are small.
Table A-1 shows the distribution of occupations by size decile, with each decile containing about 79 occupations. The largest occupation accounted for 2.7 percent of jobs in 2019 (column 4), but the smallest occupation in the top decile had less than 0.3 percent of all jobs (column 3). The average occupation in the top decile had nearly 1.3 million jobs (column 1) and accounted for about 0.8 percent of all jobs (column 2). By contrast, the average occupation in the median (fifth) decile had only 67,000 jobs and accounted for 0.04 percent of all jobs. The number of jobs per occupation and their percent shares decline further from there. Collectively, the top two deciles, 158 occupations out of 790 total, accounted for over 78.1 percent of all jobs (column 6), while the remaining 632 occupations accounted for the remaining 21.9 percent of jobs. The bottom half of the distribution (395 occupations) accounted for only about 5.3 percent of all jobs. Clearly, any reference to a particular occupation as susceptible to automation will have very different implications depending on whether it is among the few large ones or the many small ones. If the latter, then the portrait of automation’s impact can easily be unrepresentative. Even among the largest occupations, there are many that account for only 0.15 percent to 0.30 percent of all jobs.
Table A-1. Size distribution of occupations by decile, 2019 Decile Mean jobs per occupation (thousands) Percent of all jobs in decile's mean occupation Percent of jobs in decile's smallest occupation Percent of jobs in decile's largest occupation Total jobs in decile Decile's percent share of all jobs 1 1,267.9 0.78 0.29 2.69 100,167.3 61.53 2 341.7 0.21 0.14 0.28 26,996.7 16.58 3 168.1 0.10 0.08 0.14 13,283.4 8.16 4 106.9 0.07 0.05 0.08 8,445.6 5.19 5 67.3 0.04 0.03 0.05 5,316.5 3.27 6 44.0 0.03 0.02 0.03 3,432.8 2.11 7 29.8 0.02 0.02 0.02 2,382.5 1.46 8 19.0 0.01 0.01 0.01 1,497.2 0.92 9 11.6 0.01 0.01 0.01 894.9 0.55 10 4.7 0.00 <0.001 0.01 379.7 0.23 All deciles 206.1 0.13 0.00 2.69 162,796.6 100.00
As an aside, because the largest approximately 20 percent of occupations account for nearly 80 percent of all jobs, it is natural to ask whether occupational size follows a power law. According to this mathematical model, the natural log of an occupation’s size is related linearly to the natural log of its rank, sorted from largest to smallest.[71] Chart A-1 plots log occupation size against log occupation rank. The relationship is far from linear, and even a quadratic function (see chart A-2) shows significantly poor fit at either end, where actual occupational employment is much lower than predicted. Occupational size does not follow a power law. In fact, the distribution of occupational employment is strikingly close to a lognormal distribution (see chart A-3), which is also consistent with a highly skewed size distribution containing a large mass of relatively small occupations and a long tail containing a small number of large occupations.[72]
Chart A3. Distribution of the natural logarithm of occupational employment, 2019 Range Normal distribution Kernel density estimate -1.0 0.00 0.00 -0.9 0.00 0.00 -0.8 0.00 0.00 -0.7 0.00 0.00 -0.6 0.00 0.00 -0.5 0.00 0.01 -0.4 0.00 0.01 -0.3 0.01 0.01 -0.2 0.01 0.01 -0.1 0.01 0.01 0.0 0.01 0.01 0.1 0.01 0.01 0.2 0.01 0.01 0.3 0.01 0.01 0.4 0.02 0.02 0.5 0.02 0.02 0.6 0.02 0.02 0.7 0.03 0.03 0.8 0.03 0.03 0.9 0.03 0.03 1.0 0.04 0.04 1.1 0.04 0.04 1.2 0.05 0.05 1.3 0.05 0.06 1.4 0.06 0.06 1.5 0.06 0.07 1.6 0.07 0.08 1.7 0.08 0.08 1.8 0.08 0.09 1.9 0.09 0.10 2.0 0.10 0.11 2.1 0.11 0.13 2.2 0.12 0.14 2.3 0.12 0.15 2.4 0.13 0.16 2.5 0.14 0.17 2.6 0.15 0.18 2.7 0.16 0.19 2.8 0.17 0.20 2.9 0.18 0.21 3.0 0.19 0.22 3.1 0.19 0.22 3.2 0.20 0.23 3.3 0.21 0.23 3.4 0.21 0.24 3.5 0.22 0.24 3.6 0.23 0.24 3.7 0.23 0.24 3.8 0.23 0.24 3.9 0.24 0.24 4.0 0.24 0.24 4.1 0.24 0.23 4.2 0.24 0.23 4.3 0.24 0.23 4.4 0.24 0.22 4.5 0.24 0.22 4.6 0.23 0.22 4.7 0.23 0.21 4.8 0.23 0.21 4.9 0.22 0.20 5.0 0.21 0.19 5.1 0.21 0.19 5.2 0.20 0.18 5.3 0.19 0.17 5.4 0.19 0.16 5.5 0.18 0.15 5.6 0.17 0.14 5.7 0.16 0.14 5.8 0.15 0.13 5.9 0.14 0.13 6.0 0.13 0.12 6.1 0.12 0.12 6.2 0.12 0.11 6.3 0.11 0.10 6.4 0.10 0.09 6.5 0.09 0.08 6.6 0.08 0.07 6.7 0.08 0.07 6.8 0.07 0.06 6.9 0.06 0.06 7.0 0.06 0.05 7.1 0.05 0.05 7.2 0.05 0.04 7.3 0.04 0.04 7.4 0.04 0.04 7.5 0.03 0.03 7.6 0.03 0.03 7.7 0.03 0.03 7.8 0.02 0.02 7.9 0.02 0.02 8.0 0.02 0.02 8.1 0.01 0.02 8.2 0.01 0.02 8.3 0.01 0.01 8.4 0.01 0.01 8.5 0.01 0.01 8.6 0.01 0.01 8.7 0.01 0.00 8.8 0.00 0.00 8.9 0.00 0.00 9.0 0.00 0.00
Table A-2 shows occupations at different points of the size distribution and the number of jobs and share of all jobs they represent. In addition to the 20 smallest and 20 largest occupations, the table includes twenty occupations increasing in size from the median. These figures provide another perspective on how rapidly the size of occupations drops and can serve as reference points for the discussion of automation’s possible impacts. Some of the large occupations, such as material movers, heavy truck drivers, and household and commercial cleaners, figure prominently in discussions of the increasing power of robots and AI, but others, like those related to nursing and personal care aides, do not. Occupations like retail salespersons, cashiers, and bookkeeping, accounting, and auditing clerks are somewhat more ambiguous, because they have been affected by prior waves of computing technologies, such as microcomputers, e-commerce, and self-checkout, in addition to any effects of newer technology like robots and AI.
| 2022-12-01T00:00:00 |
https://www.bls.gov/opub/mlr/2022/article/growth-trends-for-selected-occupations-considered-at-risk-from-automation.htm
|
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|
59 AI Job Statistics: Future of U.S. Jobs | National University
|
59 AI Job Statistics: Future of U.S. Jobs
|
https://www.nu.edu
|
[
"Timothy Prestianni"
] |
Since 2000, automation has resulted in 1.7 million U.S. manufacturing jobs lost. 40% of employers expect to reduce their workforce where AI can automate tasks.
|
Artificial intelligence (AI) is rapidly transforming the U.S. job market, sparking widespread conversations and concerns about AI and job loss, AI taking jobs, and how many jobs AI will replace. This statistical roundup compiles the latest data and trends on AI replacing jobs, job loss statistics, and the broader impact of artificial intelligence on employment.
30% of current U.S. jobs could be automated by 2030
By 2030, 30% of current U.S. jobs could be fully automated, while 60% will see significant task-level changes due to AI integration. This shift highlights the urgent need for workers to adapt through upskilling and technological proficiency.
General AI Impact on the U.S. Job Market
AI is accelerating automation across sectors, with profound implications for employment in the United States. From job losses and workforce reductions to shifting career paths and retraining needs, these statistics outline the scale and speed of AI’s disruption.
30% of current U.S. jobs could be automated by 2030; 60% will have tasks significantly modified by AI. 300 million jobs could be lost to AI globally, representing 9.1% of all jobs worldwide. 23.5% of U.S. companies have replaced workers with ChatGPT or similar AI tools. 49% of companies using ChatGPT say it has replaced workers. In May 2023, 3,900 U.S. job losses were directly linked to AI, making it the seventh-largest eliminator of jobs that month. 13.7% of U.S. workers report having lost their job to a robot or AI-driven automation. Since 2000, automation has resulted in 1.7 million U.S. manufacturing jobs lost. 40% of employers expect to reduce their workforce where AI can automate tasks. By 2030, 14% of employees globally will have been forced to change their career because of AI. 20 million U.S. workers are expected to retrain in new careers or AI use in the next three years. 30% of U.S. workers fear their job will be replaced by AI or similar technology by 2025. Automating half of current tasks worldwide could take another 20 years. Entry-level jobs are especially vulnerable, with nearly 50 million U.S. jobs at risk in coming years. As entry-level roles decline, salary expectations are also shifting downward. AI could impact nearly 60% of jobs in advanced economies, but only 26% in low-income countries. AI’s impact is expected to be most disruptive in the next 10–30 years, with a possible 50% of jobs automated by 2045.
Which Jobs Will Thrive?
While many jobs face automation, certain roles are expected to flourish in the AI era. These statistics highlight occupations projected to grow, including those in technology, healthcare, skilled trades, and emerging AI-related fields. This section also explores why some jobs are more resilient to automation and the new opportunities created by AI adoption
Software developers are projected to see a 17.9% increase in employment from 2023 to 2033. Job postings for entry-level software engineers grew 47% between October 2023 and November 2024. The share of jobs in STEM fields grew from 6.5% in 2010 to nearly 10% in 2024, an almost 50% increase Installation, repair, and maintenance jobs are at lower risk from AI and remain in demand. Construction and skilled trades are among the least threatened by AI automation. Personal services (e.g., food service, medical assistants, cleaners) are less likely to be replaced by AI and have rebounded post-pandemic, with food preparation and serving jobs expected to add over 500,000 positions by 2033 as in-person services remain essential. Healthcare roles (nurses, therapists, aides) are projected to grow as AI augments rather than replaces these jobs; for example, nurse practitioners are projected to grow by 52% from 2023 to 2033, much faster than the average for all occupations. AI and data science specialists are among the fastest-growing job categories in 2025. Cybersecurity professionals are in growing demand due to increased digital threats with a 32% growth in information security analyst jobs from 2022 to 2032, far outpacing the average for all occupations. Renewable energy technicians (solar, wind) are projected to see double-digit growth rates, with solar photovoltaic installers expected to grow by 22% and wind turbine technicians by 44% from 2022 to 2032. AI trainers, ethicists, and explainability experts are emerging roles created by AI adoption. AI support roles (prompt engineers, AI operations) are new job types with rapid growth. Personal financial advisors will likely continue to see strong employment growth despite AI, with the BLS projecting a 13% increase in jobs from 2022 to 2032, as clients continue to value human expertise for complex financial decisions.
Which Jobs Will Disappear?
Not all roles will survive the AI revolution. This section details the occupations most vulnerable to automation, from clerical and administrative positions to routine manufacturing and customer service jobs.
Clerical and administrative roles (secretaries, data entry clerks) are among the first to be automated. Bank tellers and cashiers are seeing rapid declines as digital banking and self-checkout expand. Employment of bank tellers is projected to decline by 15% from 2023 to 2033, eliminating about 51,400 jobs, while cashier employment is projected to decline by 11% (a reduction of 353,100 jobs) over the same period Routine manufacturing jobs: 1.7 million lost since 2000 due to automation. Telemarketers and call center agents are increasingly replaced by AI-driven chatbots. Medical transcriptionists’ employment is projected to decline by 4.7% from 2023 to 2033. Customer service representatives’ employment is projected to decline by 5.0% from 2023 to 2033. Credit analysts’ employment is projected to decline by 3.9% from 2023 to 2033. Low-paid service work has seen flat or declining employment since 2019, with AI cited as a contributing factor.
Education and Skills for Future-Proof Careers
As AI transforms the nature of work, the demand for new skills is rising sharply. The skillsets required for job security and advancement are evolving with the growing importance of technological literacy, human-centric abilities, and lifelong learning.
39% of key job skills in the U.S. are expected to change by 2030, down from 44% in 2023. 59% of workers will require upskilling or reskilling by 2030. Technological skills are projected to grow in importance faster than any other skill category over the next five years. Eight of the top ten most requested skills in U.S. job postings are durable (human) skills. Communication, leadership, metacognition, critical thinking, collaboration, and character skills each appear in ~15 million U.S. job postings annually. 66% of all tasks in 2030 will still require human skills or a human-technology combination. Employers expect creative thinking, resilience, flexibility, and agility to rise sharply in importance by 2030. Analytical thinking, curiosity, and lifelong learning are among the top 10 skills on the rise for future jobs. Data literacy is now considered “the new workplace currency” and is critical across all major U.S. industries, as businesses must interpret and act on an estimated 182 zettabytes of data by 2025. AI and machine learning skills are increasingly fundamental, not just for tech workers but for all professionals. Cybersecurity and technological literacy are among the fastest-growing skill demands in the U.S. job market. Project management and UX design are among the most recommended upskilling paths for U.S. workers in 2025. Lifelong learning and upskilling are now a top priority for 75% of U.S. employers.
Impact by Generation
AI’s influence on employment is not uniform across age groups. This section explores how different generations are experiencing and responding to AI-driven changes, with a focus on the heightened vulnerability of younger workers, shifting attitudes toward education, and generational trends in upskilling and workforce participation.
Workers aged 18–24 are 129% more likely than those over 65 to worry AI will make their job obsolete. 49% of Gen Z job seekers believe AI has reduced the value of their college education. Entry-level jobs, disproportionately filled by young workers, are especially at risk, with nearly 50 million U.S. jobs affected. 14% of all workers have already been displaced by AI, but the rate is higher among younger and mid-career workers in tech and creative fields.
Impact by Gender
The impact of AI on jobs also varies by gender, with women disproportionately represented in roles at high risk of automation. These statistics analyze the gendered effects of AI-driven job displacement, the challenges of underrepresentation in tech fields, and the potential for AI to both mitigate and exacerbate workplace inequalities
79% of employed women in the U.S. work in jobs at high risk of automation, compared to 58% of men. Globally, 4.7% of women’s jobs face severe disruption potential from AI, versus 2.4% for men. In high-income nations, 9.6% of women’s jobs are at highest risk for AI automation, compared to 3.2% for men. Women are underrepresented in AI and STEM fields, limiting access to new, high-paying tech jobs created by AI. AI in HR and recruitment could help reduce gender bias if designed carefully, but may also perpetuate or worsen bias if algorithms are not transparent and inclusive.
Conclusion
The rise of artificial intelligence is reshaping the U.S. job market with speed and scale. As shown in these 59 AI job statistics, AI is not only displacing certain roles but also creating new opportunities that demand advanced technical skills, human-centered abilities, and continuous learning. While jobs in areas like administration and manufacturing face growing risks, professions in healthcare, technology, skilled trades, and AI itself are projected to expand.
The data makes one thing clear: preparing for the future of work requires adaptability. Individuals who invest in upskilling, embrace lifelong learning, and build resilience will be better positioned to thrive in an AI-influenced economy. At the same time, institutions and employers must focus on equitable access to education and training to ensure that all generations and all communities can benefit from the opportunities AI brings.
Artificial intelligence is not just replacing jobs, it’s redefining them. Understanding these trends is the first step in preparing for what’s ahead.
Sources
| 2025-05-30T00:00:00 |
2025/05/30
|
https://www.nu.edu/blog/ai-job-statistics/
|
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AI Replacing Jobs Statistics - Content Detector AI
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AI Statistics Finder: Get Instant Data & Facts for Any Topic
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https://contentdetector.ai
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[] |
Only 14% of workers have had their roles replaced by automation. Those who experienced job automation overestimated the number at 47%, while those who hadn't ...
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AI Moxby
Hi, I'm Moxby and can handle any tasks or workflows you like. But for this page specifically, I'm specialized in statistics finder tasks. What can I help you with today?
| 2022-12-01T00:00:00 |
https://contentdetector.ai/articles/ai-replacing-jobs-statistics/
|
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|
Automation Statistics You Need to Know—The Mega-List
|
Automation Statistics You Need to Know—The Mega-List
|
https://www.windwardstudios.com
|
[] |
McKinsey insights reveal about 24% of currently employed women and 28% of men could lose their jobs by 2023. The number of automated jobs increases by 14% every ...
|
Industry
Marketing, Email, Job Loss, RPA, & 14 More
Automation is increasingly influencing the way we do things in different industries. With the pandemic wide-spreading, automation is now no longer a distant-future endeavor, but a real necessity. Many businesses and industries are beginning to adopt automated processes with some spending more than others to achieve a higher degree of automation.
In this blog, we compile the most important industrial automation stats of 2020 in various sectors. Let's go:
Automation in Sales
Automation in sales boosts productivity within the department by 14.5% while bringing down marketing costs by 12.2%.
Sales teams can close 30% more deals when they automate Salesforce. It also reduces the sales cycle by 18% and saves 14 percent administration time.
Lead nurturing sales automation has shown an over 200% increase in conversions for different brands.
Sales automation has helped B2B marketers to improve their sales pipeline by an average of 10 percent.
47% of people who use marketing automation say their main concern is increasing sales revenue.
Marketing automation is credited for improving the quality of leads generated for 60% of the people who use it.
The same people who use marketing automation to improve the quality of leads report 3 times more leads generated every month.
Sales can also be increased by 20% through automating offers using past browsing history to personalize the offers.
Automation can increase the average order size by more than 360% using one-click upsells.
By 2023, 30% of sales, front desk, and customer experience activities will involve robotic process automation.
Automation in Marketing
67% of marketing leaders are utilizing automation platforms in one way or another.
With an annual growth of 14%, marketing automation spending will exceed $25 billion by 2023.
Some of these statistics are unsurprising, for example: 74% of marketers surveyed agreed that the main reason marketers and business owners use automation software is to save time.
Spending on marketing automation solutions is justified by the results: conversions are estimated to increase by 77%.
91% of online marketers see automation as a very crucial factor in the success of their marketing activities.
58% of top marketing automation users assert that conversions and revenue are the best way to measure automation success.
A study by Omnisend confirmed that automating omnichannel marketing is likely to create a customer retention rate of 90%. The same study shows engagement and purchase rates increase by 250% using omnichannel automation.
92% of marketing automation users say they use it to improve their lead generation followed by customer retention and nurturing.
A survey found that 59% of Fortune 500 companies are using marketing automation and the number is steadily on the rise.
Automation in Finance
CEOs could save 20% of the time they spend on financial tasks that could be automated. These include analyzing reports by McKinsey.
In 2017, McKinsey forecast a second wave of automation and AI that will see machines handling between 10 and 25% of banking tasks.
Bot interactions with humans in the banking sector is expected to reach a 90% success rate by 2022.
ICICI Bank was able to cut down time spent redressing complaints about ATM cash disbursal from 12 hours to 4 hours a day by automating the redress process. ICICI deployed over 750 robots to handle daily transactions and has recorded 100% accuracy.
Accounts Payable Automation implementation can improve the processing time for invoices by 10%. According to MineralTree, automating invoice capture saves 1 hour a day. Accounts Payable Automation is an investment that quickly pays for itself in 6 to 18 months.
In 2016, the influence of automated finance advice was overestimated by $1.2 trillion. Different insights had forecast that by 2020 $2.2 trillion would be under asset management.
Jobs Lost to Automation Statistics
More men than women are likely to be affected by automation. McKinsey insights reveal about 24% of currently employed women and 28% of men could lose their jobs by 2023.
The number of automated jobs increases by 14% every year with junior workers the most affected by the trend.
At the same time, automation may force between 40 million and 160 million women to transition between jobs.
About 55% of jobs that do not require college degrees are at risk of being automated.
76% of information workers are not worried about their jobs being automated because they believe they have the skills to get another job at their company.
86% of employees surveyed believe that automation will help them do their work more efficiently to improve productivity and growth.
A survey of employers shows that only 11% are looking to automation as a substitute for human labor.
70% of employees are ready to compete with robots for jobs. Research shows employees will improve their skills to be able to do just as much or better than robots.
The United States and China are projected to see the most impact of automation taking over human jobs. Product improvement will be the biggest push behind the trend.
Humans have worried about losing their jobs to robots since the 1930s.
Automation accounts for one-third of new jobs created since 1930. This has fueled the belief that with automation comes opportunity.
On the other hand, economists believe that modern automation will lead to more job losses than creation by 2030.
In England, automation replaced 850,000 workers between 2001 and 2017, but it also created 3.6 million opportunities.
Automation in Social Media
Almost all tasks involved in posting on social media can be automated. A study shows that automating posts can save 6 hours every day.
83% of marketers agree that scheduling posts for social media is the most suitable digital marketing element to automate. One survey, however, shows there is much more that can be utilized.
Automation in Cyber Security
Research suggests robotic automation can cut down the detection and response time to phishing attacks by as much as 70%.
By 2017, 44% of companies were using AI to detect and deter cyber intrusions. One survey predicted a rise in automated security in 2020.
According to LearnBonds, 68% of major global companies are planning to increase spending on automated cybersecurity solutions.
Investing in automated cybersecurity could increase global revenue opportunities by $5.2 trillion, according to a report by Accenture.
One report indicates that 51% of enterprises surveyed in 2019 were depending primarily on AI to detect cyber threats.
80% of telecom executives surveyed indicated that AI was their main hope for combating cyber-attacks now and in the future.
According to Webroot, 84% of IP professionals in America and Japan are convinced they face the threat of criminals using AI to launch attacks.
Insurance Statistics
By 2030, the United States is forecast to see 46% of its insurance claims processing jobs automated. One report predicts that customer service and claims adjustment jobs, however, will see only 16% displacement.
Quote generation time for insurance agents in California was cut from 14 days to 4 minutes using automation. The insurance firm recorded a 70% increase in sales.
By 2018, claims review was already being considered as one of the biggest processes to be automated. 30% of insurers were considering robotic process automation for this task.
In 2017, Deloitte insight predicted the loss of 22.7 million insurance jobs in the US as a result of automation and the creation of 13.6 million new ones over 10 years.
Automation in Manufacturing
McKinsey Institute estimates that automating different tasks can raise productivity growth to 1.4% annually.
In terms of dollars, an Oxford report estimates that implementing automation in the manufacturing industry could increase $4.9 trillion every year by 2030.
According to a report, the world can save 749 billion working hours by automating 64 percent of manufacturing tasks.
7 out of 10 workers believe adopting automation in their organization will create better opportunities for them in higher-skilled jobs.
The World Economic Forum predicts that by 2022, 42% of the time spent on manufacturing tasks will be automated using robots.
Meanwhile, Oxford Economics says that 20 million manufacturing jobs will be lost to automation by 2030.
Another study says that the main reason 57% of employers want to automate is to improve the productivity of their workforce. Only 24% of employers have a reduction in operating costs as their main reason for automating their business.
The use of automation in three key industries including manufacturing will contribute $15.7 trillion to the global economy by 2030, according to a PWC study.
Automation in Advertising
Google automated bidding is one of the most used advertising automated software. In 2019, it was being used by 70% of advertisers.
Businesses that automate their advertising strategies can save $130,000 annually in advertising costs.
Programmatic advertising is increasingly being adopted by advertisers. In 2018, it accounted for over 80% of digital display marketing in the US.
By the end of 2020, over 90% of mobile display ads will be negotiated automatically using programmatic ads.
By 2022, most companies will automate all their advertising processes. It is forecast to be about 80% of all processes.
In 2016, only 32% of display ads were automated. More research in 2019, however, showed the figure had more than doubled to 72%.
Automation in the Health Industry
The healthcare automation growth statistics indicate the industry is set to record gains of over $63 million by 2026. The global healthcare automation market report estimates an 8.41% growth within 7 years.
A combination of healthcare and pharmacy industries will boost the automation industry with revenue of over $900 million by the end of 2026.
With the number of older people in need of assisted care going up, the purchase of medical robots has risen by 50% each year. The global medical robotics market will hit $20 billion by 2023 as some surgery procedures become fully automated.
According to McKinsey predictions, medicine and pharma can save as much as $100 billion by depending on AI and big data.
Bot interaction in the health sector without the need for human operators is predicted to be at 75% by 2022.
The US is expected to save $150 billion in the healthcare economy by 2026 through the application of AI in the health sector.
Automation in Email
Over 269 billion emails are sent globally on a daily basis and about 2.4 million emails per second. And on an average a business employee sends 40 emails and receives 121 emails everyday. No wonder, almost half of the small and medium businesses surveyed have integrated automated emails as part of their marketing strategy as automating their email and other repetitive tasks is worth nearly $273/hour.
Companies that use automated marketing software to send out emails receive twice the number of leads and 58% more conversions than those that simply send out email blasts.
Businesses that automate follow up emails and outreach campaigns can improve their response rate by 250%.
Adding automated emails to the lead nurturing process can improve revenue by 10%.
Emails sent as part of triggered campaigns account for more than 75% of B2B revenue from emails.
A survey showed that 14% of marketers combine segmentation and automation to improve personalization.
Automation in Cloud
84% of business leaders that have adopted cloud automation have seen an increase in revenue and reduction in operation costs.
Year on year growth for businesses using cloud automation is estimated at 15%.
The market for cloud automation is forecast to grow by $103.9 billion motivated by the global growth of 24.7%.
84% of IT companies are happy with the improvement cloud automation has made to the agility of their company. 81% of IT companies feel that they have been able to improve innovation by using cloud automation.
86% of IT leaders have seen a remarkable improvement in client satisfaction as a result of cloud automation. 59% of IT leaders have used cloud automation to improve the deployment of engineers to work on higher-value tasks.
Automation in Public Relations
By 2023, 38% of PR skillsets could be replaced by automation. Research by CIPR indicates that currently 12% can either be complemented or replaced by AI.
According to the Office of National Statistics UK, 27% of PR and marketing jobs are currently at risk due to automation.
A 2013 research paper by the University of Oxford indicates there is an 18% chance of automation replacing PR jobs.
PR emails sent to media houses or journalists using automated software have a higher open rate. One source says PRs who use their software has a 46% open rate.
PR teams could save 2 hours a week by automating tasks. A survey reveals 98% of PR professionals spend most of their time using email.
Automation in IT
AI-augmented automation will be adopted by 40% of large enterprise I and O teams by 2023 which will push productivity within IT departments and improve scalability.
The number of automation architects within enterprises is forecast to rise by 70% by 2025 up from 20% in 2020.
There will be a reduced need for IT specialists by 2023 fueled by AI-enabled automation used for data management. About 20% of IT specialists will be affected.
As more organizations adopt a combination of hyper-automation and redesign operational processes, they will experience a 30% reduction in operation costs by 2024.
Research by Gartner suggests that I and O staff will get paid 40% more for their automation skills in 2022 as they manage automation implementation.
Automation in Human Resources
43% of HR professionals are optimistic about the introduction of automation to their industry. They feel it will make it better.
42% of recruiters do not think automation will affect their job in anyway. In 2017, the majority of recruiters (55%) did not think automation would cause the loss of jobs by 2020 within their organization.
14% of headhunters are afraid of being replaced by automation technology in their workplace.
Meanwhile, according to Oxford Economics, 1.7 million jobs have already been lost to automation since 2000.
72% of HR officers surveyed revealed they believe the way they recruit will have to change as automation becomes more prevalent.
67% of recruiters believe automation in HR will save or is saving time in recruitment meanwhile 43% feel it removes human bias.
Automation is being used by 20% of companies to eliminate bias and support diversity recruitment. HubSpot reveals diversity recruitment is a concern for many organizations
In 2019, 75% of HR professionals did not have the confidence to effectively use automation to improve their recruitment process.
Talk about robots and automation within the workplace has grown by over 70% but most of the time it is presented in a positive light — Adobe Digital Insights reveal.
Automating repeated tasks like administrative work could save companies as much as $5 trillion annually as well as 69 working days. Talking about robots taking over jobs, it indeed frees up our time to do more productive things.
33% of organizations look to enterprise content management as a solution to having to deal with paper documents.
Out of 3000 business executives interviewed by Boston Consulting Group, 75% would like to use automation and AI as an opportunity to explore other business opportunities.
2550 of 3000 business executives interviewed see AI as an opportunity to gain a competitive advantage.
Labor savings from adopting automation in the office can guarantee an organization between 30 and 200% return on investment in the first year.
Robotic Process Automation Stats
Robotic process automation (RPA) is growing fast. Forrester forecasts that by 2021 it will be worth $2.9 billion from $250 million 5 years ago.
A forecast by Grand View Research for the period between 2017 and 2025 puts RPA worth at $3.11 billion by 2025.
According to Deloitte, the current race for robotic automation will reach near-universal adoption within 5 years.
Research by Deloitte indicates that the majority of companies are just beginning to adopt RPA. 58% confirmed this.
Only 17% of organizations adopting RPA have reported resistance from employees. The majority see it as a good thing.
78% of organizations beginning to adopt RPA are planning to scale up within 3 years.
By 2026, workflow automation statistics indicate small and medium businesses are forecast to create market opportunities of $1600 million by taking up workflow automation according to Global Wire.
Automation in Ecommerce
Almost 50% of eCommerce companies use marketing automation software to attract more shoppers.
By 2025, 95% of interactions between customers and retailers will be handled by automated systems. This will include phone calls and live chat.
The number of consumers open to AI handling customer service is more than those who oppose it. 73% say they are fine with automated customer care as long as it improves service.
Experian reveals customers who get automated emails about incomplete purchases in their shopping carts are 2.4 times more prone to complete the purchase.
Tractica forecast that warehousing and logistics automated shipments will reach 620,000 units by 2021 driven by high demand for faster deliveries.
Automated warehouses are 40% more likely to ship orders within one day of the order being made. Robotics Business Review indicates it also reduces labor costs.
Automation in Agriculture
The agricultural robot market is expected to grow at a rate of 34.5% between 2020 and 2025 by the end of this period it will be worth $20.3 billion.
Meanwhile, by 2022, the agricultural robot market will be worth $12 billion as more farmers see improved yields after adopting the technology.
More than 35,000 robotic milking systems are currently in use around the world. Most farmers utilize them to minimize labor costs.
The market for automated farming equipment (drones and robots) is expected to rise to $23.06 billion by 2028 driven by global demand for food.
| 2022-12-01T00:00:00 |
https://www.windwardstudios.com/blog/automation-statistics-mega-list
|
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Jobs Lost To Automation Statistics | Study Hub - Edoxi
|
Jobs Lost To Automation Statistics
|
https://www.edoxi.com
|
[
"Jothi Kumar",
"Software",
"It Trainer",
"Julie Archer",
"Shaheer Kurikkal",
"Shaheen M"
] |
25% of American jobs are highly susceptible to automation, with 73 million jobs potentially lost in the U.S. over the next five years.
|
Automation and job displacement are increasingly becoming one of the major topics of discussion nowadays. Automation has become a major trend in many industries because it increases efficiency and productivity. However, this shift towards automation has a negative side effect, as it can lead to job displacement.
Automation has already had a significant impact on many sectors, and the trend is expected to continue as technology advances. This is especially true in industries such as manufacturing and retail, where machines have replaced human labour as devices can perform tasks faster and more accurately than humans.
Despite the potential benefits of automation, it could result in a reduction of the labour force and a displacement of workers, leading to economic and social challenges.
As technology continues to evolve, it is estimated that automation will lead to a significant loss of jobs in the near future and that nearly 20 million jobs will be replaced by automation.
This is a huge number, and it is crucial to prepare for the potential impacts of automation on the workforce. However, many experts believe that automation could actually create more jobs, as companies would need to hire new employees to manage automation systems and robots.
This blog checks into jobs lost to automation statistics, and high-risk occupations due to automation shedding light on the evolving employment landscape and its implications for individuals and society.
What is Automation?
Automation is the use of machines, technology, and software to automate tasks that would otherwise be done by humans. Automation involves the development of systems and software capable of performing repetitive, rule-based, or data-driven activities with minimal human intervention. Automation has become increasingly prevalent in various industries, transforming how we work and interact with technology. Manufacturing, transportation, healthcare, and retail have been utilizing automation for centuries, and its use has increased significantly in recent years.
What are the different types of automation?
There are three main types of automation. The first is;
Robotic automation: Robotic Automation uses robots to perform tasks. Cognitive Automation: Cognitive Automation is done using Artificial Intelligence (AI) and machine learning algorithms to automate tasks. Process Automation: In Process Automation, automated systems are used to streamlining the processes.
What impact will automation create on human labour?
Automation is expected to have a significant impact on the workforce, as it is projected to eliminate millions of jobs in the coming years. The impact of automation on human labour can vary depending on the industry and job role. However, it is likely to lead to job losses in certain sectors, as machines and algorithms can perform tasks with greater speed and accuracy than humans. While other jobs may require new skills or training to adapt to changing technologies.
Some estimates predict that up to 47% of jobs are at risk of automation by 2030. In general, jobs that involve repetitive tasks, such as data entry and customer service, are the most vulnerable to automation.
Automation could reduce wages in certain industries, as companies no longer need to pay human workers for tasks that can be done by machines or robots.
As such, automation could have a major impact on income inequality, as those with the necessary skills to operate and manage automated systems will be better positioned to take advantage of the benefits of automation.
Additionally, automation could lead to a shift in the types of jobs available in the market, as companies will need to focus on developing skills related to automation, robotics, and AI. This could result in an increase in new types of jobs that require different skills.
As automation could contribute to job displacement and income inequality, it is important to consider the potential implications of automation and explore strategies to support workers who may be impacted.
What factors lead to the increased adoption of automation in industries?
Factors driving automation adoption in industries include:
Cost Savings: Automation can reduce the need for human labour, which can help keep labour costs low and optimise resource allocation.
Automation can reduce the need for human labour, which can help keep labour costs low and optimise resource allocation. Increased Accuracy and Efficiency: Automation can help reduce errors and increase accuracy, as machines can be programmed to perform tasks with speed and precision.
Automation can help reduce errors and increase accuracy, as machines can be programmed to perform tasks with speed and precision. Improved Customer Experience: Automation can improve customer experience, as automated systems can streamline processes, reduce wait times, and provide customers with personalized services.
Automation can improve customer experience, as automated systems can streamline processes, reduce wait times, and provide customers with personalized services. Increased Safety:Automation can also increase safety, as automated systems can be programmed to avoid hazardous situations by improving workplace safety and reducing the risk of accidents.
Automation and Job Loss Statistics Worldwide
Automation has been steadily increasing in recent decades, with an estimated 1.8 million robots being deployed in industrial settings across the world in 2020. This has had a significant effect on the global job market, as more and more jobs are being replaced by automation. In the US alone, an estimated 800,000 jobs have been lost due to automation since the year 2000.
According to the Bureau of Labor Statistics, the number of jobs lost to automation between 2010 and 2024 is estimated to be between 9 million and 47 million.
A 2019 Gallup poll found that 28% of employed adults in the US are worried that their jobs will become automated in the next 10 years.
A 2020 report from McKinsey & Company suggests that up to 375 million workers worldwide may have to switch occupations by 2030 due to automation.
According to the World Economic Forum, 75 million jobs could be lost to automation by 2022, while 133 million new jobs could be created.
According to a recent study, an estimated 20 million manufacturing jobs could be lost by 2030 due to automation.
The Organisation for Economic Co-operation and Development (OECD) estimates that up to 14 percent of jobs in the OECD countries are at high risk of automation.
The International Labour Organisation estimates that up to 20% of jobs worldwide could be automated by 2030.
Check out: Will Artificial Intelligence Take Over Human Jobs by 2030?
World Automation and Job Loss Statistics By Industry
Automation has had a dramatic impact on the global job market, creating both job losses and opportunities for new roles. More than 247 million jobs are projected to be at risk across industries more susceptible to automation, such as construction and agriculture, by 2040, according to a report. Below, we have divided these statistics by the most affected industry. Let’s check it out.
Retail and Customer Service - 800 Million
Automotive - 300 Million
Manufacturing - 20 million
Transportation and Logistics- 15 million
Financial Services - 10 Million
IT - 9 Million
Automation and Job Loss Statistics Around the World in Retail and Customer Service
According to a report by the McKinsey Global Institute, automation is expected to affect up to 800 million jobs in the retail and customer service sector by 2030. This is due to the increasing use of AI and robotic process automation (RPA) in customer service and retail operations.
According to the World Economic Forum, the retail industry is estimated to be one of the most affected by automation, with an estimated 41 million jobs at risk by 2040.
Automated customer service technologies such as chatbots, virtual assistants, and voice recognition systems are becoming increasingly popular, and are expected to replace many customer service jobs in the near future.
Automation is expected to reduce the need for workers in areas such as checkout and inventory management while creating new opportunities in areas such as data analysis and customer service. Additionally, automation is expected to lead to fewer hours for existing workers in the retail sector, as well as reduced wages.
Automation and Job Loss Statistics in the Automotive Industry
AI could potentially replace many of the jobs in the automotive industry, from assembly line workers to technicians. It could also enable car manufacturers to produce more efficient vehicles with fewer resources and fewer emissions. As AI continues to advance, it will be interesting to see how it affects the automotive industry and its workforce in the near future.
Automated vehicle technology is being developed to reduce human error in driving and make the roads safer for everyone. AI is also being used to develop smarter cars that can predict traffic patterns and provide better navigation solutions.
The automotive industry has seen the highest levels of automation, with robots increasing their share of total employment in the automotive sector from 22% in 2014 to 28% in 2020. The impact of AI on the automotive sector is far-reaching and will continue to shape the future of this industry for years to come.
According to the International Federation of Robotics, the number of industrial robots in the automotive industry increased from approximately 1.3 million in 2014 to 1.8 million in 2020. In the USA, the number of industrial robots in the automotive sector increased from about 0.7 million in 2014 to 1.1 million in 2020.
In 2018, autonomous driving was worth $5.6 billion and is expected to reach $60 billion in 2030. By 2030, experts predict that AI could replace up to 300 million full-time jobs in the automotive industry.
Automation and Job Loss Statistics in the Manufacturing Industry
According to a recent study, an estimated 20 million manufacturing jobs could be lost by 2030 due to automation. 25% of American jobs are highly susceptible to automation, with 73 million jobs potentially lost in the U.S. over the next five years.
The Organisation for Economic Co-operation and Development (OECD) estimates that up to 14 percent of jobs in the OECD countries are at high risk of automation.
The International Labour Organisation estimates that up to 20% of jobs worldwide could be automated by 2030. To cope with such a transformation, constant skilling, reskilling and upskilling of the existing and future workforce is extremely important.
The new jobs, however, are likely to move away from traditional manufacturing and instead be added in the areas of IoT, mechatronics, robotics, 3D printing, AI, machine and deep learning, analytics, virtual collaboration, automotive design and computational thinking.
Automation and Job Loss Statistics Around the World in the Transportation and Logistics Industry
Automation is expected to have a significant impact on the transportation and logistics industry in the coming years. According to the World Economic Forum, the transportation industry is expected to see a significant decrease in the number of jobs due to automation, with an estimated 15 million jobs at risk of being replaced by automation by 2030.
Automated cars, trucks, ships, and drones are becoming increasingly common and are expected to take over many transportation tasks that are currently performed by human workers.
Automated systems, such as logistics and tracking systems, are also expected to replace many transportation jobs. An estimated 8 million jobs are at risk of being automated by 2030 in the logistics industry. This is due to the increasing use of AI and robotic process automation (RPA) in the logistics industry.
Automation is expected to reduce the need for workers in areas such as supply chain management while creating new opportunities in areas such as data analysis and customer service.
Automation and Job Loss Statistics Around the World in the Financial Services Industry
Automation is expected to have a significant impact on the financial services industry in the coming years.
According to the World Economic Forum, the financial services industry is expected to see a decrease in the number of jobs due to automation, with an estimated 10 million jobs in the financial services industry at risk of being replaced by automation by 2030.
Automated systems, such as robotic process automation (RPA) and artificial intelligence (AI) systems, are becoming increasingly common and are expected to take over many tasks that are currently performed by human workers.
Automation is expected to reduce the need for workers in areas such as bookkeeping and accounting while creating new opportunities in areas such as data analysis and risk management.
Automation and Job Loss Statistics Around the World in the IT Industry
Automation is also expected to have a significant impact on the IT industry in the coming years.
According to the World Economic Forum, the IT industry is expected to see a decrease in the number of jobs due to automation, with an estimated 9 million jobs in the IT industry at risk of being replaced by automation by 2030.
Automation is expected to reduce the need for workers in areas such as software development and IT infrastructure while creating new opportunities in areas such as data analysis and customer service.
Check out: Why Upskill For the Age of Artificial Intelligence?
Automation and Job-loss Statistics by Country
Country Automation Statistics Report USA AI will replace some 85 million jobs by 2025. Source: World Economic Forum.
By 2030, the number of jobs lost to automation in the US will reach 73 million. Source: Forbes.
By 2030, up to 73 million US jobs will be lost to automation. Source: PwC. UK According to the report, AI could displace about seven million jobs in the UK from 2017 - 2037. However, around 7.2 million new jobs will appear in the process. (World Economic Forum)
Automation could potentially impact up to 30% of UK jobs by the early 2030s, affecting different types of workers and industries at different times (PwC).
Over 30% of jobs in Britain could be taken over by artificial intelligence (AI).- PwC. India 69% of India's jobs are threatened by automation.
By 2040, 63 million jobs are expected to be lost to automation, with more than 247 million jobs expected to be in jeopardy across industries that are more susceptible to automation, such as construction and agriculture. - Forrester. UAE According to the International Labour Organization, an estimated 1.5 million jobs are at risk of being replaced by automation by 2030 in the UAE where it is expected to reduce the need for workers in areas such as manufacturing and logistics.
According to the UAE's Ministry of Labour, an estimated 500,000 jobs in the UAE will be at risk of being replaced by automation by 2030.
Top 10 Occupations At Risk of Automation
Based on industry research, the top 10 occupations at risk of automation include the following.
Data Entry & Data Analysis
People Affected- Data Entry Operators & Data Analysts
Tech Jobs
People Affected- Coders, Computer Programmers, Software Engineers, Data Analysts
Media Jobs
People Affected- Ad Copy Writers, Content Writers, Technical Writers, Journalists.
Finance Jobs (Bookkeeping, the Actual Tax Filing, the Mundane Tasks)
People Affected- Financial Analysts, Personal Financial Advisors, Accountants, Traders, Market Researchers.
Legal Jobs
People Affected- Paralegals, Legal Assistants.
Graphic Design
People Affected- Graphic Designers
Researchers
People Affected- Market Research Analysts.
Teachers
Customer Service - Retail, Food Services and more.
People Affected - Customer Service Agents.
Drivers
People Affected - Delivery drivers, truck drivers, and taxi drivers.
Advice: It is important for individuals in these occupations to stay informed on industry trends and acquire new skills to remain competitive in the job market. Consider exploring alternative career paths that require skills that are difficult to automate, such as critical thinking, creativity, and problem-solving.
The Changing Employment Landscape
The current employment landscape is undergoing significant transformation due to the increasing prevalence of automation. Various industries are embracing automation technologies to enhance productivity and efficiency. Manufacturing, transportation, retail, and customer service are at the forefront of automation implementation. As a result, certain job roles are becoming increasingly vulnerable to automation. Understanding the evolving employment landscape is crucial for individuals and policymakers to adapt to the changing demands of the job market and explore opportunities for upskilling and reskilling.
Check out: Top Artificial Intelligence Applications
Mitigating the Impact and Embracing Opportunities
As automation reshapes the job market, it is crucial to address its impact on individuals and society proactively. Mitigating job losses and embracing new opportunities require a multifaceted approach that involves policy measures, exploring emerging job sectors, and considering ethical considerations.
Reimagining the Workforce
To decrease job loss due to automation statistics, it becomes crucial to reimagine the workforce and equip individuals with the necessary skills to thrive in the automated era.
These platforms provide convenient and accessible opportunities for learning, enabling workers to adapt to the evolving demands of the job market. Edoxi offers VMware vRealize Automation Training, Automation testing, and various other automation courses. By embracing online education, individuals can gain the knowledge and expertise required to transition into new roles and industries.
Policy Measures
Governments support automation-affected individuals through retraining and upskilling.
UBI mitigates job loss impact by guaranteeing a minimum income for all.
Regulations are needed for labor rights, job displacement, and workplace safety in automation.
Embracing New Opportunities
Entrepreneurship empowers individuals and fosters innovation.
Investing in sustainability creates jobs and addresses climate change.
Continuous learning and upskilling support adaptability in the job market.
Ethical Considerations
Transparent, automated systems prevent biases and ethical issues.
Ethics must protect personal data in automation.
Balancing automation and human involvement maintains ethical decision-making.
Check out: What is Generative AI And How Does it Work
Conclusion
Automation has made a significant impact on the job market, both positively and negatively. On the one hand, automation has led to increased productivity and efficiency in many industries, allowing companies to produce more goods and services with fewer employees.
This has led to job losses in certain sectors, such as manufacturing and assembly line work. On the other hand, automation has also created new job opportunities in fields such as technology, programming, and robotics. Additionally, it has led to the development of new industries and businesses.
The overall impact of automation on the job market is complex and varies by industry, job type, and location. In general, jobs that involve repetitive tasks or can be easily replaced by machines are more at risk, while jobs that require human skills such as creativity, problem-solving, and social interaction are less likely to be automated.
However, by embracing proactive measures, such as reskilling, policy interventions, and ethical considerations, we can navigate the challenges and open doors to the opportunities presented by automation, creating a sustainable and inclusive future of work.
Locations Where Edoxi Offers Artificial Intelligence Course
Here is the list of other major locations where Edoxi offers Artificial Intelligence Course
Artificial Intelligence Course in Dubai | Artificial Intelligence Course in Qatar |
| 2022-12-01T00:00:00 |
https://www.edoxi.com/studyhub-detail/jobs-lost-to-automation-statistics
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50+ AI Replacing Jobs Statistics 2024 - AIPRM
|
50+ AI Replacing Jobs Statistics 2024 · AIPRM
|
https://www.aiprm.com
|
[] |
In 2013, Oxford economists Carl Benedikt Frey and Michael Osborne estimated that nearly half (47%) of all jobs in the US workforce could be replaced by AI, ...
|
A Forbes Advisor Survey undertaken in 2023 found that 77% of respondents were “concerned” that AI will cause job loss within the next 12 months, with 44% “very concerned”. However, this level of opinion is not conclusive for all workers. Another survey by Jitterbit reports that 85% of office workers believe AI will help to enhance their roles rather than replace them.
So, are jobs really at risk of being replaced by AI on a large scale? To find out more, AIPRM has compiled over 50 AI statistics on job displacement as a result of AI usage, which jobs appear safe, and the impact across different industries.
Top AI replacing jobs statistics>
Top AI replacing jobs statistics #
Three in 10 (30%) US workers are concerned that they may be replaced by AI
Seven in 10 (71%) US workers are worried that AI may impact human resources decision-making
More than a third (37%) of companies say AI replaced certain workers’ jobs in 2023
83% of companies say demonstrating AI skills can help employees retain their jobs
Eight out of 10 women in the US workforce are in occupations ‘highly exposed’ to generative AI automation, compared to three out of five men
The National Library of Healthcare believes AI could help healthcare professionals work more effectively by cutting down on administrative tasks
Over the year to November 2023, English-language job adverts on LinkedIn mentioning ChatGPT or GPT saw a 21x increase
The World Economic Forum cites agriculture, logistics, and teaching as industries that appear safe from AI replacement
How will AI affect the job market?>
How will AI affect the job market? #
According to research from the Heldrich Center for Workforce Development (November 2023), three in 10 (30%) US workers are ‘very’ or ‘somewhat’ concerned that their jobs may be eliminated by AI.
These worries aren’t limited to just the possibility of AI job loss either. Seven in 10 (71%) US workers are concerned that AI may be utilized by employers in human resources decision-making, such as for promotions. This indicates that workers are worried that AI may impact their ability to rise up the ranks in the job market.
AI job loss predictions>
AI job loss predictions #
The National Bureau of Economic Research reported that between 50% and 70% of changes to US wages since 1980 can be attributed to the relative wage decline of blue-collar workers, whose jobs have been either degraded or replaced altogether by automation.
Following this line of research, it appears likely that this pattern may increase as we enter an altogether new stage of automation. In fact, in 2023, investment bank Goldman Sachs predicted that 300 million jobs across the United States and Europe could be lost or degraded as a result of AI adoption.
However, these predictions rarely tell the whole story. A survey of 750 business leaders by Resume Builder reported that more than a third (37%) of companies using AI say that the technology replaced workers in their organization in 2023 because “they were no longer needed”.
Moreover, in 2024, 44% who use AI or plan to by next year say that employees will “definitely” (21%) or “probably” (23%) be laid off due to artificial intelligence.
Despite this, 91% of companies using or planning to use AI in 2024 will hire new employees in 2025, and 96% state that having AI skills will be beneficial for candidates to have hands-on experience working with artificial intelligence.
A further 83% say demonstrating AI skills will help current employees have more job security than those who don’t. Workers willing to adapt to technology demands and sharpen up on AI tools may be able to mitigate the impact of these job loss predictions.
Are men or women more vulnerable to AI-related job loss?>
Are men or women more vulnerable to AI-related job loss? #
Analysis by the Kenan Institute of Private Enterprise found that eight out of 10 women (58.87 million) in the US workforce are in occupations highly exposed to generative AI automation, compared to six out of 10 men (48.62 million). This means that 21% more women than men in the US workforce are exposed to AI automation.
This is likely a result of the distribution of genders between white and blue-collar jobs. While women make up 70% and 30% in these areas, respectively, men are more evenly distributed (roughly 50/50).
This is based on data from Goldman Sachs on the 15 occupations most vulnerable to replacement by generative AI — a technology that teaches AI systems to create content independently. ‘Highly exposed’ constitutes a level where a quarter to half (25-50%) of tasks in this occupation could be automated by generative AI.
Job displacement due to AI: Will AI replace human jobs?>
Job displacement due to AI: Will AI replace human jobs? #
Artificial intelligence replacing healthcare jobs>
Artificial intelligence replacing healthcare jobs #
Of the six industries evaluated for their rate of generative AI adoption by Fishbowl, the healthcare industry has the lowest uptake, at 15%. This is less than half the adoption rate of marketing and advertising (37%).
The National Library of Healthcare stated that the role of AI in healthcare will “complement” rather than replace doctors and healthcare providers.
Forbes supports this view, stating that AI will be able to help offload the time-consuming administrative tasks required in healthcare. A study from the Mayo Clinic points out that doctors spend half of their working day completing administrative work, such as updating patient records and scheduling appointments – tasks that can be filled by generative AI.
Read more about AI in healthcare statistics.
Artificial intelligence replacing jobs in the creative industry>
Artificial intelligence replacing jobs in the creative industry #
With Goldman Sachs’ research suggesting that generative AI has the potential to automate a quarter (26%) of tasks in arts, media, and entertainment, AI is of particular concern in the creative sector.
The American Federation of Television and Radio Artists undertook a near-four-month strike in 2023, with the proposed use of AI in the industry as one of the driving factors. A particular concern was the use of generative AI to replace the roles of extras on set.
According to data from the Harris Poll, almost three-fifths of US adults think content created by generative AI is less impressive than that made by a human, and 44% think it’s easy to tell the difference between the two.
Analysis from the poll argues for reframing how we view the use of this technology in the creative industry, stating it should be seen as a tool for professionals, rather than a competitor. When viewed this way, public opinion changes, with only 15% of adults against creators using AI to assist in content creation, and even fewer (12%) against companies making use of it.
Artificial intelligence replacing jobs in the customer service industry>
Artificial intelligence replacing jobs in the customer service industry #
Customer-facing jobs are notoriously stressful, with Nextiva reporting that call center jobs have an average annual turnover rate of 30% in the US, as employees face burnout.
Many experts agree that, rather than replacing these roles, AI can be used to help augment them by automating tasks. In a survey of office workers in the US and UK by Jitterbit, “reducing time spent gathering information from work systems and applications” is one of the top anticipated benefits of AI, allowing more time for “thoughtful work” and “larger projects”.
Forbes reports that nearly half (47%) of Gen Z consumers will not return to a brand after a single bad customer service experience; therefore, the inability of AI to replicate human skills such as empathy poses a risk to brand perception.
By automating more menial tasks, customer service agents are better able to handle the human connection side of the industry, which brands hope will reduce employee turnover rates.
This trend of automation has already seen success at Klarna. The financial services provider reports that, in the first month following the launch of its AI assistant, two-thirds of customer service chats have been automated, with similar customer satisfaction levels recorded.
What jobs will AI create?>
What jobs will AI create? #
While certain jobs are likely to be replaced by some type of AI automation, the history of technology in the workplace indicates that new developments also lead to job creation.
In fact, the founder of the World Economic Forum, Klaus Schwab, dubbed this process the ‘Fourth Industrial Revolution.’
Are workers looking for AI-related jobs?>
Are workers looking for AI-related jobs? #
LinkedIn has reported that, between December 2022 and September 2023, conversations around AI in the business and employment workspace increased by 70%. These conversations are largely driven by male millennials.
Men account for 58% of these conversations, compared to 31% of women. Meanwhile, millennials account for nearly half (45%), followed by Gen Z (26%), Gen X (21%), and finally, Boomers (4%).
Also during this period, LinkedIn noted that views for AI-based and related jobs increased by 12% across seven major economies: Australia, Brazil, France, Germany, India, the UK, and the US. Actual applications have seen a similar increase, at 11%.
Interest in AI-based and related jobs is highest in the United States, with a 21% increase in advert views, and a 19% increase in applications. Over the year to November 2023, English-language job adverts on LinkedIn mentioning ChatGPT or GPT saw a 21x increase.
New job opportunities created by AI>
New job opportunities created by AI #
In 2013, Oxford economists Carl Benedikt Frey and Michael Osborne estimated that nearly half (47%) of all jobs in the US workforce could be replaced by AI, arguing that this automation would be focused on “low skill, low income” jobs.
Despite reaffirming this belief in 2023, Frey has now also stated that job replacement is “not the right way” to think about the job market. Instead, “more competition” is one of the biggest challenges in the workplace, as more people have access to tools such as ChatGPT, allowing a wider group of people to create high-quality work.
Therefore, rather than considering automation to be a threat to job security, AI adoption should be seen as a gateway to “democratization and competition.” As a wider pool of people are given access to AI tools, Frey believes that more roles will become available and wages will decrease as a result.
What jobs will AI not replace?>
What jobs will AI not replace? #
Whilst AI has enormous capabilities, certain skills simply cannot be replicated by machines: leadership, emotional intelligence, and nuanced communication are just some of these.
This is not to mention that AI cannot replace certain manual labor jobs. In fact, industry expectations of machines replacing manual and physical work have decreased.
Companies surveyed for the World Economic Forum’s ‘Future of Jobs’ report in 2020 anticipated that 47% of these tasks could be automated by 2025, whilst the 2023 report indicates a revised estimate of 42% automation by 2027.
Data on the projected increase in job growth across certain industries can be indicative of the shortfall of AI in these areas.
Agriculture>
The World Economic Forum has predicted a 30% increase in professional agricultural roles by 2028, equal to 30 million jobs.
This job growth is due to the expected impact of shorter supply chains (as smaller farms sell directly to consumers) combined with the need for in-person manual labor. This indicates that AI will not be able to fill the increased demand in agriculture.
Teaching>
By 2027, the World Economic Forum predicts three million new jobs will have been created in vocational and higher education (growth of 10%).
As these roles rely on skills such as empathy, active listening, leadership, and social influence, it is unlikely AI will be able to fill these roles, as these are uniquely human attributes.
Logistics>
As with the agricultural industry, the market shift to localized supply chains is expected to drive an increase of 2.5 million jobs in the supply chain and logistics sector.
The World Economic Forum cites low expectations on the impact of autonomous drivers as evidence that these jobs are unlikely to be replaced by AI in the near future.
AI replacing jobs FAQs>
AI replacing jobs FAQs #
Will AI take my job? Although the integration of AI boasts a huge myriad of opportunities in the workplace, many employers are viewing the tool as an ‘add-on’ to current roles, rather than a replacement. The World Economic Forum has predicted that, though 85 million jobs may be set to be replaced, 97 million new jobs are also likely to be created by the ‘AI Revolution,’ dubbing this joint trend ‘automation and augmentation.’ In this vein, LinkedIn reported a 12% increase in adverts for AI-based and related jobs across seven major economies between December 2022 and September 2023, indicating that companies are already following this trend of adapting existing jobs to include AI skills. How many jobs will AI replace? Investment bank Goldman Sachs predicted in 2023 that as many as 300 million jobs could be replaced by AI, particularly those focused on administrative tasks. However, reports from other businesses suggest this prediction is unlikely to materialize, instead arguing AI will lead to job adaptation, rather than replacement. Nine in 10 companies planning to use AI in 2024 stated that they were likely to hire more workers as a result of this, with 96% favoring candidates who can demonstrate hands-on experience working with AI. When will AI take over jobs? Despite numerous organizations each offering their own predictions of when the AI revolution will reach its apex, we are also witnessing these forecasts being constantly revised. Companies interviewed for the World Economic Forum’s 2020 ‘Future of Jobs’ report anticipated that 47% of manual labor jobs could be automated by 2025, but by 2023’s report this prediction had already been reduced to 42% automation by 2027. These revisions indicate that it is impossible to say when AI will take over large swathes of the workforce, in any definitive sense. How will AI affect the job market? Although it is difficult to discuss displacement in the job market in terms of hard numbers, research is better able to discuss the impact of AI on the market with regards to who appears most vulnerable. The Kenan Institute of Private Enterprise found that eight out of 10 women (58.87 million) in the US workforce are in occupations highly exposed to generative AI automation, compared to six out of 10 men (48.62 million). This research indicates that US women are at a higher risk of job insecurity as a result of AI development. Moreover, although LinkedIn has reported an increase in AI-related conversations on site, far more men are fuelling this conversation than women (58% vs 31%, respectively).
Glossary>
Deep learning>
Deep learning #
This element of AI imitates the information processing of the human brain. Rather than using algorithms which perform only one task, this type of machine learning can learn from unstructured data without human supervision.
Generative AI>
Generative AI #
This technology trains an AI system using large datasets, allowing it to recognise patterns and structures. The system can then create new content alone, including images, text, and code.
Large language model (LLM)>
Large language model (LLM) #
Usually referred to as an LLM, this system is trained on large sets of text, allowing it to understand language and generate human-like text.
Machine Learning>
Machine Learning #
This type of learning focuses on developing algorithms and models that in turn help machines predict trends and behaviors from large datasets, without human intervention. It works with elements of coding, computer science, and mathematics.
Turing test>
Turing test #
This test, created by 20th century mathematician and computer scientist Alan Turing, aims to evaluate a machine’s ability to exhibit intelligence on an equal level to humans - particularly in terms of language and behavior.
A human evaluator compares a conversation between a human and a machine, and if they are unable to determine which is which, the machine passes the Turing test.
Sources>
https://www.forbes.com/advisor/business/artificial-intelligence-consumer-sentiment/
https://www.heldrich.rutgers.edu/news/us-workers-assess-impacts-artificial-intelligence-jobs-press-release
https://www.nber.org/papers/w28920
https://www.goldmansachs.com/intelligence/pages/generative-ai-could-raise-global-gdp-by-7-percent.html
https://www.resumebuilder.com/1-in-3-companies-will-replace-employees-with-ai-in-2024/
https://kenaninstitute.unc.edu/kenan-insight/will-generative-ai-disproportionately-affect-the-jobs-of-women/
https://www.statista.com/statistics/1361251/generative-ai-adoption-rate-at-work-by-industry-us/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10328041/
https://www.forbes.com/sites/bernardmarr/2024/03/13/how-generative-ai-will-change-the-jobs-of-doctors-and-healthcare-professionals/
https://www.sciencedirect.com/science/article/pii/S0025619618309388?via=ihub
https://theharrispoll.com/briefs/generative-ai-jobs/
https://www.klarna.com/international/press/klarna-ai-assistant-handles-two-thirds-of-customer-service-chats-in-its-first-month/
https://economicgraph.linkedin.com/research/future-of-work-report-ai
https://oms-www.files.svdcdn.com/production/downloads/academic/The_Future_of_Employment.pdf
https://fortune.com/2023/02/07/chatgpt-economist-says-disrupt-job-market-lower-wages-ai/
https://www.oxfordmartin.ox.ac.uk/news/generative-ai-can-potentially-disrupt-labour-markets-say-oxford-experts-10-years-after-ground-breaking-study
https://www.weforum.org/agenda/2023/05/jobs-ai-cant-replace/
https://www.coursera.org/articles/ai-terms
| 2024-07-11T00:00:00 |
2024/07/11
|
https://www.aiprm.com/ai-replacing-jobs-statistics/
|
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What the history of automation can tell us about AI's impact on jobs
|
What the history of automation can tell us about AI’s impact on jobs
|
https://eviden.com
|
[] |
A major study by CEPR (the Centre of Economic Policy Research) refutes the idea that additional robotics lead to job loss. On the contrary, CEPR estimates that ...
|
A brief history of automation
Automation is a key part of technology’s evolution and of modern economics. It is particularly pertinent to certain eras, the industrial revolution being a primary example. The mechanization of the factory in the late 18th to early 19th century was a logical shift for owners: they could maintain or increase productivity while lowering operational costs. This understandably led to protest and unrest. In industrial Britain, the Luddites famously destroyed the looms that threatened their livelihoods. And, more recently, General Motors experienced significant discontent after new automation machinery led to layoffs.
Humans have long been at risk of unemployment brought by technological change. Automation is not the only cause of making humans or even animals redundant — it’s the constant improvement of tools in general. The greater the efficacy of the tool; the less effort for the user.
While such change has erased some jobs, it has also brought a shift in the kinds of work available, for example, a trend away from manual to intellectual labor. In the UK over the last 50 years, employment in manufacturing has fallen from 25% to about 10% today, whereas the services sector now stands at over 80%.
The net-gain economic view
Many academics argue that there is a net gain over time. There are numerous academic studies on automation and its impact on jobs, particularly within the manufacturing industry. A major study by CEPR (the Centre of Economic Policy Research) refutes the idea that additional robotics lead to job loss. On the contrary, CEPR estimates that each additional robot per 1,000 workers in vehicle production actually increased overall employment by 1.3%. In the popular book Human + Machine: Reimagining Work in the Age of AI, AI is specifically heralded as a technology that will create new jobs if companies invest in it.
The net-gain argument states that, like all automation, AI can be an aggregate wealth creator because it creates new opportunities. By delegating tedious tasks to a machine, we give ourselves time to focus on more complex problems, share knowledge and be creative.
Generally speaking, this view comes with the caveat that with each advancement in automation technology, there is a short-term loss but a long-term gain.
The net-loss economic view
This is the hot topic right now. Not necessarily because the outcome is more likely, but because fear gets clicks. While doomsaying might get more attention, it doesn’t mean researchers and thought leaders don’t have a point. An economic study focused on US manufacturing during the ‘90s and ‘00s concluded that each robot replaced 5.6 human workers and reduced wages by 0.5%.
In its 2018 Future of Jobs report, The World Economic Forum predicted automation would destroy 75 million jobs but create 133 million over the next four years. The 2020 WEF report concluded that 85 million jobs will be displaced, while 97 million will be created by 2025. In the 2023 Future of Jobs report, 83 million jobs are predicted to be destroyed versus 69 million jobs created, “leading to a contraction of global labor markets by 14 million jobs in the next five years”. This suggests the trend is downward for human employees and upward for robots and algorithms.
Some predictions prior to the digital revolution claimed that we’d have new and unimaginable jobs in the future, but as Max Tegmark points out in Life 3.0, this prediction was way off. “… the vast majority of today’s occupations already existed a century ago.” The main trend, Tegmark asserts, isn’t that we’re “moving to new professions but that we’re crowding into those pieces of terrain … that haven’t been submerged by the rising tide of technology.”
The horse lesson
The role of horses in industry has an interesting story to tell. After all, the rate at which work is done in a vehicle is still measured in horse power. Horses were essential to agriculture and travel for millennia. Their role started to diminish during the second agricultural revolution, which is partially attributed to improvements to the plow “so that it could be pulled with fewer oxen or horse”. During the 20th century, their numbers fell from 20 million to 4.5 million in the USA as they were replaced by motor vehicles. The same logic can be applied to people: if a machine can do it better, the human is replaced.
If this view is to play out over the coming years, how can a business sell goods or services if there is no one with any disposable income to consume them? One fear is that the wealth gap will widen, with a small percentage in control of vast, automated powerhouses; and then the rest of us. We’ll explore possible solutions later. But first, let’s understand the impact AI is likely to have.
The scope of impact
It is well documented that AI will impact numerous types of workers: cashiers, paralegal assistants, bakers, bus drivers and construction workers to name just a few from this well-respected academic study by Oxford University. The list is long, and the jobs considered safe (archeology for example) are in relatively low demand. If the stats hold true, we’re set for a complete sociological sea change.
The Oxford study claimed there was a high probability that hospitality workers such as waiters, tour guides and bartenders would also be replaced by automation. It may be true that robotics and algorithms will outperform humans in these tasks, but this does not factor-in our visceral desire to interact with other human beings. The front-of-house hospitality staff is part of the authentic experience, and studies have shown that customers value this. Similar research highlights preferences for humans within other customer services.
It would be unfair to purely focus on AI’s impact on job retention and creation. In various sectors, automation and AI have proven to be lifesaving. Highly dangerous jobs have been replaced by robots. In many industries, this shift has saved lives. For example, robots can dive autonomously into flooded mines — deemed too dangerous for human divers — and use cameras to analyze deposits of increasingly rare and valuable minerals.
While we are right to worry about the job market as a whole, we should not overlook the benefits of automation in lowering the risk to human life and health. This helps us understand why automation is so desirable. It doesn’t just do the dull jobs. It does the dangerous and dirty ones, too.
Looking at what lies ahead, it can be hard to imagine what the next generation of jobs will be, just as it would have been hard for someone in the 19th century to imagine what our present-day jobs are. In Home Deus, the author Yuval Noah Harari, ponders this. One such example he gives is the virtual world creator, which is a job involving imagination and design skills. However, he argues that even this is not safe because, even in its early stages, AI has proven to be creatively adept. It can write R&B music that garners millions of views. It can even write classical music. And so, it seems there are very few sectors safe from the impact, and its scope can only be limited by major environmental or political factors.
What to do about it
At this moment in time, there are four exponential industries — economic sectors that are changing at an exponential rate: computing, energy, manufacturing and synthetic biology. AI already features in all of them. What happens within these industries and what occurs when they overlap hugely impacts our personal lives, culture and society. What presents an immediate risk is how far behind this exponential change our businesses, governments and laws are, as Azeem Azar has well articulated in his writing. These institutions need to evolve faster to ensure we control the technology and not the opposite. “That humans have created new systems before should give us some hope,” he concludes.
In many sectors, both the technology and the business are already present. In others, this alignment is imminent. The salient theme that emerges from discourse on this topic is about being able ““to manage that displacement.”Doing this would also buy governments crucial time to find ways to close the exponential gap.
What if that fails
Should there be mass redundancy and aggregate losses, there are various well-documented ideas that might secure our futures, such as universal basic income and a Robotics and AI tax. Both of these approaches have limited case studies, so it’s up to our governments to trial potential solutions and share the results.
There’s a whole chapter dedicated to how the Amish adopt new technology in Kevin Kelly’s What Technology Wants. In their society, they carefully consider the impact of a tool before deciding whether to adopt it or not. The economic system many of us live in does not allow for such a mindful and cautious approach. But should we reach a state of joblessness, we’d do well to learn from the Amish culture, where they choose to build houses by hand and, by doing so, give themselves a sense of purpose. We all want to be useful and valued.
Don’t focus on positives and negatives
Historically, there is plenty of evidence to show that jobs have been both destroyed and created by automation. Assuming that the loss trend is only over the medium-term and that there’s a net gain over the long-term, this still presents a problem for the current working population for two reasons:
Governments are slow to respond to or anticipate the impact of technological change. Due to financial limitations, family responsibilities and aging, it is difficult to retrain in a new career.
This is why it’s so important to make the distinction between job losses and job displacement. Whether you align with the positive or negative economic view of our future, it may be wiser to focus on managing the rate of change than the use of automation itself.
| 2022-12-01T00:00:00 |
https://eviden.com/insights/blogs/what-the-history-of-automation-can-tell-us-about-ais-impact-on-jobs/
|
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"position": 37,
"query": "job automation statistics"
},
{
"date": "2024/04/01",
"position": 27,
"query": "robotics job displacement"
},
{
"date": "2024/05/24",
"position": 42,
"query": "artificial intelligence employment"
},
{
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{
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{
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{
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"position": 42,
"query": "job automation statistics"
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{
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"position": 23,
"query": "robotics job displacement"
}
] |
|
[Infographic] 8 Statistics about impact of Automation on Jobs - Quixy
|
[Infographic] 8 Statistics about impact of Automation on Jobs
|
https://quixy.com
|
[] |
Business automation is on the rise and companies need to adopt it quickly. Here are a few statistics on impact of automation on jobs.
|
Business automation is on the rise and everyone needs to have a good idea of what it entails for the future. With its numerous benefits, companies need to get ready to implement business automation while improving the skills and capabilities of the workforce. Streamlined processes and improved timelines are possible through business automation, however businesses also need to keep their employees skilled and ready for future change. Here are a few statistics on impact of automation on jobs.
| 2020-12-01T00:00:00 |
2020/12/01
|
https://quixy.com/infographic/8-statistics-about-impact-of-automation-on-jobs/
|
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Automation, workers' skills and job satisfaction - PMC
|
Automation, workers’ skills and job satisfaction
|
https://pmc.ncbi.nlm.nih.gov
|
[
"Henrik Schwabe",
"Tik Centre",
"University Of Oslo",
"Oslo",
"Fulvio Castellacci"
] |
Such fear of future replacement does negatively affect workers' job satisfaction at present. This negative effect is driven by low-skilled workers, which are ...
|
Abstract When industrial robots are adopted by firms in a local labor market, some workers are displaced and become unemployed. Other workers that are not directly affected by automation may however fear that these new technologies might replace their working tasks in the future. This fear of a possible future replacement is important because it negatively affects workers’ job satisfaction at present. This paper studies the extent to which automation affects workers’ job satisfaction, and whether this effect differs for high- versus low-skilled workers. The empirical analysis uses microdata for several thousand workers in Norway from the Working Life Barometer survey for the period 2016–2019, combined with information on the introduction of industrial robots in Norway from the International Federation of Robotics. Our identification strategy exploits variation in the pace of introduction of industrial robots in Norwegian regions and industries since 2007 to instrument workers’ fear of replacement. The results indicate that automation in industrial firms in recent years have induced 40% of the workers that are currently in employment to fear that their work might be replaced by a smart machine in the future. Such fear of future replacement does negatively affect workers’ job satisfaction at present. This negative effect is driven by low-skilled workers, which are those carrying out routine-based tasks, and who are therefore more exposed to the risks of automation.
1. Introduction Industrial robotics and artificial intelligence (AI) have in the last few years increasingly been used in production activities. This has led to the automation of many tasks that were previously carried out by workers, and that can now be performed by smart machines. The fear that these technological advances may have dramatic consequences on the future of labor has fostered the recent development of new economics research studying the effects of automation on employment [1,2]. Recent models and empirical evidence on this topic show that automation can have negative effects on employment demand and wages, and particularly so for workers that perform routine-based tasks that can more easily be displaced [3,4]. On the other hand, however, these new technologies may also have positive effects by increasing productivity [5]. This recent research has so far focused on the effects of automation, industrial robots and artificial intelligence on labor demand and wages. However, while employment and wages are two central dimensions shaping individual workers’ well-being, it is also important to point out that other non-pecuniary aspects do contribute to shape workers’ well-being, and that automation may potentially have important impacts on these [6]. Specifically, if workers fear that their occupation might be replaced by a smart machine in the future, such prospect and uncertainty about future working conditions may arguably affect their job satisfaction at present [7,8]. Why should we care about the impacts of automation on workers’ job satisfaction? The reason is twofold. First, since individuals spend a substantial part of their life at work, job satisfaction experienced in working life does indeed represent an important component of individuals’ overall subjective well-being [9]. Second, workers that are not happy and experience dissatisfaction with their job have typically lower motivation and efforts [10], and higher turnover rates. Therefore, if a large number of workers in the economy fear to be replaced by smart machines in the future, this fear may lead to mental stress and anxiety at present, as well as hamper productivity and innovation in the economy. In spite of the relevance of this topic, to the best of our knowledge only two papers have recently explored the relationship between automation and workers’ well-being. Abeliansky and Beulmann [11] focuses on workers’ mental health in Germany; and Schwabe [12] studies workers’ life satisfaction in a sample of European countries. Neither of these studies, though, investigates explicitly the impacts of automation on job satisfaction. Further, these recent works do not study the role of workers’ skills, and how these may affect the relationship between automation and well-being. The literature on automation and employment clearly shows that the effects of the introduction of industrial robots largely differ for high-skilled and low-skilled workers. It is therefore paramount to investigate whether the effects of automation on job satisfaction can have different effects on workers’ well-being depending on their skill levels. In short, the question investigated in the present paper is the following: Does automation affect workers’ job satisfaction–and how does this effect differ for high- versus low-skilled workers? To study this question, it is useful to distinguish two related dimensions. The first side of the link between automation and job satisfaction is that the introduction of industrial robots in local labor markets will affect workers’ expectations about their future jobs, i.e. it will lead some workers to fear that part of their working tasks might be replaced by a smart machine in the future. The second dimension is that these expectations about the future, and particularly the anticipated fear of replacement, will negatively affect workers’ subjective well-being at present. Empirically, we operationalize this idea by making use of a two-stage econometric model, in which fear of replacement and job satisfaction are the dependent variables of the first and the second stage, respectively. The empirical analysis uses microdata for several thousand workers in Norway from the Working Life Barometer survey (Arbeidslivsbarometer) (four annual surveys for the period 2016–2019), combined with information on the introduction of industrial robots in Norway from the International Federation of Robotics (IFR) dataset. Our identification strategy exploits variation in the pace of introduction of industrial robots in Norwegian regions and industries between 2007 and t (i.e. the time at which each of the four surveys was carried out). The results indicate that automation in industrial firms in recent years has induced workers that are currently in employment to fear that their work might be replaced by a smart machine in the future, and that this effect is stronger for low-skilled workers. Further, our findings show that fear of future replacement does negatively affect workers’ job satisfaction at present, and that such negative effect is in particular significant for low-skilled workers, which are those carrying out routine-based tasks, and who are therefore more exposed to the risks of automation. On the whole, these results contribute to, and extend, the recent literature on automation and employment by shifting the focus to important nonpecuniary impacts that are reflected in workers’ expectations, fears and job satisfaction, and showing that workers’ skills is an important variable moderating the effects of automation on subjective well-being. The paper is organized as followed. Section 2 reviews the literature on automation and employment. Section 3 points out the conceptual mechanisms that are relevant to explain the effects of automation on job satisfaction. Section 4 presents the data and indicators. Section 5 discusses the empirical methods. Section 6 presents the results. Section 7 concludes and discusses the main contributions and implications.
2. Literature Effects of automation on employment and wages Automation, industrial robotics and artificial intelligence have in the last few years experienced substantial advances and found an increasing number of applications in production activities. Artificial intelligence and robotics have developed as two distinct scientific and technological fields for a long time, and only recently they have converged and cross-fertilized [13]. Frank et al. [2] presents relevant illustrations of this recent convergence, and it discusses challenges for research on the economic effects of AI and automation. This has spurred the recent development of a strand of scholarly research studying the effects of these new technologies on employment. A starting point of this literature is the canonical model of skilled bias [14], according to which new skilled-bias technologies lead to polarization and increasing differences in employment opportunities and wages between skilled and unskilled workers. Sachs and Kotlikoff [15] present a simple framework in which smart machines substitute directly for young unskilled labor, whereas they are complementary to older skilled workers. Young unskilled workers experience lower wages, which in turn lead to lower saving and investments in human and physical capital–thus perpetuating and strengthening the gap between young unskilled and older skilled workers over time. Such pessimistic prediction on the future of employment is however not shared by other works in this field. Taking a long-run historical perspective, Autor [16] and Mokyr et al. [1] argue that, as in other times in history, technological progress will lead to major structural changes in the quantity and content of work, but it will arguably not lead to a complete substitution of capital for labor. Houseman [17] provides empirical evidence that, although manufacturing employment in the US has declined since early 2000s, this is mainly explained by international trade and global competition effects, and there is weak support in the data for the argument that such decrease in employment is due to automation. More recently, McGuinness et al. [18] and Klenert et al. [19] present empirical studies that indicate that automation technologies and industrial robots have actually positive effects on employment. On the one hand, automation leads to a creative destruction process that may on the whole increase the overall demand for labor. On the other hand, it may contribute to reduce routine-based working tasks, which are typically monotonous and physically straining, thus improving the quality of work. A more nuanced perspective that considers both negative and positive effects of automation on employment is presented by studies of the job polarization hypothesis. In short, the main idea of this research is that automation technologies complement highly skilled labor, explaining its expansion and wage growth in recent years in most advanced countries. On the other hand, middle-skilled workers are those more negatively affected by routine-biased technical change, because their tasks are relatively easier to automate. As for low-skilled workers, and particularly those employed in personal services occupations, these often perform manual and personal communication tasks that are not that easy to automate yet. Hence, the resulting pattern is that middle-skilled workers have in recent years shifted towards low-skilled employment occupations, which have consequently grown and experienced higher wages. All in all, this explains the observed increasing polarization in the job market, with the growth of employment and wages for high- and low-skilled workers, and a corresponding decline for middle-skilled occupations [3,4,16,20]. Beaudry et al. [21] argue however that the demand for high-skilled workers has declined after 2000 due to decreasing returns to investments in information and communication technologies (ICTs), and that high-skilled have then begun to compete for lower-skilled jobs. This study, though, is based on empirical evidence on ICT investments in general, and it does not focus specifically on the effects of AI and automation. Acemoglu and Restrepo [22] present a theoretical framework that is useful to study both negative and positive effects of industrial robots on employment and wages. The model points out two contrasting effects of industrial automation: a displacement effect that negatively affects the demand for employment and the wages of workers that perform routine-based tasks; and a productivity effect that creates benefits for workers that perform non-routine tasks (in the automated sector as well as in other sectors and occupations of the economy). This study also presents empirical evidence that corroborates the model’s predictions on the effects of industrial robots on employment and wages in US manufacturing industries between 1990 and 2007. In line with evidence presented by other recent works [5,23,24], their results show that overall the displacement (negative) effect of the introduction of industrial robots has until now been stronger than the productivity (positive) effect. Effects of automation on job satisfaction This recent strand of research has so far focused on the effects of automation, industrial robots and artificial intelligence on aggregate patterns of labor demand and wages for different countries and industries. However, research has not investigated yet the impacts that these new technologies may have on individual workers’ subjective well-being. Do workers fear that their occupation might be replaced by a smart machine in the future, and if so how does that prospect affect their current job satisfaction?. Job satisfaction is the subjective well-being of workers (i.e. their own assessment of the well-being they experience at work). This is an obviously crucial dimension for economic analysis and policy. First, since individuals spend a substantial part of their life at work, job satisfaction experienced in working life represents an important component of individuals’ overall subjective well-being. Second, workers that are not happy and experience dissatisfaction with their job have typically lower motivation and efforts, and higher turnover rates. This, in turn, weakens productivity and innovation in the economy. The literature on job satisfaction is wide-ranging, and it has extensively investigated a variety of factors that explain why some individuals report higher subjective well-being than others [7,8]. However, only a few studies have so far explicitly investigated the relationships between the widespread diffusion and application of digital technologies and job satisfaction [25]. Kaplan and Schulhofer-Wohl [6], using data from the American Time Use survey, discusses the nonpecuniary implications of changes in the occupational structure in the US in recent decades, i.e. the effects of these structural changes on different aspects of job satisfaction such as reported happiness, stress and meaning at work. The work indicates that the changing occupational structure has not only led to polarization in terms of skills and wages, but it has also determined substantial changes in workers’ feelings about the job they have and the tasks they perform. Two recent papers explore the relationship between automation and workers’ well-being. Abeliansky and Beulmann [11] present an empirical study on the impact of automation on the mental health of workers (which is one important dimension reflecting stress and weak job satisfaction). The analysis uses individual-level data from the German Socioeconomic Panel for the period 2002–2014 linked to industry-level data on use of industrial robots in 21 manufacturing sectors in Germany. The results indicate that automation negatively affects workers’ mental health, and this effect is related to the fear of having lower wages and worse economic conditions in the future. Schwabe [12] makes use of worker-level data from the Eurobarometer survey for European countries (period 2012–2017) to investigate the relationships between fear of replacement and workers’ subjective well-being (measured by life satisfaction, which is as well-known an evaluative dimension of individuals’ well-being). The results of this study find that fear of replacement affects life satisfaction, but the direction of this effect does largely depend on age. In line with models of skill-bias and job polarization (see section 2.1), younger workers regard replacement as a possible threat to their job opportunities in the future, whereas older workers look at it as a positive technological development that is not likely to affect them directly, and that will arguably enhance well-being and prosperity in the society. These two studies provide an important starting point for the present work. None of them, though, investigates explicitly the role of workers’ skills, which is however a key dimension in the literature on the employment effects of automation briefly reviewed in section 2.1. In the job satisfaction literature too, education and skill levels represent one of the central factors affecting the job satisfaction of workers [26]. Two contrasting mechanisms link education and job satisfaction. On the one hand, a higher skill level increases the chances that an employee will have a higher wage level and a more interesting and rewarding job, which enhance job satisfaction. On the other hand, however, various empirical studies have found that–after controlling for income earnings–the correlation between education level and subjective well-being at work is negative [8,27,28]. This can be explained in the light of prospect theory [29]. When an individual invests more time in education and human capital formation, her expectations about the desired job will also be higher, and it will therefore be more likely that the worker will feel more critical and less satisfied with her actual working conditions if these high expectations are unmet. In particular, empirical research indicates that overqualified workers report significant lower levels of job satisfaction than others [26,30].
3. Question and propositions The question investigated in the present paper is the following: Does automation affect workers’ job satisfaction–and how does this effect differ for high- versus low-skilled workers? The first part of the question refers to the main impact of automation on job satisfaction, which as noted above has not been analyzed in previous research yet. The second part of the question suggests that fear of replacement can have different effects on workers’ well-being depending on their skill levels, and it seeks to investigate these moderation effects. Conceptually, the link between automation and job satisfaction can be analyzed in two steps. The first is that the introduction of industrial robots in local labor markets will arguably affect workers’ expectations about their future jobs, which means that some workers will fear that some of their tasks, or even their whole job, might be replaced by a smart machine in the future. The second step is that these expectations about the future, and particularly the anticipated fear of replacement, will affect workers’ job satisfaction at present. Our empirical analysis will consider both of these conceptual steps in a two-stage empirical model, and investigate whether the related impacts are stronger for high-skilled or for low-skilled workers. We point out below here the main effects that we expect to find in the empirical analysis, and how these can be explained in the light of the literature reviewed in this section. As noted below, some of the effects of interest are stronger for high-skilled workers, whereas others are more relevant for low-skilled workers, so that the overall net moderation effect cannot be pointed out ex-ante, but it will have to be established based on the empirical evidence. I. Fear of replacement The introduction of industrial robots in the local labor market increases the likelihood that some workers will be replaced by smart machines in the future. These technological changes and their applications in firms in local labor markets will therefore induce some workers that are currently employed to fear that they might be replaced in the future (or at least that some of their tasks might be). Moderation effects The introduction of industrial robots will arguably have different impacts for high- versus low-skilled workers. We envisage two contrasting effects. Fear of replacement is stronger for the low-skilled. These workers are more exposed to the risks of displacement from automation because they typically carry out routine tasks that can more easily be automated (see literature in section 2.1). Fear of replacement is stronger for the high-skilled. High-skilled workers are typically also more educated individuals who read more and follow media debates on robots, automation and their negative consequences for employment. Hence, high skilled workers are arguably more exposed to peer effects, which may translate in a greater fear about the future of employment. Contrary to this argument, we may however posit that workers of higher education typically have a better ability to understand and anticipate that these new technologies will also have positive effects for their future tasks and wages, as well as for the overall productivity of the economy–i.e. they are arguably be more forward-looking [31]. Proposition 1: The introduction of industrial robots in the local labor market will negatively affect low-skilled workers more than high-skilled workers if the former effect is stronger than the latter. II. Job satisfaction The second aspect of our conceptual analysis refers to the impacts that fear of replacement will have for workers’ subjective well-being. The main expectation is that fear of replacement in the future will negatively affect job satisfaction at present. The main reason is that the prospect to become unemployed, or to be taken away some of the current working tasks, will negatively affect wage and financial conditions expected for the future, thus creating uncertainty about future job prospects and personal finance, and hence lower job satisfaction. Moderation effects Fear of replacement will arguably have different impacts on job satisfaction for high- versus low-skilled workers. We posit the following contrasting effects. The negative effects on job satisfaction will be stronger for the low-skilled. If replaced, these workers will on average have fewer possibilities to find another occupation in the labor market. Acemoglu and Restrepo [22] and Blanas et al. [20] document in fact that displacement effects of industrial robots on employment and wages are stronger and more significant for low-education workers. On the other hand, as noted in section 2.1, extant research suggests that automation technologies can have more positive effects on high-skilled workers, increasing the demand for labor, wages and the complexity and interest of their tasks [18]. The negative effects on job satisfaction will be stronger for the high-skilled. According to prospect theory [29], individuals that invest more time in education and human capital formation will also have higher expectations about the working conditions that they desire and expect to have in the future, and be less satisfied with their job if this does not match the high expectations the individual has. Hence, highly educated workers, when facing the prospect of changing jobs and tasks in the future, may be those that have more to lose from automation, precisely because they are the individuals who have invested more in their human capital formation, and they have therefore higher expectations about the working conditions that they feel they deserve. Proposition 2: Fear of replacement will negatively affect the job satisfaction of low-skilled workers more than that of high-skilled workers if the former effect is stronger than the latter.
4. Data Individual-level data We use the Working Life Barometer survey (Arbeidslivsbarometer), which provides annual microdata for several thousand Norwegian workers. The survey is provided by the Confederation of Vocational Unions (YS), a politically independent umbrella organization for labor unions, and organized by the Work Research Institute in Norway. TNS Gallup collects the data targeting a large random sample of Norwegian workers aged 18–67 years. Our analysis makes use of the four surveys carried out in the years 2016 to 2019, which include information on the main variables of interest for this study, and particularly workers’ subjective assessments of the threats of automation, and their job satisfaction. The main target variable in the study is job satisfaction, which is measured by means of responses to the survey question: “How satisfied are you with your job?”. Respondents indicate their satisfaction level on a 1–5 scale (“Very dissatisfied”; “Pretty dissatisfied”; “Neither satisfied nor dissatisfied”; “Pretty satisfied”; “Very satisfied”). The main explanatory variable is fear of replacement. This is measured by means of responses to the following survey question: “Do you think some of your current tasks could be done by machine instead?”. Fear of replacement is a dummy variable: respondents who answer yes to this question take value 1, whereas workers who do not think that their tasks could be replaced by a machine take value 0. It is important to observe that this survey question measures workers’ assessment of the possibility that their tasks could be replaced by machines (cognitive reaction), and not directly the fear to lose their job as a consequence of automation (emotional reaction). However, as we will show later in the results section, this survey question is closely related to other survey questions that measure workers’ fear of losing their job, and it is therefore reasonable to use it as a proxy measure of fear of replacement. It is also worthwhile to note that only workers who are currently employed are asked to answer the question on fear of replacement, whereas unemployed individuals must skip this part of the questionnaire. Hence, our analysis focuses on the beliefs of workers who are potentially exposed to automation, but it does not consider those individuals that have already been laid off due to automation. Next, another important variable in our study is the skill-level of workers, which is measured by their education level, distinguishing workers with a completed University degree versus those without tertiary education. In terms of control variables, the Working Life Barometer survey also provides employee-level demographic and socio-economic information such as age, gender, income, union membership, and occupation type. In total, we analyze responses from 10,051 workers aged 19–68 years. Table 1 presents a list of the variables used in the analysis, and Table 2 reports some descriptive statistics. Table 1. Variables. Variable Definition Individual level variables Job satisfaction Respondents indicate their job satisfaction ranging from 1 “Very dissatisfied”; 2 “Pretty dissatisfied”; 3 “Neither satisfied nor dissatisfied”; 4 “Pretty satisfied”; 5 “Very satisfied”. Machine replacement Respondents indicate whether they believe that a machine can perform some of their job tasks. Union membership Dummy indicating whether the respondent is unionized. Age Age of respondent. Women Dummy indicating the gender of the respondent. University degree Dummy indicating whether the respondent has a university degree. Working in industry Dummy indicating whether the respondent is an industry worker. Regional level variables ΔRobot exposure Industry-region’s long-term robot adoption per thousand workers. More detailed definition in main text. Unemployment benefit recipients Share of regional population that are registered recipients of unemployment benefits. Business building broadband infrastructure availability Fixed broadband penetration per 100 inhabitants. Population Log of regional population. GDP per capita Log of regional GDP per capita. Tertiary education Regional share of population (aged 25–64) with tertiary education. Share of big industrial companies Big industrial companies as share of total firm population by region. Open in a new tab Table 2. Descriptive statistics. Variable Obs. Mean Std. Dev. Min Max Job satisfaction* 10,051 3.99 0.84 1.00 5.00 Machine replacement 10,051 0.40 0.49 0.00 1.00 Union membership 10,051 0.69 0.46 0.00 1.00 Age 10,051 46.44 11.67 19.00 68.00 Income scale 10,051 4.81 1.81 1.00 9.00 Women 10,051 0.52 0.50 0.00 1.00 University degree 10,051 0.56 0.50 0.00 1.00 Working in industry 10,051 0.08 0.26 0.00 1.00 Robot exposure 10,051 0.06 0.03 0.00 0.20 Log(GDP per capita) 10,051 12.89 0.53 12.14 13.69 Tertiary education (share of population) 10,051 43.81 6.68 35.5 54.3 Business building broadband infrastructure availability 10,051 0.74 0.14 0.56 0.97 Log(population) 10,051 14.09 0.20 13.74 14.33 Unemployment benefit recipients (share of population) 10,051 4.34 0.45 3.41 5.12 Share of big industrial companies 10,051 10.72 1.20 8.05 12.61 Open in a new tab Robot data To measure the introduction of industrial robots in local labor markets in Norway, we make use of a dataset provided by the International Federation of Robotics (IFR), which contains information on robot stock and deliveries in Norwegian industrial firms since 1993. The IFR defines an industrial robot as an “automatically controlled, reprogrammable multipurpose [stationary or mobile machine]” [32]. Following this definition, industrial robots are autonomous machines capable of operating without human intervention and that could potentially substitute or complement human labor. The IFR provides detailed data on robot stock and deliveries for the period 1993–2017, which can be broken down by application or industry. Robot stock for years 2018 and 2019 are extrapolated assuming a 9 percent annual growth in operational stock as projected by IFR [33]. IFR data have recently been used to analyze the impact of automation on employment and wages [22,34,35], as well as on workers’ well-being [11,12]. We allocate robots in regional labor markets following extant research [22,34,36], assuming that robots are distributed across region and industries by their respective employment shares. Employment shares are calculated based on Eurostat’s Labor Force Survey data dating back to 2008. The long-term change in robot adoption occurs between years 2008 and t based on initial regional employment composition in each industrial category (industry, agriculture, construction, and services), with the change in robot adoption per 1,000 workers fixed at the starting level in year 2008. Δ r o b o t e x p o s u r e r , s , t = ∑ s ∈ S e m p r , s , 2008 e m p r , 2008 * ( r o b o t s s , t − r o b o t s s , 2008 e m p s , 2007 ) (1) In this setup, robot exposure is measured as national robot adoption allocated at the region-industry level (r,s). Each regional labor market r is scaled by the nation’s total employment emp c . In short, the instrumental variable that we will use in our empirical analysis is the long-term change in the adoption of industrial robots by Norwegian firms in each local labor market (i.e. in each region-industry r,s). This measures the extent to which workers have been exposed to automation from 2007 onwards (see a further discussion of the empirical identification strategy in section 3.2 below). Regional-level control variables We use the Eurostat’s Labor Force Survey to obtain regional-level variables on GDP per capita, population share with tertiary education, and population size. From Statistics Norway, we retrieve data on firms by size for each region. Further, we collect data on unemployment benefit recipients as a share of total population from the Norwegian Labour and Welfare Administration (NAV), for each region and each year of our dataset. To avoid omitting the possible conflating influence of ICTs when analyzing automation, previous studies have included ICT capital or investment as an additional control variable [34,37]. However, others argue that more specific measures of ICT utilization are necessary for micro-level studies [38]. Unlike existing studies that have analyzed the impact of high-speed broadband developments in Norway [39,40], we use as additional control variable the broadband internet availability in office buildings instead of households in each region. Data on office buildings with at least 8/8 Mbit/s speeds are provided by the Norwegian Communications Authority (Nkom), and matched against individuals through regional identifiers.
5. Empirical methods The econometric analysis sets out to study the relationship between fear of replacement and job satisfaction. Fear of replacement is the subjective assessment that each worker does on the possibility that her working tasks will be replaced by a smart machine in the future. Such subjective assessment may arguably depend on unobserved and idiosyncratic factors such as e.g. ability, attitude towards risk, and technological/digital competencies. Therefore, unobserved individual factors might possibly influence both the outcome variable (job satisfaction) and the main explanatory variable (fear of replacement). To address endogeneity concerns, we follow recent research and use the lagged introduction of robots in local labor markets (industry-regions) as an instrument for individual workers’ fear of replacement [11,12]. Existing studies on robot implications for labor markets where robot adoption is the main explanatory variable address endogeneity issues by incorporating spillover effects from robot adoption across industries in other countries as an instrument in a 2SLS setup [22,34,36]. Unlike these studies, we approach subjective responses to structural inroads of robot technology in local labor markets to identify learning effects from past automation. Specifically, our instrumental variable is the one defined in (1) above, i.e. the change in the adoption of industrial robots by Norwegian firms in each local labor market (industry-region) between 2008 and year t (i.e. one of the survey years 2016–2019). This variable measures the extent to which workers in each of the 16 industry-regions considered in this study have been exposed to rising automation in recent years. We thus exploit (lagged) variation in robot adoption over time and across industry-regions in Norway to instrument for individual fear of replacement at time t. The underlying idea of this identification strategy is that workers that are employed in local labor markets that have more rapidly been exposed to automation (i.e. in industry-regions where firms have increasingly used industrial robots) will be more likely to consider automation as a possible threat, and therefore fear that some of their working tasks could be replaced by a machine in the future. In other words, we posit that workers learn from past robot adoption in their local labor markets, because they are subject to peer effects [41]. Although it is reasonable to posit that these peer effects work through changes in robot adoption over time, we cannot exclude the possibility that the same mechanism may also work through the absolute levels of robot adoption (i.e. workers may fear replacement when they experience a high intensity of industrial robots in the industry-region where they work). To consider this possibility, we have also calculated our instrumental variable in levels rather than as changes over time, and reported additional regressions in the online appendix (see Table A5 in S1 File, whose results are in line with the main results presented in the paper). Norwegian firms have invested in sophisticated robotics and automation technologies to keep pace with the Digital Single Market strategy [42], and our empirical analysis exploits this exogenous source of tempo-spatial variations to identify the effects of automation on workers’ job satisfaction. Fig 1 illustrates the dynamics of industrial robots adoption in Norway in the last decades, showing a much faster pace since 2014. Table 3 shows that most robots have so far been used by firms within manufacturing, and less so in other branches such as agriculture, construction and services. However, Table 3 also shows that the introduction of robots by service firms has been quite rapid in the last decade. Fig 2 illustrates the trend in robot adoption since 2010, indicating a rising trend in all 16 industry-regions considered in this study, and particularly so in manufacturing and services. Fig 1. Robot deliveries and operational stock for Norway between 1993 and 2019. The data for 2018 and 2019 are estimated (see data section). Open in a new tab Table 3. Adoption of robots (operational stock) in Norwegian regions and industries. Region Sector 2008 2017 Oslo & Akershus Agriculture, forestry and fishing 0 1 Industry 113 140 Construction 0 0 Services 3 13 Eastern Norway Agriculture, forestry and fishing 3 4 Industry 296 294 Construction 0 1 Services 3 11 Southern & Western Norway Agriculture, forestry and fishing 3 4 Industry 444 521 Construction 0 1 Services 3 14 Mid- and Northern Norway Agriculture, forestry and fishing 3 5 Industry 141 173 Construction 0 0 Services 3 13 Open in a new tab Fig 2. Robot adoption by industry-region, 2010–2019. Open in a new tab To get a further overview of the diffusion and use of industrial robots in Norway, it is also useful to get some descriptive figures from Eurostat’ survey on “ICT usage and e-commerce in enterprises (2018)” (see Tables A1 to A4 in S1 File). Large firms are the main adopters of both industrial and service robot technologies, and capital-intensive firms appear to invest in and integrate both technologies in their operations. Operating machines represent about 60% of all industrial robots in Norwegian firms in 2017. Whereas large firms use service robots for mostly logistics and transportation purposes, small and medium enterprises (SMEs) deploy robots in more product-related purposes, such as inspection, assembly or construction works. Although our paper focuses on industrial automation, workers in knowledge-intensive service occupations may rather fear competition from new artificial intelligence technologies. Table A4 in S1 File presents some descriptives on Norwegian firms’ use of Big Data in their business operations. Large firms are more likely to use Big Data than SMEs. Large firms use smart sensors (e.g. Internet of Things) and geo-data to a greater extent than SMEs. On the other hand, SMEs more actively collect data from social media for marketing purposes. In sum, smart machines are swiftly making inroads in the Norwegian economy, and this pace has accelerated in the last five years. Fig 3 shows the time trend of the variable machine replacement for each of the 16 industry-regions in the more recent period 2016–2019 to which our survey data refers. Although this is a relatively short span (which does not make it possible to assess long run trends), Fig 3 indicates that fear of replacement due to automation has increased steadily in most of the industry-regions considered in this study, and that there is by and large a correspondence between the time trends reported in Fig 2 (robot adoption) and Fig 3 (machine replacement). Fig 3. Share of workers who believe that their job can be replaced by machines, by region and industry. Open in a new tab Based on the identification strategy noted above, we estimate a two-stage instrumental variables (IV) model: the first stage (3) investigates how robot exposure and other control factors explain variations in workers’ fear of replacement, whereas the second stage (2) estimates the relationship between job satisfaction and anticipated replacement: J S i r s t = α 1 + γ m a c h i n e r e p l a c e m e n t i r s t + δ x i r s t ' + η r + θ t + ε i r s t (2) m a c h i n e r e p l a c e m e n t i r s t = α 2 + μ z r s t + ρ x i r s t ' + τ r + φ t + ∈ i r s t (3) JS is reported job satisfaction, machine replacement is the dummy variable indicating whether the respondent believes a machine can perform her/his job tasks, z is the instrumental variable (industry-region lagged pace of robot adoption), and x is a set of covariates (measured for individuals in each survey wave). The subscript r denotes the geographical region of residence of each worker i, s denotes the industry in which the worker is employed, and the subscript t refers to survey year. Among the set of covariates, the skill variable is particularly relevant for the present study, as we seek to investigate whether the relationship between fear of replacement and job satisfaction differs for high- versus low-skilled workers. To test these moderation effects, we interact the skill variable with the robots variable in the first stage equation, and with the fear of replacement variable in the second stage equation. For model identification, the vector x in Eqs (2) and (3) does also include detailed demographic and socio-economic characteristics expected to correlate with job satisfaction and anticipated replacement, such as age, gender, income, union membership, and industry employment. According to previous studies, these factors are relevant to explain variation in job satisfaction, labor dynamics and technological automation diffusion [8,22,31,34,37,43–45]. Finally, both equations also include a full set of regional dummies and time dummies that control for unobservable determinants of job satisfaction within each region over time. It is important to note that our identification strategy is based on the assumption that robot exposure in each industry-region will affect workers’ job satisfaction only through its effects on fear of replacement, and we therefore exclude a direct impact of robot exposure on job satisfaction. Conceptually, we cannot exclude that robot adoption in a given firm may potentially affect employees’ job satisfaction directly, and not only indirectly through fear of replacement. However, we think that this conceptual argument is not a particular reason of concern in our empirical study. The reason for this is that robot adoption in Norway, although it has increased rapidly during the last few years, it is still relatively low in absolute levels (around 6%, see Table 2). This means that our dataset and estimations do not refer to workers who already use robots in their current job, but for the great majority they refer to workers that are exposed to (i.e. observe) automation being introduced in other firms in the industry-region where they work, and that due to these peer effects fear that machines could replace some of their working tasks in the future. The econometric model is estimated as a two-stage bivariate recursive ordered probit maximum likelihood setup, which accommodates the ordinal character of the outcome and main explanatory variable [46,47]. This model estimates response probabilities of two variables, one ordered and one dichotomous, and the exogenous variable robot exposure is included in the first stage [48,49]. Estimations are performed with Roodman’s [50] conditional mixed process (CMP) program. Because the instrument is measured at the industry-region level, estimations are likely to contain grouped structures, and we therefore cluster standard errors in all regressions [41,51,52].
7. Conclusions The swift pace of introduction of industrial robots, AI and smart machines in production activities in recent years represents a new major process of Schumpeterian creative destruction. This process will in the near future lead to dramatic consequences for employment in many sectors and regions, and it will at the same time create new unprecedented opportunities for productivity growth, wealth and well-being. As for other major transformations in the past, this structural change and the related transition and adjustment process will arguably not be smooth and swift: it will unfold over a period of several years, and it will lead to important negative impacts in the short-run before the long-run economic and societal benefits will eventually emerge. Studying the effects of automation on employment, extant research has so far mostly focused on aggregate impacts that industrial robots and AI have on employment demand and wages for different industries and countries. The present paper has argued that it is important to shift the focus to the micro-level of analysis and study the impacts of automation technologies on individual workers’ well-being. Specifically, we have put forward the idea that the relevant impacts that it is important to study are not only pecuniary (i.e. related to workers’ employment conditions and wages) but also nonpecuniary (i.e. related to workers’ expectations and future job prospects). Ceteris paribus, workers that fear that their working tasks might be replaced by a smart machine in the future may have a lower job satisfaction at present than workers who have more secure job prospects and less uncertainty about the future. We have investigated this idea by considering a large sample of workers in Norway for the period 2016–2019, and studying the extent to which the introduction of industrial robots in local labor markets affect workers’ fear of being replaced in the future, and in this way hamper their subjective well-being. Our data and results provide a quite striking picture. 40% of Norwegian workers in our sample think their working tasks might be replaced by a machine, and our analysis shows that this fear of replacement significantly lowers their job satisfaction at present. We also find that this transmission mechanism is driven by low-skilled workers, which are those carrying out routine-based tasks, and who are therefore aware to be more exposed to the risks of automation. On the whole, we think that our empirical findings are not only relevant for Norway (the country to which our dataset refers), but they can in principle have more general lessons for other countries too. Automation is by now an important trend that is rapidly diffusing worldwide, and its effect on workers’ health and well-being is therefore a topic of high societal relevance. Schwabe (2019) provides related evidence using a different dataset for a larger sample of European countries. The present work calls therefore for further research that may investigate and extend this research topic in a variety of different countries and continents. A first important policy implication of our results is that the current process of structural change and creative destruction will in the short-run likely lead to stronger fear of replacement and uncertainty about the future for low-skilled workers carrying out routine work in factories, thus possibly leading to further polarization not only in terms of employment and wages, but also in terms of subjective well-being. To mitigate these negative consequences, which are already visible at present, national authorities should actively support training and re-training policies in such a way that workers that are exposed to future replacement may build up new competencies that can increase their ability to work with smart machines, as well as increase their qualifications and the likelihood to find a new job if this will become necessary in the future. If fear of replacement triggers workers to participate in such training is an interesting question for future studies. In other words, by giving better future prospects to more vulnerable workers, training policies will also contribute to enhance their subjective well-being at present. Our results also suggest a second reflection and possible policy implication. As noted above, 40% of Norwegian workers in our sample think that their working tasks might be replaced by a machine. According to the Eurobarometer survey, the extent of fear of replacement is roughly the same for workers in other European countries [12]. This number is quite high indeed. Is it reasonable that so many workers fear competition from smart machines, and why is it so? Extant research on automation and employment has not yet reached a consensus on the direction and size of these effects, and it still presents a vivid debate between those that emphasize negative consequences and those that point out positive economic and societal effects. Hence, there is no clear scientific evidence and consensus at present that could provide the basis for individual workers to form rational and well-informed assessments and expectations about their job prospects in the future. It is therefore reasonable to ask whether the generalized fear of competition from smart machines is actually exaggerated and not based on extant research and established knowledge. The concrete risk is that–in the current phase of rapid and disruptive technological change–societal debates in the media on robots, automation and AI may tend to exaggerate risks and depict gloomy future scenarios, while often neglecting possible long-run benefits for the economy and the society, which are indeed even hard to imagine at the moment [1]. Since media debates on this topic are often biased and tend to overemphasize the negative impacts of automation (which are arguably more “catchy” and attractive for the uninformed audience), this may contribute to explain why so many workers report to fear future machine replacement. However, our paper has shown that such subjective individual assessments about the future may indeed hamper job satisfaction at present. This can also lead to anxiety, mental stress and low motivation at work, which may in turn depress creativity, productivity and innovation in the workplace. In short, we should not disregard the possibility that a biased and uninformed presentation of this topic in the media may indeed have concrete negative consequences on workers’ subjective well-being by affecting their beliefs about future job prospects. The policy implications of this are certainly not easy to draw. A major point, though, is to stress the importance of having better informed societal debates in the media, and particularly in State-owned channels, that take a more balanced view of the negative and positive consequences of automation, and that avoid spreading fears and gloomy scenarios that are not based on solid evidence and arguments.
Supporting information S1 File. Online appendix: Additional information and robustness tests. Containing Tables A.1 to A.15. (DOCX)
Data Availability The main dataset used to produce the results presented in the study are third-party data available from the Working Life Barometer survey (Arbeidslivsbarometer).The survey is provided by the Confederation of Vocational Unions (YS), a politically independent umbrella organization for labor unions, and organized by the Work Research Institute in Norway. URL: https://ys.no/ys-jobber-med/ys-arbeidslivsbarometer/ Data are available from the YS for researchers who meet the criteria for access to confidential data. To get access, users have to create a user account at YS. The authors had no special access privileges to the data. For further information about the access to this dataset, users can contact YS by e-mail: [email protected]
Funding Statement - Initials of authors: FC & HS - Grant number: 247921 - Funder: Research Council of Norway - URL: https://www.forskningsradet.no/ - The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
| 2020-11-30T00:00:00 |
2020/11/30
|
https://pmc.ncbi.nlm.nih.gov/articles/PMC7703879/
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The Impact of Artificial Intelligence - Widespread Job Losses
|
The Impact of Artificial Intelligence - Widespread Job Losses
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https://www.iotforall.com
|
[] |
AI and automation are anticipated to displace millions of jobs globally, impacting even professional sectors, causing widespread concern about livelihoods.
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The Impact of Artificial Intelligence - Widespread Job Losses
- Last Updated: April 19, 2025
In schools, the impact of artificial intelligence and automation is often portrayed in a good light. Think back to learning about the water wheel, mills, printing presses, steam engines, and assembly lines. Often, the underlying narrative is that these were great innovations that reduced the burden of labor on humans. However, the technological advances of our time seem to be less well-received. Perhaps it is because of our proximity to these examples of automation. Our closeness prevents us from seeing only the benefits and instead pushes us to see how much our lives and livelihood will be impacted by artificial intelligence.
McKinsey & Company reckons that, depending upon various adoption scenarios, automation will displace between 400 and 800 million jobs by 2030, requiring as many as 375 million people to switch job categories entirely. How could such a shift not cause fear and concern, especially for the world’s vulnerable countries and populations?
The Brookings Institution writes of a "new" kind of automation with more advanced robotics and AI that can bring work displacement to college graduates and professionals as much as it has to vehicle drivers and retail workers.
With frightening yet like these, it's no wonder AI and automation keep many of us up at night.
“Stop Being a Luddite”
Although we may remember from our textbooks that the 19th century brought significant innovations to factories, this does not mean that it was welcomed with open arms by the people then. The Luddites were textile workers who protested against automation, eventually attacking and burning factories because “they feared that unskilled machine operators were robbing them of their livelihood.” The Luddite movement occurred all the way back in 1811, so concerns about job losses or job displacements due to automation are far from new.
When fear or concern is raised about the potential impact of artificial intelligence and automation on our workforce, a typical response is thus to point to the past; the same concerns are raised time and again and prove unfounded.
In 1961, President Kennedy said, “the major challenge of the sixties is to maintain full employment at a time when automation is replacing men.” In the 1980s, the advent of personal computers spurred “computerphobia” with many fearing computers would replace them.
Frame Breaking — The Luddites
So what happened?
Despite these fears and concerns, every technological shift has ended up creating more jobs than were destroyed. When particular tasks are automated, becoming cheaper and faster, you need more human workers to do the other functions in the process that haven’t been automated.
“During the Industrial Revolution, more and more tasks in the weaving process were automated, prompting workers to focus on the things machines could not do, such as operating a machine, and then tending multiple machines to keep them running smoothly. This caused output to grow explosively. In America during the 19th century, the amount of coarse cloth a single weaver could produce in an hour increased by a factor of 50, and the amount of labour required per yard of cloth fell by 98 percent. This made cloth cheaper and increased demand for it, which in turn created more jobs for weavers: their numbers quadrupled between 1830 and 1900. In other words, technology gradually changed the nature of the weaver’s job, and the skills required to do it, rather than replacing it altogether.” -The Economist, Automation and Anxiety
Impact of Artificial Intelligence — A Bright Future?
Looking back on history, it seems reasonable to conclude that fears and concerns regarding AI and automation are understandable but ultimately unwarranted. Technological change may eliminate specific jobs, but it has always created more in the process.
Beyond net job creation, there are other reasons to be optimistic about the impact of artificial intelligence and automation.
“Simply put, jobs that robots can replace are not good jobs in the first place. As humans, we climb up the rungs of drudgery — physically tasking or mind-numbing jobs — to jobs that use what got us to the top of the food chain, our brains.” -The Wall Street Journal, The Robots Are Coming. Welcome Them.
By eliminating the tedium, AI and automation can free us to pursue careers that give us a greater sense of meaning and well-being. Careers that challenge us, instill a sense of progress, provide us with autonomy, and make us feel like we belong; all attributes of a satisfying job.
And at a higher level, AI and automation will also help to eliminate disease and world poverty. Already, AI is driving great advances in medicine and healthcare with better disease prevention, higher accuracy diagnosis, and more effective treatment and cures. When it comes to eliminating world poverty, one of the biggest barriers is identifying where help is needed most. The AI and Global Development Lab is recreating historical and geographical human-development trajectories from satellite images starting from 1984 to measure poverty and the effects of foreign aid.
Impact of Artificial Intelligence — A Dark Future
I am all for optimism. But as much as I’d like to believe all of the above, this bright outlook on the future relies on seemingly shaky premises. Namely:
The past is an accurate predictor of the future.
We can weather the painful transition.
There are some jobs that only humans can do.
The Past Isn’t an Accurate Predictor of the Future
As explored earlier, a common response to fears and concerns over the impact of artificial intelligence and automation is to point to the past. However, this approach only works if the future behaves similarly. There are many things that are different now than in the past, and these factors give us good reason to believe that the future will play out differently.
In the past, the technological disruption of one industry didn’t necessarily mean the disruption of another. Let’s take car manufacturing as an example; a robot in automobile manufacturing can drive big gains in productivity and efficiency, but that same robot would be useless trying to manufacture anything other than a car. The underlying technology of the robot might be adapted, but at best that still only addresses manufacturing
AI is different because it can be applied to virtually any industry. When you develop AI that can understand language, recognize patterns, and problem-solve, disruption isn’t contained. Imagine creating an AI that can diagnose diseases and handle medications, address lawsuits, and write articles like this one. No need to imagine: AI is already doing those exact things.
Another important distinction between now and the past is the speed of technological progress. Technological progress doesn’t advance linearly, it advances exponentially. Consider Moore’s Law: the number of transistors on an integrated circuit doubles roughly every two years.
In the words of University of Colorado physics professor Albert Allen Bartlett, “The greatest shortcoming of the human race is our inability to understand the exponential function.” We drastically underestimate what happens when a value keeps doubling.
What do you get when technological progress is accelerating and AI can do jobs across a range of industries? An accelerating pace of job destruction.
“There’s no economic law that says ‘You will always create enough jobs or the balance will always be even’, it’s possible for a technology to dramatically favour one group and to hurt another group, and the net of that might be that you have fewer jobs.” -Erik Brynjolfsson, Director of the MIT Initiative on the Digital Economy
In the past, yes, more jobs were created than were destroyed by technology. Workers were able to reskill and move laterally into other industries instead. But the past isn’t always an accurate predictor of the future. We can’t complacently sit back and think that everything is going to be ok.
This brings us to another critical issue ...
The Transition Will Be Extremely Painful
Let's pretend for a second that the past actually will be a good predictor of the future; artificial intelligence will impact some jobs but more jobs will be created to replace them. This brings up an absolutely critical question, what kinds of jobs are being created, and what kinds of jobs are being destroyed?
“Low- and high-skilled jobs have so far been less vulnerable to automation. The low-skilled jobs categories that are considered to have the best prospects over the next decade — including food service, janitorial work, gardening, home health, childcare, and security — are generally physical jobs, and require face-to-face interaction. At some point, robots will be able to fulfill these roles, but there’s little incentive to robotize these tasks at the moment, as there’s a large supply of humans who are willing to do them for low wages.” -Slate, Will robots steal your job?
Blue-collar and white-collar jobs will be eliminated—basically, anything that requires middle skills (meaning that it requires some training, but not much). This leaves low-skill jobs, as described above, and high-skill jobs that require high levels of training and education.
There will assuredly be an increasing number of jobs related to programming, robotics, engineering, etc. After all, these skills will be needed to improve and maintain the AI and automation being used around us.
But will the people who lost their middle-skilled jobs be able to move into these high-skill roles instead? Certainly not without significant training and education. What about moving into low-skill jobs? Well, the number of these jobs is unlikely to increase, particularly because the middle class loses jobs and stops spending money on food service, gardening, home health, etc.
The transition could be very painful. It’s no secret that rising unemployment has a negative impact on society; less volunteerism, higher crime, and drug abuse are all correlated. A period of high unemployment, in which tens of millions of people are incapable of getting a job because they simply don’t have the necessary skills, will be our reality if we don’t adequately prepare.
So how do we prepare? At the minimum, by overhauling our entire education system and providing means for people to re-skill.
To transition from 90 percent of the American population farming to just 2 percent during the first industrial revolution, it took the mass introduction of primary education to equip people with the necessary skills to work. The problem is that we’re still using an education system that is geared toward the industrial age. The three Rs (reading, writing, and arithmetic) were once the important skills to learn to succeed in the workforce. Now, those are the skills quickly being overtaken by AI.
For a fascinating look at our current education system and its faults, check out this video from Sir Ken Robinson:
[embed]https://www.ted.com/talks/ken_robinson_says_schools_kill_creativity?language=en[/embed]
In addition to transforming our whole education system, we should also accept that learning doesn’t end with formal schooling. The exponential acceleration of digital transformation means that learning must be a lifelong pursuit, constantly re-skilling to meet an ever-changing world.
Making huge changes to our education system, providing means for people to re-skill, and encouraging lifelong learning can help mitigate impact of artificial intelligence, but is that enough?
Are We F*cked? Will All Jobs Be Eliminated?
When I originally wrote this article a couple of years ago, I believed firmly that 99 percent of all jobs would be eliminated. Now, I'm not so sure. Here was my argument at the time:
The claim that 99 percent of all jobs will be eliminated may seem bold, and yet it’s all but certain. All you need are two premises:
We will continue making progress in building more intelligent machines.
Human intelligence arises from physical processes.
The first premise shouldn’t be at all controversial. The only reason to think that we would permanently stop progress, of any kind, is some extinction-level event that wipes out humanity, in which case this debate is irrelevant. Excluding such a disaster, technological progress will continue on an exponential curve. And it doesn’t matter how fast that progress is; all that matters is that it will continue. The incentives for people, companies, and governments are too great to think otherwise.
The second premise will be controversial, but notice that I said human intelligence. I didn’t say “consciousness” or “what it means to be human." That human intelligence arises from physical processes seems easy to demonstrate: if we affect the physical processes of the brain we can observe clear changes in intelligence. Though a gloomy example, it’s clear that poking holes in a person’s brain results in changes to their intelligence. A well-placed poke in someone’s Broca’s area and voilà—that person can’t process speech.
With these two premises in hand, we can conclude the following: we will build machines that have human-level intelligence and higher. It’s inevitable.
We already know that machines are better than humans at physical tasks, they can move faster, more precisely, and lift greater loads. When these machines are also as intelligent as us, there will be almost nothing they can’t do—or can't learn to do quickly. Therefore, 99 percent of jobs will eventually be eliminated.
But that doesn't mean we'll be redundant. We'll still need leaders (unless we give ourselves over to robot overlords) and our arts, music, etc., may remain solely human pursuits too. As for just about everything else? Machines will do it—and do it better.
“But who’s going to maintain the machines?” The machines.
“But who’s going to improve the machines?” The machines.
Assuming they could eventually learn 99 percent of what we do, surely they'll be capable of maintaining and improving themselves more precisely and efficiently than we ever could.
The above argument is sound, but the conclusion that 99 percent of all jobs will be eliminated I believe over-focused on our current conception of a "job." As I pointed out above, there's no guarantee that the future will play out like the past. After continuing to reflect and learn over the past few years, I now think there's good reason to believe that while 99 percent of all current jobs might be eliminated, there will still be plenty for humans to do (which is really what we care about, isn't it?).
"The one thing that humans can do that robots can’t (at least for a long while) is to decide what it is that humans want to do. This is not a trivial semantic trick; our desires are inspired by our previous inventions, making this a circular question." -The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future, by Kevin Kelly
Perhaps another way of looking at the above quote is this: a few years ago I read the book Emotional Intelligence, and was shocked to discover just how essential emotions are to decision-making. Not just important, but essential. People who had experienced brain damage to the emotional centers of their brains were absolutely incapable of making even the smallest decisions. This is because, when faced with a number of choices, they could think of logical reasons for doing or not doing any of them but had no emotional push/pull to choose.
So while AI and automation may eliminate the need for humans to do any of the doing, we will still need humans to determine what to do. And because everything that we do and everything that we build sparks new desires and shows us new possibilities, this "job" will never be eliminated.
If you had predicted in the early 19th century that almost all jobs would be eliminated, and you defined jobs as agricultural work, you would have been right. In the same way, I believe that what we think of as jobs today will almost certainly be eliminated too. But this does not mean that there will be no jobs at all, the "job" will instead shift to determining, what do we want to do? And then working with our AI and machines to make our desires a reality.
Is this overly optimistic? I don't think so. Either way, there's no question that the impact of artificial intelligence will be great and it's critical that we invest in the education and infrastructure needed to support people as many current jobs are eliminated and we transition to this new future.
Originally published on April 1, 2017. Updated on January 30, 2023.
| 2022-12-01T00:00:00 |
https://www.iotforall.com/impact-of-artificial-intelligence-job-losses
|
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|
Technological unemployment - Wikipedia
|
Technological unemployment
|
https://en.wikipedia.org
|
[] |
The World Bank's 2019 World Development Report argues that while automation displaces workers, technological innovation creates more new industries and jobs on ...
|
Unemployment caused by technological change
A pharmacy robot delivering to a nurses station at San Jose's Good Samaritan Hospital, in the United States, in 2008: In the 21st century, robots are beginning to perform roles not just in manufacturing but also in the service sector – in healthcare, for example.
The term technological unemployment is used to describe the loss of jobs caused by technological change.[1][2][3] It is a key type of structural unemployment. Technological change typically includes the introduction of labour-saving "mechanical-muscle" machines or more efficient "mechanical-mind" processes (automation), and humans' role in these processes are minimized.[4] Just as horses were gradually made obsolete as transport by the automobile and as labourer by the tractor, humans' jobs have also been affected throughout modern history. Historical examples include artisan weavers reduced to poverty after the introduction of mechanized looms. Thousands of man-years of work was performed in a matter of hours by the bombe codebreaking machine during World War II. A contemporary example of technological unemployment is the displacement of retail cashiers by self-service tills and cashierless stores.
That technological change can cause short-term job losses is widely accepted. The view that it can lead to lasting increases in unemployment has long been controversial. Participants in the technological unemployment debates can be broadly divided into optimists and pessimists. Optimists agree that innovation may be disruptive to jobs in the short term, yet hold that various compensation effects ensure there is never a long-term negative impact on jobs, whereas pessimists contend that at least in some circumstances, new technologies can lead to a lasting decline in the total number of workers in employment. The phrase "technological unemployment" was popularised by John Maynard Keynes in the 1930s, who said it was "only a temporary phase of maladjustment".[5] The issue of machines displacing human labour has been discussed since at least Aristotle's time.[6][7]
Prior to the 18th century, both the elite and common people would generally take the pessimistic view on technological unemployment, at least in cases where the issue arose. Due to generally low unemployment in much of pre-modern history, the topic was rarely a prominent concern.[citation needed] In the 18th century fears over the impact of machinery on jobs intensified with the growth of mass unemployment, especially in Great Britain which was then at the forefront of the Industrial Revolution. Yet some economic thinkers[who?] began to argue against these fears, claiming that overall innovation would not have negative effects on jobs. These arguments were formalised in the early 19th century by the classical economists. During the second half of the 19th century, it stayed apparent that technological progress was benefiting all sections of society, including the working class. Concerns over the negative impact of innovation diminished. The term "Luddite fallacy" was coined to describe the thinking that innovation would have lasting harmful effects on employment.
The view that technology is unlikely to lead to long-term unemployment has been repeatedly challenged by a minority of economists.[who?] In the early 1800s these included David Ricardo. There were dozens of economists[who?] warning about technological unemployment during brief intensifications of the debate that spiked in the 1930s and 1960s. Especially in Europe, there were further warnings in the closing two decades of the twentieth century, as commentators[who?] noted an enduring rise in unemployment suffered by many industrialised nations since the 1970s. Yet a clear majority of both professional economists and the interested general public held the optimistic view through most of the 20th century.
Advances in artificial intelligence (AI) have reignited debates about the possibility of mass unemployment, or even the end of employment altogether. Some experts, such as Geoffrey Hinton, believe that the development of artificial general intelligence and advanced robotics will eventually enable the automation of all intellectual and physical tasks, suggesting the need for a basic income for non-workers to subsist.[8][9] Others, like Daron Acemoglu, argue that humans will remain necessary for certain tasks, or complementary to AI, disrupting the labor market without necessarily causing mass unemployment.[10][11] The World Bank's 2019 World Development Report argues that while automation displaces workers, technological innovation creates more new industries and jobs on balance.[12]
History [ edit ]
Classical era [ edit ]
Roman Emperor Vespasian, who refused a low-cost method of transport of heavy goods that would put laborers out of work
According to author Gregory Woirol, the phenomenon of technological unemployment is likely to have existed since at least the invention of the wheel.[13] Ancient societies had various methods for relieving the poverty of those unable to support themselves with their own labour. Ancient China and ancient Egypt may have had various centrally run relief programmes in response to technological unemployment dating back to at least the second millennium BC.[14] Ancient Hebrews and adherents of the ancient Vedic religion had decentralised responses where aiding the poor was encouraged by their faiths.[14] In ancient Greece, free labourers could find themselves unemployed due to both the effects of ancient labour saving technology and to competition from slaves ("machines of flesh and blood"[15]). Sometimes, these unemployed workers would starve to death or were forced into slavery themselves although in other cases they were supported by handouts. Pericles responded to perceived technological unemployment by launching public works programmes to provide paid work to the jobless. Pericle's programmes were criticized for wasting public money but these criticisms were defeated.[16]
Perhaps the earliest example of a scholar discussing the phenomenon of technological unemployment occurs with Aristotle, who speculated in Book One of Politics that if machines could become sufficiently advanced, there would be no more need for human labour.[17] Similar to the Greeks, ancient Romans responded to the problem of technological unemployment by relieving poverty with handouts (such as the Cura Annonae). Several hundred thousand families were sometimes supported like this at once.[14] Less often, jobs were directly created with public works programmes, such as those launched by the Gracchi. Various emperors even went as far as to refuse or ban labour saving innovations.[18][19] In one instance, the introduction of a labor-saving invention was blocked, when Emperor Vespasian refused to allow a new method of low-cost transportation of heavy goods, saying "You must allow my poor hauliers to earn their bread."[20] Labour shortages began to develop in the Roman empire towards the end of the second century AD, and from this point mass unemployment in Europe appears to have largely receded for over a millennium.[21]
Post-classical era [ edit ]
The medieval and early renaissance period saw the widespread adoption of newly invented technologies, as well as older ones which had been conceived yet barely used in the Classical era.[22] Some were invented in Europe while others were invented in more Eastern countries like China, India, Arabia and Persia. The Black Death left fewer workers across Europe. Mass unemployment began to reappear in Europe, especially in Western, Central and Southern Europe in the 15th century, partly as a result of population growth, and partly due to changes in the availability of land for subsistence farming caused by early enclosures.[23] As a result of the threat of unemployment, there was less tolerance for disruptive new technologies. European authorities would often side with groups representing subsections of the working population, such as Guilds, banning new technologies and sometimes even executing those who tried to promote or trade in them.[24]
16th to 18th century [ edit ]
Elizabeth I, who refused to patent a knitting machine invented by William Lee, saying "Consider thou what the invention could do to my poor subjects. It would assuredly bring them to ruin by depriving them of employment, thus making them beggars."
In Great Britain, the ruling elite began to take a less restrictive approach to innovation somewhat earlier than in much of continental Europe, which has been cited as a possible reason for Britain's early lead in driving the Industrial Revolution.[25] Yet concern over the impact of innovation on employment remained strong through the 16th and early 17th century. A famous example of new technology being refused occurred when the inventor William Lee invited Queen Elizabeth I to view a labour saving knitting machine. The Queen declined to issue a patent on the grounds that the technology might cause unemployment among textile workers. After moving to France and also failing to achieve success in promoting his invention, Lee returned to England but was again refused by Elizabeth's successor James I for the same reason.[26]
After the Glorious Revolution, authorities became less sympathetic to workers concerns about losing their jobs due to innovation. An increasingly influential strand of Mercantilist thought held that introducing labour saving technology would actually reduce unemployment, as it would allow British firms to increase their market share against foreign competition. From the early 18th century workers could no longer rely on support from the authorities against the perceived threat of technological unemployment. They would sometimes take direct action, such as machine breaking, in attempts to protect themselves from disruptive innovation. Joseph Schumpeter notes that as the 18th century progressed, thinkers would raise the alarm about technological unemployment with increasing frequency, with von Justi being a prominent example.[27] Yet Schumpeter also notes that the prevailing view among the elite solidified on the position that technological unemployment would not be a long-term problem.[26][23]
19th century [ edit ]
It was only in the 19th century that debates over technological unemployment became intense, especially in Great Britain where many economic thinkers of the time were concentrated. Building on the work of Dean Tucker and Adam Smith, political economists began to create what would become the modern discipline of economics.[note 1] While rejecting much of mercantilism, members of the new discipline largely agreed that technological unemployment would not be an enduring problem. In the first few decades of the 19th century, several prominent political economists did, however, argue against the optimistic view, claiming that innovation could cause long-term unemployment. These included Sismondi,[28] Malthus, J S Mill, and from 1821, David Ricardo himself.[29] As arguably the most respected political economist of his age, Ricardo's view was challenging to others in the discipline. The first major economist to respond was Jean-Baptiste Say, who argued that no one would introduce machinery if they were going to reduce the amount of product,[note 2] and that as Say's law states that supply creates its own demand, any displaced workers would automatically find work elsewhere once the market had had time to adjust.[30] Ramsey McCulloch expanded and formalised Say's optimistic views on technological unemployment, and was supported by others such as Charles Babbage, Nassau Senior and many other lesser known political economists. Towards the middle of the 19th century, Karl Marx joined the debates. Building on the work of Ricardo and Mill, Marx went much further, presenting a deeply pessimistic view of technological unemployment; his views attracted many followers and founded an enduring school of thought but mainstream economics was not dramatically changed. By the 1870s, at least in Great Britain, technological unemployment faded both as a popular concern and as an issue for academic debate. It had become increasingly apparent that innovation was increasing prosperity for all sections of British society, including the working class. As the classical school of thought gave way to neoclassical economics, mainstream thinking was tightened to take into account and refute the pessimistic arguments of Mill and Ricardo.[31]
20th century [ edit ]
Critics of the view that innovation causes lasting unemployment argue that technology is used by workers and does not replace them on a large scale.
For the first two decades of the 20th century, mass unemployment was not the major problem it had been in the first half of the 19th. While the Marxist school and a few other thinkers continued to challenge the optimistic view, technological unemployment was not a significant concern for mainstream economic thinking until the mid to late 1920s. In the 1920s mass unemployment re-emerged as a pressing issue within Europe. At this time the U.S. was generally more prosperous, but even there urban unemployment had begun to increase from 1927. Rural American workers had been suffering job losses from the start of the 1920s; many had been displaced by improved agricultural technology, such as the tractor. The centre of gravity for economic debates had by this time moved from Great Britain to the United States, and it was here that the 20th century's two great periods of debate over technological unemployment largely occurred.[32]
The peak periods for the two debates were in the 1930s and the 1960s. According to economic historian Gregory R Woirol, the two episodes share several similarities.[33] In both cases academic debates were preceded by an outbreak of popular concern, sparked by recent rises in unemployment. In both cases the debates were not conclusively settled, but faded away as unemployment was reduced by an outbreak of war – World War II for the debate of the 1930s, and the Vietnam War for the 1960s episodes. In both cases, the debates were conducted within the prevailing paradigm at the time, with little reference to earlier thought. In the 1930s, optimists based their arguments largely on neo-classical beliefs in the self-correcting power of markets to reduce any short-term unemployment via compensation effects. In the 1960s, belief in compensation effects was less strong, but the mainstream Keynesian economists of the time largely believed government intervention would be able to counter any persistent technological unemployment that was not cleared by market forces. Another similarity was the publication of a major Federal study towards the end of each episode, which broadly found that long-term technological unemployment was not occurring (though the studies did agree innovation was a major factor in the short term displacement of workers, and advised government action to provide assistance).[note 3][33]
As the golden age of capitalism came to a close in the 1970s, unemployment once again rose, and this time generally remained relatively high for the rest of the century, across most advanced economies. Several economists once again argued that this may be due to innovation, with perhaps the most prominent being Paul Samuelson.[34] Overall, the closing decades of the 20th century saw most concern expressed over technological unemployment in Europe, though there were several examples in the U.S.[35] A number of popular works warning of technological unemployment were also published. These included James S. Albus's 1976 book titled Peoples' Capitalism: The Economics of the Robot Revolution;[36][37] David F. Noble with works published in 1984[38] and 1993;[39] Jeremy Rifkin and his 1995 book The End of Work;[40] and the 1996 book The Global Trap.[41] Yet for the most part, other than during the periods of intense debate in the 1930s and 60s, the consensus in the 20th century among both professional economists and the general public remained that technology does not cause long-term joblessness.[42]
21st century [ edit ]
Opinions [ edit ]
There is a prevailing opinion that we are in an era of technological unemployment – that technology is increasingly making skilled workers obsolete. Prof. Mark MacCarthy (2014) [ 43 ]
The general consensus that innovation does not cause long-term unemployment held strong for the first decade of the 21st century although it continued to be challenged by a number of academic works,[44][45] and by popular works such as Marshall Brain's Robotic Nation[46] and Martin Ford's The Lights in the Tunnel: Automation, Accelerating Technology and the Economy of the Future.[47]
Since the publication of their 2011 book Race Against the Machine, MIT professors Andrew McAfee and Erik Brynjolfsson have been prominent among those raising concern about technological unemployment. The two professors remain relatively optimistic, however, stating "the key to winning the race is not to compete against machines but to compete with machines".[48][49][50][51][52][53][54]
Concern about technological unemployment grew in 2013 due in part to a number of studies predicting substantially increased technological unemployment in forthcoming decades and empirical evidence that, in certain sectors, employment is falling worldwide despite rising output, thus discounting globalization and offshoring as the only causes of increasing unemployment.[55][26][56]
In 2013, professor Nick Bloom of Stanford University stated there had recently been a major change of heart concerning technological unemployment among his fellow economists.[57] In 2014 the Financial Times reported that the impact of innovation on jobs has been a dominant theme in recent economic discussion.[58] According to the academic and former politician Michael Ignatieff writing in 2014, questions concerning the effects of technological change have been "haunting democratic politics everywhere".[59] Concerns have included evidence showing worldwide falls in employment across sectors such as manufacturing; falls in pay for low and medium skilled workers stretching back several decades even as productivity continues to rise; the increase in often precarious platform mediated employment; and the occurrence of "jobless recoveries" after recent recessions. The 21st century has seen a variety of skilled tasks partially taken over by machines, including translation, legal research and even low level journalism. Care work, entertainment, and other tasks requiring empathy, previously thought safe from automation, have also begun to be performed by robots.[55][26][60][61][62][63]
Former U.S. Treasury Secretary and Professor of Economics at Harvard University Lawrence Summers stated in 2014 that he no longer believed automation would always create new jobs and that "This isn't some hypothetical future possibility. This is something that's emerging before us right now." Summers noted that already, more labor sectors were losing jobs than creating new ones.[note 4][64][65][66][67] While himself doubtful about technological unemployment, professor Mark MacCarthy stated in the fall of 2014 that it is now the "prevailing opinion" that the era of technological unemployment has arrived.[43]
At the 2014 Davos meeting, Thomas Friedman reported that the link between technology and unemployment seemed to have been the dominant theme of that year's discussions. A survey at Davos 2014 found that 80% of 147 respondents agreed that technology was driving jobless growth.[68] At the 2015 Davos, Gillian Tett found that almost all delegates attending a discussion on inequality and technology expected an increase in inequality over the next five years, and gives the reason for this as the technological displacement of jobs.[69] 2015 saw Martin Ford win the Financial Times and McKinsey Business Book of the Year Award for his Rise of the Robots: Technology and the Threat of a Jobless Future, and saw the first world summit on technological unemployment, held in New York. In late 2015, further warnings of potential worsening for technological unemployment came from Andy Haldane, the Bank of England's chief economist, and from Ignazio Visco, the governor of the Bank of Italy.[70][71] In an October 2016 interview, US President Barack Obama said that due to the growth of artificial intelligence, society would be debating "unconditional free money for everyone" within 10 to 20 years.[72] In 2019, computer scientist and artificial intelligence expert Stuart J. Russell stated that "in the long run nearly all current jobs will go away, so we need fairly radical policy changes to prepare for a very different future economy." In a book he authored, Russell claims that "One rapidly emerging picture is that of an economy where far fewer people work because work is unnecessary." However, he predicted that employment in healthcare, home care, and construction would increase.[73]
Other economists[who?] have argued that long-term technological unemployment is unlikely. In 2014, Pew Research canvassed 1,896 technology professionals and economists and found a split of opinion: 48% of respondents believed that new technologies would displace more jobs than they would create by the year 2025, while 52% maintained that they would not.[74] Economics professor Bruce Chapman from Australian National University has advised that studies such as Frey and Osborne's tend to overstate the probability of future job losses, as they don't account for new employment likely to be created, due to technology, in what are currently unknown areas.[75] Looking deeper into this, small and mid-sized businesses have created a large amount of new jobs around the world, which allows for entrepreneurs and investors to have the freedom to create and grow businesses, which is extremely vital with new technologies emerging everyday.[76] With all of these new buinesses there will be a large number of workers that will be required to work for these companies, which would improve the world's employment situation, replacing jobs that were previously lost.
General public surveys have often found an expectation that automation would impact jobs widely, but not the jobs held by those particular people surveyed.[77]
Studies [ edit ]
A number of studies have predicted that automation will take a large proportion of jobs in the future, but estimates of the level of unemployment this will cause vary. Research by Carl Benedikt Frey and Michael Osborne of the Oxford Martin School showed that employees engaged in "tasks following well-defined procedures that can easily be performed by sophisticated algorithms" are at risk of displacement. The study, published in 2013, shows that automation can affect both skilled and unskilled work and both high and low-paying occupations; however, low-paid physical occupations are most at risk. It estimated that 47% of US jobs were at high risk of automation.[26] In 2014, the economic think tank Bruegel released a study, based on the Frey and Osborne approach, claiming that across the European Union's 28 member states, 54% of jobs were at risk of automation. The countries where jobs were least vulnerable to automation were Sweden, with 46.69% of jobs vulnerable, the UK at 47.17%, the Netherlands at 49.50%, and France and Denmark, both at 49.54%. The countries where jobs were found to be most vulnerable were Romania at 61.93%, Portugal at 58.94%, Croatia at 57.9%, and Bulgaria at 56.56%.[78][79] A 2015 report by the Taub Center found that 41% of jobs in Israel were at risk of being automated within the next two decades.[80] In January 2016, a joint study by the Oxford Martin School and Citibank, based on previous studies on automation and data from the World Bank, found that the risk of automation in developing countries was much higher than in developed countries. It found that 77% of jobs in China, 69% of jobs in India, 85% of jobs in Ethiopia, and 55% of jobs in Uzbekistan were at risk of automation.[81] The World Bank similarly employed the methodology of Frey and Osborne. A 2016 study by the International Labour Organization found 74% of salaried electrical & electronics industry positions in Thailand, 75% of salaried electrical & electronics industry positions in Vietnam, 63% of salaried electrical & electronics industry positions in Indonesia, and 81% of salaried electrical & electronics industry positions in the Philippines were at high risk of automation.[82] A 2016 United Nations report stated that 75% of jobs in the developing world were at risk of automation, and predicted that more jobs might be lost when corporations stop outsourcing to developing countries after automation in industrialized countries makes it less lucrative to outsource to countries with lower labor costs.[83]
The Council of Economic Advisers, a US government agency tasked with providing economic research for the White House, in the 2016 Economic Report of the President, used the data from the Frey and Osborne study to estimate that 83% of jobs with an hourly wage below $20, 31% of jobs with an hourly wage between $20 and $40, and 4% of jobs with an hourly wage above $40 were at risk of automation.[84] A 2016 study by Ryerson University (now Toronto Metropolitan University) found that 42% of jobs in Canada were at risk of automation, dividing them into two categories - "high risk" jobs and "low risk" jobs. High risk jobs were mainly lower-income jobs that required lower education levels than average. Low risk jobs were on average more skilled positions. The report found a 70% chance that high risk jobs and a 30% chance that low risk jobs would be affected by automation in the next 10–20 years.[85] A 2017 study by PricewaterhouseCoopers found that up to 38% of jobs in the US, 35% of jobs in Germany, 30% of jobs in the UK, and 21% of jobs in Japan were at high risk of being automated by the early 2030s.[86] A 2017 study by Ball State University found about half of American jobs were at risk of automation, many of them low-income jobs.[87] A September 2017 report by McKinsey & Company found that as of 2015, 478 billion out of 749 billion working hours per year dedicated to manufacturing, or $2.7 trillion out of $5.1 trillion in labor, were already automatable. In low-skill areas, 82% of labor in apparel goods, 80% of agriculture processing, 76% of food manufacturing, and 60% of beverage manufacturing were subject to automation. In mid-skill areas, 72% of basic materials production and 70% of furniture manufacturing was automatable. In high-skill areas, 52% of aerospace and defense labor and 50% of advanced electronics labor could be automated.[88] In October 2017, a survey of information technology decision makers in the US and UK found that a majority believed that most business processes could be automated by 2022. On average, they said that 59% of business processes were subject to automation.[89] A November 2017 report by the McKinsey Global Institute that analyzed around 800 occupations in 46 countries estimated that between 400 million and 800 million jobs could be lost due to robotic automation by 2030. It estimated that jobs were more at risk in developed countries than developing countries due to a greater availability of capital to invest in automation.[90] Job losses and downward mobility blamed on automation has been cited as one of many factors in the resurgence of nationalist and protectionist politics in the US, UK and France, among other countries.[91][92][93][94][95]
However, not all recent empirical studies have found evidence to support the idea that automation will cause widespread unemployment. A study released in 2015, examining the impact of industrial robots in 17 countries between 1993 and 2007, found no overall reduction in employment was caused by the robots, and that there was a slight increase in overall wages.[96] According to a study published in McKinsey Quarterly[97] in 2015 the impact of computerization in most cases is not replacement of employees but automation of portions of the tasks they perform.[98] A 2016 OECD study found that among the 21 OECD countries surveyed, on average only 9% of jobs were in foreseeable danger of automation, but this varied greatly among countries: for example in South Korea the figure of at-risk jobs was 6% while in Austria it was 12%.[99] In contrast to other studies, the OECD study does not primarily base its assessment on the tasks that a job entails, but also includes demographic variables, including sex, education and age. In 2017, Forrester estimated that automation would result in a net loss of about 7% of jobs in the US by 2027, replacing 17% of jobs while creating new jobs equivalent to 10% of the workforce.[100] Another study argued that the risk of US jobs to automation had been overestimated due to factors such as the heterogeneity of tasks within occupations and the adaptability of jobs being neglected. The study found that once this was taken into account, the number of occupations at risk to automation in the US drops, ceteris paribus, from 38% to 9%.[101] A 2017 study on the effect of automation on Germany found no evidence that automation caused total job losses but that they do effect the jobs people are employed in; losses in the industrial sector due to automation were offset by gains in the service sector. Manufacturing workers were also not at risk from automation and were in fact more likely to remain employed, though not necessarily doing the same tasks. However, automation did result in a decrease in labour's income share as it raised productivity but not wages.[102]
A 2018 Brookings Institution study that analyzed 28 industries in 18 OECD countries from 1970 to 2018 found that automation was responsible for holding down wages. Although it concluded that automation did not reduce the overall number of jobs available and even increased them, it found that from the 1970s to the 2010s, it had reduced the share of human labor in the value added to the work, and thus had helped to slow wage growth.[103] In April 2018, Adair Turner, former Chairman of the Financial Services Authority and head of the Institute for New Economic Thinking, stated that it would already be possible to automate 50% of jobs with current technology, and that it will be possible to automate all jobs by 2060.[104]
Premature deindustrialization [ edit ]
Premature deindustrialization occurs when developing nations deindustrialize without first becoming rich, as happened with the advanced economies. The concept was popularized by Dani Rodrik in 2013, who went on to publish several papers showing the growing empirical evidence for the phenomena. Premature deindustrialization adds to concern over technological unemployment for developing countries – as traditional compensation effects that advanced economy workers enjoyed, such being able to get well paid work in the service sector after losing their factory jobs – may not be available.[105][106]
Some commentators, such as Carl Benedikt Frey, argue that with the right responses, the negative effects of further automation on workers in developing economies can still be avoided.[107]
Artificial intelligence [ edit ]
Since about 2017, a new wave of concern over technological unemployment had become prominent, this time over the effects of artificial intelligence (AI).[108] Commentators including Calum Chace and Daniel Hulme have warned that if unchecked, AI threatens to cause an "economic singularity", with job churn too rapid for humans to adapt to, leading to widespread technological unemployment. However, they also advise that with the right responses by business leaders, policy makers and society, the impact of AI could be a net positive for workers.[109][110]
Morgan R. Frank et al. cautions that there are several barriers preventing researchers from making accurate predictions of the effects AI will have on future job markets.[111] Marian Krakovsky has argued that the jobs most likely to be completely replaced by AI are in middle-class areas, such as professional services. Often, the practical solution is to find another job, but workers may not have the qualifications for high-level jobs and so must drop to lower level jobs. However, Krakovsky (2018) predicts that AI will largely take the route of "complementing people", rather than "replicating people". Suggesting that the goal of people implementing AI is to improve the life of workers, not replace them.[112] Studies have also shown that rather than solely destroying jobs AI can also create work: albeit low-skill jobs to train AI in low-income countries.[113]
Following Russian president Vladimir Putin's 2017 statement that whichever country first achieves mastery in AI "will become the ruler of the world", various national and supranational governments have announced AI strategies. Concerns on not falling behind in the AI arms race have been more prominent than worries over AI's potential to cause unemployment. Several strategies suggest that achieving a leading role in AI should help their citizens get more rewarding jobs. Finland has aimed to help the citizens of other EU nations acquire the skills they need to compete in the post-AI jobs market, making a free course on "The Elements of AI" available in multiple European languages.[114][115][116] Oracle CEO Mark Hurd predicted that AI "will actually create more jobs, not less jobs" as humans will be needed to manage AI systems.[117]
Martin Ford argues that many jobs are routine, repetitive and (to an AI) predictable; Ford warns that these jobs may be automated in the next couple of decades, and that many of the new jobs may not be "accessible to people with average capability", even with retraining.
Certain digital technologies are predicted to result in more job losses than others. For example, in recent years, the adoption of modern robotics has led to net employment growth. However, many businesses anticipate that automation, or employing robots would result in job losses in the future. This is especially true for companies in Central and Eastern Europe.[119][120][121]
Other digital technologies, such as platforms or big data, are projected to have a more neutral impact on employment.[119][121]
Issues within the debates [ edit ]
Long-term effects on employment [ edit ]
There are more sectors losing jobs than creating jobs. And the general-purpose aspect of software technology means that even the industries and jobs that it creates are not forever. Lawrence Summers [ 64 ]
Participants in the technological employment debates agree that temporary job losses can result from technological innovation. Similarly, there is no dispute that innovation sometimes has positive effects on workers. Disagreement focuses on whether it is possible for innovation to have a lasting negative impact on overall employment. Levels of persistent unemployment can be quantified empirically, but the causes are subject to debate. Optimists accept short term unemployment may be caused by innovation, yet claim that after a while, compensation effects will always create at least as many jobs as were originally destroyed. While this optimistic view has been continually challenged, it was dominant among mainstream economists for most of the 19th and 20th centuries.[122][123] For example, labor economists Jacob Mincer and Stephan Danninger developed an empirical study using data from the Panel Study of Income Dynamics, and find that although in the short run, technological progress seems to have unclear effects on aggregate unemployment, it reduces unemployment in the long run. When they include a 5-year lag, however, the evidence supporting a short-run employment effect of technology seems to disappear as well, suggesting that technological unemployment "appears to be a myth".[124] Other studies, on the other hand, suggest that the labour-market effects of technologies such as industrial robots strongly depend on domestic institutional context.[125]
The concept of structural unemployment, a lasting level of joblessness that does not disappear even at the high point of the business cycle, became popular in the 1960s. For pessimists, technological unemployment is one of the factors driving the wider phenomena of structural unemployment. Since the 1980s, even optimistic economists have increasingly accepted that structural unemployment has indeed risen in advanced economies, but they have tended to attribute this on globalisation and offshoring rather than technological change.[citation needed] Others claim a chief cause of the lasting increase in unemployment has been the reluctance of governments to pursue expansionary policies since the displacement of Keynesianism that occurred in the 1970s and early 80s.[122][126][44] In the 21st century, and especially since 2013, pessimists have been arguing with increasing frequency that lasting worldwide technological unemployment is a growing threat.[123][55][26][127]
Compensation effects [ edit ]
John Kay inventor of the Fly Shuttle AD 1753, by Ford Madox Brown, depicting the inventor John Kay kissing his wife goodbye as men carry him away from his home to escape a mob angry about his labour-saving mechanical loom. Compensation effects were not widely understood at this time.
Compensation effects are labour-friendly consequences of innovation which "compensate" workers for job losses initially caused by new technology. In the 1820s, several compensation effects were described by Jean-Baptiste Say in response to Ricardo's statement that long-term technological unemployment could occur. Soon after, a whole system of effects was developed by Ramsey McCulloch. The system was labelled "compensation theory" by Karl Marx, who criticized its ideas, arguing that none of the effects were guaranteed to operate. Disagreement over the effectiveness of compensation effects has remained a central part of academic debates on technological unemployment ever since.[44][128]
Compensation effects include:
By new machines. (The labour needed to build the new equipment that applied innovation requires.) By new investments. (Enabled by the cost savings and therefore increased profits from the new technology.) By changes in wages. (In cases where unemployment does occur, this can cause a lowering of wages, thus allowing more workers to be re-employed at the now lower cost. On the other hand, sometimes workers will enjoy wage increases as their profitability rises. This leads to increased income and therefore increased spending, which in turn encourages job creation.) By lower prices. (Which then lead to more demand, and therefore more employment.) Lower prices can also help offset wage cuts, as cheaper goods will increase workers' buying power. By new products. (Where innovation directly creates new jobs.)
The "by new machines" effect is now rarely discussed by economists; it is often accepted that Marx successfully refuted it.[44] Even pessimists often concede that product innovation associated with the "by new products" effect can sometimes have a positive effect on employment. An important distinction can be drawn between 'process' and 'product' innovations.[note 5] Evidence from Latin America seems to suggest that product innovation significantly contributes to the employment growth at the firm level, more so than process innovation.[129] The extent to which the other effects are successful in compensating the workforce for job losses has been extensively debated throughout the history of modern economics; the issue is still not resolved.[44][45] One such effect that potentially complements the compensation effect is job multiplier. According to research developed by Enrico Moretti, with each additional skilled job created in high tech industries in a given city, more than two jobs are created in the non-tradable sector. His findings suggest that technological growth and the resulting job-creation in high-tech industries might have a more significant spillover effect than anticipated.[130] Evidence from Europe also supports such a job multiplier effect, showing local high-tech jobs could create five additional low-tech jobs.[131]
Many economists pessimistic about technological unemployment accept that compensation effects did largely operate as the optimists claimed through most of the 19th and 20th century. Yet they hold that the advent of computerisation means that compensation effects have become less effective. An early example of this argument was made by Wassily Leontief in 1983. He conceded that after some disruption, the advance of mechanization during the Industrial Revolution increased the demand for labour as well as increasing pay due to effects that flow from increased productivity.[132] While early machines lowered the demand for muscle power, they were unintelligent and needed large numbers of human operators to remain productive. Yet since the introduction of computers into the workplace, there is now less need not just for muscle power but also for human brain power. Hence even as productivity continues to rise, the lower demand for human labour may mean less pay and employment.[44][26]
Luddite fallacy [ edit ]
If the Luddite fallacy were true we would all be out of work because productivity has been increasing for two centuries. Alex Tabarrok [ 133 ]
The term "Luddite fallacy" is sometimes used to express the view that those concerned about long-term technological unemployment are committing a fallacy, as they fail to account for compensation effects. People who use the term typically expect that technological progress will have no long-term impact on employment levels, and eventually will raise wages for all workers, because progress helps to increase the overall wealth of society. The term is originating on from the Luddites, members of an early 19th century English anti-textile-machinery organisation. During the 20th century and the first decade of the 21st century, the dominant view among economists has been that belief in long-term technological unemployment was indeed a fallacy. More recently, there has been increased support for the view that the benefits of automation are not equally distributed.[123][134][135]
There are two different theories for why long-term difficulty could develop.
Traditionally ascribed to the Luddites (accurately or not), that there is a finite amount of work available and if machines do it, there can be none left for humans. Economists may call this the lump of labour fallacy, arguing that in reality no such limitation exists. A long-term difficulty can arise that has nothing to do with any lump of labour. In this view, the amount of work that can exist is infinite, but
machines can do most of the "easy" work that requires less skill, talent, knowledge, or insight
the definition of what is "easy" expands as information technology progresses, and
the work that lies beyond "easy" may require greater brainpower than most people have.
This second view is supported by many modern advocates of the possibility of long-term, systemic technological unemployment.
In his 2018 book Bullshit Jobs, David Graeber argues that the real reason that mass unemployment has never materialised, despite a widespread expectation, that total hours worked and the length of the workweek have not substantially declined since the 1930s, and that instead overwork is considered a pervasive problem, is that genuinely necessary jobs that have been lost to automation have been replaced by jobs, typically white-collar jobs, whose relevance to the economy is unclear and which do not respond to any genuine market demand (as opposed especially to essential labour such as care work, part of critical infrastructure), and which even those who are employed in these jobs themselves often cannot justify and find pointless.[136]
Skill levels and technological unemployment [ edit ]
A frequent view among those discussing the effect of innovation on the labour market has been that it mainly hurts those with low skills, while often benefiting skilled workers. According to scholars such as Lawrence F. Katz, this may have been true for much of the twentieth century, yet in the 19th century, innovations in the workplace largely displaced costly skilled artisans, and generally benefited the low skilled. While 21st century innovation has been replacing some unskilled work, other low skilled occupations remain resistant to automation, while white collar work requiring intermediate skills is increasingly being performed by autonomous computer programs.[137][138][139]
Some recent studies however, such as a 2015 paper by Georg Graetz and Guy Michaels, found that at least in the area they studied – the impact of industrial robots – innovation is boosting pay for highly skilled workers while having a more negative impact on those with low to medium skills.[96] A 2015 report by Carl Benedikt Frey, Michael Osborne and Citi Research agreed that innovation had been disruptive mostly to middle-skilled jobs, yet predicted that in the next ten years the impact of automation would fall most heavily on those with low skills.[140]
Geoffrey Colvin at Forbes argued that predictions on the kind of work a computer will never be able to do have proven inaccurate. A better approach to anticipate the skills on which humans will provide value would be to find out activities where we will insist that humans remain accountable for important decisions, such as with judges, CEOs, bus drivers and government leaders, or where human nature can only be satisfied by deep interpersonal connections, even if those tasks could be automated.[141]
In contrast, others see even skilled human laborers being obsolete. Oxford academics Carl Benedikt Frey and Michael A Osborne have predicted computerization could make nearly half of jobs redundant;[142] of the 702 professions assessed, they found a strong correlation between education and income with ability to be automated, with office jobs and service work being some of the more at risk.[143] In 2012 co-founder of Sun Microsystems Vinod Khosla predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[144]
The issue of redundant job places is elaborated by the 2019 paper by Natalya Kozlova, according to which over 50% of workers in Russia perform work that requires low levels of education and can be replaced by applying digital technologies. Only 13% of those people possess education that exceeds the level of intellectual computer systems present today and expected within the following decade.[145]
Empirical findings [ edit ]
There has been a significant amount of empirical research that attempts to quantify the impact of technological unemployment, mainly at the microeconomic level. Most existing firm-level research has found a labor-friendly nature of technological innovations. For example, German economists Stefan Lachenmaier and Horst Rottmann find that both product and process innovation have a positive effect on employment. They also find that process innovation has a more significant job creation effect than product innovation.[146] This result is supported by evidence in the United States as well, which shows that manufacturing firm innovations have a positive effect on the total number of jobs, not just limited to firm-specific behavior.[147]
At the industry level, however, researchers have found mixed results with regard to the employment effect of technological changes. A 2017 study on manufacturing and service sectors in 11 European countries suggests that positive employment effects of technological innovations only exist in the medium- and high-tech sectors. There also seems to be a negative correlation between employment and capital formation, which suggests that technological progress could potentially be labor-saving given that process innovation is often incorporated in investment.[148]
Limited macroeconomic analysis has been done to study the relationship between technological shocks and unemployment. The small amount of existing research, however, suggests mixed results. Italian economist Marco Vivarelli finds that the labor-saving effect of process innovation appears to have affected the Italian economy more negatively than the United States. On the other hand, the job creating effect of product innovation could only be observed in the United States, not Italy.[149] Another study in 2013 finds a more transitory, rather than permanent, unemployment effect of technological change.[150]
Measures of technological innovation [ edit ]
There have been four main approaches that attempt to capture and document technological innovation quantitatively.[citation needed] The first one, proposed by Jordi Gali in 1999 and further developed by Neville Francis and Valerie A. Ramey in 2005, is to use long-run restrictions in a vector autoregression (VAR) to identify technological shocks, assuming that only technology affects long-run productivity.[151][152]
The second approach is from Susanto Basu, John Fernald and Miles Kimball.[153] They create a measure of aggregate technology change with augmented Solow residuals, controlling for aggregate, non-technological effects such as non-constant returns and imperfect competition.[citation needed]
The third method, initially developed by John Shea in 1999, takes a more direct approach and employs observable indicators such as research and development (R&D) spending, and number of patent applications.[154] This measure of technological innovation is widely used in empirical research, since it does not rely on the assumption that only technology affects long-run productivity, and fairly accurately captures output variation based on input variation. However, there are limitations with direct measures such as R&D. For example, since R&D only measures the input in innovation, the output is unlikely to be perfectly correlated with the input. In addition, R&D fails to capture the indeterminate lag between developing a new product or service, and bringing it to market.[155]
The fourth approach, constructed by Michelle Alexopoulos, looks at the number of new titles published in the fields of technology and computer science to reflect technological progress, which he found to be consistent with R&D expenditure data.[156] Compared with R&D, this indicator captures the lag between changes in technology.
Solutions [ edit ]
Preventing net job losses [ edit ]
Banning/refusing innovation [ edit ]
"What I object to, is the craze for machinery, not machinery as such. The craze is for what they call labour-saving machinery. Men go on 'saving labour', till thousands are without work and thrown on the open streets to die of starvation." — Gandhi, 1924 [ 157 ]
Historically, innovations were sometimes banned due to concerns about their impact on employment. Since the development of modern economics, however, this option has generally not even been considered as a solution, at least not for the advanced economies. Even commentators who are pessimistic about long-term technological unemployment invariably consider innovation to be an overall benefit to society, with J. S. Mill being perhaps the only prominent western political economist to have suggested prohibiting the use of technology as a possible solution to unemployment.[128]
Gandhian economics called for a delay in the uptake of labour saving machines until unemployment was alleviated, however this advice was largely rejected by Nehru who was to become prime minister once India achieved its independence. The policy of slowing the introduction of innovation so as to avoid technological unemployment was, however, implemented in the 20th century within China under Mao's administration.[158][159][160]
Shorter working hours [ edit ]
In 1870, the average American worker clocked up about 75 hours per week. Just prior to World War II working hours had fallen to about 42 per week, and the fall was similar in other advanced economies. According to Wassily Leontief, this was a voluntary increase in technological unemployment. The reduction in working hours helped share out available work, and was favoured by workers who were happy to reduce hours to gain extra leisure, as innovation was at the time generally helping to increase their rates of pay.[132]
Further reductions in working hours have been proposed as a possible solution to unemployment by economists including John R. Commons, Lord Keynes and Luigi Pasinetti. Yet once working hours have reached about 40 hours per week, workers have been less enthusiastic about further reductions, both to prevent loss of income and as many value engaging in work for its own sake.[citation needed] Generally, 20th-century economists had argued against further reductions as a solution to unemployment, saying it reflects a lump of labour fallacy.[161] In 2014, Google's co-founder, Larry Page, suggested a four-day workweek, so as technology continues to displace jobs, more people can find employment.[65][162][163]
Public works [ edit ]
Programmes of public works have traditionally been used as way for governments to directly boost employment, though this has often been opposed by some, but not all, conservatives. Jean-Baptiste Say, although generally associated with free market economics, advised that public works could be a solution to technological unemployment.[164] Some commentators, such as professor Mathew Forstater, have advised that public works and guaranteed jobs in the public sector may be the ideal solution to technological unemployment, as unlike welfare or guaranteed income schemes they provide people with the social recognition and meaningful engagement that comes with work.[165][166]
For less developed economies, public works may be an easier to administrate solution compared to universal welfare programmes.[132] A partial exception is for spending on infrastructure, which has been recommended as a solution to technological unemployment even by economists previously associated with a neoliberal agenda, such as Larry Summers.[167]
Education [ edit ]
Improved availability to quality education, including skills training for adults, is a solution that in principle at least is not opposed by any side of the political spectrum, and welcomed even by those who are optimistic about long-term technological employment. Improved education paid for by government tends to be especially popular with industry. However, several academics have argued that improved education alone will not be sufficient to solve technological unemployment, pointing to recent declines in the demand for many intermediate skills, and suggesting that not everyone is capable in becoming proficient in the most advanced skills.[137][138][139] Kim Taipale has said that "The era of bell curve distributions that supported a bulging social middle class is over... Education per se is not going to make up the difference."[168] while back in 2011 Paul Krugman argued that better education would be an insufficient solution to technological unemployment.[169]
Living with technological unemployment [ edit ]
Welfare payments [ edit ]
The use of various forms of subsidies has often been accepted as a solution to technological unemployment even by conservatives and by those who are optimistic about the long-term effect on jobs. Welfare programmes have historically tended to be more durable once established, compared with other solutions to unemployment such as directly creating jobs with public works. Despite being the first person to create a formal system describing compensation effects, Ramsey McCulloch and most other classical economists advocated government aid for those suffering from technological unemployment, as they understood that market adjustment to new technology was not instantaneous and that those displaced by labour-saving technology would not always be able to immediately obtain alternative employment through their own efforts.[128]
Basic income [ edit ]
Several commentators have argued that traditional forms of welfare payment may be inadequate as a response to the future challenges posed by technological unemployment, and have suggested a basic income as an alternative.[170] People advocating some form of basic income as a solution to technological unemployment include Martin Ford, [171] Erik Brynjolfsson,[58] Robert Reich, Andrew Yang, Elon Musk, Zoltan Istvan, and Guy Standing. Reich has gone as far as to say the introduction of a basic income, perhaps implemented as a negative income tax is "almost inevitable",[172] while Standing has said he considers that a basic income is becoming "politically essential".[173] Since late 2015, new basic income pilots have been announced in Finland, the Netherlands, and Canada. Further recent advocacy for basic income has arisen from a number of technology entrepreneurs, the most prominent being Sam Altman, president of Y Combinator.[174]
Skepticism about basic income includes both right and left elements, and proposals for different forms of it have come from all segments of the spectrum. For example, while the best-known proposed forms (with taxation and distribution) are usually thought of as left-leaning ideas that right-leaning people try to defend against, other forms have been proposed even by libertarians, such as von Hayek and Friedman. In the United States, President Richard Nixon's Family Assistance Plan (FAP) of 1969, which had much in common with basic income, passed in the House but was defeated in the Senate.[175]
One objection to basic income is that it could be a disincentive to work, but evidence from older pilots in India, Africa, and Canada indicates that this does not happen and that a basic income encourages low-level entrepreneurship and more productive, collaborative work. Another objection is that funding it sustainably is a huge challenge. While new revenue-raising ideas have been proposed such as Martin Ford's wage recapture tax, how to fund a generous basic income remains a debated question, and skeptics have dismissed it as utopian. Even from a progressive viewpoint, there are concerns that a basic income set too low may not help the economically vulnerable, especially if financed largely from cuts to other forms of welfare.[173][176][177][178]
To better address both the funding concerns and concerns about government control, one alternative model is that the cost and control would be distributed across the private sector instead of the public sector. Companies across the economy would be required to employ humans, but the job descriptions would be left to private innovation, and individuals would have to compete to be hired and retained. This would be a for-profit sector analog of basic income, that is, a market-based form of basic income. It differs from a job guarantee in that the government is not the employer (rather, companies are) and there is no aspect of having employees who "cannot be fired", a problem that interferes with economic dynamism. The economic salvation in this model is not that every individual is guaranteed a job, but rather just that enough jobs exist that massive unemployment is avoided and employment is no longer solely the privilege of only the very smartest or highly trained 20% of the population. Another option for a market-based form of basic income has been proposed by the Center for Economic and Social Justice (CESJ) as part of "a Just Third Way" (a Third Way with greater justice) through widely distributed power and liberty. Called the Capital Homestead Act,[179] it is reminiscent of James S. Albus's Peoples' Capitalism[36][37] in that money creation and securities ownership are widely and directly distributed to individuals rather than flowing through, or being concentrated in, centralized or elite mechanisms.
Broadening the ownership of technological assets [ edit ]
Several solutions have been proposed which do not fall easily into the traditional left-right political spectrum. This includes broadening the ownership of robots and other productive capital assets. Enlarging the ownership of technologies has been advocated by people including James S. Albus[36][180] John Lanchester,[181] Richard B. Freeman,[177] and Noah Smith.[182] Jaron Lanier has proposed a somewhat similar solution: a mechanism where ordinary people receive "nano payments" for the big data they generate by their regular surfing and other aspects of their online presence.[183]
Structural changes towards a post-scarcity economy [ edit ]
The Zeitgeist Movement (TZM), The Venus Project (TVP) as well as various individuals and organizations propose structural changes towards a form of a post-scarcity economy in which people are 'freed' from their automatable, monotonous jobs, instead of 'losing' their jobs. In the system proposed by TZM all jobs are either automated, abolished for bringing no true value for society (such as ordinary advertising), rationalized by more efficient, sustainable and open processes and collaboration or carried out based on altruism and social relevance, opposed to compulsion or monetary gain.[184][185][186][187][188] The movement also speculates that the free time made available to people will permit a renaissance of creativity, invention, community and social capital as well as reducing stress.[184]
Other approaches [ edit ]
The threat of technological unemployment has occasionally been used by free market economists as a justification for supply side reforms, to make it easier for employers to hire and fire workers. Conversely, it has also been used as a reason to justify an increase in employee protection.[126][189]
Economists including Larry Summers have advised a package of measures may be needed. He advised vigorous cooperative efforts to address the "myriad devices" – such as tax havens, bank secrecy, money laundering, and regulatory arbitrage – which enable the holders of great wealth to avoid paying taxes, and to make it more difficult to accumulate great fortunes without requiring "great social contributions" in return. Summers suggested more vigorous enforcement of anti-monopoly laws; reductions in "excessive" protection for intellectual property; greater encouragement of profit-sharing schemes that may benefit workers and give them a stake in wealth accumulation; strengthening of collective bargaining arrangements; improvements in corporate governance; strengthening of financial regulation to eliminate subsidies to financial activity; easing of land-use restrictions that may cause estates to keep rising in value; better training for young people and retraining for displaced workers; and increased public and private investment in infrastructure development, such as energy production and transportation.[64][65][66][190]
Michael Spence has advised that responding to the future impact of technology will require a detailed understanding of the global forces and flows technology has set in motion. Adapting to them "will require shifts in mindsets, policies, investments (especially in human capital), and quite possibly models of employment and distribution".[note 6][191]
See also [ edit ]
Notes [ edit ]
^ Smith did not directly address the problem of technological unemployment, but the Dean had, saying in 1757 that in the long term, the introduction of machinery would allow more employment than would have been possible without them. ^ Typically the introduction of machinery would both increase output and lower cost per unit. ^ Unemployment and technological change(Report no. G-70, 1940) by Corrington Calhoun Gill of the 'National Research Project on Reemployment Opportunities and Recent changes in Industrial Techniques'. Some earlier Federal reports took a pessimistic view of technological unemployment, e.g. Memorandum on Technological Unemployment (1933) by Ewan Clague Bureau of Labor Statistics. Some authorities – e.g. Udo Sautter in Chpt 5 of Three Cheers for the Unemployed: Government and Unemployment Before the New Deal (Cambridge University Press, 1991) – say that in the early 1930s there was near consensus among US experts that technological unemployment was a major problem. Other's though like Technology and the American economy (1966) by the 'National Commission on Technology, Automation, and Economic Progress' In the 1930s, this study was(Report no. G-70, 1940) by Corrington Calhoun Gill of the 'National Research Project on Reemployment Opportunities and Recent changes in Industrial Techniques'. Some earlier Federal reports took a pessimistic view of technological unemployment, e.g.(1933) by Ewan Clague Bureau of Labor Statistics. Some authorities – e.g. Udo Sautter in Chpt 5 of(Cambridge University Press, 1991) – say that in the early 1930s there was near consensus among US experts that technological unemployment was a major problem. Other's though like Bruce Bartlett in Is Industrial Innovation Destroying Jobs (Cato Journal 1984) argue that most economists remained optimistic even during the 1930s. In the 1960s episode, the major Federal study that bookmarked the end of the period of intense debate was(1966) by the 'National Commission on Technology, Automation, and Economic Progress' established by president Lyndon Johnson in 1964 Archived 4 March 2016 at the Wayback Machine ^ Other recent statements by Summers include warnings on the "devastating consequences" for those who perform routine tasks arising from robots, 3-D printing, artificial intelligence, and similar technologies. In his view, "already there are more American men on disability insurance than doing production work in manufacturing. And the trends are all in the wrong direction, particularly for the less skilled, as the capacity of capital embodying artificial intelligence to replace white-collar as well as blue-collar work will increase rapidly in the years ahead." Summers has also said that "[T]here are many reasons to think the software revolution will be even more profound than the agricultural revolution. This time around, change will come faster and affect a much larger share of the economy. [...] [T]here are more sectors losing jobs than creating jobs. And the general-purpose aspect of software technology means that even the industries and jobs that it creates are not forever. [...] If current trends continue, it could well be that a generation from now a quarter of middle-aged men will be out of work at any given moment." ^ Labour-displacing technologies can be classified under the headings of mechanization automation , and process improvement. The first two fundamentally involve transferring tasks from humans to machines. The third often involves the elimination of tasks altogether. The common theme of all three is that tasks are removed from the workforce, decreasing employment. In practice, the categories often overlap: a process improvement can include an automating or mechanizing achievement. The line between mechanization and automation is also subjective, as sometimes mechanization can involve sufficient control to be viewed as part of automation. ^ Spence also wrote that "Now comes a ... powerful, wave of digital technology that is replacing labor in increasingly complex tasks. This process of labor substitution and disintermediation has been underway for some time in service sectors – think of ATMs, online banking, enterprise resource planning, customer relationship management, mobile payment systems, and much more. This revolution is spreading to the production of goods, where robots and 3D printing are displacing labor." In his view, the vast majority of the cost of digital technologies comes at the start, in the design of hardware (e.g. sensors) and, more important, in creating the software that enables machines to carry out various tasks. "Once this is achieved, the marginal cost of the hardware is relatively low (and declines as scale rises), and the marginal cost of replicating the software is essentially zero. With a huge potential global market to amortize the upfront fixed costs of design and testing, the incentives to invest [in digital technologies] are compelling." Spence believes that, unlike prior digital technologies, which drove firms to deploy underutilized pools of valuable labor around the world, the motivating force in the current wave of digital technologies "is cost reduction via the replacement of labor." For example, as the cost of 3D printing technology declines, it is "easy to imagine" that production may become "extremely" local and customized. Moreover, production may occur in response to actual demand, not anticipated or forecast demand. "Meanwhile, the impact of robotics ... is not confined to production. Though self-driving cars and drones are the most attention-getting examples, the impact on logistics is no less transformative. Computers and robotic cranes that schedule and move containers around and load ships now control the Port of Singapore, one of the most efficient in the world." Spence believes that labor, no matter how inexpensive, will become a less important asset for growth and employment expansion, with labor-intensive, process-oriented manufacturing becoming less effective, and that re-localization will appear globally. In his view, production will not disappear, but it will be less labor-intensive, and all countries will eventually need to rebuild their growth models around digital technologies and the human capital supporting their deployment and expansion.
References [ edit ]
Citations [ edit ]
Sources [ edit ]
| 2022-12-01T00:00:00 |
https://en.wikipedia.org/wiki/Technological_unemployment
|
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|
Automation Affects Workers of Color the Most. How Can We Lessen ...
|
Automation Affects Workers of Color the Most. How Can We Lessen the Impact?
|
https://www.chicagofed.org
|
[
"Kristen Broady"
] |
The Covid-19 pandemic accelerated existing trends toward workforce automation as employers sought to minimize labor costs and new work-from-home imperatives ...
|
The Covid-19 pandemic accelerated existing trends toward workforce automation as employers sought to minimize labor costs and new work-from-home imperatives increased digitalization of workplaces across many sectors. Putting pressure on employers to automate jobs were factors including competition for workers amid high employment rates, spread of illness among workers in close physical proximity to each other, and political and market pressures to bring strategically significant manufacturing jobs back within U.S. borders. Employees themselves helped drive the push toward remote work, with almost half of workers being able to do so during the pandemic’s early months—and significant proportions of them continuing to do so now, more than three years in.
While there has been considerable research into the ways that automation may eliminate or diminish the number of nonspecialized jobs, much less attention has been paid to how this vocational displacement may interact with continuing structural issues of racial inequality in the United States. In our paper, building on previous research done at The Brookings Institution, we examine the pandemic-spurred growth in automation and show that Black and Hispanic workers are overrepresented in jobs most likely to be affected by automation.
Underpinning the discussion is the reality that these workers are also overrepresented in jobs where physical presence at a job site and nearness to other people are necessary and underrepresented in jobs best suited to benefit from the perceived advantages of new work-from-home options.
Background
The Covid pandemic caused rapid, immediate job losses in the United States: about 20.5 million jobs from March to April 2020, by far the largest one-month decline the Bureau of Labor Statistics has ever recorded. Since then, the economy has built back up to its present rate of unemployment, low by historical standards.
But that’s not to say the country is back to a pre-pandemic “normal” or that everything is rosy for the average worker. In between then and now, intersecting trends around consumer behavior, labor markets, and the degree of automation have wrought meaningful change, much of which has fallen on the shoulders of workers earning low and moderate incomes.
Massive increases in online purchasing pushed e-commerce companies, such as Amazon, further in their adoption of and experimentation with automated technologies, including warehouse robots and autonomous delivery vehicles, that seem destined to slowly displace human workers .
The rapidly increasing digitization of work threatens to leave behind those without digital skills or resources. Statistics from the pandemic suggest that in some ways it already has. In addition to the proliferation of online meetings and their attendant platforms, businesses ramped up their use of cloud computing, online contracting, and digital payments systems, among other examples. Working from home was not an equal opportunity: Two-thirds of those with a bachelor’s degree were able to telework, versus one-fourth of those with a high-school diploma, according to a U.S. Bureau of Labor Statistics report. Among White workers, 49% could work from home, compared to 39% of Black workers and 29% of Hispanic workers in the labor force. Ability to telework was much greater for those in higher- versus lower-paying jobs, and, as expected, remote work was much more feasible in the traditional “white-collar” occupations.
Employers , scrambling to find workers amid the many constraints imposed by the pandemic, accelerated their use of automation. Orders for robots in North America, mostly in the U.S., increased by 20% in the first quarter of 2021. At airports and on planes, sanitation robots were deployed to cleanse surfaces and the air using UV light sterilization and air sanitizers. In airports, in senior living facilities, and on college campuses, robots were put to work in food and retail delivery. At grocery stores, robots began processing transactions, cleaning floors, stocking shelves, and delivering groceries.
This is a dynamic situation that will require years of study and the perspective of time to fully understand. But we can begin to take stock of which jobs are endangered by automation and what the demographics are of those who currently hold them.
In our study, we found that Black workers are overrepresented in 17 of the 30 U.S. occupations with the highest risk of being automated (as classified by a 2017 study by Frey and Osborne), and Hispanic workers are overrepresented in 22 of these occupations. Retail salespeople, cashiers, construction workers, laborers, and secretaries are the five categories of endangered jobs that employ the highest number of workers. At the other end of the spectrum, Black workers are overrepresented in just 5 of the 30 positions at low risk of being automated, jobs including early childhood educators and dietitians/nutritionists.
As automation takes hold, it is expected to significantly impact Black and Hispanic workers. For example, in a 2017 study, I found that Black workers were more than one-and-a-half times more likely than White workers to be cashiers, cooks, food servers, production workers, and laborers and freight/stock/material movers. I found that Black workers were more than three times more likely than White workers to be security guards, bus drivers, and taxi drivers/chauffeurs.
Policy considerations
Experts have spoken of a coming Fourth Industrial Revolution, one that will be driven by increased automation (including the burgeoning field of artificial intelligence). For all workers to thrive and to avoid increasing racial disparities, policymakers should consider new approaches in education and workforce training that will help prepare this population for the coming changes.
High schools and community colleges may need to beef up their offerings in courses that teach robotics, programming, and other relevant skills, especially in communities most vulnerable to education and job loss. Policymakers could also actively involve businesses, which will need workers to manage automation, in developing such programs and ensuring they are available particularly in vulnerable communities.
Further, because risk of displacement drops significantly for both Black and White workers who hold a bachelor’s degree , investments in higher education, particularly in historically Black colleges and universities (HBCUs) and institutions that focus on increasing access for students of color, can increase educational attainment and lower displacement risk from automation. Such investment might include funding for technical infrastructure, along with initiatives to increase educator-employer connections, such as the engineering program that links Toyota, the University of Kentucky, and the HBCU Kentucky State University.
Education can and should continue on the job. Studies show companies often recapture the costs of training programs through productivity gains from workers whose skills have been boosted. Many companies, however, are reluctant to make this investment. Policymakers could help by offering government incentives such as expanded tax credits for on-the-job education and training expenses.
Another promising area is in working to narrow a racial gap in apprenticeship programs, which combine work-based training and classroom instruction to develop a specific vocational skillset. In 2019, Black workers made up about 10% of the more than 280,000 people completing a Registered Apprenticeship Program, while Black Americans were about 13% of the U.S. labor force overall.
The data show that jobs held by people of color are disproportionately vulnerable to automation. Combatting this reality will require policy responses that pay extra attention to the issue.
Opinions expressed in this article are those of the author(s) and do not necessarily reflect the views of the Federal Reserve Bank of Chicago or the Federal Reserve System.
| 2022-12-01T00:00:00 |
https://www.chicagofed.org/research/content-areas/mobility/policy-brief-automation
|
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|
Automation Engineer Demographics and Statistics [2025] - Zippia
|
Automation Engineer Demographics and Statistics [2025]: Number Of Automation Engineers In The US
|
https://www.zippia.com
|
[] |
Automation Engineer demographics and statistics in the US · There are over 91,711 automation engineers currently employed in the United States. · 13.0% of all ...
|
Automation Engineer employment statistics
Most automation engineers work for a public in the technology industry.
Company size where automation engineers work
Below, you can see the size of companies where automation engineers work.
< 50 employees 50 - 100 employees 100 - 500 employees 500 - 1,000 employees 1,000 - 10,000 employees > 10,000 0% 20% 40% 60% 80% 100%
Automation Engineer jobs by employer size Company size Percentages < 50 employees 2% 50 - 100 employees 8% 100 - 500 employees 15% 500 - 1,000 employees 6% 1,000 - 10,000 employees 23% > 10,000 employees 47%
Automation Engineer jobs by company type
Employees with the automation engineer job title have their preferences when it comes to working for a company. For instance, most automation engineers prefer to work at public companies over private companies.
Education Public Private 0% 20% 40% 60% 80% 100%
Automation Engineer jobs by sector Company type Percentages Education 1% Public 52% Private 46% Government 0%
Automation Engineer jobs by industry
The most common industries for automation engineers are technology, manufacturing and finance.
| 2021-01-29T00:00:00 |
2021/01/29
|
https://www.zippia.com/automation-engineer-jobs/demographics/
|
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Robotic Process Automation: Top Statistics, Trends & Application
|
Robotic Process Automation: Top Statistics, Trends & Application
|
https://www.strategicmarketresearch.com
|
[] |
Robots are anticipated to be able to deliver 20% of FTE capacity on average. RPA reduces 80% of labor-intensive jobs. RPA can reduce expenses for financial ...
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Robotic Process Automation: Top Statistics, Trends, and Application
Robotic process automation is crucial for every firm to help with human resources. In addition to improving business processes, robotic process automation can lower expenses. With RPA, organizations can quickly automate routine administrative tasks so that staff can focus on important responsibilities.
The market for robotic process automation in 2021 was USD 3.05 Bn and by 2030 it will be worth USD 24 Bn, growing at a 27% CAGR.
Advent of ML and AI technologies have made significant developments in different fields. By 2025, intelligent automation will eliminate 40% of service desk operations, unlocking unmanaged types of automation that combine RPA with AI & ML.
RPA healthcare market will be worth USD 6.2 Bn, growing at a 26% CAGR by 2030. Implementation of RPA in healthcare will enhance patient care and improve efficiency. RPA is being utilized in healthcare to automate tasks such as data entry and scheduling appointments.
Top RPA Statistics
The market for robotic process automation in 2021 was USD 3.05 Bn and by 2030 it will be worth USD 24 Bn, growing at a 27% CAGR.
The market for business process management was USD 3.38 billion in 2019, and sales will increase by 6.26% annually to USD 4.78 Bn by 2025.
The service segment led the market with a revenue share of nearly 62.3% in 2022, on the basis of type.
By deployment, the on-premise segment ruled the market with a share of 79.2% in 2022.
Large enterprise segment was the global market leader with a share of 67.2% in 2022. The SME segment will grow at a rapid CAGR by 2030.
BFSI was the largest sector with a revenue share of nearly 29.2% on the basis of application.
North America was the market leader with a revenue share of 37.5% in the year 2022.
RPA technology will play a significant part in automating up to 40% of transactional accounting work by 2025.
In the year 2020, the RPA software revenue grew by 11.9% from 2021.
RPA Adoption Statistics
Nearly 20% of businesses have adopted RPA as of June 2021, increasing from 20% in 2020.
According to 98% of IT leaders, automating company operations is essential for generating financial gains.
Over the next ten years, RPA technology will be most widely adopted in the healthcare sector.
85% of very large and large enterprises used RPA by 2022.
The digital workforce has grown for 72% of the early RPA adopters.
Adoption by Industry
In 2021, manufacturing was the top industry that utilizes RPA more than any other sector (35%). Technology (31%), Medical (10%), Retail and CPG (8%), Money (8%), Public sector (5%), and Learning (3%) were behind manufacturing.
Over the next three years, 78% of companies that have already deployed RPA will see a major rise in investment.
Cognitive RPA Adoption Rate
Organizations prefer to use cognitive technology with greater ambition as RPA adoption advances. Only 6% of those that have not deployed RPA are making progress with cognitive automation, compared to 28% of those who are implementing and scaling RPA.
More than 90% of C-level executives claim that their companies already have some form of intelligent automation.
Results of implementing RPA
61% of participants in total said that cost reduction objectives were fulfilled or exceeded.
In order to support their RPA implementation, 63% of participants intend to work with an RPA implementation partner, while 15% intend to handle RPA internally and 19% intend to use RPA vendors.
Over 90% of C-level executives who use intelligent automation claim that their company outperforms the industry average when it comes to managing organizational transformation in response to new commercial trends.
In 2016, 75% of the firms profiled stated that they have used labor arbitrage to achieve cost-saving goals.
The exception handling portion of automation, which accounts for 80% of the labor, can be enhanced with better process comprehension.
Although technology will not replace workers, it is predicted that 861,000 public sector employment will be eliminated by 2030, saving the public sector £17 billion in payments compared to 2015.
Benefits of RPA
Merging hyper-automation technologies with newly developed operational processes will enable enterprises to reduce operational costs by 30% by 2024.
An RPA bot typically costs one-third that of an offshore person and one-fifth that of an onshore staff.
Robots will perform a sizable amount of current transactional operations. Robots are anticipated to be able to deliver 20% of FTE capacity on average.
RPA reduces 80% of labor-intensive jobs.
RPA can reduce expenses for financial services by 20% to 60% compared to baseline FTE rates.
Digital workplace service workers will be able to cut 30% of the time currently spent on endpoint support and maintenance to continuous engineering by 2024 thanks to endpoint analytics and automation.
According to 85% of respondents, RPA delivered non-financial benefits like accuracy, speed, and flexibility that met or surpassed their expectations.
Using intelligent automation, more than 50% of C-level executives have identified important operational activities that may be improved or automated.
Companies using RPA technology typically save between 35% and 65% on costs.
Businesses receive a typical return of USD 6.74 for every USD 1 invested in RPA technology.
71% of businesses claim that RPA technology has increased employee satisfaction.
RPA Challenges
The biggest obstacle to RPA adoption is a lack of internal and IT resources, followed by staff opposition, fragmented processes, a lack of a clear vision, implementation costs, data preparation, and fear of disruption.
Due to a shortage of specialized expertise, implementation for the majority of firms (63%) will necessitate collaboration with a committed third-party partner.
The majority of CEOs think their company lacks the data science, machine learning, and other AI/cognitive skills required for process automation.
For each automation capability, the following percentages of executives believe they lack the necessary skills: 90% for fundamental process automation, 89% for more sophisticated automation, and 75% for IBM's intelligent process automation.
Top industry using RPA
Banking
In the banking sector, awareness of RPA software is rapidly expanding. BY 2025, RPA in banking will reach USD 1.13 Bn.
RPA adoption reduces labor and operational costs in banks. Additionally, it significantly reduces human jobs and errors. For instance, by speeding up banking automation with RPA technology, processing costs can drop by 30% to 70%.
Benefits of RPA in the banking sector
Cost-effectiveness : Banks and financial institutions can cut processing costs and time by up to 50% by automating repetitive processes.
Increased customer satisfaction and service : By incorporating RPA to handle repetitive tasks, staff members concentrate on company operations & offer clients an improved experience.
Growth with legacy data: The application of robotic process automation closes gaps between different processes by merging essential legacy & new data into one system. Additionally, this makes it possible for banks to produce reports more quickly and with greater depth to support corporate expansion.
Due to the implementation of enhanced internal processes & activities, RPA is now more accurate, productive, and operationally efficient
Use Cases of Banking in RPA
Onboarding of both consumers and employees is facilitated by RPA using the optical character recognition (OCR) approach. This enhances the user experience by removing manual mistakes, cutting down on waiting time, and making dispute resolution easier. Similar to this, welcome emails, email ID creation, and other forms of employee onboarding are done automatically for new hires.
The traditional methods for validating and approving credit card and loan applications could take weeks. RPA facilitates quick choices regarding a customer's eligibility and expedites the process of acquiring customer information, conducting credit and background checks, and background checks.
The cost of fraud and cybercrime was USD 6 trillion by 2021, banks have a huge responsibility to secure customer financial information. By using an "if-then" mechanism to track, examine, and transmit any red flags for the investigation to the relevant department, RPA lessens this burden.
The critical and data-intensive KYC compliance process costs banks at least $384 million a year. By reducing time and money, RPA implementation to gather, screen, and validate consumer data enhances the process cycle.
Healthcare
The healthcare RPA market will be worth USD 88.9 Bn growing at a 8.4% CAGR by 2028 due to the rising worry about access to affordable healthcare. A 2019 survey in the U.S. found that 35% of C-suite executives said their healthcare business had implemented robotic process automation (RPA).
33% of work performed by healthcare providers may be automated.
50% of US healthcare providers plan to invest in RPA during the next three years.
Application of RPA in the healthcare sector
Tracking Assets
An annual 6,000 hours are lost by a nurse looking for misplaced equipment. Additionally, inefficient asset location might have a negative impact on the patient's experience of care by lengthening wait times and delaying critical care.
RPA in conjunction with digital sensors and cloud-based control panels enables personnel to readily discover assets, confirm that equipment inventories are accurate and up-to-date, and monitor asset condition to ensure replacement of assets as needed.
RPA is being used, for instance, by T-Systems in the UK to provide a variety of health solutions, such as assuring smooth organ transplant operations by recording time and location.
Electronics Record & Data Saving
Doctors, third-party portals, insurance companies, appointment scheduling software, and databases of medical records are a few sources of health information. Merging all sources of health data into one requires lot of money. RPA can cut down on the amount of resources used in healthcare for clerical work by processing patient records effectively.
Payments, Billing, and Claim Management
Using RPA systems, it is also possible to combine expenses for things like testing, medications, food, and doctor visits into a single payment for medical care. By precisely and swiftly converting bills into invoices, healthcare providers avoid billing mistakes and save time. If there are payment delays, RPA can also email patients specific reminders.
RPA can similarly handle time-consuming health claims processing. Compared to 85 seconds for a human, an RPA solution just needs 12 seconds to check the status of a health insurance claim. Administrative blundering results in the denial of about 25% of claims, including problems with eligibility and registration. An RPA solution lessen this needless financial loss given that each claim costs about $118.
Appointment Scheduling
Prior to the pandemic, almost 88% of appointments were scheduled by hand, which resulted in a 2 month delay between the first referral & the actual visit. Missed appointments cost US healthcare providers USD 150 Bn every year. One possible solution for hospitals to decrease their no-show rates, which can range from 5% to 39% depending on the healthcare specialty, is to eliminate the need for human data entry and integrate RPA medical bots into their appointment scheduling and patient engagement software. This approach can potentially improve efficiency & reduce errors in the process.
Manufacturing
The market for RPA manufacturing in 2021 was USD 7.60 Bn and will grow at a 33.2% CAGR by 2030. Robotic process automation in the manufacturing sector is one of the cutting-edge strategies that firms can use to boost productivity, cut costs, and improve customer satisfaction in the era of Industrial Revolution 4.0. According to industry experts, 85% of large enterprises have implemented RPA by 2022.
According to McKinsey, at least 87% of factory workers' routine and manual tasks can be automated.
Benefits:
Reduced operational costs by up to 40%
Enhanced control over procedures
Improved performance of the workforce
Reduced downtime significantly and improved quality
Telecom
The operations of telecom operators cover a wide spectrum, including infrastructure and IT services, database administration, customer service management, and purchase order management. RPA may be used across processes and departments since it is versatile. While other duties can be automated to the necessary amount, some tasks, including repetitive back office processes, can be automated completely.
No matter how much automation RPA adds to the tasks, cost savings and improved efficiency are observed. RPA is frequently utilized as a lifeline to release back office staff members from daily, monotonous, repetitive chores.
The potential of RPA to automate customer service procedures is one of its main advantages. RPA can be used by telecom businesses, for instance, to update client accounts and automatically reply to user inquiries and survey results. As a result, less manual data entry is required, allowing customer support representatives to concentrate on more challenging duties.
| 2022-12-01T00:00:00 |
https://www.strategicmarketresearch.com/blogs/robotic-process-automation-statistics
|
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|
Automation In Job Statistics - Are Skilled Trades Safe?
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Automation In Job Statistics
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https://bluecollarbrain.com
|
[] |
Data shows that 30% of the workforce might lose their jobs by the mid-2030s due to automation. The global industrial robot market was valued at $33.90 billion ...
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Undoubtedly, digital transformation and automation drive tremendous productivity improvement in the workplace. However, due to rapid developments, many individuals and businesses are negatively influenced, with some positions and jobs entirely replaced by robots. How will your job be affected?
Data shows that 30% of the workforce might lose their jobs by the mid-2030s due to automation. The global industrial robot market was valued at $33.90 billion in 2021 and is expected to reach $61.09 billion by 2026. Robots were responsible for up to 670,000 lost jobs in the U.S. between 1990 and 2007. South Korea, Singapore, Japan, Germany, and Sweden are the world’s top five most automated countries. Male workers are more likely than female workers to have high probability of automation employment by 2030, with a rate estimate of 34% vs. 26%.
Labor automation is the process of replacing human labor with technology to accomplish specific tasks or jobs. On the one hand, automation frequently produces as many employment opportunities as it eliminates over time. Workers who can use machines are more productive than those who cannot. Machines contribute to lowering the costs and prices of goods and services, making customers spend more. Ultimately, these sales result in the creation of new jobs.
The global industrial robot market was valued at $33.90 billion in 2021 and is expected to reach $61.09 billion by 2026 .
On the other hand, some workers lose out, particularly those who are immediately displaced by robots and must now compete with them. A recent report predicts that 25% more workers than previously estimated will need to find new jobs by 2030 due to factors such as automation, e-commerce, and remote work. Further data support the development of this trend by stating that, by the mid-2030s, 30% of jobs have the potential to get automated across territories.
Manufacturing tops the list of the first industries to deploy robots on a larger scale in recent years. The deployment comes as a result of industrial robotics’ help in increasing productivity and efficiency by lowering manufacturing costs and the overall purchase price of products.
How Many Jobs Have Been Automated?
Job automation, at this point, is growing and shows no signs of stopping. Data shows that in the U.S., robots were responsible for up to 670,000 lost jobs between 1990 and 2007, and that number is expected to rise as the stock of industrial robots will quadruple.
Further data support the negative impact by showing us that for every robot added per 1,000 workers in the U.S., wages decline by 0.42%, and the employment to population ratio goes down by 0.2 percentage points.
The use of industrial robots in factories is rapidly increasing worldwide. According to World Robot Report 2021, the global average robot density in manufacturing is 126 robots per 10,000 employees, nearly double the number five years ago (2015: 66 units).
| 2022-12-09T00:00:00 |
2022/12/09
|
https://bluecollarbrain.com/automation-job-loss-statistics/
|
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The Impact of AI on the Job Market and Employment Opportunities
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Ways AI Impacts the Job Market and Employment Trends
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https://onlinedegrees.sandiego.edu
|
[] |
On one hand, AI and automation have replaced certain roles, particularly those involving repetitive or routine tasks. This shift has led to job displacement in ...
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Artificial intelligence (AI) is reshaping the modern job market in significant and complex ways.
On one hand, AI and automation have replaced certain roles, particularly those involving repetitive or routine tasks. This shift has led to job displacement in various industries, redefining traditional employment structures.
On the other hand, AI is also a powerful driver of job creation. It has paved the way for new roles — like AI ethicist and machine learning engineer — that didn’t exist just a few years ago.
As AI continues to evolve, its impact on the workforce grows more profound. Staying informed about these changes is crucial for professionals, job seekers and educators navigating this shifting landscape. Keep reading to explore how AI is shaping jobs and employment opportunities.
AI’s Role in Automating and Replacing Existing Jobs
The capability of AI to automate tasks is a double-edged sword. While it enhances operational efficiency, it also raises concerns about AI causing unemployment.
Earlier predictions, such as a 2013 study from the University of Oxford, speculated that nearly 47% of US jobs could be automated by AI over the following two decades. This prediction seems to have been overstated, however, with more recent insights indicating that experts widely agree on how AI serves as a tool to enhance worker efficiency rather than significantly disrupt employment rates, as many fear.
The Future of Jobs Global Report 2025 shows that employers expect 39% of key skills required in the job market will change by 2030, with technological skills leading the way in importance over the next five years.
This raises an important question: Which jobs will AI impact the most?
Sectors Most at Risk
Certain industries appear to be more susceptible to the automation wave driven by AI. Here are some sectors that face a higher risk of automation:
Customer service and experience: Customer service helplines now commonly use chatbots powered by natural language processing (NLP) algorithms. These bots gather details about customer concerns, allowing support agents to handle inquiries more efficiently.
Customer service helplines now commonly use chatbots powered by natural language processing (NLP) algorithms. These bots gather details about customer concerns, allowing support agents to handle inquiries more efficiently. Banking and insurance: AI is streamlining banking by automating paperwork, speeding up issue resolution, and improving customer service. It also enhances security by detecting potential fraudulent transactions more effectively.
AI is streamlining banking by automating paperwork, speeding up issue resolution, and improving customer service. It also enhances security by detecting potential fraudulent transactions more effectively. Transportation: Autonomous driving is one of AI’s most transformative applications. Companies like Tesla have brought self-driving cars into the mainstream, while Uber explores their potential. Beyond personal transport, self-driving trucks promise faster deliveries and lower costs by eliminating rest stops and reducing labor expenses.
Jobs Facing Automation
In addition to customer service representatives and drivers, other roles that could be impacted by automation automation include:
Computer programmers: Even programming tasks can be automated to some extent with AI, though more complex tasks still require a human touch.
Even programming tasks can be automated to some extent with AI, though more complex tasks still require a human touch. Research analysts: Automation can handle data collection and preliminary analysis, enabling analysts to focus on deeper interpretation.
Automation can handle data collection and preliminary analysis, enabling analysts to focus on deeper interpretation. Paralegals: Document review and other routine tasks can be automated, freeing up time for more complex legal work.
Document review and other routine tasks can be automated, freeing up time for more complex legal work. Factory/warehouse workers: AI-driven robots are increasingly taking over repetitive tasks in factories and warehouses.
Jobs Less Likely to be Replaced
Certain jobs remain more secure due to their inherent human-centric nature. These professions require a level of empathy, understanding and creativity that AI is far from replicating. The impact of artificial intelligence on employment in these careers is relatively low:
Teacher
Nurse
Social worker
Therapist
Handyperson
Lawyer
HR specialist
Writer
Artist
AI as a Job Creator
Apart from automating tasks, AI is also creating new jobs. These roles are crucial for developing, managing and ensuring the ethical use of AI technologies.
Here are several new roles created from advancements in AI:
Machine learning (ML) engineer: Machine learning engineers develop and refine algorithms that enable systems to learn from data and improve their performance over time. By designing, testing and optimizing these models, they help AI mimic human learning processes. From applications like facial recognition and predictive analytics to automation, machine learning is transforming industries.
Machine learning engineers develop and refine algorithms that enable systems to learn from data and improve their performance over time. By designing, testing and optimizing these models, they help AI mimic human learning processes. From applications like facial recognition and predictive analytics to automation, machine learning is transforming industries. Natural language processing (NLP) specialist: Chatbot developers specialize in AI and NLP to create bots that can understand users, communicate naturally, and provide valuable assistance. As virtual assistants and automated customer support become increasingly popular, these developers play a crucial role in shaping effective, human-like interactions, making their expertise more essential than ever.
Chatbot developers specialize in AI and NLP to create bots that can understand users, communicate naturally, and provide valuable assistance. As virtual assistants and automated customer support become increasingly popular, these developers play a crucial role in shaping effective, human-like interactions, making their expertise more essential than ever. AI ethicist: An AI ethicist ensures the responsible and ethical development, deployment and use of AI technologies. They evaluate the potential social, legal and moral implications of AI systems, addressing concerns like bias, privacy and transparency. AI ethicists help ensure that AI benefits society while minimizing harm and promoting fairness.
An AI ethicist ensures the responsible and ethical development, deployment and use of AI technologies. They evaluate the potential social, legal and moral implications of AI systems, addressing concerns like bias, privacy and transparency. AI ethicists help ensure that AI benefits society while minimizing harm and promoting fairness. AI prompt engineer: An AI prompt engineer designs and refines prompts to optimize the responses generated by AI systems. They focus on crafting clear, effective input that guides the AI to produce accurate, relevant and contextually appropriate outputs.
AI’s Influence on Hiring
AI is transforming the landscape of human resources (HR) and recruitment by streamlining numerous processes. Its introduction to HR has brought about advantages while also posing certain challenges.
Advantages of AI in Hiring
AI is transforming recruitment and workforce management by optimizing key processes.
One of its most significant benefits is improving hiring efficiency. AI quickly sorts through applications and screens candidates, saving time and resources. It also helps predict talent needs within a company or department, allowing businesses to adopt proactive recruitment strategies.
Beyond hiring, AI provides valuable insights into employee retention. By analyzing turnover data, it identifies patterns that contribute to dissatisfaction, helping organizations improve workplace conditions and reduce attrition.
AI also plays a role in promoting fair hiring practices. By evaluating candidates based on objective criteria, it minimizes human biases and supports a more equitable selection process.
Disadvantages and Challenges
However, the potential for bias still exists. If AI systems are trained on datasets primarily representing a certain demographic, they might fail to provide an expansive pool of candidates. This reflects the biases present in the training data or programming. Addressing these biases to ensure a fair recruitment process is a significant challenge that requires meticulous attention.
AI and the Demand for New Skills
AI is reshaping the skills landscape across various industries. Machines taking on more routine and data-driven tasks boosts the demand for specific human skills. These skills are essential for interacting with AI systems, contributing to their development and applying them in problem-solving scenarios.
Meta Skills
Meta skills are foundational abilities that help individuals navigate different challenges and environments effectively. These include skills like adaptability, communication, and critical thinking, which are crucial for continuous learning and success in a rapidly evolving world.
Top Skills of the Future
According to the World Economic Forum 2025 Future of Jobs Report, individuals should consider building the following skills to stay competitive and relevant in the AI-driven job market:
Analytical thinking
AI and big data
Networks and cybersecurity
Technology literacy
Creative thinking
Resilience
Flexibility and agility
Curiosity and lifelong learning
These skills underscore the evolving nature of work and the shift toward more cognitive, creative and interpersonal skills. They will be essential in leveraging AI and automation to solve complex problems, drive innovation and ensure meaningful human contribution in the workforce.
Preparing for an AI-Driven Job Market
The rise of AI is ushering in a new era in the job market. To stay relevant and secure in their careers, professionals must adapt to the evolving landscape. Here are some actionable steps shared by Indeed.com that professionals can take to position and protect themselves amid AI-induced changes:
1. Be Flexible
In a rapidly changing job market, flexibility is key. Being open to new roles, responsibilities and learning opportunities can help professionals navigate the shifts caused by AI and automation.
2. Foster Your “People Skills”
AI lacks the human touch, making people skills more valuable than ever. Communication, empathy and teamwork are crucial and can set individuals apart in an AI-driven environment.
3. Build Your Network
Building a professional network can provide support, insights and potential job opportunities as the AI landscape evolves.
4. Continue to Learn About AI
Having a solid understanding of AI and its applications in your field can be a significant advantage. Professionals can take online courses, attend workshops or read books and articles to keep up with the latest developments.
5. Use AI to Your Benefit
Leveraging AI tools and platforms can increase efficiency, provide new insights and free up time for more strategic tasks. Embracing AI, rather than resisting it, can help professionals stay ahead in their careers.
How Can a Master’s in Applied Artificial Intelligence Help?
Amid the transforming job market, education serves as a powerful tool to secure a solid footing. The Master of Science in Applied Artificial Intelligence program at the University of San Diego equips students with the necessary knowledge and skills to thrive in the AI-driven landscape.
Here’s how the program positions students for success:
Comprehensive Curriculum
The curriculum is meticulously crafted to cover a broad spectrum of AI-related topics. From machine learning and deep learning to ethics in AI, students get a well-rounded education that prepares them for various roles in the AI domain.
Practical Experience
The program emphasizes real-world applications of AI, providing students with hands-on experience through projects with fellow students, instructors, and potential industry partners. This practical approach ensures students are job-ready and well-versed in applying AI solutions to real-world problems.
Expert Faculty
The program boasts a faculty of professionals and academics in the field of AI. Their varied expertise provides students with a rich learning experience and insights into the latest developments in AI.
Networking Opportunities
With connections to industry leaders and potential employers, the program offers numerous networking opportunities that can open doors for students as they transition into the professional world.
Lifelong Learning
The rapidly evolving nature of AI necessitates a commitment to lifelong learning. The program instills a culture of continuous learning, ensuring that students stay updated with the evolving AI landscape.
Get Your Free Checklist: 8 Key Factors to Consider Before Choosing an AI Master’s Program(PDF)
AI and the Job Market: Final Thoughts
AI has a significant impact on the job market, leading to both job displacement and the creation of new roles. It also affects hiring processes and demands new skills from professionals to stay relevant.
AI education, such as the Master’s in Applied Artificial Intelligence program at the University of San Diego, prepares individuals to navigate these changes with ease.
For more detailed information on choosing the right AI Master’s Program, we encourage you to download our eBook, 8 Questions to Ask Before Selecting an Applied Artificial Intelligence Master’s Program.
FAQs
| 2024-02-21T00:00:00 |
2024/02/21
|
https://onlinedegrees.sandiego.edu/ai-impact-on-job-market/
|
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{
"date": "2023/05/01",
"position": 86,
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},
{
"date": "2023/06/01",
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},
{
"date": "2023/06/01",
"position": 83,
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},
{
"date": "2023/07/01",
"position": 7,
"query": "AI impact jobs"
},
{
"date": "2023/07/01",
"position": 21,
"query": "AI job creation vs elimination"
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"date": "2023/08/01",
"position": 7,
"query": "AI impact jobs"
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{
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{
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"position": 86,
"query": "job automation statistics"
},
{
"date": "2023/09/01",
"position": 10,
"query": "AI impact jobs"
},
{
"date": "2023/09/01",
"position": 20,
"query": "AI job creation vs elimination"
},
{
"date": "2023/09/01",
"position": 84,
"query": "job automation statistics"
},
{
"date": "2023/10/01",
"position": 88,
"query": "AI economic disruption"
},
{
"date": "2023/10/01",
"position": 6,
"query": "AI labor market trends"
},
{
"date": "2023/10/01",
"position": 97,
"query": "automation job displacement"
},
{
"date": "2023/11/01",
"position": 7,
"query": "AI impact jobs"
},
{
"date": "2023/11/01",
"position": 6,
"query": "AI labor market trends"
},
{
"date": "2023/12/01",
"position": 8,
"query": "AI impact jobs"
},
{
"date": "2023/12/01",
"position": 7,
"query": "AI labor market trends"
},
{
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"position": 92,
"query": "automation job displacement"
},
{
"date": "2023/12/01",
"position": 83,
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},
{
"date": "2024/01/01",
"position": 20,
"query": "AI job creation vs elimination"
},
{
"date": "2024/02/21",
"position": 3,
"query": "AI economic disruption"
},
{
"date": "2024/02/01",
"position": 31,
"query": "AI employment"
},
{
"date": "2024/02/21",
"position": 25,
"query": "AI hiring"
},
{
"date": "2024/02/01",
"position": 7,
"query": "AI impact jobs"
},
{
"date": "2024/02/01",
"position": 19,
"query": "AI job creation vs elimination"
},
{
"date": "2024/02/01",
"position": 7,
"query": "AI labor market trends"
},
{
"date": "2024/02/21",
"position": 2,
"query": "AI unemployment rate"
},
{
"date": "2024/02/21",
"position": 13,
"query": "future of work AI"
},
{
"date": "2024/02/01",
"position": 52,
"query": "job automation statistics"
},
{
"date": "2024/02/21",
"position": 4,
"query": "machine learning job market"
},
{
"date": "2024/02/21",
"position": 7,
"query": "machine learning workforce"
},
{
"date": "2024/02/21",
"position": 3,
"query": "robotics job displacement"
},
{
"date": "2024/03/01",
"position": 20,
"query": "AI job creation vs elimination"
},
{
"date": "2024/03/01",
"position": 6,
"query": "AI labor market trends"
},
{
"date": "2024/03/01",
"position": 78,
"query": "artificial intelligence hiring"
},
{
"date": "2024/03/01",
"position": 75,
"query": "automation job displacement"
},
{
"date": "2024/04/01",
"position": 7,
"query": "AI impact jobs"
},
{
"date": "2024/04/01",
"position": 85,
"query": "automation job displacement"
},
{
"date": "2024/05/01",
"position": 7,
"query": "AI impact jobs"
},
{
"date": "2024/05/01",
"position": 20,
"query": "AI job creation vs elimination"
},
{
"date": "2024/05/01",
"position": 5,
"query": "AI labor market trends"
},
{
"date": "2024/05/01",
"position": 89,
"query": "automation job displacement"
},
{
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"position": 20,
"query": "AI job creation vs elimination"
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{
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},
{
"date": "2024/07/01",
"position": 82,
"query": "automation job displacement"
},
{
"date": "2024/08/01",
"position": 20,
"query": "AI job creation vs elimination"
},
{
"date": "2024/08/01",
"position": 7,
"query": "AI labor market trends"
},
{
"date": "2024/08/01",
"position": 86,
"query": "automation job displacement"
},
{
"date": "2024/09/01",
"position": 7,
"query": "AI impact jobs"
},
{
"date": "2024/09/01",
"position": 6,
"query": "AI labor market trends"
},
{
"date": "2024/09/01",
"position": 86,
"query": "job automation statistics"
},
{
"date": "2024/10/01",
"position": 7,
"query": "AI impact jobs"
},
{
"date": "2024/10/01",
"position": 89,
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},
{
"date": "2024/11/01",
"position": 8,
"query": "AI impact jobs"
},
{
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"position": 7,
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},
{
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"position": 81,
"query": "job automation statistics"
},
{
"date": "2024/12/01",
"position": 19,
"query": "AI job creation vs elimination"
},
{
"date": "2024/12/01",
"position": 83,
"query": "automation job displacement"
},
{
"date": "2025/01/01",
"position": 19,
"query": "AI job creation vs elimination"
},
{
"date": "2025/02/01",
"position": 14,
"query": "AI job creation vs elimination"
},
{
"date": "2025/02/01",
"position": 38,
"query": "automation job displacement"
},
{
"date": "2025/03/01",
"position": 31,
"query": "artificial intelligence employment"
},
{
"date": "2025/03/01",
"position": 39,
"query": "automation job displacement"
},
{
"date": "2025/05/01",
"position": 7,
"query": "AI impact jobs"
},
{
"date": "2025/05/01",
"position": 22,
"query": "AI job creation vs elimination"
},
{
"date": "2025/05/01",
"position": 5,
"query": "AI labor market trends"
},
{
"date": "2025/05/01",
"position": 19,
"query": "artificial intelligence employment"
},
{
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"position": 3,
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},
{
"date": "2025/06/01",
"position": 21,
"query": "AI job creation vs elimination"
}
] |
The Impact of Automation on Employment: Just the Usual Structural ...
|
The Impact of Automation on Employment: Just the Usual Structural Change?
|
https://www.mdpi.com
|
[
"Vermeulen",
"Kesselhut",
"Pyka",
"Saviotti",
"Pier Paolo",
"Ben Vermeulen",
"Jan Kesselhut",
"Andreas Pyka",
"Pier Paolo Saviotti"
] |
Only 25.5% of jobs have a normalized automatability score of 0 . 5 (interpreted as that half of the activities of an occupation can be fully automated within 10 ...
|
4.1. Technology-Driven Structural Change in Employment in NAICS Sectors
The BLS data enable us to study the projected structural change of employment across actual sectors and to infer on the different countervailing effects at work in the various types of sectors. Following the method described in Section 3.2 , we obtained Employment Projections for all occupations affected by the focal technologies and broken down by NAICS sector. In Figure A3 in Appendix B , we report the employment increase for these filtered occupations by major SOC group, for each of the affected sectors. A first observation is that the number of sectors as well as the number of jobs directly affected is limited. In the last two columns on the right, the projections in total number and percentage of increase in number of jobs are reported for all occupations per major group, not just the filtered occupations. A closer look at the projected shifts in employment within the same sector (vertically) and across sectors (horizontally) are in line with displacement and various countervailing effects and fully captured within our multisectoral framework of structural change.
Overall, there is a relatively small job loss (−75.3 k, about 0.0%) in occupations affected by the focal technologies, but a loss is in itself already in stark contrast with the high number of jobs expected to be created in the economy as a whole (11,519 k, about 7.4%). When looking at the projected growth in the number of jobs by major group of occupations, we see that there is almost exclusively a decline in the number jobs in Production (SOC 51) and Office and Administrative Support (SOC 43) occupations. In Production, this is almost all due to a decrease in the Manufacturing sector (NAICS 31–33). Note that the job loss overall (−406.9 k, −4.3%) is much lower than the job loss in occupations affected by technology (−681.9 k people, −13.4%), underlining the effect of technological unemployment. In Office and Administrative Support occupations, the story is quite different: the number of administrative jobs in a wide range of sectors such as Manufacturing (NAICS 31–33), Government (NAICS 90), Education (NAICS 61), Wholesale trade (NAICS 42), Information (NAICS 51), and several others is declining, while the number of administrative jobs in Health Care (NAICS 62), Management support and services (NAICS 56), and Retail trade (NAICS 44–45) is increasing. Detailed analysis reveals that any (partially technology-driven) decrease affects mostly secretaries and office clerks across the aforementioned sectors, while the increase is due to job creation in service representatives, information and stock clerks. That said, the rate of increase in administrative jobs in the economy as a whole (0.6%) is relatively low compared to the total rate of job creation (7.4%), which may well be (partially) caused by a technology-driven productivity increase.
There also are several occupations in which there is a strong increase in the number of jobs, notably Computer and Mathematical (SOC 15), Management (SOC 11), and Architecture and Engineering (SOC 17) occupations. While the biggest increase occurs in the Professional, scientific, and technical services (NAICS 54) overall, there may be a shift of employment across occupations within this sector as well as in the Information (NAICS 51), and Finance and Insurance (NAICS 52) sectors. In these sectors, a technology-driven loss of jobs in Administrative jobs (SOC 43) seem to be offset by a gain of jobs in Computer and Mathematics related jobs (SOC 15), possibly created to reap complementarities (or where people may be hired to automate processes and thus cause technological unemployment). For example, in the Information sector (in which data is created, processed, and transferred), there is a loss of 43.5 k administrative jobs but an increase of Computer and Mathematical jobs.
When looking at the projected growth in the number of jobs by sector, the change is much in line with “classical” effects of automation, demographic developments and progressive outsourcing. There is a particularly strong employment growth in the Professional, scientific and technical services (NAICS 54 with about 331.8 k people), of which the Occupational Utilization reveals that there is an increasing demand for engineers, e.g., due to robotization and automation (SOC 17), but also the use of advanced digital and internet-connected devices, and outsourcing of security (SOC 15). Moreover, we observe a decrease in Office and Administrative Support (SOC 43) due to automation and an increase in Business Operations and Management (SOC 13, 11) due to outsourcing and purchasing training and consulting services. Health care (NAICS 62, 251.5 k) sees a rise on several occupations due to the aging population and several organizational changes such as the introduction of reception services and team-based structures to cope with that. The Administrative and management services (NAICS 56, 105 k people) sector sees an increase in employment, e.g., for customer representatives, as there is more outsourcing in other sectors. Moreover, there is substantial job loss in the Manufacturing sectors (NAICS 31–33, −751.1 k people), notably due to substitution, but also offshoring. Obviously, the job loss due to substitution is not (entirely) offset by intra -sectoral complementary jobs. It may, however, be (partially) compensated by the development of software, robot and AI as well as the production and servicing thereof, which reflects in the statistics for other sectors described before.
Illustratively, of the thirty fastest growing occupations reported in the BLS Employment Projections (see Figure A4 in Appendix C ), a total of seventeen occupations are directly or indirectly related to healthcare, either as healthcare practitioner (SOC 29) or support (SOC 31), personal care (SOC 39) or by providing training for healthcare (SOC 25). This, again is to be attributed to demographics and aging, but arguably also local demand spillover, which is related to disposable income. Five occupations pertain to computers and mathematics (SOC 15). Of the thirty fastest declining occupations reported, sixteen are Production-related occupations (SOC 51) such as operators, assemblers, setters, etc., and six Office and administrative occupations (SOC 43) such as computer and telephone operators, typists, data keyers, etc. In addition, in our subset of occupations (that are affected by the focal technologies), we see a confirmation of the trend that there is a decline in low- and medium-skilled jobs (such as assemblers) and growth in high-skill, high-productivity jobs (such as software engineers) (see e.g., [ 6 ]). Surprisingly, there is an increase in medium- to low-skilled jobs for occupations such as stock clerks and order fillers, receptionists and information clerks, which appear to be automatable. Supposedly, this is, on the one hand, due to a demand-side increase and, on the other hand: (i) a low acceptance for digital services in occupations in which human contact is appreciated; (ii) yet relatively poor performance of automated services (so limited substitutability); and (iii) lowering of unit costs actually compensating automation with higher demand.
Conclusively, the occupational outlook data features structural change in line with displacement and countervailing effects discussed above, but also reveals that several countervailing effects and other factors (demographics and offshoring) may be counteracting.
| 2018-05-14T00:00:00 |
2018/05/14
|
https://www.mdpi.com/2071-1050/10/5/1661
|
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},
{
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{
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Business Process Automation Stats for 2025 - Kissflow
|
Business Process Automation Stats for 2025
|
https://kissflow.com
|
[] |
By 2030, automated solutions can take away 20 million jobs from the manufacturing sector. The most at-risk industries in terms of job replacement are storage, ...
|
Process automation is everywhere. All businesses require efficiency and productivity, which results in reducing repetitive tasks and optimizing existing processes. This allows workers the freedom to focus on bringing real value to the table and pursue high-value endeavors.
Today, process automation is essential for an organization to stay competitive. And knowing the state of the industry is extremely important. We have compiled some of the most important statistics for 2024 to tell you where business process automation is headed.
The Growing Popularity of Automation
A large number of businesses are moving towards automation because of the countless advantages it gives them. With fierce competition in the markets across the boards, you need an edge to maintain excellent performance standards. BPM can help with that’s why it’s so popular.
Here’s what’s happening in 2024:
The business process automation market had grown to 8 billion US dollars in 2020. By 2026, this number will have risen to 19.6 billion US dollars. AI consulting in logistics can significantly aid in optimizing business operations, achieving impressive outcomes similar to automation technologies. Companies that successfully integrate AI into their logistics see vast improvements not just in efficiency, but also in strategic planning and day-to-day management.
About 80 percent of businesses are speeding up process automation, while 50 percent of them are planning to automate all repetitive tasks.
It is expected that about 69 percent of all managerial work will be completely automated by 2024.
A McKinsey report on the future of work estimates that the adoption of automation and AI in businesses will continue to increase
50 percent of business leaders believe that they can successfully automate up to 30 percent of their workload.
Learn More: Business Process Management
Automation in Productivity Enhancement
Eliminating repetitive tasks from a typical workday, reducing the chances of human error, and ensuring consistency is only the tip of the iceberg. All businesses that use business process automation agree that it significantly enhances the overall productivity of the organization. Here’s what they think:
[6] 73% of IT leaders
[7] About 30%
Over half of all automation tools target efficiency. Moreover, [8] 78%
About [9] 42% workflow automation saves time and frees up their employees to pursue other things rather than repetitive tasks. Another survey indicates that this sentiment is not limited to repetitive tasks only. About 85% of business leaders believe that automation allows employees to focus on goals that matter to the company.
workflow automation saves time and frees up their employees to pursue other things rather than repetitive tasks. Another survey indicates that this sentiment is not limited to repetitive tasks only. About 85% of business leaders believe that automation allows employees to focus on goals that matter to the company. [10] 50%
The Effect on Employment Opportunities
New technology always brings fear of being replaced. However, there is another side of the coin too. It also creates new opportunities and marketable skills. Here are the latest trends:
The fear of automation is common. A recent survey revealed that about [11] 37% of professionals believe
By 2030, automated solutions can take away [12] 20 million jobs from the manufacturing sector.
The most at-risk industries in terms of job replacement are [13] storage, logistics, and manufacturing.
In the United States alone, about [14] 25% of all jobs are disrupted
By the end of 2022, about [15] 54% of the workforce will require upskilling
There are several positive aspects as well when it comes to the effect of automation on the job market. Here are a few facts that indicate the improvement in the job market because of business process automation:
[16] . Automation has streamlined HR processes significantly
About [17] 61% of leaders believe that process automation
Automation can sustain industrial growth by [18] 1.4% of global GDP every year
[19] About 33% of occupations
[20] 70% of professionals see automation
Automation in Sales and Marketing
Sales and marketing teams are leveraging the power of automation technology to enhance their performance metrics. By automating their conversion processes, lead capture, and deal closures, they're not just streamlining operations but also boosting efficacy. The proactive approach of automating the buyer's journey fosters better customer engagement and interaction, providing a personalized experience that propels business growth. These intriguing statistics serve as a testament to the strategic importance of sales and marketing automation in today's competitive business landscape.
Sales departments report[21] about a 15 percent increase in productivity and a 12 percent decrease in marketing costs through automation.
Sales automation improves the performance of B2B channels by an average of 10 percent every year.
About 67 percent of marketers[23] rely on automation for consistent performance and demand generation.
Conversions are estimated to increase by 77 percent by spending on marketing automation solutions.
Automation in manufacturing, healthcare, retail, finance, banking, Insurance, and cybersecurity
Automation is reshaping industries. It's boosting manufacturing efficiency, aiding financial data analysis and fraud detection, speeding up insurance claims, and enhancing threat detection in cybersecurity. The healthcare sector is leveraging automation for everything from patient scheduling to predictive diagnostics. Retailers are using it to personalize customer experiences and streamline inventory management. Simply put, automation is a key driver for progress and efficiency today.
More than 50 percent of CEOs of banking and financial organizations are focusing on simplifying their products and operations by adopting process automation
Mckinsey expects automation tools to handle up to 25 percent of banking tasks.
In cyber security, automation can improve detection to reduce the chances of security threats by a whopping 70 percent.
68 percent of global businesses seek ways to improve their cyber security through automation.
A report indicates that automating 64 percent of manufacturing tasks can save billions of working hours for the industry.
Retail Automation Market is about to rise from USD 16550 million in 2022 to USD 34530 million by 2030, with a CAGR of 9.8
Automation in healthcare ranges from chatbots to new uses for generative AI, helping meet the demands of clinicians and patients. It's also being used to process the industry's 30 billion transactions per year, which cost an estimated $250 billion.
Final Thoughts
Automation is everywhere and it will continue to grow. The concept of business process automation is not limited to enterprises. It can be implemented across various teams, functions, and organizations irrespective of size or industry.
A comprehensive work platform like Kissflow not only helps you automate your business processes but vastly improves the way you work. You can now do away with multiple solutions for multiple problems—do more and diverse work within a single platform. It is the ideal solution for organizations looking to ease the tensions between business and IT while significantly improving productivity, reducing human errors, and consistently driving customer retention.
Solve your workflow challenges with Kissflow and optimize your team's productivity.
Experience the rush of no-code process automation with Kissflow Get Started
Frequently Asked Questions (FAQs)
What are the latest trends in business process automation?
Latest trends in business process automation include AI-powered decision-making, hyperautomation combining multiple technologies (RPA, AI, process mining), no-code platforms democratizing automation, process intelligence providing real-time insights rather than periodic analysis, and increased focus on sustainability metrics within process performance dashboards.
How does automation impact business efficiency?
Automation impacts business efficiency by eliminating manual handoffs that cause delays, standardizing execution for consistent outcomes, reducing errors from manual data entry, providing real-time visibility into process status, enabling 24/7 operations without fatigue, scaling to handle volume spikes without staffing changes, and freeing knowledge workers from routine tasks for higher-value activities.
What industries lead in automation adoption?
Industries leading in automation adoption include financial services (loan processing, claims handling), healthcare (patient scheduling, insurance verification), manufacturing (quality assurance, maintenance workflows), legal services (contract management, case processing), and government agencies (permit applications, citizen service requests).
What are real-world examples of BPA success?
Real-world examples of BPA success include a major insurance company reducing claims processing time by 70% using AI to extract information from submitted documents, a global bank cutting account opening time from days to minutes while improving compliance, a healthcare network reducing billing errors by 80% through automated validation, and a manufacturer decreasing production planning time by 60% through integrated systems.
How do businesses measure automation success?
Businesses measure automation success through both quantitative metrics (cycle time reduction, error rate decreases, processing volume increases, cost savings) and qualitative indicators (employee satisfaction, customer experience improvements, compliance enhancement). Leading organizations establish baseline measurements before implementation and track improvements over time, comparing results against strategic objectives beyond mere efficiency gains.
| 2022-12-01T00:00:00 |
https://kissflow.com/workflow/bpm/business-process-automation-statistics/
|
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|
AI in the Workplace Statistics 2024 - AIPRM
|
AI in the Workplace Statistics 2024 · AIPRM
|
https://www.aiprm.com
|
[] |
This is around a fifth (20%) more than the number of jobs expected to be created in this period (69 million), suggesting that AI's development could have a ...
|
Conversations around AI’s effect on the workplace have been prevalent for decades. From excitement about how the technology could enhance productivity, to fears around job displacement, AI’s presence in the workplace inspires much debate.
Since the beginning of the AI boom in 2022, these conversations have accelerated, with topics that once felt like part of a distant future now highly relevant to the lives of workers and businesses worldwide.
But what does the future hold for the use of AI in the workplace? To shed more light on this much-discussed topic, AIPRM has collated a whole host of AI in the workplace statistics, covering AI adoption by industry, the jobs most at risk from AI, and more.
Top 10 AI in the workplace statistics>
Top 10 AI in the workplace statistics #
Three-quarters (75%) of surveyed workers were using AI in the workplace in 2024.
Of these, nearly half (46%) began doing so within the last six months.
75% of companies said they were looking to adapt to AI within the next five years in 2023.
Over a third (37%) of marketing and advertising sector workers had adopted AI at work in 2023.
More than two-thirds (68%) of business leaders feel they have struggled to attract adequate talent to manage their AI solutions.
In 2024, 45% of workers claimed they were worried about AI replacing them at work.
Nine out of ten (90%) surveyed workers claimed that AI helped them save time on tasks in 2024.
29% of business leaders said they approved of AI in the workplace, with a further 12% saying they strongly approved.
Programmers using AI were able to code 126% more projects per week than programmers not using the technology.
Nearly two-thirds (65%) of surveyed workers claimed they’d used ChatGPT in the workplace.
How many people use AI at work?>
How many people use AI at work? #
Recent AI in the workplace statistics from Microsoft found that three-quarters (75%) of surveyed workers were using AI in the workplace in 2024, with only one in four (25%) yet to use the technology.
A breakdown of the percentage of surveyed respondents using AI in the workplace (2024)>
A breakdown of the percentage of surveyed respondents using AI in the workplace (2024) #
Of the workers using AI at work, nearly half (46%) began doing so within the last six months, with the remaining 54% having used it for longer.
The same survey found that nearly four-fifths (79%) of company leaders feel their company needs to adopt AI to stay competitive, with around three-fifths (59%) worried about their ability to measure the productivity gains of AI.
Additionally, a further 60% of business leaders said they were worried that their organization’s leadership lacked a plan or vision to implement AI in the workplace.
How are companies planning to adapt to AI?>
How are companies planning to adapt to AI? #
A 2023 survey from the World Economic Forum reported that around 75% of companies were looking to adapt to AI within the next five years. Digital platforms and apps were the technologies most referenced by business leaders, with 86% expecting to incorporate them into company operations between 2023 and 2027.
A breakdown of the percentage of surveyed organizations who say they’re likely or highly likely to adopt various technology types between 2023 and 2027>
A breakdown of the percentage of surveyed organizations who say they’re likely or highly likely to adopt various technology types between 2023 and 2027 #
Technology Percentage of surveyed organizations who are likely or highly likely to adopt this technology between 2023 and 2027 Digital platforms and apps 86.4% Education and workforce development technologies 80.9% Big-data analytics 80% Internet of things and connected devices 76.8% Cloud computing 76.6% Encryption and cybersecurity 75.6% E-commerce and digital trade 75.3% Artificial intelligence 74.9% Environmental management technologies 64.5% Climate-change mitigation technology 62.8% Text, image, and voice processing 61.8% Augmented and virtual reality 59.1% Power storage and generation 52.1% Electric and autonomous vehicles 51.5% Robots, non-humanoid 51.3%
(Source: World Economic Forum)
AI in the workplace stats show that more than four-fifths (81%) of businesses plan to implement technology for education and workforce development over the next five years — the second most common reason.
Big data analytics was the only other purpose cited by at least four-fifths of businesses, with 80% planning to develop their resources in this area. E-commerce and digital trade are expected to be adopted by around three-quarters (75%) of businesses — 11% less than the number planning to adopt digital platforms and apps.
At the other end of the scale, just over half (51%) of companies planned to utilize robots and non-humanoid technology, with a slightly higher percentage (52%) citing the use of electric and autonomous vehicles.
What industries are adopting AI in the workplace?>
What industries are adopting AI in the workplace? #
The latest AI in the workplace statistics found that over a third (37%) of workers in the marketing and advertising sector had adopted AI at work. This was the highest percentage of any industry — 2% more than second-placed technology.
An industry-based breakdown of the percentage of workers that have adopted AI in the workplace (2023)>
An industry-based breakdown of the percentage of workers that have adopted AI in the workplace (2023) #
Three-tenths (30%) of consultants had adopted AI at work, making it the third most common industry and the only other one with an adoption rate of at least 30%.
Just under two-fifths (19%) of teachers had adopted AI at work, which was 3% more than the percentage of accountants who had done the same (16%). Healthcare had the lowest total of all sectors, with an adoption rate of 15% — less than half the number reported by the advertising and consulting industries.
Visit our AI in healthcare statistics page for key insights into how AI is transforming aspects of the healthcare industry.
How much are people using AI at work?>
How much are people using AI at work? #
A report from tech.co found that just over a third (34%) of business leaders said AI was not being used at all in the workplace. This was the most common response given, with a slightly lower number (33%) saying the technology is being used to a limited extent.
A breakdown of the extent to which AI is being used in the workplace among surveyed business leaders>
A breakdown of the extent to which AI is being used in the workplace among surveyed business leaders #
Over a fifth of business leaders claimed AI is being used moderately in the workplace, around triple the number who were using it extensively (7%).
Less than one in twenty (5%) claimed that their organization had fully embraced AI at work, which was over seven times less than the number who weren’t using it at all.
AI tools and technologies #
According to AI statistics, more than two-thirds (68%) of business leaders feel they have struggled to attract adequate talent to manage their AI solutions. This is despite the fact that companies investing in artificial intelligence have seen their customer satisfaction rates more than double, on average.
What are the most common AI tools used at work? #
ChatGPT is the most commonly used AI tool among workers, with nearly two-thirds (65%) claiming they’d used the OpenAI chatbot in the workplace. This was 12% higher than any other tool, making it the only one cited by more than half of workers, according to ChatGPT statistics.
A breakdown of the percentage of surveyed workers who reported using various AI tools in the workplace #
Just under half (48%) had used Google Gemini (formerly Bard), making it the second-most common tool. This was more than double the total of Microsoft Copilot (formerly Bing Chat) (21%) which was the only other platform referenced by over a fifth of workers.
At the other end of the scale, around than a tenth reported using either Claude AI (10%) or Jasper (9%) at work, with just 8% referencing any other tools.
What are the most common reasons people are using AI in the workplace?>
What are the most common reasons people are using AI in the workplace? #
Recent AI in the workplace statistics found that nearly a third (32%) of surveyed workers were using AI to analyze data at work. This was 6% more than any other reason, making data analysis the only one cited by over 30% of workers.
A breakdown of the most common functions people use AI for in the workplace>
A breakdown of the most common functions people use AI for in the workplace #
Over a quarter (26%) use AI to help with writing tasks like emails, reports, and presentations. This was 5% more than the next highest response, with over a fifth (21%) citing scheduling and calendar management.
The only other functions cited by at least a fifth of respondents were automated data entry, quality control, and cybersecurity, with each being mentioned by 20%.
Attitudes towards AI in the workplace>
Attitudes towards AI in the workplace #
Nine out of ten (90%) surveyed workers claimed that AI helped them save time on work tasks. This was 5% more than any other benefit, with 85% claiming that AI helps them focus on their most important work.
A breakdown of the most common improvements cited by workers as a result of AI>
A breakdown of the most common improvements cited by workers as a result of AI #
Analysis of AI in the workplace statistics shows that over four-fifths (84%) felt that AI allows them to be more creative at work, with a similar number (83%) feeling that the technology has made work more enjoyable.
What are the most common fears about AI in the workplace?>
What are the most common fears about AI in the workplace? #
According to a 2024 Microsoft survey, more than half of workers (53%) claimed they were worried that using AI for work tasks would make them look replaceable to their employers. This was the most commonly cited fear, with a further 52% expressing reluctance to admit using AI for important tasks.
A breakdown of the most common fears about AI cited by surveyed workers>
A breakdown of the most common fears about AI cited by surveyed workers #
Statistics around AI replacing jobs found that just under half (46%) of workers are considering quitting their jobs in the year ahead, with a further 45% expressing worry about AI replacing them at work.
Elsewhere, a 2023 survey from the American Psychological Association found that nearly four out of ten U.S. workers (38%) were worried AI may make some or all of their job duties obsolete in the future.
Of these, 51% said their work had a negative impact on their mental health, with this number falling to 29% among workers not worried about AI making their jobs obsolete.
Are businesses happy about AI in the workplace?>
Are businesses happy about AI in the workplace? #
There is more positivity than negativity about AI among business leaders, with around 29% saying they approve of the technology in the workplace. Additionally, a further 12.23% said they strongly approved of AI, meaning over 40% of business leaders approve of AI to some extent.
A breakdown of the happiness levels of business leaders around AI in the workplace>
A breakdown of the happiness levels of business leaders around AI in the workplace #
Conversely, just under 10% of business leaders said they disapproved of AI in the workplace, with a further 7.45% strongly disapproving. This means that the number of business leaders who approved of AI to some extent was more than double the total that disapproved.
Over two-fifths (41.83%) of respondents said they were neutral about AI in the workplace — the most common answer given.
The changing perceptions of AI among business leaders>
The changing perceptions of AI among business leaders #
More than two-thirds (68%) of business leaders claimed they’d struggled to attract adequate talent to manage their AI tools effectively, according to a 2024 report from Spiceworks. The same article reported that four-fifths (80%) of engineering and manufacturing companies had reported hiring challenges in the wake of AI.
A breakdown of the attitudes around AI in the workplace among surveyed business leaders>
A breakdown of the attitudes around AI in the workplace among surveyed business leaders #
Despite these struggles, more than half of business leaders (52%) believe AI will significantly improve their operations in the future, with over a third (35%) planning to hire AI-related talent in the near future.
What are the benefits of AI in the workplace?>
What are the benefits of AI in the workplace? #
A report from Workplace via Meta looked at how AI could be best integrated to generate measurable value. The report concluded that mere adoption alone would not significantly improve workplace performance. AI needs to be combined with new ways of working, staff reskilling, and a culture of innovation and experimentation.
The report demonstrated these views by referencing a study from the IBM Institute for Business Value. The study found that AI adopters that also outperform expectations on reskilling staff see a 36% revenue growth rate premium over standard AI adopters.
The report also defined six performance categories to determine ‘best-in-class’ AI-performing companies, they were:
Vision and strategy
An AI operating model
AI engineering and operations
Data and technology
Sufficient talent and skills
Culture and adoption.
Best-in-class companies that had developed all six of these capabilities reported an average ROI of 13% on AI projects.
AI effect on jobs>
AI effect on jobs #
Recent AI in the workplace statistics predicted that 83 million jobs could be lost globally between 2023 and 2027. This is around a fifth (20%) more than the number of jobs expected to be created in this period (69 million), suggesting that AI’s development could have a significant impact on the global workforce.
A breakdown of the projected changes in global employment between 2023 and 2027>
A breakdown of the projected changes in global employment between 2023 and 2027 #
If these projections are correct, then the global workforce will shrink by 14 million people between 2023 and 2027.
Which jobs are expected to see the biggest workforce increases between 2023 and 2027?>
Which jobs are expected to see the biggest workforce increases between 2023 and 2027? #
Agricultural equipment operators are expected to see their workforce increase by approximately 2.6 million between 2023 and 2027 — the highest of any occupation. This was roughly 18% more than any other job, making agricultural equipment operations the only one with projected increases above 2.5 million.
A breakdown of the jobs with the highest anticipated employment increases between 2023 and 2027>
A breakdown of the jobs with the highest anticipated employment increases between 2023 and 2027 #
* figures are estimates based on data tables issued by the Word Economic Forum
Heavy truck and bus drivers were the next highest profession, with projected workplace increases of around 2.2 million.
There were four other roles expecting increases of at least two million, they were:
Vocational education teachers (approx 2.1 million)
Mechanics and machinery repairs (approx 2.1 million)
Business development professionals (approx 2.1 million)
Building frame and related trades workers (approx 2 million)
Elsewhere, AI in education statistics show that special education teachers are projected to see increases of around 1.25 million workers. This is around 40% less than the increases expected in vocational education teachers.
Which jobs are expected to see the biggest workforce decreases between 2023 and 2027?>
Which jobs are expected to see the biggest workforce decreases between 2023 and 2027? #
The number of data entry clerks is expected to decline by approximately eight million globally in the 5 years from 2023. This is roughly a third (33%) more than any other profession and over double the total expected for security guards (around 3 million).
A breakdown of the jobs with the highest anticipated employment decrease between 2023 and 2027>
A breakdown of the jobs with the highest anticipated employment decrease between 2023 and 2027 #
* figures are estimates based on data tables issued by the Word Economic Forum
Administrative and executive secretaries are expected to see the next biggest impact, with a reduction of around six million jobs. This is approximately 26% more than the next highest profession, making this job the only other one with expected losses above five million.
Accountants, bookkeepers, and payroll clerks are expected to see their combined workforce shrink by approximately 4.75 million, around triple the total expected for bank tellers and related clerks (roughly 1.5 million).
AI effect on workforce skills>
AI effect on workforce skills #
A report from the World Economic Forum found that creative thinking was the skill most organizations cited as increasing in importance. Almost three-quarters (73.2%) chose creative thinking, suggesting AI’s ability to automate many functional tasks may be increasing the focus on creativity.
A breakdown of the top 10 workplace skills deemed to be increasing in importance by organizations (2023)>
A breakdown of the top 10 workplace skills deemed to be increasing in importance by organizations (2023) #
Around 72% of organizations cited analytical thinking as an increasingly important skill, making this the only other attribute chosen by over 70% of companies. Over two-thirds of organizations (67.7%) referenced technological literacy — around 1% more than the number who chose curiosity and lifelong learning (66.8%).
At the other end of the scale, just over half (53.1%) cited leadership and social influence as increasingly important skills — over 20% lower than the number that chose creative thinking.
Visit our blog for expert advice on a range of topics, including crafting effective prompts.
AI effect on productivity>
AI effect on productivity #
Despite the mixed attitudes about AI in the workplace, a collection of studies from the Nielsen Norman Group found that support agents who used AI handled 13.8% more customer inquiries per hour than those who didn’t.
The difference was even greater among business professionals, with AI users writing 59% more business documents per hour than non-users.
A breakdown of the productivity increases from AI users for various tasks>
A breakdown of the productivity increases from AI users for various tasks #
The biggest increase was seen among programmers, with AI users coding more than double (+126%) the projects per week than non-users.
This meant that, on average, AI improved worker productivity by two-thirds (66%) across all three studies.
How do workers feel about data privacy when using AI?>
How do workers feel about data privacy when using AI? #
Nearly a quarter (24%) of workers in the manufacturing and finance industries said they were ‘strongly worried’ about their privacy when using AI. This number rose to almost a third (32%) for workers describing themselves as ‘somewhat worried’, meaning more than half (56%) of workers in these industries have security concerns when using AI.
A breakdown of the levels of worry among workers in the manufacturing and finance industries about privacy while using AI>
A breakdown of the levels of worry among workers in the manufacturing and finance industries about privacy while using AI #
Just over one in ten (11%) described themselves as ‘strongly unworried’ about AI’s effects on privacy, with a further 14% saying they were ‘somewhat unworried’. Overall, this means that a quarter of respondents were unworried about security when using AI.
The future of AI in the workplace>
The future of AI in the workplace #
A 2023 report from McKinsey predicted that AI use may boost the US labor productivity by between 0.5 and 0.9 percentage points annually by 2030 in a midpoint adoption scenario. The range reflects whether the time freed up by automation is redeployed at 2022 productivity levels or the anticipated levels for 2030.
What jobs are most at risk from AI?>
What jobs are most at risk from AI? #
The health sector is expected to see significant rises in labor demand between 2022 and 2030, with 30% rises anticipated for both health professionals and aides, technicians, and workers.
This is 7% more than any other profession, with demand for STEM professionals expected to rise by just under a quarter (23%).
A breakdown of the projected changes in labor demand by industry between 2022 and 2030>
A breakdown of the projected changes in labor demand by industry between 2022 and 2030 #
At the other end of the scale, labor demand for office support is expected to drop by nearly (18%) a fifth between 2022 and 2030 — 6% more than any other profession.
Customer service and sales are expected to see a labor demand drop of 13%, making this the only other sector with expected decreases exceeding 10%.
How is AI changing the demand for skills?>
How is AI changing the demand for skills? #
In 2023, just 28% of surveyed employers considered proficiency in STEM to be a critical workforce skill. This represents a 14% decline from 2016 (42%) — the biggest individual drop of any skill.
A breakdown of the changes in the perceived importance of various workplace skills between 2016 and 2023>
A breakdown of the changes in the perceived importance of various workplace skills between 2016 and 2023 #
Basic computer and application skills also fell 9% over this period, from 40% to 31%. This suggests that AI’s ability to automate many basic computer tasks may be reducing the perceived value of this skill.
Conversely, 42% cited time management skills as a critical skill in 2023, representing a rise of 9% from 2016. Over the same period, the number of employers selecting working effectively in a team as a key skill rose 5% from 35% to 40%.
What can we learn from history to maximize the potential of AI in the workplace?>
What can we learn from history to maximize the potential of AI in the workplace? #
A report from McKinsey looked at the changes in employment levels across various industries between the end of the Industrial Revolution in 1850 and 2015. Agriculture saw its share of the US workforce decline by a huge 55.9 percentage points (pp) over this period, with manufacturing and mining falling by 3.6 pp and 1.3 pp, respectively.
Despite this, there have been significant rises across numerous other industries, most notably trade (12.8 pp), education (9.9 pp), and healthcare (9.3 pp).
A breakdown of the changes in the total employment share by sector in the US between 1850 and 2015>
A breakdown of the changes in the total employment share by sector in the US between 1850 and 2015 #
Industry Changes in employment levels between 1850 and 2015 (percentage points) Trade (retail and wholesale) 12.8 Construction 0.3 Transportation 0.2 Agriculture -55.9 Manufacturing -3.6 Mining -1.3 Household work 2.71 Professional services 5 Utilities 0.8 Business and repair services 6.1 Telecommunications 0.7 Healthcare 9.3 Entertainment 2.2 Education 9.9 Government 4.9 Financial services 5.9
(Source: McKinsey)
Overall, 13 out of the 16 industries covered in the study experienced rises in their workforce share between 1850 and 2015, suggesting that many of the job losses in agriculture were absorbed by growth in other industries.
McKinsey concludes their report by stating their findings prove the following:
Employment shifts can be painful>
Employment shifts can be painful #
While improving technology can create new jobs to offset many of those lost, the transitional process can be painful for workers. This is proven by the decline of the Industrial Revolution, in which average real wages stagnated for decades in England despite improved productivity. Therefore, ensuring workers are properly reskilled in AI will be key to ensuring the technology remains a force for good in our society.
Technology creates more jobs than it destroys, including some you can’t imagine>
Technology creates more jobs than it destroys, including some you can’t imagine #
New technologies can not only create growth in existing industries but also result in the creation of new jobs and entire industries that wouldn’t have been possible before technological advancements. This claim is backed up by a study from the Federal Reserve Bank of America that found 0.56% of new jobs in the US each year are in new occupations.
We all work less and play more, thanks to technology>
We all work less and play more, thanks to technology #
Over time, technology has improved productivity, resulting in a reduction in the length of the average working week. The report validates this claim by revealing that across advanced economies, the length of the average workweek has dropped by nearly 50% since the early 1900s.
With recent AI developments providing the opportunity to automate more tasks than ever before, it’s hoped that efficient use of the technology will result in a kinder working schedule for people across many industries!.
History of AI in the workplace>
History of AI in the workplace #
While the AI boom may make the technology feel like a new phenomenon, its origins stretch back more than a century. Below, we’ll cover some key moments in the development of AI.
1921>
Czech playwright Karel Čapek released a science fiction play titled “Rossum’s Universal Robots”. The play is credited for introducing the idea of “artificial people” which he named robots. This was the first known use of the word.
1929>
Japanese professor Makoto Nishimura built the first Japanese robot, named Gakutensoku.
1950>
Alan Turing published a paper titled “Computer Machinery and Intelligence”. The paper proposed a test of machine intelligence called The Imitation Game, now commonly referred to as ‘The Turing Test’.
1952>
American scientist Arthur Samuel developed the first AI program that could play checkers independently.
1958>
John McCarthy created the first programming language for AI research, known as LISP (an acronym for List Processing).
1966>
Joseph Weizenbaum created the first chatbot, named ELIZA. The chatbot was a mock psychotherapist that used natural language processing (NLP) to communicate with humans.
1973>
An applied mathematician named James Lighthill reported to the British Science Council that AI had not made the improvements predicted by scientists. The report resulted in a reduction in funding over the next few decades.
1984>
The AAAI warns of a pending “AI Winter” that would result in a funding and interest decrease.
1987-93>
The AI winter arrives, with the LISP-based hardware market collapsing in the wake of popular non-ai-based computer releases from IBM and Apple.
1997>
IBM software Deep Blue became the first ever AI to defeat a reigning world chess champion, resulting in increased interest and funding in the technology.
In the same year, Microsoft Windows released its first speech recognition software.
2003>
Nasa landed two rovers on Mars named Spirit and Opportunity. They navigated the surface of the planet without human intervention.
2011>
Apple released Siri, the first mainstream virtual assistant technology.
2018>
A Chinese tech group named Alibaba AI defeated a human intellect on a Stanford reading and comprehension test.
2022>
OpenAI released ChatGPT, a large language model-trained chatbot that redefined the capabilities of AI in responding to human instructions.
2023>
In response to the success of ChatGPT, Google released its own large language model trained chatbot Google Bard in March 2023. The Chatbot would later be renamed to Gemini.
AI in the workplace FAQs>
AI in the workplace FAQs #
Questions about AI in the workplace>
Questions about AI in the workplace #
How is AI used in the workplace? AI is used in a range of ways at work across many industries. Some notable examples include: Assisting with creative tasks like writing and image generation
Automating certain admin-based tasks
Analyzing data and providing customer insights
Preventing cybersecurity threats
Handling customer service queries via chatbots and virtual assistants While the above examples are notable instances of AI in the workplace, the technology is used in numerous other circumstances today and this is only likely to increase as AI develops further. How can AI help in the workplace? AI can substantially increase productivity at work by automating tasks previously carried out by humans at a much quicker rate. The technology can also analyze data to provide insight that companies can use to make key decisions, refining their approach in key business areas. By assisting with many day-to-day tasks, AI can also enhance employee productivity, giving workers more time to focus on key areas that were previously neglected due to time constraints. What happens if AI replaces humans in the workplace? While it’s possible AI may render certain jobs obsolete, the technology offers significant opportunities for growth in many industries. Additionally, developments in AI may even result in the creation of new jobs and sectors that were previously not possible before the AI boom. History shows us that with the right preparation and reskilling plans, new technology can create more jobs than it destroys. How can AI be used to increase efficiency in the workplace? AI increases efficiency in the workplace by automating tasks previously carried out by humans. Often, these tasks are performed at a significantly greater speed resulting in greater productivity, and more time for employees to focus on other key areas of their job. AI can also analyze data to provide insights to business leaders. The information gained from this data can be used to refine business practices, further increasing productivity and efficiency. Which jobs are in danger due to AI? A report from the World Economic Forum predicted that the number of data entry clerks would fall by eight million globally in the five years from 2023. Other jobs predicted to face a significant workforce decline were administrative and executive secretaries (-6 million), and accountants, bookkeepers, and payroll clerks (-4.75 million). If these figures prove to be correct, it would suggest that the increasing effect of AI automation in these roles has resulted in job losses.
AI in the Workplace Glossary>
AI in the Workplace Glossary #
Automation>
Automation is the use of technology or machines to perform tasks without significant human intervention.
Generative AI>
Generative AI #
Generative AI is a form of AI capable of generating text, images, videos, and code by following human prompts.
Large Language Model (LLM)>
Large Language Model (LLM) #
LLMs are a type of machine learning that analyze massive text-based datasets. They recognize patterns and structures in text, producing their own similar outputs that can be used to respond to human queries.
Machine learning>
Machine learning #
Machine learning is an AI process that uses computer science, mathematics, and coding to develop algorithms and models. The algorithms and models are then trained to analyze patterns and predict future trends without human assistance.
Sources>
https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part
https://workplaceinsight.net/quarter-of-people-have-now-used-or-tried-ai-in-the-workplace/
https://www3.weforum.org/docs/WEF_Future_of_Jobs_2023.pdf
https://www.statista.com/statistics/1361251/generative-ai-adoption-rate-at-work-by-industry-us/
https://tech.co/news/business-attitudes-ai-workplace
https://www.spiceworks.com/tech/artificial-intelligence/articles/ai-adoption-shaping-future-workplaces-aberdeen-data/
https://www.forbes.com/sites/bernardmarr/2024/07/16/the-best-generative-ai-workplace-productivity-tools/
https://www.businessinsider.com/ai-transforming-the-workplace-examples-2023-7
https://www.ib.barclays/content/dam/barclaysmicrosites/ibpublic/documents/our-insights/AI-impact-series/ImpactSeries_12_brochure.pdf
https://en-gb.workplace.com/blog/ai-and-the-future-of-work
https://www.apa.org/topics/healthy-workplaces/artificial-intelligence-workplace-worry
https://x.com/OECD/status/1785700751810916851
https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/ai-capabilities
https://www.ipsos.com/en-uk/only-half-uk-workers-have-received-learning-opportunities-work-1-in-2-have-not-learned-about-ai
https://www.nngroup.com/articles/ai-tools-productivity-gains/
https://www.forbes.com/sites/joemckendrick/2023/04/25/yes-ai-increases-productivity-study-suggests/
https://360learning.com/blog/ai-ethics/
https://www.servicedeskinstitute.com/five-ethical-issues-of-ai-in-the-modern-workplace/
https://cpduk.co.uk/news/exploring-ai-ethical-considerations-in-the-workplace
https://www.rospa.com/news-and-views/how-ai-could-transform-workplace-safety
https://www.hse.gov.uk/news/hse-ai.htm
https://academic.oup.com/joh/article/66/1/uiad017/7505756
https://www.mckinsey.com/mgi/our-research/generative-ai-and-the-future-of-work-in-america
https://www.mckinsey.com/featured-insights/sustainable-inclusive-growth/chart-of-the-day/a-jolt-to-jobs-from-gen-ai
https://www.ib.barclays/content/dam/barclaysmicrosites/ibpublic/documents/our-insights/AI-impact-series/ImpactSeries_12_brochure.pdf
https://www.mckinsey.com/featured-insights/future-of-work/five-lessons-from-history-on-ai-automation-and-employment
https://www.linkedin.com/pulse/brief-history-ai-business-logycco-ktfjc/
https://www.tableau.com/data-insights/ai/history
https://www.techtarget.com/searchenterpriseai/tip/The-history-of-artificial-intelligence-Complete-AI-timeline
https://verloop.io/blog/the-timeline-of-artificial-intelligence-from-the-1940
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2024/08/25
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https://www.aiprm.com/ai-in-workplace-statistics/
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},
{
"date": "2024/09/03",
"position": 91,
"query": "artificial intelligence wages"
},
{
"date": "2024/09/03",
"position": 9,
"query": "artificial intelligence workers"
},
{
"date": "2024/09/03",
"position": 34,
"query": "reskilling AI automation"
},
{
"date": "2024/09/03",
"position": 1,
"query": "workplace AI adoption"
},
{
"date": "2024/10/01",
"position": 78,
"query": "job automation statistics"
},
{
"date": "2024/11/01",
"position": 96,
"query": "job automation statistics"
},
{
"date": "2024/11/01",
"position": 18,
"query": "workplace AI adoption"
},
{
"date": "2025/01/01",
"position": 21,
"query": "workplace AI adoption"
},
{
"date": "2025/02/01",
"position": 23,
"query": "job automation statistics"
},
{
"date": "2025/03/01",
"position": 20,
"query": "job automation statistics"
},
{
"date": "2025/04/01",
"position": 18,
"query": "job automation statistics"
},
{
"date": "2025/05/01",
"position": 61,
"query": "artificial intelligence workers"
},
{
"date": "2025/05/01",
"position": 19,
"query": "job automation statistics"
},
{
"date": "2025/06/01",
"position": 63,
"query": "artificial intelligence workers"
}
] |
As ML Engineer, do you guys have on-call duty?
|
The heart of the internet
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https://www.reddit.com
|
[] |
Typically, operationalization job may have on-call duty more often than others. As MLE, how often is your on-call duty?
|
As far as I am aware, MLE works in deploying and MLOps stuff, which is basically DevOps for Machine Learning. Typically, operationalization job may have on-call duty more often than others.
As MLE, how often is your on-call duty?
Does it affect your work-life balance greatly?
Assuming you have on-call, do you still have work-life balance?
How does your company "reward" you during on-call as MLE?
| 2022-12-01T00:00:00 |
https://www.reddit.com/r/ExperiencedDevs/comments/zz0e78/as_ml_engineer_do_you_guys_have_oncall_duty/
|
[
{
"date": "2022/12/01",
"position": 3,
"query": "machine learning job market"
}
] |
|
I am a self-taught (no college degree) AI Engineer. AMA
|
The heart of the internet
|
https://www.reddit.com
|
[] |
Can be rules based engines, machine learning, etc. at the end of the day machine learning is just a skill. Software engineers, ml engineers, data scientists, ...
|
I’m hoping people that want to crack into the field, or just want to make a shift from their current tech job, see this and realize that if you’re passionate enough, and willing to be resilient that you can do it! Ask me anything and Hopefully I can share some useful experience.
| 2022-12-01T00:00:00 |
https://www.reddit.com/r/datascience/comments/zyqacx/i_am_a_selftaught_no_college_degree_ai_engineer/
|
[
{
"date": "2022/12/01",
"position": 6,
"query": "machine learning job market"
}
] |
|
Cloud Computing or Artificial Intelligence — Which career is ...
|
Cloud Computing or Artificial Intelligence — Which career is better?
|
https://taimurcloud123.medium.com
|
[
"Taimur Ijlal"
] |
A.I. jobs are typically some of the highest paying jobs year in and year out as can be seen by pretty much any salary report. While the entry barrier is high, ...
|
Cloud Computing or Artificial Intelligence — Which career is better? Taimur Ijlal 7 min read · Dec 9, 2022 -- 2 Share
Which is the best career option for newcomers?
Photo by ThisisEngineering RAEng on Unsplash
If you are newly entering the job market in 2023, then I do not blame you for being nervous
“Tech Layoffs are happening” “Jobs are scarce” “Doom and gloom everywhere”
Same old blah blah
Let us look at the advantages you have
If you are a newcomer to the industry, then you have an enormous advantage over older and more experienced people like me in that your primary goal is to nab that all-important experience
Salary and other perks while important are all secondary
Junior-level positions are also much easier to get compared to senior-level positions that have much more competition.
A common question I get asked by newcomers is which field to choose ?
I made a video a few months back on Artificial Intelligence vs Cybersecurity and it is by far the most popular video on my Youtube channel
| 2022-12-09T00:00:00 |
2022/12/09
|
https://taimurcloud123.medium.com/cloud-computing-or-artificial-intelligence-which-career-is-better-ab76d8c1fba5
|
[
{
"date": "2022/12/01",
"position": 20,
"query": "machine learning job market"
},
{
"date": "2022/12/01",
"position": 8,
"query": "artificial intelligence wages"
}
] |
C3-IoC: A Career Guidance System for Assessing Student ...
|
C3-IoC: A Career Guidance System for Assessing Student Skills using Machine Learning and Network Visualisation
|
https://link.springer.com
|
[
"José-García",
"A.Jose-Garcia Exeter.Ac.Uk",
"Department Of Computer Science",
"University Of Exeter",
"Exeter",
"Sneyd",
"A.Sneyd Sheffield.Ac.Uk",
"The University Of Sheffield",
"Sheffield",
"Melro"
] |
by A José-García · 2023 · Cited by 27 — The novelty of this AI-based solution is in using machine learning and text mining techniques that accurately relate a range of skills to existing IT jobs. The ...
|
Motivation for the C3-IoC System
In the field of AI and career guidance systems, three important aspects of current provision can be improved (De Smedt et al., 2015; Hillage & Cross, 2015; Peterson et al., 1999). Firstly, working with large datasets to create repositories of jobs and skills which are fine-tuned to specific sectors is essential to keep up-to-date with emerging and changing roles. The IT sector is a prime example where this is highly pertinent. Secondly, automating the process of skill profiling makes it less time-consuming for the user and improves understanding of skills and job roles. Lastly, providing visualisations and advanced metrics to accurately guide and place users in the job space. Given the skill gap between education and industries, especially in the IT sector, the development of the C3-IoC system was motivated by a desire to explore these three opportunities and to produce an online career guidance system for students that would give them greater awareness of their skillset and of the skills required to pursue certain roles in IT.
In developing this iteration of the C3-IoC system, we improved upon a previous pilot version, drawing on findings from student perceptions of the platform. The pilot version was essential to understand the usefulness for students in using a system with the aspects mentioned. The pilot consisted of a CV parser, a non-technical skills questionnaire and a network visualisation of the IT job space. This first version's matching process was limited to technical skills and IT job roles. We then conducted a mixed-method user trial with 71 participants using surveys and interviews. The findings from the pilot showed that students found the system useful along three main dimensions: career exploration, personalisation and skill awareness (see Fig. 1).
Fig. 1 Perceived value of the pilot version according to students Full size image
Firstly, career exploration was regarded as the most valuable aspect of the pilot system. Students perceived it as helpful in giving a broad overview of the job market, especially those who had no idea what roles to pursue. Additionally, the job role visualisation was considered more valuable over an internet search. Secondly, students appreciated a personalised career coaching experience which would guide them to job roles based on their personal skillset. They also liked being told what skills they needed to develop for certain job roles and seeing how closely their skillset matched those. Finally, in terms of skill awareness and self-reflection, students found the system useful in helping them think about their skillset and what they display in their CVs. It particularly helped them reflect on the relevance of non-technical skills in the workplace. However, although the pilot version allowed for skill awareness through a self-assessment questionnaire, it did not present a profile of non-technical skills nor positioned the user in the job space accordingly. Informed by the potentialities of the pilot version, the final version of the C3-IoC sought to address these aspects by providing improved visual feedback and relating student technical and non-technical skills with job roles.
The C3-IoC System Architecture
Building on our pilot study, the overall aims of the current C3-IoC system are to provide a personalised career exploration system and help users understand the key technical and non-technical skills required for careers in the IT sector. Although the system primarily focuses on the IT sector, it also incorporates information about a wide range of job roles at a coarse resolution to allow users who might consider themselves outside the IT sector to explore potential roles.
The system architecture underlying the C3-IoC website consists of three main modules: (1) a knowledge base of skills and job roles; (2) a user skill profiling module; and (3) a personalised job role matching and visualisation module, which is shown in Fig. 2. A typical user journey through the C3-IoC system is illustrated in Fig. 3. Users first encounter a welcome screen (a) which offers an overview of the system. They then go through a three-stage journey, with progress marked via a progress bar at the top of the screen. The first stage extracts user skills information via a CV parser and a questionnaire. The second stage allows the user to refine their skill profile. For example, the middle screenshot (b) illustrates a radar chart of non-technical skills whose levels the user can adjust. Finally, the third stage of the system allows users to explore job roles: the bottom screenshot (c) shows a network visualisation of the user’s placement in the IT job role space.
Fig. 2 C3-IoC system architecture comprised of three main modules Full size image
Fig. 3 C3-IoC screenshots: welcome screen (a), radar chart of non-technical skills ratings (b) and network map of a user in the job role space (c) Full size image
Module 1: Knowledge-base of Skills and Job Roles
The first gap in the current provision that we wanted to address with this website was the lack of a comprehensive landscape of the technical and non-technical skills required for existing and emerging jobs in the IT market. To achieve this, we used classical techniques from Natural Language Processing to process and extract information from job ads (Jurafsky & Martin, 2008). The data was scraped came from two sources, for reasons described below, and we then merged the findings from both to create a non-technical skills list and questionnaire.
Identification of Job Roles and Skills from the IT Job Corpus
The first step in this process was to curate a corpus of recent job vacancies in the UK IT sector from the “Find a job” website. This website hosts job advertisements classified into 29 categories, including IT. The website was crawled weekly from October 2018 to December 2019, and all jobs in the IT category were extracted. Collected job metadata, including the job title, job description and URL. Duplicated jobs were removed, resulting in a corpus of 22,359 jobs. We will refer to this dataset as the IT job corpus.
A comprehensive list of technical skills was then extracted using text mining techniques from the corpus. A predefined list of 100 common technical skills (e.g. “Python”, “Agile”) was used as a seed list to extract a complete list of skills. This was achieved by training a Word2Vec model (Mikolov et al., 2013) on the corpus to represent words as latent vectors. The ten most similar words to every word in the seed list were found using cosine similarity and extracted. These words were combined with the original seed list terms to generate an extended skill list consisting of 902 terms. However, the extended skill list was noisy and included some non-skill words and synonyms. We manually removed the noise terms and merged synonyms, resulting in an IT skill dictionary consisting of 195 unique skills. A manual examination of the skills revealed they could be classified into four categories: general tech, programming languages, tools and platforms, and non-technical skills, including specific training (see Table 1 for category sizes and examples). In our final knowledge base, the non-technical skills were made a separate dimension, keeping only the specific training (e.g. machine learning) as part of the IT skills, as described further in the section about the second module.
Table 1 IT job corpus skill categories: number of skills (N. Skills) and examples Full size table
The task of clustering IT job advertisements into common job roles (such as “software engineer” or “data scientist”) is non-trivial. There are no standard definitions of these roles, and the same role may have multiple names and not all job advertisements necessarily fall into one of the roles. We identified common job roles by analysing the job title data. This data was frequently noisy, including, for example, locations or salaries. The titles were first cleaned by removing all numbers and punctuation, in addition to common locations and role level signifiers (such as “graduate”). All cleaned titles which occurred more than 100 times were then extracted and manually examined to produce a list of 26 job roles. Job advertisements matching one of these roles (11,784) were assigned to that role, while those that did not match were discarded. The skills occurring in each remaining job advertisement were then extracted using the skills list described above. Finally, each job role was represented as a weighted list of skills, where the weighting of a skill is given by the proportion of ads in that role containing that skill. Table 2 shows the 26 extracted job roles, the number of jobs each role contains and its most common skills.
Table 2 The 26 job roles extracted from the IT job corpus, along with the frequency of jobs and the most common skills for that role Full size table
Identification of Job Roles and Non-technical Skills from the O*NET Database
To complement the collection of IT jobs, we drew on a second data set so that users of the system who were not currently in IT could orient themselves. This data set would allow us to provide a visualisation of how ‘far’ from their desired job in the IT sector a user was. The O*NET program conducted by the U.S. Department of Labor produces the publicly available O*NET database detailing the importance of 231 workplace skills, knowledge, and abilities for the completion of each of the 967 occupations (hereinafter referred to as job roles) recognised under the Standard Occupational Classification (SOC) System. The O*NET database is updated annually, allowing for snapshots of the relationships between job roles and skills through a continual survey of workers from each occupation. We used the most recent annual O*NET database corresponding to the year 2019 (version db_24).
A second important benefit was that the O*NET data set offered a much more nuanced overview of the non-technical skills required in a range of jobs. As these are often transversal skills, it can be challenging to determine their weighting in different job roles. O*NET’s extensive research background in matching skill levels and job roles based on interviews and surveys with employees provides a rich understanding of, for instance, how important non-technical skills like ‘attention to detail’ are for different job types.
A final benefit was that O*NET also offers a comprehensive set of questions for users to self-evaluate against these skills. This latter was important because assessing non-technical skills via questionnaires comes with limitations. On the one hand, for example, it is complex to address with only one or two questions whether someone knows how to communicate effectively in multiple forms (e.g., orally, written, visually). On the other hand, self-assessment is subjective. For this reason, it was important to have a tested and reliable survey.
From the O*NET database, we created a matrix in which each row corresponds to one of the 967 listed job roles and each column to one of the 231 skills measured by O*NET. Using the Education, Training and Experience table in O*NET, we counted the percentage of respondents who ranked the necessary qualification for each job role at level 6 (Bachelor’s Degree) or higher. We retained only job roles; over 50% of the respondents believed the job role required a level 6 or higher qualification. We did this to focus our efforts on users who are university students or recent graduates. This restriction removed a large number of job roles from consideration, most of which could be described as ‘physical’ or ‘manual’ (Alabdulkareem et al., 2018).
O*NET estimates the importance of each attribute or skill for every job role. We therefore represented job roles as vectors of skill weightings. To measure the attributes which are relevant for jobs requiring a degree, we correlated the vector, \(d\left(j\right)\), which measures the proportion of respondents who ranked the necessary qualification or skill for each job role, \(j\), as level 6 or higher with the vector \(v\), where \(v\left(j\right)\) is the importance of attribute \(s\) for occupation \(j\). If the correlation was greater than 0, we retained that skill. As a result, in this system iteration, we considered a total of 142 skills and 381 job roles from the O*NET database. We will refer to this as the general job corpus for the remainder of this paper.
Merging of Non-technical Skills from Both Data Sets
As stated above, the decision was taken to draw on O*NET questionnaires for their reliability and skill level discrimination. Since the C3-IoC uses two data sets – the IT job corpus and the general job corpus – a matching between both was therefore needed so that users’ responses to the non-technical skills questionnaire could be directed to both data sets.
To identify users’ non-technical skills, we selected a set of questions from four O*NET questionnaires: Skills (S), Abilities (A), Work Activities (WA) and Work Styles (WS). The starting point was the list of 25 non-technical skills from the IT job corpus, which were then matched with the 144 questions from O*NET. The matching between both lists followed an extensive manual review with several rounds of peer revision. The selection criteria followed two main questions: (1) which O*NET questions best describe the IT job corpus non-technical skills? (2) which non-technical skills are relevant for IT jobs based on previous findings and literature? During this process, we could not find matches for five IT job corpus skills, as displayed in Table 3, so these were not included in the final list used for the C3-IoC. Additionally, three extra skills from O*NET were included in accordance with our second criterion. For certain skills, there was a need to have more than one question to describe that skill better. The questionnaire was then presented on the website using O*NET questions, scale and examples. For instance, for “creativity and innovation”, the question How confident are you in thinking creatively? was asked, together with a scale from 1 to 7 with examples in categories (1) “change the spacing on a printed report”, (4) “adapt popular music for a high school band”, and (6) “create new computer software”. Four categories were created for the final 24 questions regarding non-technical skills: thinking/cognitive, social, personal and management. Table 3 displays the final matching between both data sets. The numbers followed by the O*NET acronyms refer to the question number of the corresponding questionnaire.
Table 3 Matching of non-technical skills between IT job corpus and general job corpus (referring to specific O*NET questions) Full size table
Module 2: User Skill Profiling
The second challenge the system seeks to address is collecting a comprehensive overview of user skills without requiring too much user input. For users to complete their technical and non-technical skills profile (also shown as ‘Personal skills’ on the website) profile without much effort, the C3-IoC provides an automated CV scraper, skill predictions, and recommendations.
Users can upload their CVs in PDF format and a list of technical skills is extracted from it. In detail, the input file is converted to plain text then matching is performed using a predefined dictionary of technical skills derived from the IT job corpus. For the non-technical skills, users need to fill out a questionnaire with 24 questions that were selected based on their relevance to the IT sector, as described in the previous section. In order to fulfil the requirements for the matching process, particularly with regard to the general job corpus, only four of the 24 questions are mandatory, and these are displayed at the top of the questionnaire. These four questions concerning four non-technical skills are asked in a certain order of relevance and represent the most informative questions, which means that just by gathering answers to these questions, we can gain enough information to place the user in the job network accurately. By using the most informative order method, we were able to determine that the four most informative non-technical skill questions correspond to: “attention to detail”, “management”, “self-control”, and “teaching and guidance” (training and teaching). This method allows us to extract as much information as possible in a minimum amount of questions. To find out the most informative order, we defined a loss function as the (Frobenius) norm of the difference between the data matrix \(X\), whose elements \({X}_{js}\) are the importance that O*NET estimates of the job \(j\) and the skill \(s\), and an estimate of \(X\), \(\overline{X }\) that is constructed from the answers to the non-technical skill questions. This estimate was constructed using the non-technical skill questions as independent variables in a regression model. That is, we solved the linear regression \({X}_{s}={X}_{Q}\beta\), where \({X}_{s}\) is the column of \(X\) corresponding to skill \(s\), \({X}_{Q}\) is the slice of \(\overline{X }\) corresponding to the answered non-technical skill questions, \(\beta\) are the unknown coefficients. We started with just one question and found the most informative question from the set, as the one resulting in the smallest loss. This turned out to be the question asking about “attention to detail”. We then took the question set found for the most informative question and found three more additional questions that would reduce the loss function the most: “management”, “self-control”, and “teaching and guidance”. As a result, we covered a broad range of work activities with four questions.
Next, after uploading their CV and filling out the questionnaire, the user is shown a list of their inferred skills, from which they can remove individual items that seem inappropriate for their profile. They are additionally prompted to consider recommended skills in the knowledge base, which they can add to their profile. This encourages the user to reflect on the skills they have developed so far and also those they have included on their CV. Once the user completes the skill list, they proceed to a page where they can visualise both their technical and non-technical skills through radar diagrams that show the rating of skills from 1 to 7, divided into four main areas. For technical skills, the four main areas are programming languages, general tech, specific training, and tools and platforms. The four domains for non-technical skills are social, thinking, personal and management skills (as shown in Fig. 2). With this interactive interface, users can refine their skills once more and watch the respective changes happening on the radar charts. This way of visualising the skills gives users an overview of the dimensions in which they have more expertise and those they need development.
Module 3: Job Role Matching and Visualisation
The final challenge the C3-IoC system seeks to address is how to make an accurate projection of the user into the job role network. This involves three phases: job role matching, construction of the jobs network visualisation and projection of the user into the latter. In this section, we address those in detail.
Job Role Matching
Both the profile and job roles are represented as weighted skill vectors. A common way to measure relatedness is to use cosine similarity, which measures the cosine of the angle between two vectors. However, this does not work in our case because we want to consider vector magnitude (corresponding to skill level). We therefore used the following metric which takes into account skill level. Let \(S\) be the fixed set of skills, \(S=\left\{{s}_{1},{s}_{2},\dots ,{s}_{N}\right\}\), where \(N\) is the total number of skills. Each job role \(j\) and user \(u\) can be represented by a set of skill weightings, \(j=\left\{{j}_{s}|s\in S\right\}\), \(u=\left\{{u}_{s}|s\in S\right\}\). Then, the distance between \(u\) and \(j\) is defined as:
$$D\left(u,j\right)=\sqrt{\sum_{s\in S}{\mathrm{max}({j}_{s}-{u}_{s},0)}^{2}}$$ (1)
Note that the distance between \(u\) and \(j\) does not increase if the user has at least the requisite skill level for the job role. Therefore, the similarity between \(u\) and \(j\) is defined as:
$$Sim\left(u,j\right)=\frac{D\left(\mathrm{\varnothing },j\right)-D(u,j)}{D(\mathrm{\varnothing },j)}=1-\frac{D(u,j)}{D(\mathrm{\varnothing },j)} ,$$ (2)
where \(\mathrm{\varnothing }\) denotes the empty set and the value bound of \(Sim(u,j)\) is \([\mathrm{0,1}]\), such that values tending towards unity indicate a better job role matching as they indicate a better correspondence between the student profile and a job role.
Job Role Visualisation
Two main techniques were used in order to allow the visualisation of the job role landscape as networks into which users can be projected: (1) the revealed comparative advantage index (RCA), which denotes the relative importance of a skill for a job role after accounting for skills which are common across many job roles. This allows students to more effectively compare their skillset to occupational requirements (Alabdulkareem et al., 2018); (2) a force-directed layout that effectively visualises related career fields, leading students to explore skills and practices associated with a specific job or occupation. Two job role networks were displayed based on the two knowledge databases: a tech-focused (from the IT job corpus) and a non-tech focused (from the general job corpus).
To create these job role networks, we denote by \(net(j, s)\) the level of importance of skill \(s \in S\) to job role \(j \in J\) where \(J\) is the set of job roles and \(net\left(j, s\right)\) indicates one of the studied databases: IT job corpus or general job corpus. Also, \(net\left(j, s\right)=5\) indicates that \(s\) is essential to \(j\), while \(net\left(j, s\right)=0\) indicates that workers of job role \(j\) need not possess or perform skill \(s\). This importance level or skill level rating is associated with the O*NET occupation and for simplicity, both databases were normalised in the range \([\mathrm{0,5}]\). Usually, job roles are the units of interest in labour dynamics, and they are identified by a set of skills possessed by workers of that occupation. Therefore, we need to quantify how useful a skill is for identifying a job role. RCA normalisation compares the relative importance of a skill in a job role to the expected importance of the skill on average. It indicates that an occupation relies on the skill more than expected. For example, most jobs require basic reading comprehension, so this skill is not discriminative, and the RCA transformation accounts for these effects. RCA is defined by:
$${\text{rca}}\left(j,s\right)=\frac{net\left(j,s\right)/{\sum }_{{s}^{\mathrm{^{\prime}}}\in S}net\left(j,{s}^{\mathrm{^{\prime}}}\right)}{{\sum }_{{j}^{\mathrm{^{\prime}}}\in J}net\left({j}^{\mathrm{^{\prime}}},s\right)/{\sum }_{{j}^{\mathrm{^{\prime}}}\in J,{s}^{\mathrm{^{\prime}}}\in S}net\left({j}^{\mathrm{^{\prime}}},{s}^{\mathrm{^{\prime}}}\right)}$$ (3)
We denote an effective need for skill \(s\) in job \(j\) by \(e(j, s)=1\) if \({\text{rca}}\left(j,s\right)>1\), and \(e(j, s)=0\) otherwise. We use the following network projection onto the space of job roles to create a similarity measure between two job roles:
$${J}\left(j,j\mathrm{^{\prime}}\right)=\frac{{\sum }_{s\in S}e(j,s)\cdot e\left(j\mathrm{^{\prime}},s\right)}{\mathrm{max}({\sum }_{s\in S}e\left(j,s\right),{\sum }_{s\in S}e\left(j\mathrm{^{\prime}},s\right))}$$ (4)
This projection identifies job role pairs that share key occupational features. Defining the similarity between job roles in this way allows the “job role space” to be visualised as a network (graph) in which individual job roles are nodes or vertices and similar job roles are linked together. Ideally, the aggregate structure in the job roles network should correspond to meaningful labour dynamics. To this end, we identify job role types using the Louvain community detection algorithm (Blondel et al., 2008). This method identifies node communities by comparing the density of connections within a community to connections between communities. This method requires no assumptions about the number of communities to be found. Figures 4 and 5 illustrate the job roles network for the general job corpus and IT job corpus databases, respectively. In the figures, each filled circle or rectangle represents a node (job role) and the grey lines indicate the relationship between the different job roles, such that the shorter the line, the greater the similarity between the job roles. The absence of a line/edge between nodes indicates that job roles have no skills with effective needs in common.
Fig. 4 The job role landscape generated based on the general job corpus has 11 communities. Each node colouring corresponds to a community (manually labelled). Individual nodes refer to job roles and are linked by their skill similarity Full size image
Fig. 5 The job role landscape generated for the IT Job corpus database has 4 communities. Each node colouring corresponds to a community (manually labelled). Individual nodes refer to job roles and are linked by their skill similarity Full size image
The final feature of this module is the projection of the user onto the job role networks. Thus, considering the created job role networks and the proposed similarity measure for the job role matching, we ranked the jobs based on their similarity score to a given user (Eq. (2)).
As an example, Fig. 6 illustrates the resulting projection of a Bioinformatician user profile onto the non-tech focused network based on their input skill profile. The top 10 positions are highlighted using different levels of colour intensities, such that the darker the colour, the better the job matching between the user skills and the job roles in the general job corpus. These top 10 job roles are also displayed to the user as a bar chart on the website. The figure shows that the user is located in the Science and Technology community based on the input skills, which is the most suitable for the given user profile. Also, notice that all the remaining predicted job roles are in the same cluster and close to each other, which supports the robustness of the job matching component of the proposed C3-IoC system.
| 2023-12-14T00:00:00 |
2023/12/14
|
https://link.springer.com/article/10.1007/s40593-022-00317-y
|
[
{
"date": "2022/12/01",
"position": 45,
"query": "machine learning job market"
}
] |
M.S. in Artificial Intelligence
|
M.S. in Artificial Intelligence
|
https://ds.njit.edu
|
[] |
DS 675: Machine Learning. or CS 670: AI. One of: DS 677: Deep Learning; DS 680 ... Job Seeker · Alumni. University Heights, Newark, NJ 07102 USA (973) 596 ...
|
To be eligible for admission, a student must have a Bachelor of Science degree with a minimum GPA of 3.0 on a 4.0 scale and have completed the following undergraduate coursework: Calculus I and II (equivalent to the NJIT courses Math 111 and Math 112)
Introduction to Programming (equivalent to the NJIT CS 100 course)
Basic programming constructs, writing and debugging programs, iteration, recursion, arrays, lists (equivalent to NJIT CS 113)
Data Structures and Algorithms (equivalent to the NJIT CS 114 course)
Probability and Statistics (equivalent to the NJIT Math 333 course)
Linear Algebra (equivalent to the NJIT Math 337 course) Students who do not meet all of the above requirements but hold a BS or BA degree in a technical scientific subject will be evaluated on a case-by-case basis and may be admitted to the program after they successfully complete a relevant graduate certificate in Artificial Intelligence, which provides a pathway to the MS program. Earning the certificate with a GPA of 3.0 or higher guarantees admission to the MS program.
Transcripts from a prior degree in computing
At least one letter of recommendation Certificates of completion of online computing courses are taken into consideration. All international applicants are required to submit scores for the following standardized tests: GRE
TOEFL, or IELTS, or Duolingo Score reports do not have to be submitted directly by the companies that administer the tests (e.g. ETS etc.) The scores can be reported by the applicant, by either uploading a pdf with the official score report or by sharing a link to their score report. Test Waiver: International applicants who hold a degree from an accredited US institution can ask for a waiver of standardized tests. To request a waiver, please first submit your application and then contact the All applicants are required to submit:Certificates of completion of online computing courses are taken into consideration. Allapplicants are required to submit scores for the following standardized tests:Score reportshave to be submitted directly by the companies that administer the tests (e.g. ETS etc.) The scores can be reported by the applicant, by either uploading a pdf with the official score report or by sharing a link to their score report.International applicants who hold a degree from an accredited US institution can ask for a waiver of standardized tests. To request a waiver, please first submit your application and then contact the NJIT admissions office , by directly emailing our staff responsible for international recruitment.
Students who receive a master's degree in AI from NJIT will be able to demonstrate their expertise in the following areas: Design and build custom AI models using a general-purpose programming language (Python) and frameworks such as Tensorflow and PyTorch.
Design and develop software in the form of scalable AI software architectures and APIs.
Process and analyze a variety of data in different formats including text, images, audio, videos, and time series data.
Formulate complex problem statements and solve them using specific AI models.
Present AI applications and methodologies effectively and clearly.
Campus Options The online program is available to both U.S. residents and international students
The online rates are available only to students fully enrolled to the online program
U.S. residents can choose a combination of face-to-face and online courses
The Jersey City program is not available to F-1 international students
F-1 students can take at most one course per semester online, or at Jersey City Tuition & Fees by Campus (based on AY 2024-2025 rates) Online: $34,290
Jersey City: $34,504-$37,200
Newark, NJ residents: $36,426-$46,450
Newark, non-NJ residents: $44,748-$63,120 For details, see NJIT's For details, see NJIT's Tuition and Fee Schedule . For information about cost of living see Tuition and Costs at NJIT
All applicants are automatically considered for financial support during admission, with no need for additional communication. However, guaranteed financial support for MS students is limited and typically reserved for doctoral candidates. After enrollment, MS students can apply for campus jobs such as grading, tutoring, research assistantships, or roles in the library and academic departments to help offset expenses. Well-performing students are often hired as graders, earning up to $9K annually.
| 2022-12-01T00:00:00 |
https://ds.njit.edu/ms-artificial-intelligence
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[
{
"date": "2022/12/01",
"position": 52,
"query": "machine learning job market"
},
{
"date": "2022/12/01",
"position": 42,
"query": "generative AI jobs"
}
] |
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Artificial Intelligence and Machine Learning
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Artificial Intelligence and Machine Learning
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https://www.resetera.com
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... job. I look forward to seeing you then. Regards, XXXXXXX". It was very ... market very competitive. The depth and breadth of knowledge needed for MLE ...
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Once upon a time, in the far-off future, a hero named Master Chief stood tall against impossible odds. He was a soldier, the last of his kind, and he was humanity's only hope against the monstrous alien alliance that threatened to destroy everything.
Master Chief was a towering figure, clad in his iconic green armor and wielding a powerful assault rifle. He was a skilled and ruthless fighter, feared by his enemies and respected by his allies.
The war against the aliens raged on for years, and Master Chief fought on the front lines, leading his team of soldiers into battle time and time again. They faced incredible danger and hardship, but Master Chief never wavered. He was a true hero, and he was determined to protect his home and his people at any cost.
In the end, Master Chief emerged victorious. The aliens were defeated, and humanity was saved. But the cost had been high, and Master Chief knew that he would carry the scars of battle with him for the rest of his life.
Despite the victory, Master Chief's work was not yet done. He continued to fight, to protect his people and to ensure that they would never again be threatened by such a monstrous foe. And though the war was over, Master Chief remained ever vigilant, always ready to defend his home and his people from any danger that might arise.
In the end, Master Chief was a true hero, and he would always be remembered as the one who saved humanity from certain destruction.
| 2022-12-01T00:00:00 |
2022/12/01
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https://www.resetera.com/threads/artificial-intelligence-and-machine-learning.659995/post-97443982
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[
{
"date": "2022/12/01",
"position": 53,
"query": "machine learning job market"
}
] |
CCP for Tech Professionals – AI Engineer
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Career Conversion Programmes (CCP) Details
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https://conversion.mycareersfuture.gov.sg
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[] |
Reskill your competencies to take on new job roles with good longer-term prospects ... The Artificial Intelligence /Machine Learning Engineer supports the ...
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CCPs are career conversion programmes targeted at mid-career Professionals, Managers, Executives and Technicians (PMETs) to undergo reskilling and move into new occupations or sectors that have good prospects.
About the Sector
Information and Communications (I&C) is a key economic growth sector that creates quality jobs and opens up exciting new opportunities for citizens. According to the Infocomm Media Development Authority (IMDA)’s Annual Survey on Infocomm Media Manpower 2020, the number of tech professionals employed across the economy grew by about 10,000 annually over the past few years, reaching around 216,000 in 2020. Demand will also extend beyond the sector into other sectors like finance, manufacturing and retail.
Job Role Description (as per ICT Skills Framework)
The AI Engineer track maps to the Artificial Intelligence/Machine Learning Engineer role in the Infocomm and Technology (ICT) Skills Framework (SFw). The Artificial Intelligence /Machine Learning Engineer supports the production of scalable and optimisedartificial intelligence (AI)/machine learning (ML) models. He/She focuses on building algorithms for the extraction, transformation and loading of large volumes of real-time, unstructured data in order to deploy AI/ML solutions from theoretical data science models. He runs experiments to test the performance of deployed models, and identifies and resolves bugs that arise in the process. He works in a team setting and is proficient in statistics, scripting and programming languages required by the organisation. He is also familiar with the relevant software platforms in which the models are deployed. He should be knowledgeable of the requirements under the Model AI Governance Framework and the Personal Data Protection Act (PDPA) in the course of his work on AI/ML models. The AI/ML Engineer is a determined individual who is comfortable working with large data sets, has a keen interest in problem solving and experimentation, and enjoys the iterative process of development and resolving issues.
About the Programme
Under the CCP for AI Engineer, you will undergo up to 100% of On-the-Job-Training (OJT), as well as optional structured classroom trainings (where applicable) to take on the role of an Artificial Intelligence/Machine Learning Engineer.
Training will be conducted over 3 – 6 months. Upon successful completion of the CCP, you will be equipped with the necessary skills to embark on a career in an Information and Technology-related role.
For more information on the Technical Skills and Competencies (TSCs) you may acquire through the programme, refer to the annex below. These TSCs are aligned with the Skills Framework (SFw) for ICT, found here.
| 2022-12-01T00:00:00 |
https://conversion.mycareersfuture.gov.sg/portal/ProgramDetails.aspx?ProgID=P00001808
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[
{
"date": "2022/12/01",
"position": 54,
"query": "machine learning job market"
}
] |
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The Impact of Technology on the Job Market and How It Is ...
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The Impact of Technology on the Job Market and How It Is Changing the Way We Work
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https://www.linkedin.com
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[
"Joyna Khurana",
"Pursuing Bcom Program At Delhi University",
"Pursuing Graphic Designing",
"Ishan Khurana",
"Equity Research Enthusiast",
"Student At Iim Amritsar",
"Nism Certified Research Analyst",
"Summer Intern At Dawaa Dost",
"Certified Investment Analyst",
"Utsav Khowala"
] |
In addition, technology has made it possible for employers to use artificial intelligence and machine learning algorithms to screen and evaluate job ...
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The Impact of Technology on the Job Market and How It Is Changing the Way We Work
Technology has had a major impact on the job market in recent years, fundamentally changing the way we work and the skills that are in demand. In this article, we will explore the ways in which technology has affected the job market and how it is continuing to shape the future of work.
One of the most significant impacts of technology on the job market has been the increase in automation and the use of machines to perform tasks that were previously done by humans. This has led to a decrease in the demand for certain types of labour, such as manufacturing jobs, and an increase in the demand for workers with technical skills, such as programming and data analysis.
According to a report from the World Economic Forum, approximately 85 million jobs could be replaced by automation by 2025, while 97 million new jobs could be created in fields such as data analysis and artificial intelligence. This shift is already happening, with many companies replacing manual labor with machines and robots in order to increase efficiency and reduce costs.
However, the impact of automation on employment is not necessarily negative. While some jobs may be replaced, others will be created as a result of increased efficiency and productivity. In addition, automation can also lead to the creation of new industries and business opportunities, as companies seek to develop and implement new technologies.
Technology is also changing the way we work by making it easier to communicate and collaborate with colleagues and clients, regardless of location. The widespread use of video conferencing, messaging apps, and project management tools has made it possible for people to work remotely or from different parts of the world.
According to a survey by the U.S. Census Bureau, between 2019 and 2021, the number of people primarily working from home tripled, from 5.7% (roughly 9 million people) to 17.9% (27.6 million people), according to the new 2021 American Community Survey (ACS). There are similar data and surveys for India as well. According to a survey by the Indian Staffing Federation, the percentage of people who work from home at least part of the time in India has increased from 9% in 2018 to 22% in 2020. This trend is expected to continue in the coming years as more companies adopt flexible work arrangements and remote work becomes more common.
The use of technology in the job market is not limited to automation and remote work. It is also changing the way that job searches and hiring processes are conducted. Online job boards and professional networking platforms, such as LinkedIn, have made it easier for job seekers to find and apply for positions, while employers can use these platforms to search for and recruit candidates.
In addition, technology has made it possible for employers to use artificial intelligence and machine learning algorithms to screen and evaluate job applications, potentially reducing the time and cost of the hiring process.
However, the increasing reliance on technology in the job market has also raised concerns about the potential for job loss and the need for workers to continually update their skills in order to remain competitive. It is important for both individuals and companies to stay up to date on the latest technologies and developments in their field in order to adapt to the changing job market.
Overall, the impact of technology on the job market has been significant, with automation and remote work leading to a shift in the types of jobs that are in demand and the way that we work. While there are potential challenges and concerns to consider, technology is also creating new opportunities and industries, and it is important for individuals and companies to stay current and adapt to these changes in order to succeed in the modern job market.
Other References-
Technology, jobs, and the future of work. (n.d.). McKinsey & Company. https://www.mckinsey.com/featured-insights/employment-and-growth/technology-jobs-and-the-future-of-work
| 2022-12-01T00:00:00 |
https://www.linkedin.com/pulse/impact-technology-job-market-how-changing-way-we-work-thesocialchai
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[
{
"date": "2022/12/01",
"position": 86,
"query": "machine learning job market"
}
] |
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Does AI create more jobs than it destroys? - PALTRON
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Does AI create more jobs than it destroys?
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https://www.paltron.com
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[
"Josef Günthner",
"Co-Founder"
] |
The World Economic Forum (WEF) estimated in 2020 that 12 million more jobs will be created than destroyed by 2025.
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In the dawn of the digital age, one question is at the centre of our collective consciousness - the question of artificial intelligence (AI) and its impact on our world of work. As we stand at the beginning of the fourth industrial revolution, we must now clarify: Does AI create more jobs than it destroys? This is a question humanity has had to ask itself before every technological revolution or innovation phase - and so far it has gone relatively well. The challenge with AI, however, is that this development is not linear, but exponential. And we humans are notoriously bad at thinking exponentially.
As we explore, we will navigate the maze of AI, which will erase traditional roles while creating new ones. A dichotomy as well as a paradox as fascinating as it is worrying. Let's start with the status quo.
The impact of AI in the world of work
Artificial intelligence now permeates every facet of our lives. And in many areas, it has been doing so for longer than we thought: from voice assistants like Siri or Alexa, to chatbots and navigation programmes, to algorithms for predicting market trends or purchasing behaviour—AI is no longer a distant concept, but a longer-standing part of our reality. But with the release of ChatGPT in Nov 2022 (and all subsequent public AI applications), applications of this technology are evolving at an unprecedented pace—and this is creating a rapid wave of disruption that is causing significant disruption in the labour market. In recruiting, too.
The labour market has always followed the currents of technology and innovation, and AI is no exception. Traditional jobs are being automated, tasks are becoming redundant and entire industries are transforming. And this is where applications like ChatGPT, Midjourney, DALL-E and GitHub Copilot come in: copywriting, marketing, programming or graphic design—there is a monumental shift happening in these industries right now. But like any technology, AI is a double-edged sword, destroying old jobs and methods while creating new ones. At this crossroads, it is critical to look at both sides of the coin. Let's look at the dark side first and examine how AI is leading to job destruction.
The Dark Side of AI – Job Destruction
As we make our way through the digital landscape, we encounter the shadow that AI casts on the labour market—the spectre of job destruction. AI-driven automation is replacing human labour across a wide range of industries. From assembly lines in manufacturing to ever-improving chatbots in customer service, AI is proving to be an excellent replacement for tasks that used to be done by humans.
Even white-collar jobs that were once considered immune to automation are faltering. These include call centre work, accounting, clerical, customer service, secretarial and more. Sophisticated algorithms are now able to generate reports, analyse data and make decisions—tasks that used to be the exclusive preserve of skilled professionals. And at this point, this particularly affects middle management, who have to master precisely these activities.
Furthermore, as already mentioned, creative skills such as copywriting, marketing, programming or graphic design are targeted by artificial intelligence and often produce high-quality results in these areas. And we all believed that creativity was humanity's last bastion against artificial intelligence.
The implications are unmistakably profound. As AI continues its inexorable march, the spectre of job destruction looms large, casting a long shadow over the workforce. But is that the whole picture? Not at all. Every shadow also has a source of light in this case, the creation of new jobs in the wake of the AI wave.
The good side of AI: job creation
While threatening security (=destroying jobs) is a primal fear, the reality looks a little rosier: New jobs are being created. And thus more than those that are being eliminated. The World Economic Forum (WEF) estimated in 2020 that 12 million more jobs will be created than destroyed by 2025 (see Fig 2).
German Bank economist Jim Reid also agrees, pointing to our history: “History teaches us, however, that technology does not lead to unemployment”. Figure 1 illustrates that unemployment fluctuates in response to business cycles, not technological waves.
(Fig 1: Unemployment as a function of business cycles, DE Research via BusinessInsider)
Consider the rise of data science, AI ethics or machine learning engineering—professions that didn't even exist a few decades ago. These professions are the result of the AI revolution and require a profound understanding of AI and its applications. As companies increasingly use AI, the demand for skilled workers who can bridge the gap between technology and business strategy is also increasing—further evidence of AI's potential to create jobs.
The emergence of the jobs just mentioned was predictable. But beyond that, AI also creates new jobs indirectly. The automation of routine tasks frees up human workers to focus on more complex, creative and ultimately more fulfilling tasks. This change leads to the creation of jobs that rely on unique human skills such as emotional intelligence, creativity and complex problem solving. And mostly in combination with AI tools.
(Fig 2: New jobs until 2025, WEF)
Goodbye skills shortage?
There is another confident aspect to the shortage of skilled workers, which is reaching frightening records, especially in IT in Germany. Because vacant positions are expensive. Deutsche Bank reckons that AI can close the skills gap. At least in the short term. The use of AI makes programming much easier and thus makes employees much more productive. However, AI in programming is still in its infancy and often produces code that does not work. On the other hand, people like Stability AI CEO Emad Mostaque say: “In five years there will be no (human) programmers”.
In areas such as care and the health system, AI can also help the often overburdened staff and reduce administrative tasks to a minimum. This means that the relief in all professions in demand leads to a reduction in the shortage of skilled workers.
(Fig 3: Occupations with the largest skills gaps, Deutsche Bank)
However, these positive sides of AI are not without challenges, which can also be seen in Figure 2. Let us therefore next address the important question: what skills do people need to succeed in an AI-driven labour market? The answer to this question reveals another facet, one that underscores the importance of adaptability in the age of AI.
Successful in an AI world: The new skill set
On the big chessboard of the AI-driven labour market, people are not mere pawns, but active players endowed with unique skills. And the point is to play these qualities to the full.
In this new world order, technical skills related to AI and data analytics are unsurprisingly of great value. But equally critical now are the “soft” skills—creativity, emotional intelligence, critical thinking and complex problem-solving. These are skills that will remain uniquely human, at least for the foreseeable future, and that are increasingly in demand in an AI-driven world. And according to the WEF, these skills also top the ranking of the most sought-after skills of the future (see Figure 4 below). And this is the big opportunity for many people who are less comfortable with the technical aspects.
(Fig 4: Top 10 skills 2025, WEF)
However, the steep evolution of AI presents another challenge—the need for constant learning and adaptability and resilience. As AI evolves, so must our skills. Lifelong learning, once a noble goal, is now a survival strategy in the AI-driven job market. Moreover, people who do not engage with AI are largely being replaced professionally by those who do.
But what does this mean for society in general? In the next chapter we will look at the impact of AI on the world of work, not only on individuals but also on society.
AI's Ripple Effect: The Societal Impact
The impact of AI goes far beyond the boundaries of the labour market and also affects society as a whole. As AI reshapes the world of work, it also shakes up societal norms and structures.
One of the most critical societal impacts of AI is economic inequality. As AI automates jobs, especially low-skilled jobs, it risks widening the gap between rich and poor. Those with the skills to succeed in an AI-driven labour market could see their wealth increase, while those whose jobs are automated could face unemployment and financial hardship.
It is therefore essential that policymakers take measures to mitigate the negative effects of the introduction of AI. This includes, above all, measures to qualify the workforce and to support employees who lose their jobs due to AI. Topics such as the universal basic income due to AI are also getting new wind.
The future of AI, the workplace and society
We are finding that the universe of AI is not just about jobs. It is a reflection of our society, our values, and our vision for the future.
As we navigate this AI landscape, it is important to remember that we are not just spectators, but active participants. Artificial intelligence is a topic that will encompass all sectors. Policymakers must become active—but above all, we ourselves must also actively embrace change.
This change is uncertain, but nevertheless promises a positive and exciting future. History teaches us that even this technological revolution can be mastered without causing great damage. It is important for us to maintain our adaptability—because this ability has brought us through a good 300,000 years of human history.
| 2022-12-01T00:00:00 |
https://www.paltron.com/insights-en/does-ai-create-more-jobs-than-it-destroys
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[
{
"date": "2022/12/01",
"position": 12,
"query": "AI job creation vs elimination"
},
{
"date": "2023/02/01",
"position": 14,
"query": "AI job creation vs elimination"
},
{
"date": "2023/07/01",
"position": 14,
"query": "AI job creation vs elimination"
},
{
"date": "2023/08/02",
"position": 7,
"query": "AI economic disruption"
},
{
"date": "2023/08/01",
"position": 14,
"query": "AI job creation vs elimination"
},
{
"date": "2023/08/02",
"position": 10,
"query": "AI labor market trends"
},
{
"date": "2023/08/02",
"position": 73,
"query": "AI workforce transformation"
},
{
"date": "2023/08/02",
"position": 7,
"query": "artificial intelligence employment"
},
{
"date": "2023/08/02",
"position": 17,
"query": "future of work AI"
},
{
"date": "2023/08/02",
"position": 42,
"query": "machine learning job market"
},
{
"date": "2023/09/01",
"position": 14,
"query": "AI job creation vs elimination"
},
{
"date": "2024/01/01",
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"query": "AI job creation vs elimination"
},
{
"date": "2024/02/01",
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"query": "AI job creation vs elimination"
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{
"date": "2024/03/01",
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{
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{
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{
"date": "2024/12/01",
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"query": "AI job creation vs elimination"
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{
"date": "2025/01/01",
"position": 14,
"query": "AI job creation vs elimination"
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{
"date": "2025/02/01",
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"query": "AI job creation vs elimination"
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{
"date": "2025/05/01",
"position": 14,
"query": "AI job creation vs elimination"
},
{
"date": "2025/06/01",
"position": 13,
"query": "AI job creation vs elimination"
}
] |
|
Analysts Estimate Fewer Jobs and More Tech in Tomorrow's Job ...
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Analysts Estimate Fewer Jobs and More Tech in Tomorrow’s Job Market
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https://ai.emory.edu
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[] |
Researchers say Covid-19 has accelerated a push to automate tasks and eliminate jobs, a trend that favors new technologies and a technical workforce.
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Researchers say Covid-19 has accelerated a push to automate tasks and eliminate jobs, a trend that favors new technologies and a technical workforce.
For many Americans, 2021 has been about rebuilding and preparing for a post-pandemic world. There is a zealousness to recover old jobs, start new businesses, and go back to work. Yet as America readies itself for its big return, analysts fear scarce job prospects due to a surge of automation and artificial intelligence.
The pandemic’s push to limit human interaction has compelled whole industries to reconfigure how they do business, a drive that has raised the demand for machine automation, AI, and online services.
In Northern California, the California Department of Transportation laid off roughly 250 Bay Area toll workers permanently as it transitioned to digital toll collection. Walmart and Sam’s Club expanded the use of robotic floor cleaners and inventory scanners. There are also a wide array of examples of robots acting as essential workers, monitoring patients, sanitizing hospitals, patrolling facilities, making deliveries, and aiding healthcare providers.
AI is contributing to this worker displacement. In recent years, the technology has gradually—and often inconspicuously—replaced the jobs of office receptionists, telemarketers, research analysts, cashiers, proofreaders, and retail clerks.
As companies downsized and restructured during the pandemic, researchers report an aggressive demand for technology and opposition to human labor. For instance, out of the roughly 40 million jobs lost during the pandemic, 32-42 percent will not return, according to an estimation from the Becker Friedman Institute at the University of Chicago.
Further, in its own analysis, McKinsey & Company projects roughly half of the tasks performed by today’s workforce can be automated, meaning certain jobs won’t necessarily disappear but may require far fewer people to fill them.
The findings prompt serious and critical questions about the evolving job market and how new tech, labor policies, and industry trends will impact America’s workforce in the coming years.
Equally important, education will be a critical component to overcoming obstacles and adapting to new technologies. Those entering or already in the workforce will be compelled to explore traditional and non-traditional education, degrees, and contemporary certificated courses.
Short-term displacement and instability
Unfortunately, while advances in AI, machine learning, and automation are expected to increase productivity and even seed new industries and jobs, it’s unclear if these inventions will benefit workers in the short term.
If history follows course, experts fear the immediate effects on the American workforce will be negative. The Economic Policy Institute reports that from 1979 to 2019, the nation’s net productivity rose 72.2 percent, while wages for middle to entry-level workers rose a mere 17.2 percent—adjusted for inflation. Researchers forecast a larger wealth gap between the rich and poor and a profusion of low-wage jobs if businesses continue to prioritize shareholder profits over employee prosperity.
“The risk is that automation could exacerbate wage polarization, income inequality, and the lack of income advancement that has characterized the past decade across advanced economies, stoking social and political tensions,” McKinsey researchers wrote.
Another catalyst for AI-based job displacement is found in the U.S. tax system. The federal government taxes employers for each employee via payroll taxes, and at the same time, allows companies to expense machinery and software purchases. The tax breaks come through Trump Administration’s 2017 Tax Cuts and Jobs Act.
Daron Acemoglu, an MIT economist specializing in automation and workforce analysis, said the tech world has only accelerated this trend with automated systems and small workforces.
“The US tax code aggressively subsidizes the use of equipment and taxes the employment of labor,” Acemoglu said in the Journal of Economic Perspectives. “A tendency towards further—and potentially excessive—automation may have been reinforced by the growing focus on automation and use of artificial intelligence for removing the human element from most of the production process.”
A dearth of investment in education and job skills development is a third major stumbling block for workers. In McKinsey’s research, professions least likely to be impacted by AI and automation were those that required a high level of education and skill. For many Americans, entering or transitioning to such professions may be a challenging task.
Even if workers have the mental capacity and fit for such professions—McKinsey spotlighting science, technology, engineering, and mathematics (STEM) careers—they may not have the funding to make the leap. Costs for education have continued to climb, and the U.S. spends less than half of what it did in the 90s for job transition programs.
Adapting to an Automated Workplace
Despite troubling forecasts and the hardships inherent in systemic change, researchers also see the potential for adaption. There will be opportunities for policy shifts, alternative pathways for job transition, and fresh job demand in yet-to-be-imagined industries and careers. These new options include technical education, certificate courses, coding boot camps, and other novel and affordable educational programs to help workers find employment.
The goal in the next decade will be bridging the skills gap to these new careers. By 2030 McKinsey estimated top job growth will come from technology-based careers. There will be great demand for professions with a high degree of social and emotional skill sets and careers that require higher-level cognitive skills.
In contrast, McKinsey predicts that by 2030 jobs centered around physical and manual tasks will contract sharpest along with jobs comprised of lower-level cognitive skills such as data-entry clerks and cashiers.
“Occupations made up of physical activities in highly structured environments or in data processing or collection will see declines,” McKinsey researchers said. “Growing occupations will include those with difficult to automate activities—such as managers—and those in unpredictable physical environments such as plumbers. Other occupations that will see increasing demand for work include teachers, nursing aides, and tech and other professionals.”
More than anything, researchers agree perennial learning will be a necessity to ensure workers keep pace with the advance in technology. An aptitude with AI assistants and robotic tools may be the norm. McKinsey said this would likely mean dealing with changes in traditional workflows and the supervision and management of autonomous and automated devices.
“As self-checkout machines are introduced in stores, for example, cashiers can become checkout assistance helpers, who can help answer questions or troubleshoot the machines,” McKinsey said. “More system-level solutions will prompt a rethinking of the entire workflow and workspace.”
The World Economic Forum predicted by 2025, more than 50 percent of all professions will need reskilling or upskilling to accommodate advances in technology.
“Increased digitization resulting from COVID-19 may accelerate this trend [in automation]. By the mid-2030s, as AI advances and becomes more autonomous, 30 percent of jobs and 44 percent of workers with low levels of education will be at risk of automation.” WEF researchers said. “In the next five years, half of all workers will require some upskilling or reskilling to prepare for changing and new jobs.”
Predictions aside, researchers say what comes next will hinge on both public sector and private sector policy decisions. These decisions will impact education, social safety net programs, labor-based tax incentives, investments in human capital, and moves to promote social mobility.
“It’s not all doom and gloom,” said MIT Economist Daron Acemoglu. “There is nothing that says technology is all bad for workers. It is the choice we make about the direction to develop technology that is critical.”
Emory University Academic Innovation works with changemakers across the university to explore new ideas, to learn new skills and ways of working, and to harness the power of innovation and entrepreneurship.
| 2022-12-01T00:00:00 |
https://ai.emory.edu/articles/future-work-automation.html
|
[
{
"date": "2022/12/01",
"position": 34,
"query": "AI job creation vs elimination"
},
{
"date": "2023/02/01",
"position": 35,
"query": "AI job creation vs elimination"
},
{
"date": "2023/04/01",
"position": 11,
"query": "automation job displacement"
},
{
"date": "2023/07/01",
"position": 34,
"query": "AI job creation vs elimination"
},
{
"date": "2023/08/01",
"position": 38,
"query": "AI job creation vs elimination"
},
{
"date": "2023/09/01",
"position": 37,
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|
AI, Automation and New Jobs - Scientific Research Publishing
|
AI, Automation and New Jobs
|
https://www.scirp.org
|
[
"Jaures Badet",
"Scientific Research Publishing"
] |
Therefore, the main new feature of our framework is that, in addition to the part of jobs that are displaced by automation, it also leads to the creation of new ...
|
AI, Automation and New Jobs ()
Our study analyzes the advantages that automation presents for the job. The main new feature of our framework is that, in addition to the part of jobs that are displaced by automation, it also leads to the creation of new, more complex versions of existing tasks, which leads to the demand for employment. We focused more on the essential factor which is the degree of skill to take advantage of these new jobs. We carry out research based on information relating to automation and jobs. Also, by using the output of the final good model, we show that the creation of new tasks in which the labor has a comparative advantage is one of the positive aspects of automation. We find that automation will create new jobs (smart jobs) and eliminates repetitive jobs which will be replaced by machines in the future. However, these new jobs will need high skills. Therefore, the level and quality of education will play important role in the new jobs that automation will generate. Workers and future students must prepare themselves by focusing their training more on the skills that new technologies will require. Automation may prepare us for a future in which workers with low skills will be forced to change occupations or lose their occupations, which will be completely occupied by machines. We find also that the job loss depends on the speed of automation in each country. Based on the economic structure, the investment policy in new technology, and the level of education of countries, the speed at which automation spread is slower in some countries and intense in others. Therefore, the job is more at risk in countries with high automation than in those with medium or low automation.
1. Introduction
An intense debate has intensified for years around the question of the future of work with the advent of machines. This debate has taken on a little more momentum these days especially in the period of the COVİD 19 pandemic when the use of technology has become essential to face the health crisis. There are many questions about automation and its impact on work. Are we going to see an era where humans will be sidelined in industries to the detriment of machines? What is the future of employment with machines? What will be the role of automation in industries? What is the place of humans in the automation system? One thing is certain: today production in most industries requires the simultaneous completion of a series of tasks. Some difficult and complex tasks are even beyond human skill and therefore require the assistance of machines. The majority of these tasks are therefore performed either with machines or with a combination of man and machines. The real question is if the tasks will be displaced definitively by machines or not. Most of the debates and works have been centered on the fact that many jobs will be replaced in the industry by machines and in the same way, automation will create the advent of new jobs. For example, according to Manyika and Sneader (2018), around 15 percent of the global workforce, or around 400 million workers, could be displaced by automation during the period 2016-2030. According to the same authors, the demand for labor due to automation until 2030 would be between 21% and 33% of the global workforce or 555 million and 890 million jobs. This demand for labor can largely compensate for the number of jobs lost caused by automation. They also support the idea that all occupations can’t be affected by automation. Only about 5% of occupations could be fully automated by technology. About 30 percent of the activities in 60 percent of all professions could be automated. In addition, automation affects less-educated workers and employees in less educated jobs. The displacement of human labor by automation will create a displacement effect and reduce labor demand. But this displacement effect can be counterbalanced by other economic factors like productivity, capital accumulation, the deepening of automation, and the creation of new tasks. Those factors lead to an increase in labor demand. Moreover, automation will lead to the decline in the share of labor in national income, but at the same time by the creation of new tasks, this negative effect will be offset. New tasks increase the demand for labor and tend to raise the labor share (Acemoglu & Restrepo, 2018). Automation causes jobs to be lost in some industries and jobs to increase in others (Bessen, 2017). According to Zinser et al. (2015), a forecast made by a global consulting group, the share of tasks performed by robots in all manufacturing industries today will increase by 15 percent by 2025 (from a global average of around 10% to around 25%). Acemoglu and Restrepo (2016) think that, in a static version where the capital is fixed and technology is exogenous, automation leads to a reduction in employment and the share of labor, but the creation of new tasks generated by this automation causes an increase of labor demand.
Other authors argue that not all jobs can be automated despite the advent of machines. According to Arntz et al. (2016), many workers specialize in tasks that cannot be automated easily, which doesn’t put in danger the employment market too much. Other studies point out that automation mainly affects jobs where workers have low skill levels. According to a European survey about skills and employment from Cedefop, “around 14% of jobs in the EU are at risk of displacement by computer algorithms. The jobs most likely to be affected are those which depend more on routine tasks and which require few transversals and interpersonal skills” (CEDEFOP, n.d.). In addition, automation will affect low-skilled workers more than skilled workers (Arntz et al., 2016). Graetz and Michaels (2015) argue also that the use of robots in industries reduces the employment share of low-skilled workers rather than total employment. The threat of automation on jobs is less and heterogeneous. It varies depending on the economic policy, the level of education, and the investment of each country in new technology. Acemoglu and Restrepo (2017) and Chiacchio et al. (2018) underline the fact that automation affect wages and employment in two ways: The direct displacement of workers from the tasks they previously performed creates displacement effect which negatively affects employment and the increase in demand for labor by industries which creates productivity effect which positively affects wages and employment.
As we saw, many points are often covered in studies about automation and employment. For example, the non-automation of all sectors of industry, the advent of new jobs caused by high automation, the collaboration between robots and humans, and the displacement of certain jobs by machines. The second point, that of the advent of new jobs caused by automation, is discussed more in our study. Our study is therefore more focused on the advantages that automation presents for the job. The dimension of displacement of jobs by automation is not discussed too much in this study. We do so for a reason: The predictions of automation causing massive job losses are not too much in line with reality. According to some reports, automation will even create more jobs than it will displace. Therefore, the main new feature of our framework is that, in addition to the part of jobs that are displaced by automation, it also leads to the creation of new, more complex versions of existing tasks, which leads to the demand for employment. We focused more on the essential factor which is the degree of skill to take advantage of these new jobs.
In this study, Section 2 presents the relevant literature on the impact of AI and automation on jobs. Section 3 focuses on automation and new jobs; especially this section sheds light on the creation of new jobs by automation. In Section 4 the relationship between skills, technologies, and jobs is discussed. In Section 5, how automation leads to the creation of new jobs is shown by using the output of the final good model used by Acemoglu and Restrepo (2016) and Zeira (1998). Finally, in Section 6 a discussion on AI, automation, and new jobs were conducted.
2. Relevant Literature
In this section, the relevant literature on the impact of AI and automation on jobs is surveyed. Acemoglu and Restrepo (2017) analyze the effect of the increase in industrial robot usage between 1990 and 2007 on US local labor markets. They show that robots may reduce employment and wages and that the local labor market effects of robots can be estimated by regressing the change in employment and wages on the exposure to robots in each local labor market-defined from the national penetration of robots into each industry and the local distribution of employment across industries. They find that one more robot per thousand workers reduces the employment to population ratio by about 0.18 - 0.34 percentage points and wages by 0.25 - 0.5 percent. Frey and Osborne (2017) examine the expected impacts of future computerization on labor market outcomes by implementing a novel methodology to estimate the probability of computerization for 702 detailed occupations. They find that around 47% of total US employment is at high risk in the future. According to them, most occupations such as transportation and logistics, office and administrative support occupations, and jobs in production industries are at risk. Dauth et al. (2017) analyze the impact of rising robot exposure on the careers of individual manufacturing workers, and the equilibrium impact across industries and local labor markets in Germany. They find that robots do not cause total job losses but affect the composition of overall employment. Robots decrease overall employment in Germany by nearly 23 percent in the period 1994-2014. This represents approximately 275.000 jobs. They also find that this loss is fully offset by additional jobs in the service sector. Berriman and Hawksworth (2017) conclude that almost 30% of jobs in the UK could be automated in the early 2030s. According to them, education will play an important role in this automation process. In the UK, automation affects more jobs requiring a lower level of education (46%) than those requiring a high level of education. According to the same authors, automation will create new jobs in the field of digitalization. Chiacchio et al. (2018), by studying the impact of industrial robots on employment and wages in six European Union countries, which make up 85.5 percent of the EU industrial robots market, find that robot per thousand workers reduces the employment rate by 0.16 - 0.20 percentage points. They also find that the displacement effect is particularly evident for workers of middle education and young cohorts, while men are more affected than women are.
3. Automation and New Jobs
The example of Britain with new industries and advent of new jobs as engineers, machinists in 19th century and an America with the mechanization of agriculture at the beginning of 20th century has shown that the intensive automation leads to the emergence of new jobs and news tasks in the industries. The majority of work on automation and job claim that automation will eliminate a lot of jobs but also create a lot of new ones. According to Acemoglu and Restrepo (2018), automation will harm the demand for labor but at the same time, will leads to the creation of new jobs and new tasks. Atkinson and Wu (2017) also support the idea that technology not only eliminates jobs but creates them as well. The emergence of technology may eliminate certain professions, leaving room for other more productive professions. Likewise, according to Manyika and Sneader (2018), automation will result in more job creation than job loss (21% and 33% of the global workforce, or 555 million and 890 million jobs until 2030). According to Gartner (2017), “AI will create 2.3 million jobs in 2020 while eliminating 1.8 Million”, an estimated number that exceeds the number of jobs that are eliminated by the AI in 2020. Chowdhry (2018) also supports the idea that “the growth of artificial intelligence could create 58 million net new jobs in the next few years”.
Thus, we can conclude that automation will displace many jobs especially the jobs which require a low level of education and/or skill will be easily automated. However, it will create some new jobs (smart jobs), which require high skills. For example, according to studies, in the future, we will no longer need a taxi driver to move anywhere we want thanks to autonomous cars1. The advent of autonomous vehicles in the future is almost inevitable. So what is its impact on taxi drivers? We believe that the advent of autonomous vehicles will generate two situations: The collaboration between taxi drivers and these vehicles and/or the definitive replacement of these jobs by vehicles creating new jobs, which are not identical to the old ones but more complex. Autonomous vehicles can navigate on their own without human intervention but cannot do these kinds of things humans do. For example, drivers repair vehicles or bring them in for repairs when they break down on the way. The job of taxi driving will not go away completely but automation at this level will help taxi drivers do their jobs more easily and better. We may assist in a collaboration between the AI of these cars and the taxi drivers. It is also important to note that automation at this level will require a high degree of skill. Therefore, taxi drivers must requalify their skill level to know how to manage and use these vehicles. Furthermore, if autonomous vehicles replace taxi drivers, it will lead to the creation of other jobs, which may be more complex. Companies will need, for example, engineers, technicians, software developers, and designers to build and manage these autonomous vehicles. These vehicles will break down sometimes and will need humans to repair them. The advent of autonomous vehicles may not benefit taxi drivers if they replace them but will allow other employees with high skills to find work. In any case, the workforce will have an advantage over the machines.
4. Skill, Technologies and New Jobs
As it is said in our framework in the previous sections, intensive automation will lead to new tasks and new jobs. But these new jobs need new skills. So the use of efficient machines in industries will require workers who will be able to acquire new skills. According to Mckinsey Global Institute (2017), in developing countries, the rate of employment growth is highest for occupations that require a college diploma or higher. In China, for example, there is a high demand for occupations currently requiring university degrees and above. At the same time, nearly 60 million jobs currently require a high school diploma. Moreover, even with the effect of automation, in India, the demand for new employees requiring high school education is nearly 100 million (Mckinsey Global Institute, 2017: p. 85). Automation requires higher skill requirements. The acquisition of more digital skills and the complementarity of key skills are essential in the adaptation of individuals to digital change and automation (CEDEFOP, n.d.). Thus, the industry will need highly qualified professionals whose talents will be in great demand and will be able to train other workers to better master and better adapt to the new tasks that automation will generate. By 2022, 54% of employees will need to learn new skills to meet the expectations of the new tasks created by automation. Thus 35% of these workers will need at least training for six months, training for more than 6 years will be necessary for 9% of workers and 10% will need training for more than one year (World Economic Forum, 2018). But we often see a mismatch between skills and technologies because workers need to master the use of new technologies in industries but they often do not possess those skills to do those tasks. These new skills often require a high educational or experimental capacity, which workers in most situations do not have. Most of the time it is also difficult for employers to find workers that can master the new jobs and tasks induce by the intensive automation (Deloitte & the Manufacturing Institute, 2011).
To summarize, jobs displaced by automation require less skill than new jobs generated by the latter. The new jobs require high skills, which will depend on the quality of the educational system of each country. Much of the employment in the future created by automation will require high education levels. In this context, jobs requiring less educational requirements are in danger and at the same time, the demand for jobs requiring educational capacities or higher skills increases. The education system plays a decisive role in the new jobs that will lead to automation. The quality of skill depends on the quality of the educational system. If the educational system is not up to the skill requirements that new jobs require, we often see a mismatch between skills and technologies.
5. Theoretical Model
In our model, we analyze the effect of automation and AI on Jobs by considering two type of economy. Firstly, we suggest that in a technologically stagnant economy, all tasks are produced by human. Therefore, jobs are not in danger in this economy. Second, in a technologically advanced economy where most sectors are automated, automation will obviously cause the loss of many jobs. However, these jobs will be more the jobs with repetitive tasks, which do not push the workers to raise their level of thinking. At the same time, it will create new, more complex jobs, which will cause workers to think more, to have more creativity in the execution of tasks. In other words, these new jobs will need employees who are able to create new ideas and knowledge useful for the business, think quickly and smartly to solve complex problems, and who have the ability to adapt to the new technology that is in prospect change. Therefore, high skill due to high level and quality of education (only both makes qualified workers) will play an essential role in the “smart jobs” that automation will create. To evince our hypothesis, we use the output of the final good model used by Acemoglu and Restrepo (2016) and Zeira (1998). According to these authors, the output of the final good is given by:
Y = ( ∫ N − 1 N y i φ − 1 φ ) φ φ − 1 (1)
Final good Y is produced by combining a continuum of tasks y i where y i ∈ [ N − 1 ; N ] and φ denotes the elasticity of substitution between tasks.
By assuming that the range of tasks is between N− 1 and N, the creation of a new task in N corresponds to the replacement of an existing task in N − 1. The production of each task requires a combination of labor or capital. In industries, not all tasks can be produced by labor. Another task requires the help of machines (capital). Therefore, we have the automated tasks, which are produced by labor with the help of machine, and the non-automated tasks executed only by labor. If there exists I ∈ [ N − 1 , N ] such that
i ≤ I automatedtasks i > I nonautometedtasks (2)
Note that here i ≤ I is the automated tasks which are produced by labor or capital as well.
If we assume that the tasks are executed by labor or machine with a specific intermediate task q ( i ) (the technology used both for production and for the possible automation of tasks), we have:
y ( i ) = B [ β q ( i ) φ − 1 φ + ( 1 − β ) ( γ K ( i ) k ( i ) + γ L ( i ) l ( i ) ) φ − 1 φ ] φ φ − 1 for i ≤ I y ( i ) = B [ β q ( i ) φ − 1 φ + ( 1 − β ) ( γ L ( i ) l ( i ) ) φ − 1 φ ] φ φ − 1 for i > I (3)
where γ L ( i ) is the productivity of labor in task i, γ K ( i ) the productivity of capital (machine), l ( i ) denotes tasks that can be produced by human labor and k ( i ) the one that will be produced by machine. φ ∈ ( 0 ; ∞ ) and represents the elasticity of substitution between intermediates and labor, β ∈ ( 0 ; 1 ) represents the distribution parameter of this constant elasticity of substitution production function. B represents a normalizing constant and B ≡ ( 1 − β ) φ 1 − φ to simplify the algebra.
y ( i ) = ( 1 − β ) φ 1 − φ [ β q ( i ) φ − 1 φ + ( 1 − β ) ( γ K ( i ) k ( i ) + γ L ( i ) l ( i ) ) φ − 1 φ ] φ φ − 1 for i ≤ I y ( i ) = ( 1 − β ) φ 1 − φ [ β q ( i ) φ − 1 φ + ( 1 − β ) ( γ L ( i ) l ( i ) ) φ − 1 φ ] φ φ − 1 for i > I (4)
If we assume that β → 0 (the share of revenues going to intermediates is very low)2:
y ( i ) = 1 φ 1 − φ [ ( γ K ( i ) k ( i ) + γ L ( i ) l ( i ) ) φ − 1 φ ] φ φ − 1 for i ≤ I y ( i ) = 1 φ 1 − φ [ ( γ L ( i ) l ( i ) ) φ − 1 φ ] φ φ − 1 for i > I (5)
∀ φ ∈ ( 0 ; ∞ ) , ( 1 ) φ 1 − φ = 1 .
So:
y ( i ) = γ L ( i ) l ( i ) + γ K ( i ) k ( i ) for i ≤ I (a) y ( i ) = γ L ( i ) l ( i ) for i > I (b) (6)
Case (a) is currently restricted to a group of countries. This case is more observed in high-income countries and some middle-income countries. Case (b) is more frequent in industries of low-income, and certain middle-income countries. However, this case is also observable in high-income countries because even in these countries not all sectors can be automated.
So, in a technologically stagnant economy, all tasks are produced by labor.
y ( i ) = γ L ( i ) l ( i ) (7)
Jobs are not in danger since automation is not evolved in these economies. Even employees with low skills are not exposed to the risk of job loss caused by automation since there is no task produced by machines.
In a technologically advanced economy, tasks are executed totally by human in some sectors and by the humans and machines in others:
y ( i ) = γ L ( i ) l ( i ) + γ K ( i ) k ( i ) y ( i ) = γ L ( i ) l ( i ) (8)
In such an economy, the non-automated sector employees are not exposed to the risk of job loss. In automated sector, an intensive increase inI, leads to lower labor costs (l) and the creation of new tasks (more complex) generated by the use of AI and other advanced technologies in automation. The new tasks lead to an increase in the demand for labor. If γ L ( i ) increases strictly in i, labor has a comparative advantage in higher-indexed tasks3. Even in automated sector, the loss of ancient jobs leads to the creation of new ones that are more complex and will need a high skill. As is discussed in Section 3 of our work, the creation of new tasks in which the labor has a comparative advantage is one of the positive aspects of automation. Even though capital accumulation and deepening automation are important factors in increasing the labor share of national income, the creation of new tasks in which labor has a comparative advantage remains the most important aspect preventing the decline in the share of national income (Acemoglu & Restrepo, 2018). Therefore, in any case, the workforce will have an advantage over the machines. Furthermore, as we saw in the model, not all sectors can be automated. The speed at which automation spreads depends on the policy and investment in innovation and adoption of technology in each country and each company. For example, the speed of automation in The USA is not the same in most African countries because the goals of innovation and adoption of new technology are not the same in all countries. Therefore, jobs in automated industries are at a higher risk of displacement than those in non-automated or less automated industries. In addition, the quality of education especially higher education will also be important in the process of automation. The companies use advanced technologies like AI and others in automation process. The use of these technologies will need high and qualified skills in digitalization. Therefore, intensive automation will lead to the replacement of low-level jobs and the creation of new jobs that require high skill levels.
6. Conclusion
Many questions and debates intensify on the future of jobs with automation especially in the period of COVID-19 where we have seen the importance of new technology in the management of the health crisis. Different authors have different opinions on the future of employment with automation. However, most of the work on this issue supports the fact that automation will replace some jobs in the industry but at the same time create others as well. Our study is focused on the advantages that automation presents for the job. The dimension of displacement of jobs by automation is not discussed too much in this study. We do so for a reason: the predictions of AI causing massive job losses are not too much in line with reality. According to some reports, automation will even create more jobs than it will displace. Therefore, the main new feature of our framework is that, in addition to the part of jobs that are displaced by automation, it also leads to the creation of new, more complex versions of existing tasks, which leads to the demand for employment. We focused more on the essential factor which is the degree of skill to take advantage of these new jobs.
Firstly, we find that based on the economic structure, the investment policy of countries in new technology, and also the quality of education, the effect of automation on employment varies from country to country. The speed of automation is slower in some countries and very intense in others. Therefore, the job is more at risk in countries with high automation than in those with medium or low automation. In other words, the influence of automation on the job in technologically advanced countries is different from that in technologically stagnant countries.
Second, even if machines are more productive than humans are, they cannot do everything in companies. Certain jobs are properly reserved only for human capacity. So not all jobs can be automated in the industry. We may see low demand for labor in the automated sectors but at the same time a strong demand in the non-automated sectors. In addition, jobs with a low skill level are more at risk than those with a high skill level. In another word, jobs consisting of repetitive tasks do not require a high level of education. These jobs are more vulnerable to automation than those that require more thinking and more creativity (High skill). Therefore, in this dynamism of automation, the job of skilled workers is more secure than that of low-skilled workers.
Thirdly, automation will create maybe more jobs than it eliminates in the future. It will replace many jobs especially the jobs with low skill but at the same time as we said in our study, it will create many new jobs, which will need a high skill. Automation is a step forward, an important and necessary revolution for industries in increasing productivity and competitiveness. Not something, that should normally be scary. The key to the jobs that automation will create is high and digital skills. Thus, higher education and training oriented toward digitalization and coding will play a very important role in the new jobs. The future jobs will not be for everybody. To know how to create or manage sophisticated technologies, workers need to get high skills. Therefore, we need to change the way we give or take training. The educative system must be more valued and more oriented to digital skills and coding. Education in the future should be more focused on knowledge of new technologies. Automation is not a threat to us. It is an advantage but to benefit from this automation, workers and future students must prepare themselves by focusing their training more on the skills that new technologies will require. Automation may prepare us for a future in which workers with low skills or simple tasks will be forced to change occupations or lose their occupations, which will be completely occupied by machines. To successfully perform their new tasks, workers will need to acquire the necessary skills. They will therefore need training, so the duration will vary depending on the type of task and the type of skill sought. In any case, automation leaves us many job opportunities. The important thing is to know how to orient the new knowledge to be able to adapt it to the requirements of automation.
NOTES
1The advent of autonomous vehicles will not only have an impact on taxi drivers but many other areas. In this section of our study, we just approached the impact of the advent of autonomous vehicles on taxi drivers. The aim is to show the advantage of this new technology on this job.
2 Acemoglu and Restrepo (2016) use the same assumption in their work about “The Race between Man and Machine: Implications of Technology for Growth, Factor Shares and Employment’ page 8.
3Our theoretical framework builds on Acemoglu and Restrepo (2016) extends Zeira (1998) who develop a model where firms produce intermediates using labor-intensive or capital-intensive technologies.
| 2021-08-09T00:00:00 |
2021/08/09
|
https://www.scirp.org/journal/paperinformation?paperid=112070
|
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"query": "AI job creation vs elimination"
},
{
"date": "2025/01/01",
"position": 67,
"query": "automation job displacement"
},
{
"date": "2025/02/01",
"position": 42,
"query": "AI job creation vs elimination"
},
{
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"position": 44,
"query": "AI job creation vs elimination"
},
{
"date": "2025/06/01",
"position": 44,
"query": "AI job creation vs elimination"
}
] |
AI will wipe out jobs: CEOs start saying the quiet part out loud
|
AI will wipe out jobs: CEOs start saying the quiet part out loud
|
https://www.team-bhp.com
|
[] |
For sure, AI will automate a lot of jobs, and a lot of job positions being held today by humans would be replaced by AI. And, hence , companies ...
|
Jeroen Distinguished - BHPian
Join Date: Oct 2012 Location: Delhi Posts: 9,100 Thanked: 64,529 Times
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re: AI will wipe out jobs: CEOs start saying the quiet part out loud Quote: kosjam Originally Posted by Will AI eat up the repetitive and routine jobs?.
CEOs that make such claims should be called by their supervisory board why such tasks were not automated before!
AI has the potential to automate all kinds on more non repetitive and non routine and none mondaine tasks.
To what extent that will be achieved, be feasible and more to the point, desirable remains to be seen.
It will certainly have a big impact on many different industries and society as a whole.
We have witnessed the introduction of other technological solutions. In all cases there were strong doubt about we should introduce it, potential lay-offs, moral dilemmas and so on.
Although lots of folks have lost their jobs since the Industrial Revolution due to all kind of automation, not just software, in most cases you could argue that the end result was generally better than before. Of course, if you were a firemen on a steam locomotive and lost your job due to steam being replaced by electric and diesel locomotives, that is a tough call. So is self scan check outs at supermarkets instead of cashiers.
I would say the main difference today with AI compared to previous introductions of new technologies is this; it’s pushed by a handful of billionaires and or wannabe billionaires. Very few countries have appropriate legislation and oversight in place. Most regulators and politicians are way beyond the curve. To some extend that is normal. But I think potential impact of AI on our society as a whole is much bigger than anything previously seen. So appropriate oversight, healthy public debate on the use of AI and its limitations are crucial. Very little is happening. Companies are self regulating, e.g. newspapers to the extent how much AI can be used to write articles, produce photographs and so on.
I see very little public debate in Europe and less so in the USA. Very few politicians have an understanding of the ramifications of AI. The EU has of course its various digital acts, even so probably too little too late.
Jeroen Well, if it is repetitive and routine you don’t need AI to automate it all. Regular software and process automation is more than capable of dealing with anything repetitive and routine.CEOs that make such claims should be called by their supervisory board why such tasks were not automated before!AI has the potential to automate all kinds on more non repetitive and non routine and none mondaine tasks.To what extent that will be achieved, be feasible and more to the point, desirable remains to be seen.It will certainly have a big impact on many different industries and society as a whole.We have witnessed the introduction of other technological solutions. In all cases there were strong doubt about we should introduce it, potential lay-offs, moral dilemmas and so on.Although lots of folks have lost their jobs since the Industrial Revolution due to all kind of automation, not just software, in most cases you could argue that the end result was generally better than before. Of course, if you were a firemen on a steam locomotive and lost your job due to steam being replaced by electric and diesel locomotives, that is a tough call. So is self scan check outs at supermarkets instead of cashiers.I would say the main difference today with AI compared to previous introductions of new technologies is this; it’s pushed by a handful of billionaires and or wannabe billionaires. Very few countries have appropriate legislation and oversight in place. Most regulators and politicians are way beyond the curve. To some extend that is normal. But I think potential impact of AI on our society as a whole is much bigger than anything previously seen. So appropriate oversight, healthy public debate on the use of AI and its limitations are crucial. Very little is happening. Companies are self regulating, e.g. newspapers to the extent how much AI can be used to write articles, produce photographs and so on.I see very little public debate in Europe and less so in the USA. Very few politicians have an understanding of the ramifications of AI. The EU has of course its various digital acts, even so probably too little too late.Jeroen
| 2022-12-01T00:00:00 |
https://www.team-bhp.com/forum/shifting-gears/296318-ai-will-wipe-out-jobs-ceos-start-saying-quiet-part-out-loud.html
|
[
{
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"query": "AI job creation vs elimination"
},
{
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"query": "AI replacing workers"
},
{
"date": "2023/02/01",
"position": 52,
"query": "AI job creation vs elimination"
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},
{
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"query": "AI replacing workers"
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{
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"query": "AI job losses"
},
{
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"position": 48,
"query": "AI replacing workers"
},
{
"date": "2023/07/01",
"position": 50,
"query": "AI impact jobs"
},
{
"date": "2023/07/01",
"position": 55,
"query": "AI job creation vs elimination"
},
{
"date": "2023/08/01",
"position": 49,
"query": "AI impact jobs"
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{
"date": "2023/08/01",
"position": 45,
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{
"date": "2023/09/01",
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"query": "AI replacing workers"
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{
"date": "2023/10/01",
"position": 49,
"query": "AI replacing workers"
},
{
"date": "2023/10/01",
"position": 98,
"query": "universal basic income AI"
},
{
"date": "2023/11/01",
"position": 46,
"query": "AI replacing workers"
},
{
"date": "2023/12/01",
"position": 46,
"query": "AI replacing workers"
},
{
"date": "2024/01/01",
"position": 29,
"query": "AI replacing workers"
},
{
"date": "2024/02/01",
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"query": "AI replacing workers"
},
{
"date": "2024/03/01",
"position": 42,
"query": "AI replacing workers"
},
{
"date": "2024/03/01",
"position": 98,
"query": "universal basic income AI"
},
{
"date": "2024/04/01",
"position": 16,
"query": "AI job losses"
},
{
"date": "2024/04/01",
"position": 42,
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},
{
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"query": "AI replacing workers"
},
{
"date": "2024/06/01",
"position": 55,
"query": "AI job creation vs elimination"
},
{
"date": "2024/06/01",
"position": 46,
"query": "AI replacing workers"
},
{
"date": "2024/06/01",
"position": 97,
"query": "universal basic income AI"
},
{
"date": "2024/07/01",
"position": 48,
"query": "AI replacing workers"
},
{
"date": "2024/08/01",
"position": 95,
"query": "universal basic income AI"
},
{
"date": "2024/10/01",
"position": 44,
"query": "AI replacing workers"
},
{
"date": "2024/11/01",
"position": 49,
"query": "AI replacing workers"
},
{
"date": "2024/12/01",
"position": 45,
"query": "AI replacing workers"
},
{
"date": "2025/01/01",
"position": 54,
"query": "AI job creation vs elimination"
},
{
"date": "2025/01/01",
"position": 45,
"query": "AI replacing workers"
}
] |
|
Deeper Looks: The Future of Work - DNI.gov
|
Office of the Director of National Intelligence
|
https://www.dni.gov
|
[
"Odni - Nic"
] |
In the coming years, AI is likely to be applied to higher skill tasks, eliminating some jobs while significantly increasing the productivity of those workers ...
|
DEEPER LOOKS Technology and the Future of Work This paper was produced by the National Intelligence Council’s Strategic Futures Group in consultation with outside experts and Intelligence Community analysts to help inform the integrated Global Trends product, which published in March 2021. However, the analysis does not reflect official US Government policy, the breadth of intelligence sources, or the full range of perspectives within the US Intelligence Community. During the next two decades, technological innovations—including automation, online collaboration tools, artificial intelligence, and additive manufacturing—will reshape some fundamental aspects of how and where people work. The future workplace is likely to be increasingly flexible but also increasingly insecure as companies demand new skill sets while no longer providing employees with traditional benefits. A key uncertainty is whether the labor force will adjust quickly enough to meet the demands of the new working world. Scholars agree that although technological innovations will eliminate many jobs, they will also create new ones as firms shift labor into complementary tasks. However, the skills required and the locations of these jobs may not match the capabilities of the labor force—putting pressure on already stretched governments to help labor markets manage these new conditions.
TECHNOLOGY DRIVING WORKPLACE CHANGES
New technologies are reshaping the workplace through automation, online collaboration tools, artificial intelligence, and additive manufacturing. Tasks that once seemed uniquely suited to human abilities, such as driving a car or diagnosing a disease, are already automated or potentially amenable to automation in the next decade. Emerging technologies are also making possible virtual labor mobility through Internet-based freelance platforms that match customers with self-employed service providers, as well as speed-of-light commercial data and software transmission. Automation Continuing To Replace Some Jobs In advanced economies, robots are increasingly supplanting humans for routine tasks and may take on more complex tasks in the coming decades as progress is made in artificial intelligence (AI). Companies probably will apply AI breakthroughs in object recognition, machine translation, robotic controls, and natural language processing as a labor-saving measure in white-collar professions. Automation is most widespread in jobs with midlevel technical skill requirements. However, employees with nonroutine skills that complement automated processes are in high demand and have seen rising wages. These nonroutine skills include emotional intelligence and teamwork, as well as critical thinking and problem solving skills.
In the coming years, AI is likely to be applied to higher skill tasks, eliminating some jobs while significantly increasing the productivity of those workers who remain. Affected fields include law, medicine, finance, and software development. Lab technicians, chemical engineers, and optometrists are examples of professions particularly vulnerable to future AI applications.
(Graphic 1: WORK ACTIVITIES WITH HIGHER AUTOMATION POTENTIAL - Click image to enlarge) PREVIOUS WAVES OF WORKPLACE DISRUPTION Changes to work in the coming two decades will probably reflect aspects of previous waves of job market change, from the 19th century Industrial Revolution to the automation and trade globalization of recent decades. In those episodes, shifts in technology and the workforce created winners and losers, with worker skills, retraining opportunities, and the government role in labor market adjustments showing up as common denominators. Industrial Revolution. Beginning in the late 18th century, as the European population boomed, Western economies underwent massive shifts in the workforce out of agriculture and into industrial jobs. New technologies drove productivity in the agriculture, iron, and textile sectors. The adaptation of coal-powered steam engines to railroads and shipping boosted international trade. Over a period of several decades, the Industrial Revolution was especially difficult for low-skill-worker households, as wages failed to keep up with the cost of living. Many poor households lived in unhealthy conditions, with inadequate food, housing, education, and social services.
Beginning in the late 18th century, as the European population boomed, Western economies underwent massive shifts in the workforce out of agriculture and into industrial jobs. New technologies drove productivity in the agriculture, iron, and textile sectors. The adaptation of coal-powered steam engines to railroads and shipping boosted international trade. Over a period of several decades, the Industrial Revolution was especially difficult for low-skill-worker households, as wages failed to keep up with the cost of living. Many poor households lived in unhealthy conditions, with inadequate food, housing, education, and social services. Rising Labor Share. Technology and workforce changes can lead to improvements for workers, too. In the wake of World War II, several factors common to most advanced economies combined to shift firm profits away from business owners and to workers, thereby reducing income inequality. These included widespread education, worker unionization and other types of political power, demands from aging workers for retirement benefits, factory mechanization that improved the productivity of low-skill workers, and a cultural shift that emphasized the social benefits of lower income inequality.
Technology and workforce changes can lead to improvements for workers, too. In the wake of World War II, several factors common to most advanced economies combined to shift firm profits away from business owners and to workers, thereby reducing income inequality. These included widespread education, worker unionization and other types of political power, demands from aging workers for retirement benefits, factory mechanization that improved the productivity of low-skill workers, and a cultural shift that emphasized the social benefits of lower income inequality. Outsourced Manufacturing. When China joined the global trading system in the early 2000s, it was home to almost one-quarter of the world's total working-age population. Chinese manufacturing wages were as low as 3 percent of those in the United States, giving China a labor cost advantage that helped drive its rapid increase in global market share, in combination with effective use of imported technology. Western manufacturers took advantage of the cost savings and moved many factories overseas, leading to significant job losses in certain regions of advanced economies, disrupting local communities.
An automation wave is likely to hit some developing economies during the next two decades, although their current automation levels are lower than those of advanced economies. The cost of automation and digital technology continues to decline globally, reducing barriers to new investment in robots. Developing economies with workforces that probably are vulnerable to disruptive automation include Brazil, Costa Rica, Malaysia, Romania, and Russia because their working populations are older and they earn relatively high wages. Some developing economies will struggle to industrialize as automation reduces the cost of manufacturing elsewhere. For example, as Asian manufacturers adopt automation and other innovative technologies, they will gain cost advantages derived from producing large volumes of goods. Exports of these products will drive down the prices of competing goods globally, making it difficult for small-scale manufacturers in Latin America and Sub-Saharan Africa to compete in the marketplace and reducing the number of industrial jobs in those regions. Instead, employment in those regions has been gravitating toward the lower paying services sector.
A key uncertainty about automation during the next few decades will be the degree of its disruptive effect on labor markets. Automation could create such efficiency that the number of well-paid jobs created is less than the number lost. Whether workers and companies will be able to meet retraining and education needs is unclear, given the pace of change. One 2013 study concluded that up to half of all US jobs were susceptible to being overtaken by the new forms of automation. More recent studies have looked closely at which job subtasks are automatable and found a smaller percentage of jobs at risk. The newer results concluded that 9 percent to 15 percent of jobs on average could be displaced—still highly disruptive, but on a scale that may permit workers, companies, and governments time to adapt without being overwhelmed.
FROM FACTORY-FREE PRODUCTION TO OFFICE-FREE SERVICES
How far could the disruption of services sector employment by international digitalized services platforms spread? The path charted by some manufacturers over the past two decades suggests that the answer is "very far". Some manufacturers went to the logical extreme of outsourcing all production in what became known as "factory-free production"-for example, Apple-designed iPhones are assembled in Asia, and Dyson designs vacuum cleaners but subcontracts out the production. This development could point the way to a large-scale "office-free services" model, with major corporations operating with no in-house human resources staff or international financial services firms with no physical headquarters.
(Graphic 2: THE FUTURE OF WORK IN SERVICES - Click image to enlarge) Technologies Changing Where Jobs Are Performed Companies are increasingly dividing jobs into discrete tasks that can be completed by teleworking employees or outsourced to sometimes geographically distant freelancers, further disconnecting work tasks from where those tasks are performed. Digitalization and Internet connectivity have facilitated this trend with innovations such as online document sharing, cloud storage, wireless connectivity, videoconferencing, and AI-augmented process management. During the next 20 years, advances in telepresence technologies, such as virtual reality, will allow more physical tasks to be completed remotely, expanding this trend beyond traditional office jobs. Long before we began to experience the workplace disruptions caused by the COVID-19 pandemic, more than 2 million additional US workers had begun working from home at least part-time, bringing the total to 8 million US workers, according to US Census Bureau estimates, or more than 5 percent of the US workforce. Similar percentages of workers have reported working from home in Europe and Japan.
Freelancing or “gig economy” web platforms facilitate outsourcing of office tasks, such as web development, writing and translation, design and multimedia, and administrative support, as well as consumer services such as ride-sharing, delivery, and household microtasks.
Technological advances almost certainly will reshape more traditionally physical jobs as well, in industries such as mining, forestry, and oil and gas. Automated techniques allow for operations control or infrastructure inspection by remote workers who could “visit” multiple geographically distant sites in one day. Self-driving vehicles, drones, and other robotics are likely to accelerate this trend.
Parallel with the rise of offsite work within countries, companies in advanced economies are increasingly sourcing services to workers in emerging economies, particularly those in East and South Asia and Sub-Saharan Africa. International trade in digitally deliverable services—including financial and business services and computer programming—has grown at almost twice the rate of the global economy during the past 15 years and in 2018, contributed more than 3.5 percent of global economic output, double the value of international tourism that year. Workers in developing Asian countries are benefiting from new employment in cross-border freelanced services, such as software development and IT systems integration, but these freelancers receive relatively low average wages and have poor labor bargaining power.
Three developing countries are among the top 10 countries with the largest share of digitally deliverable services in their total service exports: Sierra Leone (76 percent), Ghana (73 percent), and India (66 percent).
Freelancers in Kenya are among the leading suppliers of writing and translation services globally, and in Nigeria, South Africa, Tanzania, and Zimbabwe, freelancers are among the most active in supplying software development services.
Additive manufacturing—known as 3D printing—is also changing the location and composition of jobs as companies “reshore,” or localize, production plants. Additive manufacturing offers companies several advantages, such as the ability to make lighter weight parts, avoid wasting material, and save on shipping costs. The widespread adoption of this technology could eliminate manufacturing jobs in some emerging economies while creating new jobs in advanced economies in product design, customer relations, and computer programming. Many jobs associated with additive manufacturing offer higher pay and demand greater skill than traditional manufacturing jobs, but such jobs are also scarcer. For instance, software engineers are needed to modify the 3D printer computer code when a product is customized.
The medical device industry has already widely adopted additive manufacturing, a trend probably accelerated by the COVID-19 pandemic because the health care industry has worked to quickly leverage 3D printing for ventilator valve and personal protective equipment production. The automotive and aerospace manufacturing sectors are among those likely to generate the greatest number of 3D printing tasks.
DEMOGRAPHICS, PANDEMIC ACCELERATING NEW WORK PRACTICES
Aging Workforces Spurring Automation During the next two decades, the workforces of most of today’s largest economies will shrink as aging workers retire, increasing the need for automated processes. South Korea will lose 23 percent of its working-age population (ages 15-64), Japan 19 percent, Germany 13 percent, and China 11 percent, according to UN projections. Southern Europe’s advanced economies—such as Italy, Portugal, and Spain—make up the world’s most rapidly aging region; together their working-age populations will decline by more than 17 percent during the next 20 years. Automation—traditional industrial robots and AI-powered task automation—almost certainly will spread quickly as companies look for ways to replace and augment an aging workforce. Companies with large cohorts of workers 55 and older are more likely to adopt robotics and other forms of automation as they anticipate the potential for declining workforce productivity. Industrial robots are currently concentrated in the United States, China, Germany, Japan, and South Korea, only one of which—the United States—does not have a shrinking workforce. Southern and East European economies probably will adopt more workplace automation, given the anticipated high rates of worker retirements. In particular, Italy, Russia, and Spain have lower-than-average rates of industrial robot adoption, suggesting that increased automation could spread to those countries.
Automation and AI will also be adapted for aging workers in white-collar workplaces. For example, networked and smart hearing aids could enhance communication, and robotic exoskeletons could increase personal mobility.
Pandemic Response Leading to New Ways, Locations for Working In response to the coronavirus pandemic, companies worldwide have quickly expanded technology-driven workplace changes. Some companies have encouraged employees to work remotely rather than commute to worksites, and many businesses have shifted to digitally connected virtual teams, eliminating the need for business travel. Although some of these changes are likely to remain after the pandemic ends, others may be discontinued if companies and workers decide in-person engagement is critical to performance. Videoconferencing is replacing daily commutes to an office and periodic travel for conferences. As quarantine measures were widely implemented in March 2020, office videoconferencing apps saw record downloads by users in Italy and Spain—countries that previously had below-EU-average rates of work from home. Traffic on one videoconferencing app rose from 10 million daily meeting participants in December 2019 to more than 300 million in April 2020.
For office workers already struggling with the high cost of housing in urban centers, temporary remote work protocols have raised interest in permanently working remotely from lower cost locations. When the COVID-19 crisis has ended, employers are likely to maintain larger percentages of remote and freelance workers, even while potentially facing questions related to adjusted pay scales, and mechanisms for employee monitoring and feedback.
Technology is also filling and creating new roles in the services sector, in particular where the public is demanding a shift to low contact or automated options. For example, online streaming of entertainment is eliminating jobs in movie theaters and live performance venues while digital platforms are creating new job opportunities in shopping and delivery services. Many countries are testing robots and drones—including the United States, Chile, China, Ghana, Israel, and South Korea—to replace humans in high-contact tasks, such as medical testing, surface sterilization, and package delivery.
Even in industries where telework is not an option—such as manufacturing, farming, and food processing—companies probably will pursue technological fixes to keep their businesses operating with fewer workers. As in previous crises, companies have been making capital investments in automation, which is likely to accelerate the trend in some industries.
BROADER IMPLICATIONS
The changing landscape of work raises broader implications, especially for advanced economies, concerning social stability and the role of governments on issues of inequality, social identity, and regulation. Increasing Inequality The hollowing out of the middle-income workforce in advanced economies—a process under way for the past two decades—almost certainly will continue, with profits disproportionately going to some high-skill workers and the corporations that hold the new technologies. Automation has replaced mostly middle-skill jobs even as employers add workers at both the bottom and top of the pay scale—a trend known as “job polarization.” For example, across Western Europe and the United States, the employment share for traditional middle-income jobs, such as machine operators, metalworkers, and office clerks is decreasing. At the same time, low-paying services-sector jobs, such as sales representatives, as well as high-income jobs for doctors, engineers, and other professionals are increasing. Overall compensation for workers relative to economic output probably will also shrink during the coming two decades. Automation has decreased the overall share of worker take-home pay relative to corporate shareholder profits because decreases in wages and jobs displacement have not been fully offset by the potential of automation to produce more goods and drive new employment in related industries. Although many new high paying, high-skill jobs have been created in the past 20 years, the total wages and benefits those workers earn do not surpass the lost wages and benefits of the displaced workers.
In the years ahead, some higher skill professionals are likely to face declining wages as new technologies begin to supplant the routine tasks they perform—a development that will further contribute to downward pressure on worker pay.
Digital freelance, or “gig economy,” platforms are contributing to declining aggregate worker earnings and benefits in advanced economies, since most freelancers work part-time and have lower-than-average overall take-home pay. As remote work becomes more common, companies may increasingly substitute freelancing for work performed offsite by employees as a cost-saving measure. Freelancers, who do not receive employer-provided benefits, are more vulnerable to employment loss during a business downturn. Payment is also more at risk for freelancers. At some point, about 90 percent of freelancers have been denied payment after completing a task based on what the customer claims—often unfairly, according to freelancers—is subpar work.
Even though aggregate wages probably will fall in advanced economies, a small portion of workers will see rising wages and profits. Technology innovators and workers who perform nonroutine and cognitive work that is enhanced—not replaced—by automation almost certainly will continue to see their wages increase relative to those of other workers. Overall worker compensation will further concentrate in key digital domains affected by automation, such as online search, electronic communication, and online shopping. A handful of companies enjoy near-monopolies in these domains, benefiting from network effects inherent in digital services. That is, the larger the user base of these digital services grows, the more desirable these companies’ services are to consumers, even though the marginal cost of expanding their services is close to zero. Consequently, workers in jobs with access to these monopoly profits will see higher wage growth.
The pandemic is accelerating this trend as consumers avoid brick-and-mortar stores and companies shift to e-commerce and work-from-home arrangements, giving online platforms additional market share. Although consumers probably will revert to their pre-pandemic lifestyles after the pandemic, surging shares of e-commerce companies in China, Europe, India, and the United States suggest that investors judge a substantial portion of the behavioral shift is likely to stick. The need for retraining, particularly among lower and middle-income workers, could exacerbate income inequality, particularly if workers bear the burden individually. Low-income workers are likely to be unable to afford the certification necessary to obtain higher paid employment and thus be relegated to low-skill jobs. In addition, workers who are retraining could lack information on which training programs will pay off with future work assignments, exacerbating the skills mismatch that already leaves higher skilled positions unfilled. Reshaping Social Identities Changes to the nature, location, and compensation structure of work in advanced economies during the next two decades will further reshape white-collar workers’ social identities. Many people gain self-worth from their work and tend to identify closely with their workplace goals. Physical workplaces provide social cohesion and help build institutional trust in modern society. Changes and dislocations that many blue-collar and manufacturing workers in advanced economies experienced in the late 1990s will increasingly affect white-collar and service workers as companies begin disaggregating, outsourcing, and automating knowledge-worker jobs. At the same time, many younger workers in advanced economies are eager to adopt more flexible and creative employment opportunities offered by workplace changes. Technology is enabling work to be more about tasks and less about job positions, allowing workers more choice on how and where to complete the tasks. Research shows that those who choose flexible working arrangements out of preference, not necessity, are positive about future changes to the workplace.
Polling in advanced economies during the past decade has shown that rising generations are less attached to institutional corporate structures and more eager to control their work. Similarly, younger business owners and hiring managers are more likely to allow nontraditional workplace practices, according to a survey of US firms.
Even so, many younger workers also experience anxiety and depression because of the less stable nature of gig or freelance work. A 2018 survey of Canadian millennials found that those with precarious employment—defined as permanent part-time work without traditional benefits—experienced a significantly higher prevalence of mental health concerns.
With automation poised to command a larger share of routine physical and cognitive tasks, workers will be expected to innovate, facilitate teamwork, and apply skills across different classes of tasks. Solutions to problems will be more readily crowdsourced—a method that is often more effective than relying on deep expertise alone. To succeed, workers will need to continually acquire new skills, including using insights from real-time data analytics that artificial intelligence can generate from increasingly digitized homes and workplaces.
New Issues for Governments To Manage As these trends unfold, the public is likely to increasingly demand that governments manage the dislocations caused by new technologies. Governments probably will have an incentive to play a role in managing and smoothing labor market disruptions, but many governments may not have the capacity to manage these issues when faced with a host of other challenges. Governments most likely will face tough choices between investing in the current workforce and providing benefits to displaced and aging workers. Record levels of government debt in both advanced and developing economies, along with the rising overall costs of aging populations, could limit new programs for reskilling, healthcare, and retirement. Even in Sub-Saharan countries on the verge of a youth bulge, large older worker cohorts nearing retirement probably will strain the minimal elder care programs in place. In the long term, if governments neglect education and infrastructure, growth probably will slow.
Governments will have a regulatory and legal role in defining relations between workers and employers. The new work landscape may include today’s problems of bad business practices, gender and race discrimination, and fraud, as well as new types of bias in online “gig worker” employment platforms.
The growing trend of cross-border freelancing may require new regulatory frameworks, within and among states. Online freelancers may be vulnerable to cyber fraud, cyber attacks, and outages that have the potential to damage their livelihoods and, in aggregate, the broader economy. For instance, cryptocurrencies could become digital services workers’ preferred channel for payment, but in the event of financial system stress, cryptocurrencies may not have the backing of central banks, leaving online workers at greater risk.
The current era of deceptive digital material—including manipulated images—and cyber hacking will challenge freelance employment platforms to maintain credibility with their remote workforces. The problems could range from digital reputation manipulation and large-scale theft of other employees’ work to the possible macroeconomic disruption of core digital-economy platforms being taken offline through malice or natural disaster. These types of potential market failures in the nonvirtual economy are often the subject of government regulation.
| 2022-12-01T00:00:00 |
https://www.dni.gov/index.php/gt2040-home/gt2040-deeper-looks/future-of-work
|
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|
AI and the Workforce: Preparing for the Jobs of the Future | Thoughtful
|
AI and the Workforce: Preparing for the Jobs of the Future
|
https://www.thoughtful.ai
|
[
"Nicole Singleton"
] |
While some jobs may be eliminated, new ones will emerge, and existing ones will evolve. It is important for businesses and workers to understand the impact of ...
|
Understanding the Impact of AI on the Workforce
Artificial intelligence (AI) is changing how businesses operate, and its impact on the workforce is clear. AI can automate repetitive work, improve accuracy, and boost efficiency. But it also raises concerns about job loss, and highlights the growing need for upskilling and reskilling.
According to McKinsey Global Institute, up to 375 million people may need to change jobs or learn new skills by 2030 as automation and AI advance. While some roles will disappear, others will evolve or emerge. To prepare for the jobs of the future, workers and businesses need to understand how AI is reshaping the workforce.
AI is already handling repetitive tasks like data entry, customer service, and financial analysis, freeing workers to focus on more complex, human-led work like decision-making, problem-solving, and critical thinking. In healthcare RCM, Thoughtful’s AI Agents are already automating processes like eligibility verification and claims processing, which helps staff focus their efforts on patient care.
That said, not every industry benefits equally. Jobs in manufacturing, transportation, and retail are at higher risk as machines take on more work. To stay competitive, workers need to build new skills and adjust to changing roles.
The impact of AI on the workforce is nuanced. It brings speed and scale, but it also demands adaptability. Preparing for the jobs of the future starts with understanding both the risks and the opportunities.
In the next section, we’ll look at what skills will matter most and how to build them.
Preparing for the Future: Skills and Training
As AI continues to grow across industries, people need to prepare for the jobs of the future. That means building skills that will stay in demand as AI reshapes the workforce.
Here are some of the areas that will matter most:
Data analysis: As businesses rely more on AI, the ability to gather, interpret, and present data will be key. This includes data mining, data visualization, and statistical analysis.
As businesses rely more on AI, the ability to gather, interpret, and present data will be key. This includes data mining, data visualization, and statistical analysis. Programming: Languages like Python, Java, and R are the foundation for many AI systems. Knowing how to use them at any level can open new doors.
Languages like Python, Java, and R are the foundation for many AI systems. Knowing how to use them at any level can open new doors. Machine learning: Understanding how machine learning works, and how to apply it, is essential for improving and maintaining AI tools.
Understanding how machine learning works, and how to apply it, is essential for improving and maintaining AI tools. Communication: AI may handle the workflows, but people still need to connect the dots. Clear communication (both with coworkers and systems) will continue to set great teams apart.
AI may handle the workflows, but people still need to connect the dots. Clear communication (both with coworkers and systems) will continue to set great teams apart. Creativity: AI is fast and efficient, but it’s not original. Creative thinking, problem-solving, and new ideas still depend on people.
You don’t need a degree to gain these skills. Boot camps, online courses, and certifications can be just as effective. The goal is to stay current, stay curious, and keep learning as AI evolves.
Employers have a role too. The most forward-thinking companies are already investing in training and giving teams the tools to adapt. This pays off by building a workforce that’s equipped to grow with new technology.
Success in an AI-driven workforce depends on action. The sooner workers and companies commit to upskilling, the better prepared they'll be for what’s ahead.
Next, we’ll look at the benefits and risks AI brings to the workplace.
Advantages and Considerations of AI in the Workplace
As AI becomes more common at work, it’s important to look at both the benefits and the risks it brings.
One of the biggest advantages of AI in the workplace is increased efficiency. Automating repetitive and routine tasks lets employees focus on more creative or strategic work. This shift can boost productivity and speed up how quickly teams get things done. Another key benefit is improved accuracy. AI systems often outperform humans on precision, which matters most in areas like healthcare and finance where small errors can lead to serious consequences.
AI also helps businesses cut costs. By reducing the need for manual labor and increasing operational speed, companies can run leaner without sacrificing quality. For instance, Thoughtful deploys its AI Agents to automate repetitive workflows across healthcare RCM, helping organizations lower administrative costs.
Another major advantage is availability. Unlike humans, AI systems can operate 24/7, which is especially useful for global businesses or teams that need round-the-clock customer support.
Still, there are real challenges to consider. One of the most pressing is job loss. As AI systems get more advanced, they may take over roles that rely on structured, rules-based work. That could lead to layoffs or reduced demand for certain jobs.
Another issue is the loss of human interaction. AI can complete tasks, but it can’t replace the value of collaboration, empathy, or personal connection that teams rely on to work well together.
Bias is another risk. If AI systems are trained on biased data, they can produce biased results. This can create unfair outcomes or reinforce existing inequalities unless organizations put safeguards in place.
Last but not least, security is also a concern. AI systems often manage sensitive data and critical operations. Without strong security protocols, businesses risk data breaches, service outages, or worse.
AI offers major advantages, but businesses need to act thoughtfully. That means balancing speed with oversight, automation with accountability, and cost savings with long-term workforce planning.
How AI is Changing the Job Landscape
Artificial intelligence is already reshaping how we live, work, and communicate. It’s become a part of daily life, and its influence on the job landscape is clear. AI hasn’t just created new types of jobs, it’s also changed the way many existing roles function.
One major shift is the automation of repetitive tasks. AI-powered tools now handle work that once relied on human input, including data entry, customer support, and even surgical procedures. This kind of automation boosts speed and consistency, leading to stronger productivity across industries.
At the same time, AI is opening the door to new careers. As technology evolves, companies are looking for people who can design, manage, and optimize AI systems. This demand has led to the rise of roles like AI engineers, data scientists, and machine learning specialists. These jobs didn’t exist a decade ago, but now they’re at the core of digital operations.
Still, the transition to an AI-powered workplace isn’t without its challenges. One of the biggest concerns is job displacement. The World Economic Forum estimates that AI and automation could eliminate 85 million jobs by 2025. That doesn’t mean the future is bleak; it means workers need to prepare. Adapting to this shift requires new skills and a mindset focused on continuous learning.
For individuals, this means building capabilities that AI can’t easily replace. Skills like data analysis, software development, and creative problem-solving are becoming more valuable across sectors. On the employer side, companies need to invest in training and support systems so their teams can work effectively with AI and grow into new roles as jobs evolve.
In short, AI is transforming the job landscape by streamlining old tasks and creating space for new ones. It brings uncertainty, but also opportunity. With the right focus on training and skill development, both workers and businesses can stay ahead of the curve.
As this shift continues, it’s worth paying close attention to how the future of work is unfolding. The next section looks at what’s coming next, and what leaders and employees should expect in the years ahead.
The Future of Work: Perspectives and Predictions
As AI and automation continue to evolve, the future of work remains a moving target. Some experts predict widespread job displacement, while others believe new job opportunities will emerge.
According to a report by the World Economic Forum, machines and algorithms could take on more work tasks than humans by 2025. At the same time, the report projects that AI will create 97 million new jobs, especially in areas like data analysis, software development, and cybersecurity.
Many also expect a shift in the kinds of skills employers value. As AI takes over more repetitive tasks, demand will grow for skills like critical thinking, creativity, and emotional intelligence.
The rise of remote work and the gig economy is also expected to continue. The COVID-19 pandemic accelerated these trends, and many companies are now leaning into the benefits of a flexible workforce.
Still, concerns remain about job security and income inequality. As AI becomes more common, some roles may disappear, particularly jobs that rely on routine, task-based work. This could widen the gap between highly skilled workers and those with fewer technical qualifications.
But with the right preparation, workers can adapt to these changes and find new opportunities. That might mean building new skills, embracing remote work, or staying current on emerging technologies. Companies like Thoughtful AI are helping smooth this transition by automating repetitive tasks and giving teams more time to focus on higher-level work.
The future of work may be uncertain, but one thing is clear. AI and automation will continue to reshape the job landscape. By staying informed and flexible, workers can prepare for what’s next and position themselves for the jobs of the future.
| 2022-12-01T00:00:00 |
https://www.thoughtful.ai/blog/ai-and-the-workforce-preparing-for-the-jobs-of-the-future
|
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|
How Artificial Intelligence Is Creating New Job Opportunities - EMPEQ
|
How Artificial Intelligence Is Creating New Job Opportunities – EMPEQ
|
https://empeq.co
|
[] |
One of the most significant ways that AI can create jobs is by enhancing efficiency and productivity. By reducing manual labor and streamlining processes, ...
|
Artificial Intelligence (AI) has become a buzzword in recent years. We’ve heard countless stories about how AI could potentially eliminate jobs, particularly in the engineering and contracting realm. However, we tend to forget that AI is also capable of creating new opportunities for employment and growth. I’d like to explore exactly how AI can help create jobs for engineers and other professionals in the contracting industry.
AI Enhances Demand for Skilled Workers
One of the most significant ways that AI can create jobs is by enhancing efficiency and productivity. By reducing manual labor and streamlining processes, organizations are able to focus their energy on more complex tasks that require human expertise. This shift means a greater need for skilled labor, which means more job openings for engineers and other professionals. For example, AI can be used to automate mundane tasks such as data entry or administrative work, allowing humans to focus their attention on more technical projects – and this means engineers have more time to create solutions that change the world.
AI Improves Productivity
Another way that AI can create job opportunities is by improving productivity across industries. By leveraging predictive analytics and pattern recognition tools, companies are able to gain better insights into their operations and make better decisions regarding resources management. This leads to improved efficiency and performance, which then translates into an increased demand for skilled workers who can manage these systems effectively. As companies continue to invest in AI technology, they will need people with specialized skillsets who are able to use it effectively.
AI Expands Possibilities for Entry Level Workers
Finally, AI can create job opportunities by expanding possibilities within industries. For example, machine learning algorithms have enabled organizations to produce more accurate data-driven insights than ever before. Further, AI is being used to help fill skill gaps in energy engineering and commercial mechanical contracting by allowing lower skilled workers to conduct much of the preliminary and routine onsite work. For example, AI on handheld devices, like that offered by EMPEQ’s Fast Site SurveyTM, can remove the need for a vast reservoir of knowledge when performing initial equipment inventories and site surveys.
It’s clear that Artificial Intelligence has the potential to both disrupt existing workflows and create new job opportunities at the same time. By automating mundane tasks or leveraging predictive analytics tools, organizations are able to increase efficiency – and this means greater demand for skilled workers with specialized skill sets such as mechanical and energy engineers familiar with building sustainable solutions in commercial buildings. AI also promises to provide new opportunities to entry level workers by separating an engineer’s more mundane tasks from the high-level analysis that requires advanced education and training. Solutions like Fast Site Survey provide engineers more time to create the incredible solutions they’re trained to produce, while simultaneously providing new career pathways for unskilled workers. With its ability to enhance efficiency and expand possibilities within industries, Artificial Intelligence is undoubtedly here to stay—and we encourage you to see what AI can do for your organization with a free demo of Fast Site Survey today!
| 2022-12-01T00:00:00 |
https://empeq.co/how-artificial-intelligence-is-creating-new-job-opportunities/
|
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|
10 Jobs That Will Disappear by 2030 Because of AI | Resumeble
|
10 Jobs That Will Disappear by 2030 Because of AI
|
https://www.resumeble.com
|
[] |
By 2030, automation and AI will eliminate 20 million manufacturing jobs, with 25% of American employment highly susceptible to automation.
|
The professional landscape is beginning to shift due to automation and AI. As computing power grows, so does AI's likelihood of eventually replacing human workers. In fact, AI is already being used in several sectors to streamline operations and boost productivity. This results in the workforce worrying about the long-term effects of AI on the job market.
While there are recession-proof jobs, there will still be different job types that are set to disappear by 2030 because of various factors, including automation. In this article, we will discuss some of the jobs that are at risk of becoming obsolete in the near future.
10 Jobs That Will Be Eliminated by AI
By 2030, automation and AI will eliminate 20 million manufacturing jobs, with 25% of American employment highly susceptible to automation. This results in 73 million job losses over the next five years.
Take a closer look at the jobs that will be gone by 2030:
1. Data Entry and Manual Data Processing Specialists
Jobs that AI and automation will impact include those that require data entry and manual data processing. Manual data entry refers to information added to or changed in a computerized database. Specialists in this field transfer data from documents such as invoices, receipts, quotes, ledgers, and product details. It’s also a safe bet when finding fixed and flexible schedule jobs.
Since technology has advanced, systems and tools can now process data and carry out operations like data entry much more quickly and accurately than human workers. As more artificial intelligence (AI) tools emerge to automate data entry, those working in this industry can anticipate its decline in the coming years.
2. Assemblers
Toys, electronics, motor vehicles, and airplanes are just some of the products that rely heavily on the work of assembly line workers. Assemblers, however, are rapidly becoming extinct as machines and robotics take over assembly processes and repetitive tasks across various industries. With the advancement of industrial robots and automation systems powered by super AI, machines can now perform repetitive tasks more efficiently and accurately. In fact, the US Bureau of Labor Statistics projects a 6% decline in this field from 2021 to 2031. That’s why more workers in this sector are at risk of unemployment, as this is one of the jobs that will disappear by 2030.
3. Telemarketers
Ever received unwanted sales calls offering you a product or service? You might be pleased to hear they’re one of the ten jobs that will disappear by 2030, but still, it’s not good news for actual telemarketers. Telemarketers are people whose job is to call potential customers and ask them to buy something or donate over the phone. Their responsibilities include maintaining customer contact lists, selling their services, and collecting payments when required.
With the spread of AI proliferation, automated sales calls become more common, thus risking human telemarketer jobs. Companies in the telemarketing industry have quickly adopted this new method because it eliminates the need for new hires and allows them to interact with potential clients at any time of day or night. In other cases, however, such as when dealing with complex negotiations or sales strategies, a human touch is still necessary.
4. Content Writers and Content Generators
With content writing, you can write anywhere and anytime you want. But how long does it take a human to write one unique page of content about any topic? Most of us need considerable time to prepare, deliberate, and carry out the required action. Writing and content creation are likely among the jobs affected by AI and automation because an AI-powered entity can execute the task in a matter of seconds. With the development of language models like ChatGPT and Jasper AI, many mundane writing tasks have become fully automated, including creating news summaries, product descriptions, blogs, and basic reports.
However, creative writing, such as fiction and poetry, and content editing and reviewing offered by professional resume services still have significant exceptions that humans can only write due to the high emotional intelligence, empathy, and critical thinking required.
5. Financial Analysts and Record-Keepers
Financial analysis and record-keeping are now within reach with the help of AI-powered tools that can do a better job than humans. Before, professionals manually kept records and performed analyses. They investigate discrepancies in the books and make notes about their findings. Now, many of the mundane tasks in these industries can be carried out automatically with little to no human intervention required. Even though this is one of the jobs that will be in a couple of years, humans will still be needed for complex financial analysis, interpreting financial data, and making decisions.
Administrator
6. Administrative Jobs
Administrators play an essential role in any successful business. They provide necessary office support for an employee or group. Their duties include answering phones, greeting and directing visitors, typing documents, making spreadsheets and presentations, and filing. With this, the future of administrative positions is in jeopardy because businesses can utilize AI tools to perform some administrative tasks. The next decade will see further improvements in automation and digitalization, reducing the need for humans to perform these tasks. For those seeking to transition into more secure roles, such as positions within the government, federal resume writing can be a valuable resource to tailor your experience for these opportunities.
7. Cashiers
With the emergence of contactless payments and cryptocurrencies, there has been widespread discussion in recent years about the possibility of a cashless society. Companies have already implemented such measures. For instance, Amazon is among the first companies to employ cashierless checkout. Customers can use the Amazon Go app to pay for groceries without human intervention. It’s a novel method for most people, but simplifies transactions and lessens operational costs. While some may still prefer to pay in cash to monitor their spending habits, the days of human payment processors are ending. Indeed, this is among the jobs that will cease to exist in 2030.
8. Fast Food Workers
By 2030, robots and computer kiosks will replace most fast food roles due to advancements in artificial intelligence. Moreover, fast food chains are not hiding that they are trying to hire fewer people. Self-service checkout lanes and stations are gradually replacing cashiers in fast food establishments, including McDonald's.
Additionally, companies in the US are already using robot technology to flip burgers and assemble sandwiches. In the future, only a few humans may be required to fulfill orders from robots at your favorite fast-food restaurant.
9. Travel Agents
There was a time when planning a summer vacation abroad involved visiting a travel agency, perusing brochures, and having a helpful salesperson put it all together on a computer. These days, it’s now possible for anyone to plan their vacation thanks to the plethora of user-friendly travel websites. With the help of websites like Airbnb, Trivago, and Agoda, you can find cheap flights and hotels that fit your travel dates and budget with just a credit card and some free time. This is also why travel agents are one of the jobs on the verge of disappearing by 2030. Many airlines, hotels, and tour groups are eliminating brick-and-mortar locations to expand their online presence.
10. Letter Carriers
Traditional letter carriers may be in trouble in the future. It’s doubtful that demand for these services will increase over the following decades, as bills and statements can be viewed and paid online. Junk mail has also moved from the mailbox to the inbox, and letter writing is a dying art form. Despite this, there’s still a need for couriers to deliver packages, so letter carriers still have a chance to recuperate once this job becomes obsolete in 2030.
Jobs That No Longer Exist Due to Technology
As technology continues to advance quickly, it has led to the obsolescence of various jobs that were once crucial in daily life, replacing them with more efficient and automated solutions. Some of the jobs that no longer exist include:
Typewriter repair technicians: When the typewriter was the go-to technology, these technicians were always in demand. However, since the introduction of more versatile typing solutions like computers, this profession is no longer viable.
Switchboard operators: These individuals were responsible for manually connecting telephone calls by plugging and unplugging cables on a switchboard. However, with the development of automatic telephone switching systems, this job has become obsolete.
Elevator operators: Before automated systems took over, elevator operators manually controlled lifts and assisted passengers. Today's elevators are equipped with automatic controls, eliminating the need for human operators.
All these jobs were once essential and are a testament to how technological advancements reshape our world. As we keep innovating, it is fascinating to reflect on how technology continuously redefines the workplace.
IT Jobs That Are Unlikely to Disappear
Despite the significant disruption caused by technology, some IT jobs are here to stay. These roles are critical for maintaining and advancing the technological infrastructure we use today. They include:
Cybersecurity experts: These professionals protect sensitive information and ensure the security of networks against cyber attackers. Their role involves monitoring networks, developing security policies, and defending against attacks, making them crucial as our digital reliance grows. As long as digital systems exist, cybersecurity experts will remain essential.
Software developers: Software developers are the architects of our digital world, creating the applications and systems that we use today. Whether building mobile apps or complex enterprise software, their skills are vital for innovation and will remain in demand as technology advances.
IT support specialists: They provide hands-on help with technical issues to ensure that tech-driven environments operate smoothly. Their expertise is irreplaceable, especially as technology evolves.
AI and machine learning engineers: These engineers develop algorithms that enable intelligent systems to learn and make decisions. Their work drives innovation across various fields, and demand for their skills will continue to rise.
Should Employees Be Worried?
It’s reasonable to feel concerned if your profession is on the verge of becoming extinct by 2030. However, a transfer to a different position is more likely than a layoff, and you could potentially pick up and hone some useful new skills along the way. Taking a more optimistic approach, you could view the evolution of your role as an opening to pursue a new career path and begin training for the jobs of the future immediately. There is no denying that automation and AI will soon permeate every aspect of our lives. It’s only high time we embrace this trend and use it as leverage to further our careers. So, don’t be worried if your current job is one of the ten jobs that will disappear by 2030. You can still find ways to upskill and use AI to your advantage.
A professional resume might not fit in a gift box, but it could open a hundred doors. Explore Resumeble’s Gift Resume option and give a loved one the gift of confidence - and career growth.
| 2022-12-01T00:00:00 |
https://www.resumeble.com/career-advice/jobs-that-will-be-gone-2030
|
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Artificial Intelligence and Job Automation: Challenges for Secondary ...
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Artificial Intelligence and Job Automation: Challenges for Secondary Students’ Career Development and Life Planning
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https://www.mdpi.com
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"Lawrence P. W. Wong"
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Given AI systems' powerful learning and content creation capabilities, job automation has become a significant concern, potentially leading to job ...
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Based on the empirical setting presented above, an overview of how AI disruptive forces could impact the career development of secondary school students is urgently needed given the lack of research in this area [ 16 17 ]. This review is a valuable resource, providing consolidated career information to help educators and career guidance professionals identify the skills students need to develop before they enter the future world of work. The first part of this paper reviews research on AI’s workplace impact globally and regionally, while the second part discusses how school-based career guidance and counseling can be enhanced.
Artificial intelligence refers to systems that demonstrate intelligent behavior by evaluating their environment and acting autonomously. This technology allows computers and machines to emulate human abilities such as learning, problem-solving, decision-making, and creativity [ 8 ]. Generative AI, for example, is one type of artificial intelligence technology [ 9 ]. The rapid progress of AI is expected to reduce the unique role of human labor in providing cognitive skills. This is because AI now surpasses humans in many cognitive functions, allowing for the automation of numerous tasks traditionally performed by human workers [ 10 11 ]. Given AI systems’ powerful learning and content creation capabilities, job automation has become a significant concern, potentially leading to job displacements as AI systems replace human workers as the primary providers of cognitive skills [ 10 11 ]. In other words, artificial intelligence will automate jobs and reduce the need for human workers. From this perspective, job automation poses a threat to humans because machines can perform the same tasks that humans used to do. This change necessitates a reevaluation of career development planning and skills development for future employment [ 12 ]. In this vein, understanding and managing the risks associated with AI-induced job automation is a crucial agenda for the career development and planning of secondary students [ 13 ]. The relevant literature in this topic, however, appears to be focused more on the effects AI-induced disruption has on the career development needs of university students and adult workers [ 11 13 ]. The needs of secondary students are less understood. Another weakness of the current literature is that studies investigating the impact that job automation has on career development (e.g., employment) have deployed different methodologies conceptualizing this issue, which often leads to an inconclusive and inconsistent understanding of the subject matter [ 14 15 ]. There is an immediate need to re-evaluate how we analyze and address this problem, starting from its core from the perspective of secondary school students. This is because empirical research has already shown that secondary school students are likely to aspire to work in an occupational role that is linked to a high risk of automation [ 16 ].
One primary motivation for students to pursue postsecondary education is to acquire cognitive and vocational skills valued by employers [ 1 ]. In some regions, the intense pressure to excel academically can lead to tragic outcomes, including student suicides, as matriculation exam results are often used as the only measurement of a job candidate’s cognitive abilities [ 2 4 ]. Even though research evidence has already shown that schoolteachers’ career-related care and support can help students thrive in such a stressful situation [ 5 ], the availability of career-related teacher support varies across education contexts due to a shortage of staff with knowledge about the world of work [ 1 6 ]. The advent of AI is expected to complicate the existing landscape by automating job tasks, thus challenging and displacing the traditional roles of human workers in the workplace [ 7 ].
Career guidance holds a strategic position in the educational system by assisting students in making well-informed decisions regarding their future educational and career paths. Effective career guidance helps students recognize their strengths, values, and interests while also providing them with the essential skills needed for a seamless transition from school to the workplace [ 1 ]. Effective career guidance can help students develop the skills and knowledge necessary for the future job market, including an understanding of how technological advancements like artificial intelligence (AI)-induced automation affect career opportunities, enabling students to make informed choices and necessary adjustments about their career plans [ 1 ].
Current evidence indicates that AI is replacing human labor, either fully or partially, at a faster pace than anticipated across various job sectors. These roles include voice actors, forex traders in investment banking, news writers, screenwriters, software engineers, coders, data analysts, lawyers, and graphic designers [ 18 ]. For instance, industry experts predicted that most outsourced coders in India will lose their jobs within two years due to the rapid advancement of artificial intelligence technology [ 19 ]. Additionally, the Writers Guild of America is in legal proceedings with Hollywood studios to limit AI applications, which threatens the job security of screenwriters, actors, and other production staff [ 20 21 ]. News outlets like Sports Illustrated and News Corp. have already begun using AI to produce news stories [ 22 23 ]. In investment banking, established banks such as UBS are restructuring their workforce, deeming human coders and forex traders redundant as machines can now perform their tasks [ 24 25 ]. AI-powered automated mutual fund trading algorithms have proven significantly superior to human traders [ 26 ]. Banks like Morgan Stanley are developing large language models to perform research analysts’ tasks, such as summarizing documents and generating investment solutions [ 27 ]. Boutique digital financial services firms, such as Betterment, are emerging, offering automated services that rival traditional banks [ 28 ]. In the legal field, recent studies indicate that GPT-4 can pass the Uniform Bar Examination with a score near the 90th percentile, greatly surpassing many trainee lawyers and far exceeding the passing threshold [ 29 ].
Because of their powerful content creation capability, AI systems are set to disrupt established educational and occupational developmental pathways pervasively. Recent studies have shown that humans are forming emotional dependencies on AI models [ 10 ]. Students, in particular, have been using AI to assist with cognitive tasks like writing assignments [ 41 ]. Research from the University of Pennsylvania revealed that students who used ChatGPT for test preparation scored lower than those who did not. This raises concerns about over-reliance on AI and its negative impact on cognitive skill development [ 42 ]. If students develop reliance on AI without cultivating the underlying essential cognitive skills, it raises questions about their relevance to future employers, especially when intelligent machines can perform much of their work [ 43 ]. While students may not fully grasp its impact, AI’s rapid advancement has already sparked fears of job displacement among adults [ 44 ]. This is evidenced by the career choices made by the students who had scored a perfect score in the 2023–2024 cohort of the International Baccalaureate Diploma Program examination. Among the 23 students in Hong Kong, China, who have scored a perfect score in the exam, none of them aspired to become an artificial intelligence scientist or work in a field that is related to AI development [ 45 ]. Some of them chose to pursue occupational roles (e.g., lawyers and psychologists) that have been suggested by experts as being highly exposed to risks of job displacement due to AI-induced job automation [ 7 46 ], which is very similar to the trend that had been earlier reported in England [ 16 ]. This worrisome situation challenges our theoretical and practical approaches to preparing students for future employment. This is because elite students often perpetuate established norms and choose careers (e.g., Lawyer, Investment Banker, and Medical Doctor) that are associated with social prestige and wealth [ 47 48 ], without considering the risks of technological disruptions [ 16 46 ]. Experts have warned that this trend could signal issues for policymakers, as academic success does not necessarily lead to economic and technological growth, especially with a shortage of AI talent [ 49 50 ].
It is clear that AI can enhance students’ creativity and critical thinking. However, researchers may have overlooked the fact that it is the AI interface that possesses these abilities but not the students themselves [ 35 ]. Recent findings suggest that AI has both positive and negative impacts on student development [ 36 37 ]. For instance, Ahmad et al. [ 38 ] found that in a sample of 285 students from universities in Pakistan and China, AI significantly impacted decision-making, increased laziness, and raised privacy concerns. Similarly, Abbas et al. [ 39 ] showed that ChatGPT usage among 659 university students was linked to higher levels of procrastination and memory loss, especially under high academic pressure. Among primary and secondary school students in Hong Kong (= 502), a survey study conducted by the Hong Kong Academy for Gifted Education [ 40 ] revealed that while students believed that generative AI devices such as ChatGPT could help them to complete school assignments, these devices may negatively influence their problem-solving and critical thinking skills. These findings underscore the need to reassess AI’s suitability in education as it may impede adolescents’ cognitive and behavioral development.
At the classroom level, some researchers reported that students found AI-powered devices enjoyable to use, fostering creativity and academic success due to their human-like capabilities. For example, in their experimental study involving 123 Grade 10 students, Chiu et al. [ 30 ] found that with adequate teacher support, students developed intrinsic motivation to use chatbots for learning. Kim et al. [ 31 ] discovered through in-depth interviews with 20 university students that AI applications can improve academic writing skills by serving as both a writing assistant and teacher. Students felt more confident in writing scientific articles with AI support. Marrone et al.’s [ 32 ] focus group study examined students’ views on AI and creativity. Students with a higher self-reported understanding of AI were found to be more positive about using AI to enhance their academic engagement and creativity. Chang and Tsai [ 33 ] found that design thinking significantly enhanced AI learning, attitudes, and creativity, particularly in novelty, value, functionality, and elaboration. Rong et al. [ 34 ] reported that in the context of art education in middle schools, digital technology has transformed traditional teaching. The integration of AI and virtual reality has significantly improved students’ concentration and creativity through deep learning.
Early studies indicate that AI has both positive and negative impacts. While it enhances productivity, it also increases susceptibility to procrastination, memory loss, and poor decision-making among students and professionals [ 70 71 ]. Additionally, AI can generate highly convincing fake information due to algorithmic flaws, undermining trust in reliable sources [ 72 73 ]. This complicates career planning for students, as they struggle to verify the authenticity of career information due to their limited knowledge about the world of work. Furthermore, students’ ability to clearly express their thoughts, understandable by both humans and machines, significantly impacts their effectiveness in prompting AI [ 74 ]. Therefore, developing high proficiency in English is crucial, as it is the global lingua franca and the primary language AI systems are designed to respond to [ 75 76 ].
To further exacerbate these problems, massification of higher education has raised concerns about training quality [ 51 ], thus reducing the value of academic credentials as indicators of skills competency [ 66 ]. Skills-based hiring now dominates, necessitating continuous skill updates due to rapid technological advancements [ 67 ]. Consequently, traditional university degrees are becoming less relevant indicators of a worker’s capabilities [ 66 ]. Additionally, adolescents often struggle with mental health and social interactions due to excessive internet and social media use [ 68 ]. As AI technologies advance, adolescents face increased risks of misinformation and fake news, yet interventions to promote critical thinking skills are scarce and under-researched [ 69 ].
Additionally, the quality of school-based career services is significantly influenced by school management support [ 62 ]. Teachers are often overwhelmed by teaching and administrative duties, making it difficult for them to allocate time to support their students’ career planning [ 63 ]. When teachers attempt to organize career guidance activities, they may face scrutiny from administrators who are concerned about these activities disrupting academic learning [ 64 ]. Additionally, teachers seeking professional development often face administrative hurdles, such as the requirement to reschedule all their classes to attend training sessions [ 65 ].
Globally, the survey results of a study conducted by the Organization for Economic Co-operation and Development (OECD) [ 47 ] on the occupational expectations of 15-year-olds around the world. These occupational expectations were then matched with the expert analysis performed by Frey and Osbourne in 2013 [ 7 ] and the Department of Education in 2023 [ 46 ]. As shown in Table 1 , Frey and Osborne [ 7 ] initially classified only a few of these desired job roles as having a high risk of automation. However, a decade later, roles once deemed low-risk in 2013, such as psychologists and lawyers, are now highly susceptible to AI-induced job automation. This change is likely due to major advancements in AI technologies, especially in natural language processing and machine learning, which greatly enhance AI’s capability to handle complex tasks involving sophisticated rules and human emotions [ 46 61 ]. Additionally, it is crucial to recognize that creative roles like voice actors and screenwriters were initially considered to have low exposure to AI. However, as AI technology rapidly progresses, these roles, exemplified by the situation in Hollywood, are now at significant risk of AI-induced job displacement [ 20 21 ].
Empirical research shows that career-related information provided by schoolteachers influences students’ career planning most significantly [ 1 53 ]. However, valuable career insights are typically derived from industry experience and private professional networks. Companies often keep detailed career information confidential [ 55 ]. Consequently, schoolteachers, who spend most of their careers in a school setting, often struggle to provide insightful career information and foresee the challenges students may face in the AI era [ 6 59 ]. This elucidates why school-based career guidance and counseling programs may not be able to sufficiently equip students to confront the imminent threats posed by AI-induced job automation, which is generating anxiety within the global workforce [ 44 50 ]. This issue is apparent in a set of well-established career guidance benchmarks developed over a decade ago in England [ 54 ]. These benchmarks no longer adequately address the current career development needs of students, as the structure of the world of work has fundamentally changed following the COVID-19 pandemic and global economic recessions [ 56 ]. This is exemplified by the real-life employment situations in the United Kingdom, where students are encountering significant difficulties in securing graduate jobs [ 58 ]. Despite warnings from the UK Department of Education [ 46 ] and the European Parliament [ 50 ] about the clear threat of AI-induced job displacement, these benchmarks have not been updated to address the imminent risks associated with AI-driven job displacement and the evolving skills requirements in the global workplace. Recent research suggests that one possible cause of this is the inability of schoolteachers to keep pace with the development of the world of work [ 1 60 ].
From a practical standpoint, the global expansion of higher education makes it harder to ensure quality employment outcomes for secondary students after they have completed their university education/vocational training [ 51 ]. Furthermore, secondary students worldwide face considerable challenges due to the inconsistent quality of school-based career-related teacher support [ 6 53 ]. Frameworks such as the Gatsby Benchmarks in England emphasize that adequate career-related teacher support is essential for effective career guidance in schools [ 54 ]. Despite its significance, the specific nature and types of this support are often not thoroughly examined [ 53 ]. In the context of AI-induced job automation, these frameworks communicated little to no details on how schoolteachers should facilitate their students’ career planning as AI is now powerful enough to replace human workers in both low- and high-skilled roles [ 7 46 ].
Considering the extensive impact of the current AI disruption, it raises doubts about whether the current theoretical foundation in adolescent career development research can effectively guide educational and counseling professionals in preparing adolescents for the future workforce. Given that the current generation of AI (e.g., GPT-4) is already capable of replacing human intelligence [ 109 110 ], a comprehensive overview of how AI could affect the career transitions of secondary students is urgently needed. This information will be crucial for secondary students’ career planning.
Lastly, recent research in adolescent career development has shifted from traditional job-matching and employability enhancement to framing career problems as if they were mental health issues, explored through positive psychology. These studies employ quantitative methods to investigate the relationships between positive psychology concepts such as courage, career constructs (e.g., career adaptability), and mental health variables (e.g., well-being) (see e.g., [ 103 ]). This approach shares the criticisms of positive psychology research. That is, these studies often lack real-world applicability and practical insights about career development [ 104 105 ]. The study of positive psychology itself is criticized for subjective definitions, weak theorizing, and promoting unrealistic positivity, leading to pathologization of negative emotions [ 104 107 ]. In fact, as revealed in Lee et al.’s study [ 108 ], employees’ levels of career adaptability positively influenced their turnover intentions when experiencing lower levels of supervisor and coworker support, suggesting that career adaptability can be a negative construct [ 106 ].
Second, career theories often assume that career decision-making is a one-dimensional process guided by a few universal principles [ 88 ]. Consequently, the specific factors mediating career development in real-world contexts are often not clearly identified [ 89 92 ]. For instance, the super short form of the Career Adapt-Abilities Scale [ 93 ] contained four items, which are “Planning how to achieve my goals”, “Keeping upbeat”, “Exploring my surroundings”, and “Solving problems”. These items are so universal and unspecific that their ability to provide meaningful explanations of career development issues in the real world is highly uncertain [ 55 94 ]. Furthermore, the item “Solving problems” specifically relates to the measurement of confidence, but this non-specific measurement contradicts with the original conceptualization of self-efficacy. According to Albert Bandura [ 95 96 ], the predictive power of self-efficacy is valid only when assessed in relation to specific tasks within a particular context. This example questions the validity of career adaptability [ 97 ] and highlights the researchers’ bias in pursuing only statistical significance without considering conceptual and clinical/empirical implications [ 98 102 ].
First, these concepts often overlook the interpersonal, affective, and emotional aspects of work. With AI’s rapid advancement, the notion that humans are the sole providers of cognitive skills is increasingly challenged. Industry experts now emphasize the need for developing emotional and affective skills like leadership, persuasion, and communication to highlight human uniqueness in an automated environment [ 82 84 ]. The concept of career adaptability is particularly deficient, lacking an interpersonal perspective despite the essential nature of collaboration [ 17 ]. Only recently, “cooperation” has been proposed to address this critical flaw [ 85 ]. Likewise, some scholars have attempted to apply Amartya Sen’s Capability Approach to explain career behaviors. However, in the context of career guidance and counseling, this approach exhibits significant limitations due to its egalitarian principles, inherent subjectivity, insufficient theoretical foundation, and challenges in measuring and comparing capabilities [ 86 ]. This underscores the urgent need to reassess these career concepts in light of AI job disruption. As AI outperforms humans in cognitive tasks [ 87 ], the industry now prioritizes human-centric skills such as empathy, persuasion, openness to new ideas, and creativity, which are uniquely human attributes [ 82 84 ].
Following the classification and presentation styles used in state-of-the-art expert reports produced by expert informants such as the World Economic Forum [ 120 122 ]. The information identified in the selected research studies was categorized according to their geographical locations and job sectors. Only published peer-reviewed materials and reports published by expert informants and global/governmental organizations (e.g., UNESCO) were included. Nonpeer-reviewed materials were all excluded. Articles were included if they met the following criteria: (1) described and explained the mechanism behind technological disruption and its impact on the workforce, (2) assessed the extent of how AI could impact student populations and the workforce, (3) offered predictions and forecasts of the impact and extent of AI disruption could have on education and work, and (4) theoretical and practical frameworks containing any of the keywords. It was found that 270 articles met the criteria presented in the above. After close inspection, 60 articles were finally chosen for further analysis and summarization. Figure 1 presents a flowchart illustrating the process of conducting the literature search and selecting materials for review.
Keyword searches were also conducted in major academic journals that specialized in career development. These are, for example, the International Journal for Educational and Vocational Guidance, the British Journal of Guidance and Counseling, the Journal of Career Assessment, the Journal of Vocational Behavior, and the Journal of Career Development. Keywords, or a combination of keywords in English such as “AI job automation”, “artificial intelligence workforce”, “generative AI student work”, and “AI work report”, were used to identify and locate relevant materials. The identified articles, papers, and reports were then downloaded from the internet.
This review does not aim to be comprehensive; instead, it seeks to provide an overview of the current developments of AI-induced job automation and how it could impact the workforce, which in turn influences the career planning of secondary students. The advantage of this approach is that it emphasizes surveying the literature and highlighting the existing knowledge base on the subject [ 14 115 ].
The main objective of conducting a systematic literature review is to improve the replicability of research, allowing other researchers to use the same methods and confirm the results [ 111 ]. However, not all literature reviews are systematic; their nature depends on the research purpose [ 111 ]. Narrative reviews, for instance, are more flexible and interpretative. They aim to synthesize and discuss the literature rather than strictly replicate quantitative findings, offering a broad overview of a topic and highlighting gaps or inconsistencies in the existing research [ 112 113 ]. A narrative review is most suitable when the literature review is designed to examine the theoretical conceptualization of a research topic, focusing on its overall theoretical meaning rather than the statistical significance of the findings [ 114 ]. Due to the use of inconsistent research methods in studying the impact of AI-induced job automation on adolescents’ career development, our understanding of this topic is thus fragmented, inconsistent, and incomprehensive. It is, therefore, necessary to conduct a narrative review on this topic to identify gaps and inconsistencies in the existing literature [ 14 15 ]. Such a process is crucial for improving the scientific rigor and replicability of future research on this topic [ 111 ].
In summary, global surveys indicated that AI will transform work processes and boost productivity. While AI is expected to replace many job roles, it will also create new ones. However, automation may exacerbate inequality and anxiety, particularly among workers with limited access to technology. Women and younger employees lacking in technological proficiency are more vulnerable to job displacement due to automation or a lack of AI skills. Unlike previous technological revolutions, this AI-driven wave will significantly disrupt both skilled blue-collar jobs and cognitively demanding white-collar jobs, which require specialized knowledge obtained through university education. Roles involving repetitive tasks or adherence to pre-existing rules, such as Lawyers, Financial Analysts, and Accountants, are highly susceptible to automation. In contrast, jobs requiring human emotions and creativity, like Schoolteachers and Fine Artists, are less likely to be automated.
Last, a global survey by Goldman Sachs [ 136 ] explored the potential impacts of AI adoption on global economic development. AI adoption is anticipated to drive significant labor cost savings and productivity improvements. Nearly two-thirds of current jobs in the United States and Europe are exposed to some degree of AI automation, with up to one-fourth of current work potentially being automated, affecting up to 300 million jobs worldwide. The report suggested that AI-induced job automation could boost annual US labor productivity growth by nearly 1.5 percentage points over a decade following widespread adoption. Globally, AI could increase annual GDP by 7%. However, the actual impact will depend on AI’s capabilities and the timeline for its adoption. The report also indicated that up to 30% of existing job roles in Hong Kong, China, particularly in the finance and insurance sectors, could benefit from automation, that is, the highest ratio among surveyed regions. This suggested that the Hong Kong labor market will be significantly impacted by AI automation soon. These findings aligned with other global survey reports, such as those by Microsoft and LinkedIn [ 134 ] and the World Economic Forum [ 120 ], indicating that AI-induced job replacement and displacement are becoming evident [ 137 ].
Fourth, the International Monetary Fund’s [ 135 ] analysis of the global economy predicted that AI would transform the global economy. Around 40% of jobs around the world are exposed to the risks of automation. In advanced economies, exposure to AI-induced job automation increases to 60% of the workforce. AI is projected to increase productivity, but job automation will also result in the widening of income inequality. College-educated workers are expected to adapt better to the changing work landscape, while older workers, women, and young people face substantial exposure to higher risks of AI-induced job displacement.
Third, PricewaterhouseCoopers in 2024 [ 44 ] conducted a global workforce survey with more than 56,000 professionals from 50 countries. The survey revealed that over half of the workers felt overwhelmed by rapid changes at work, with 47% worrying about their job security. The findings also suggested that AI is a double-edged sword. While 73% of the respondents commented that AI would help them to be more creative at work, 52% of the respondents were concerned that the adoption of AI would lead to increased bias and misleading information.
Second, Microsoft and LinkedIn [ 134 ] investigated the impact of AI automation on the global workforce through a survey of 31,000 professionals across 31 countries. The findings revealed that 75% of respondents are currently using AI in their daily work, with 46% having adopted it in the past six months to reduce their workload. AI is positively perceived for enhancing productivity and creativity, aiding in time management, prioritizing tasks, and increasing job satisfaction. However, business leaders may resist this change as AI fundamentally alters the workforce and work processes. AI is also reshaping the labor market by increasing the demand for AI skills and changing job roles. Many organizations lack a clear strategy to leverage AI upskilling effectively. Additionally, companies are facing challenges in retaining AI-skilled workers, with 46% of these employees globally considering leaving their current jobs.
Conversely, roles that enhance work automation and digitization, such as data scientists, AI and machine learning specialists, big data specialists, robotics engineers, user experience designers, information security specialists, and software engineers, are expected to grow [ 122 ]. Rapid digitization in some job sectors raises new issues concerning employee well-being as remote work increases [ 120 ]. To adapt to these changes, employees are expected to acquire technological skills (e.g., deep learning), statistical skills (e.g., regression), and communication skills for accurate self-expression [ 120 ]. Additionally, they should strengthen human qualities such as leadership, flexibility, originality, and persuasion and negotiation [ 120 121 ]. Psychological well-being and self-management skills, such as resilience, stress tolerance, mindfulness, gratitude, and kindness, are also essential as workers are likely to undergo several job transitions due to automation. It is imperative that they possess the ability to manage their own well-being effectively.
Globally, AI is projected to replace around 85 million jobs and create 97 million new roles by 2025 [ 121 ]. Rapid advancements in computer processing power, high-speed internet, cloud computing, and big data are expected to disrupt approximately 44% of workers’ skills in the coming years [ 122 ]. A net contraction of 14 million jobs globally is projected between 2023 and 2027. White-collar roles that are routine-based and adhere to established norms, such as accountants, financial auditors, financial analysts, business administration managers, cashiers, telemarketers, bank tellers, and lawyers, are most impacted by AI automation [ 119 ].
First, the World Economic Forum’s “Future of Jobs Report” series [ 120 122 ] examined the interplay between socioeconomic development and technological advancement. To adopt new technologies, companies are employing staff with technological expertise and outsourcing current functions. Concurrently, they are reducing their full-time workforce through automation and enhancing productivity with technology. Retraining and upskilling are common strategies to address skill gaps, with staff lacking new technological skills being strategically reduced. It is estimated that two-thirds of the current global workforce will be affected by this transition [ 120 ].
In the empirical workplace, a Harvard Business School study with Boston Consulting Group [ 133 ] examined AI’s impact on management consultants’ performance. The study revealed that AI significantly improved productivity and quality for tasks within its capabilities but struggled with more complex tasks. Consultants using AI completed 12.2% more tasks and produced 40% higher-quality results. However, for tasks beyond AI’s capabilities, consultants using AI were 19% less likely to achieve accurate solutions compared to those without AI assistance. These results suggested that AI usage is linked to reduced overall cognitive functioning among humans.
Preliminary findings suggest that students may not be adequately prepared to address the risks of job automation before entering the workforce. For example, a university-to-work transition program developed by a Hong Kong university’s English department did not address AI-induced job displacement risks and the related psychological disturbance (e.g., anxiety). The program’s developers interviewed 40 employers and 69 graduates to identify necessary skills that can facilitate students’ transition to the workplace [ 125 126 ]. The researchers found that graduates generally lacked soft skills and suggested improvements. However, the validity of these claims is questionable. First, the authors lack training and qualifications in psychology and career guidance. This undermines the validity and reliability of the research. Second, subjective definitions of “soft” and “hard” skills were used, which may reflect researcher bias [ 127 ]. Additionally, in terms of research methods, soft skills are often subjectively defined and overlap with each other [ 128 ]. This creates measurement problems that can lead to wrongful interpretation of the subject matter [ 5 ]. These issues highlight the biases or misinformation that can arise when researchers lack relevant expertise, potentially leading to the wrongful pathologization and medicalization of career problems [ 129 130 ]. Insufficient knowledge may also lead to ethical breaches and result in interventions that are ineffective or even potentially harmful to students, despite being well-intentioned [ 131 132 ].
The recent breakthrough in computer engineering, where graphics processing units (GPUs) replace traditional processors, has finally met the computational demands of AI after a long stagnation [ 123 ]. This advancement paves the way for the exponential growth of AI applications in all aspects of life. Jensen Huang, NVIDIA’s CEO, stated that generative AI’s rise means “nobody will have to program”, rendering coding education less relevant [ 124 ]. The computerization of cognitive tasks that are typically performed by humans is beneficial for society from an efficiency standpoint, as computers can operate continuously with accuracy and impartiality [ 7 ].
Within the school context, educators and policymakers can explore how AI technologies can be deployed to facilitate students’ career planning. First, computer systems powered by artificial intelligence technology can be used to help students become more aware of their career interests [ 184 ]. Second, AI systems can be used to help students conduct self-regulated learning and explore job opportunities in a particular occupation field [ 185 ]. Third, AI systems can also help learners to identify job opportunities and learn about the recent developments of the occupational fields that suit their career aspirations [ 186 ]. Last, artificially intelligent technologies such as deep learning have been found to be able to promote the formation of entrepreneurial intentions and motivate students to engage in active career planning [ 187 ].
Last, AI adoption led to both optimism and fear [ 44 ]. In Europe, AI adoption has increased employment in AI-exposed occupations without widespread job loss. In Asia Pacific, 41% of professionals were optimistic about AI, but 16% feared AI-induced job replacement. For example, in Hong Kong, up to 30% of jobs are exposed to AI automation [ 136 ]. To get past the fear of being replaced by AI, workers should emphasize adaptability and lifelong learning to mitigate these fears. One solution will be for workers to focus on transitioning into AI-related roles and develop proficiency in using AI. Alternatively, they can focus on developing skills in job sectors (e.g., education) where human-centric skills are irreplaceable [ 82 122 ].
Second, there are significant regional disparities in the extent of automation. For example, in the United Kingdom, 10–30% of jobs could be automated, with significant regional disparities [ 154 ]. In Japan, high costs and a lack of expertise limited AI adoption [ 147 ]. These varying results suggest that workers in highly affected regions should seek training in emerging fields and industries and gain expertise to mitigate the risks brought about by technological disruptions.
AI-induced job automation impacts the workforce in the following ways. First, AI boosts productivity [ 120 139 ]. Working professionals are generally optimistic about adopting AI at work, as it allows them to focus on meaningful tasks [ 121 ]. Younger employees are particularly active AI users [ 146 ]. Automation, however, also leads to job displacement and inequality. Policies and programs aimed at reskilling and social safety nets are thus crucial [ 122 ]. At the same time, workers should acquire skills that complement AI technologies. Continuous learning and upskilling in areas less likely to be automated, such as critical thinking, creativity, and complex problem-solving, are essential [ 82 122 ].
Overall, AI is transforming work by enhancing productivity and generating new job opportunities. However, it also induces anxiety, job displacement, and inequality, particularly affecting those with limited technology access, women, and younger workers [ 44 ]. Both skilled blue-collar and cognitively demanding white-collar jobs are vulnerable. Repetitive tasks, such as those in accounting and law, are highly susceptible to automation, whereas roles requiring creativity and emotional intelligence, like teaching and fine arts, are less likely to be automated [ 7 121 ]. Regional impacts vary, with significant disparities in AI adoption and job exposure. In schools, the use of AI technologies excites students and educators [ 30 ] but raises concerns about academic integrity and its potential negative effects on critical thinking and problem-solving skills [ 39 ].
In Africa, the AI adoption rate remains low. As an example, a mere 9% of African media companies are making extensive use of AI tools, while 48% are employing them to a limited extent. The low rate of AI adoption is primarily due to high implementation costs and a lack of skilled personnel, with many organizations remaining hesitant to adopt AI [ 150 ]. Furthermore, 52% of businesses have yet to integrate AI tools into their operations, and 44% have not completed AI-specific training for their employees [ 151 ]. Overall, the adoption of AI is progressing slowly, hindered by inadequate infrastructure and resources, with 450 million individuals still without access to mobile broadband networks [ 156 ]. Like nations in the West, young people and women are more exposed to the risks of automation because they tend to work in low-skilled job roles [ 145 ].
In the Middle East, the impact of automation on workers was examined by McKinsey & Company [ 152 ]. Among the six countries in the region—Bahrain, Egypt, Kuwait, Oman, Saudi Arabia, and the United Arab Emirates—it was predicted that up to 45% of existing work could be automated by 2030. Notably, workers with low-to-medium levels of education and experience are particularly susceptible to automation shocks. At the same time, AI adoption was projected to drive growth and enhance productivity when the workforce is equipped with the necessary skills. Job automation is projected to bring significant benefits to the region, potentially adding around150 billion in value. However, the current rate of AI adoption remains low. Challenges to adoption include the lack of a regulatory framework, a shortage of skilled professionals, and data privacy concerns. Despite these hurdles, the outlook for AI adoption is optimistic as Middle Eastern economies are shifting away from oil and gas, investing more in technology and education to develop future AI talent. The region has already witnessed its recent rapid growth in e-commerce, which was driven by its large technologically adept young population [ 153 183 ].
In Singapore, an analysis by the International Monetary Fund [ 149 ] indicated that a significant portion of the workforce is at risk of automation due to the high concentration of highly skilled job roles. Approximately 77% of current job roles are highly susceptible to AI-induced automation. Among these roles, there is an equal distribution between those with high and low AI complementarity. High-complementarity roles, such as managers and professionals, may see productivity gains, while low-complementarity roles, such as clerical and administrative positions, face a higher risk of job displacement. Women and younger workers are particularly vulnerable to the adverse effects of job automation. Although job automation has the potential to exacerbate inequality, Singapore’s world-class infrastructure and skilled workforce position the country to benefit from these technological advancements.
In other Chinese regions, such as Hong Kong, Goldman Sachs [ 136 ] predicted that up to 30% of current job roles within the local labor market are exposed to AI automation, with the financial services sector facing the highest levels of impact. A recent study by Amazon Web Services [ 181 ] revealed that acquiring AI skills could significantly enhance the career prospects of Hong Kong workers, as well as boosting their productivity. The research indicated that workers with AI expertise could see salary increases of up to 28%. Despite the obvious need for more AI-skilled talent, 73% of employers face challenges in recruiting suitable candidates due to a lack of training resources and financial constraints. In addition, while professionals in Hong Kong were positive about AI, they were at the same time cautious about its adoption as they fear their jobs could be eventually replaced by AI [ 44 ].
In China, a survey study performed by the SAS Institute [ 148 ] showed that China leads globally in AI adoption, with 83% of Chinese respondents claiming that they are using the technology. This surpasses the global average of 54% and the 65% adoption rate in the United States. In terms of the impact AI has on employment, Wang et al. [ 179 ] used a novel task-based quantification approach to predict the effects of AI automation in the Chinese workforce using characteristics of the American job market. Findings indicated that 54% of jobs in China could be replaced by AI in the coming decades. Unit heads would be relatively secure, while jobs requiring perceptive and manipulative skills are most vulnerable to AI displacement. Contrary to the popular belief that AI will displace humans, Shen [ 180 ] demonstrated that AI increased manufacturing jobs in China, enhancing productivity and market size, and benefiting labor-intensive industries and women in digitally advanced regions.
In Japan, a recent Reuters survey [ 147 ] revealed that while nearly a quarter of Japanese companies have adopted AI, over 40% have no plans to utilize the technology. The survey, conducted by Nikkei Research, showed that the major reasons for adopting AI included addressing workforce shortages and reducing labor costs. However, challenges such as employee anxiety over job cuts, high costs, and lack of expertise hinder broader adoption.
In India, PricewaterhouseCoopers [ 178 ] surveyed 600 professionals from various job sectors in 2018. Results showed that there was a high demand for affordable, reliable services, with a preference for AI assistants over real humans in job roles such as Travel Agent, Financial Advisor, and Tax Preparer. However, for health and education, human interaction was preferred. While embracing the power of AI, the workforce feared that AI-run services would result in a loss of human touch. Another downside of AI adoption was that it created anxiety about job loss. According to the Work Trend Index 2023 compiled by Microsoft, 74% of Indian employees were concerned that AI might replace their jobs. However, 83% were open to delegating a significant portion of their workload to AI to reduce the risk of job loss posed by the technology. Comparable results were observed in the survey conducted by EY and FICCI [ 157 ]. The study indicated that approximately 37% of the Indian workforce requires reskilling. As a result of AI-induced job automation, the workforce will transition into entirely new roles that do not currently exist, many of which will demand substantially different skill sets.
In Australia, research by McKinsey & Company [ 177 ] and ServiceNow [ 155 ] indicated that jobs involving repetitive and technical tasks were at high risk of being affected by automation. By 2030, 1.3 million workers, or 9% of the workforce, may need to change professions due to AI-driven automation. Jobs in office support, customer service, sales, and food services are most at risk.
Deloitte [ 146 ] surveyed over 11,900 young employees and students and found that these young people were leading the adoption of AI, resulting in increased productivity and new skill development opportunities. In total, 76% of the respondents in China and 29% of the participants in Australia believed that AI had significantly impacted their career decision outcomes. The study underscores the need for businesses and policymakers to adapt to this rapidly evolving technology to fully leverage its benefits.
In this region, a survey of 19,500 professionals in 14 territories across Asia Pacific conducted by PricewaterhouseCoopers [ 44 ] revealed that 41% of employees in the Asia-Pacific region were optimistic about AI’s ability to enhance productivity. However, approximately 16% were concerned that AI might replace their jobs. Meanwhile, around 34% perceive AI as a chance to acquire new skills.
The European Skills Agenda has been launched across the European Union to assist young people in developing a broad range of skills for sustainable living and working with AI-powered technologies [ 167 168 ]. These skills, known as, encompass transversal competencies, including knowledge, abilities, values, and attitudes essential for eco-friendly practices [ 169 170 ]. Green skills, as defined by the OECD and the European Centre for the Development of Vocational Training [ 171 ], are crucial for adapting products, services, and processes to climate change, technological advancements, and environmental regulations. These include technical expertise in eco-friendly technologies and transversal competencies for environmentally conscious decision-making. Additionally, AI can significantly enhance green skills, boosting productivity while promoting environmental sustainability [ 172 173 ]. The shift to sustainable practices offers significant opportunities for upskilling and reskilling for emerging green jobs such as Sustainability Manager [ 174 175 ]. Integrating these holistic green skills into global training systems is thus vital [ 176 ].
AI’s impact on job displacement varies by gender, with AI-induced job automation primarily affecting white-collar, medium-skilled jobs. Women, who dominate office administration, healthcare, education, and social services, face significant job loss risks due to increased automation [ 166 ]. Jobs with well-defined routines and physical/manual tasks, such as agriculture and clerical work, are highly susceptible to automation. In contrast, jobs that are unpredictable (e.g., gardening and childcare) or require social intelligence and empathy are less automatable. Professions in IT, management, science, teaching, humanities, social sciences, media, law, medicine, and nursing will remain in demand [ 165 ].
Within the European Union, Albanesi et al. [ 144 ] investigated the relationship between labor market trends and new technology adoption in 16 European countries from 2011 to 2019. They found that employment shares in AI-exposed occupations increased, particularly among younger skilled workers. Wages showed little correlation with AI exposure. The European Parliament [ 164 165 ] further indicated that AI adoption between 2011 and 2020 did not lead to widespread job losses as previously feared. This was partly due to the relatively low AI adoption rate, with only 42% of EU enterprises having implemented at least one AI technology [ 166 ].
The Institute for the Future of Work [ 163 ] surveyed 1012 senior executives from UK firms with over 20 employees. Results showed that 79% of these firms have adopted new technologies for physical and cognitive tasks in the past three years. Small and medium-sized enterprises were automating cognitive tasks at rates similar to larger firms. While the overall impact on job creation and skill enhancement was positive, significant regional disparities persisted due to inadequate investments in education and connectivity. Improved technological readiness is essential for boosting job opportunities and skill levels across the UK workforce.
The Institute for Public Policy Research [ 154 ] analyzed the potential impact AI-induced job automation had on the UK workforce, predicting significant job displacement. AI could disrupt up to 59% of tasks, especially routine cognitive ones. Initially, back-office, entry-level, and part-time jobs, such as secretarial and administrative roles, are most at risk. As AI technology advances, higher-earning occupations involving non-routine cognitive tasks may also be affected. The findings aim to guide policymakers in leveraging AI benefits while mitigating workforce disruptions. Companies should be incentivized to provide upskilling training to enhance productivity and minimize job displacement.
In the United Kingdom, the Department of Education of the UK government [ 46 ] conducted an extensive analysis to assess the scale of disruption AI adoption could bring to the UK workforce. The study reveals that 10–30% of jobs could be automated by AI and large language models, with finance, law, and business management roles being particularly vulnerable, especially in accounting and finance. London and the South East face the highest exposure, correlating with higher qualifications. The report identifies roles like Psychologists, Management Consultants, Business Analysts, Accountants, Civil Engineers, Actuaries, Statisticians, and Economists as highly susceptible to AI automation. Conversely, jobs such as Truck Drivers, Professional Athletes, Cleaners, Launderers, and Bricklayers are least at risk, primarily due to their reliance on manual dexterity rather than advanced university-level training.
According to a recent study conducted by the International Labor Organization and the World Bank, AI adoption could eliminate up to 5% of jobs in Latin America. The integration of AI in various sectors, such as manufacturing, retail, agriculture, legal services, and marketing, is transforming traditional roles, optimizing processes, and enhancing customer experiences. This widespread adoption signifies a transformative trend, empowering professionals to excel in decision-making and thrive in a rapidly evolving landscape. At the same time, AI adoption gives rise to inequality issues, with women and younger workers being more vulnerable to job loss due to automation [ 162 ].
Last, McKinsey Global Institute [ 161 ] predicted that by 2030, up to 30% of current work hours could be automated, particularly affecting office support, customer service, and food service roles due to AI-induced job automation. The American workforce will have to undergo substantial job-related transitions as automation continues to progress. It is predicted that workers working in lower-wage jobs are 14 times more likely to change jobs or face job redundancy when compared to higher-wage workers. As AI systems now have the capability to rival human intelligence, traditional merit-based academic credentials may not serve as an appropriate indicator when it comes to employee selection. Employers are encouraged to focus on skills-based hiring and workforce reskilling to navigate through the changes brought about by technological disruption.
Fourth, a survey by the National Bureau of Economic Research [ 160 ] revealed that AI technology was utilized across all job sectors among 850,000 US companies. However, the overall adoption rate was low, with fewer than 6% implementing any AI-related technologies. Adoption was highest among larger firms and among more educated and younger workers.
Third, Pew Research Center’s analysis of federal data in 2023 [ 59 ] revealed that 19% of the US workforce are working in jobs highly exposed to AI. Occupations requiring analytical skills, such as critical thinking, writing, science, and mathematics, are more susceptible to job replacement. The study also highlighted that jobs requiring higher education and analytical skills are more susceptible to AI integration. Women, Asian, university-educated, and higher-paid white-collar professionals are being more affected. These job roles are, for example, Web Developers, Tax Preparers, Lawyers, Financial Auditors and Budget Analysts. In contrast, 23% of the workforce are working in jobs with low AI exposure. These jobs are such as Barber, Firefighter, and Dishwasher. Younger workers and persons with less formal education were also found to be less exposed to the risks of AI automation. Despite concerns about the potential threats of AI automation, many workers in AI-exposed industries remain optimistic about AI’s impact on enhancing their productivity. However, schoolteachers have expressed their concerns about the imminent widespread adoption of AI. About 25% of US K-12 teachers believed AI tools could cause more harm than good in education, while 32% see a balance of benefits and drawbacks [ 159 ].
Second, The White House [ 158 ] assessed AI’s economic impact on US workforces, noting that despite low overall adoption, AI will significantly transform industries, workflows, and productivity. However, it also poses challenges like job displacement, increased inequality, and the need for workforce reskilling. AI technologies can now automate non-routine tasks by using algorithms trained on examples rather than explicit rules, potentially replacing and outperforming educated workers in roles requiring significant education and experience.
In the United States, first in their seminal study, Frey and Osbourne [ 7 ] developed an algorithm to evaluate the impact of automation on the US labor market, analyzing 702 job roles from the O*Net database. They found that 47% of these roles are highly susceptible to automation (Probability ≥ 0.09). Common characteristics of these jobs included repetitive manual tasks (e.g., Hand Sewers, probability = 0.99) or rule-based activities (e.g., Accountants and Financial Auditors, probability = 0.94; Paralegals and Legal Assistants, probability = 0.94). Conversely, roles such as Psychologists (probability = 0.0043), Fine Artists (probability = 0.0042), Mechanical Engineers (probability = 0.0011), and Secondary Schoolteachers (probability = 0.0078), which require human emotions and creativity to address individual needs, are less likely to be automated due to their non-repetitive nature.
In Canada, artificial intelligence was perceived to have both beneficial and detrimental effects in the workplace. According to Future Skills Centre [ 142 ], 20% of jobs in Canada were significantly vulnerable to automation. Canadian workers are lagging in adopting AI compared to their global counterparts. Only 25% of Canadian respondents use AI tools monthly, versus 36% globally [ 44 ]. Another recent report by Deloitte in 2024 [ 143 ] revealed that 56% of Canadian employers did not use AI and are not planning to adopt it soon. Only 15% of workers currently use AI, and nearly half of employers believed their employees were unprepared for AI integration.
Overall, AI adoption is rising globally, with new AI technology usage doubling in 2023 [ 139 ]. Currently, 65% of organizations around the world regularly use AI at work. Denmark leads in adoption among 11 OECD countries, followed by Belgium, Italy, Portugal, and France. Adoption rates are lower in Israel, Japan, and South Korea [ 140 ]. Across geographical locations, a significant “AI divide” exists between the Global North and South due to disparities in infrastructure and readiness [ 141 ].
According to OECD [ 138 ], regions with a higher share of tertiary-educated workers, a strong service sector, and a large urban population tend to have a lower risk of AI-induced job automation. Conversely, areas with a higher concentration of routine-based jobs are more susceptible to automation. The chronological evolution of AI automation and regional disparities in AI adoption across different geographical regions are summarized in Table 2 and Table 3
9. Discussion
50, Schools play a pivotal role in fostering innovation and developing skills valued in the labor market. With rapid technological advancements, AI could soon possess the cognitive capacity to outperform a significant portion of the adult population [ 124 ]. To stay relevant, schools must adapt and understand how AI impacts employment and education for young people [ 46 134 ]. This approach ensures that adolescents can keep pace with technological advancements and enhance their career prospects. As AI advances, upskilling and reskilling will become essential throughout a worker’s career [ 121 122 ]. Effective school-based career guidance initiatives are vital for supporting lifelong learning and facilitating work transitions, especially as job automation becomes more prevalent. Given the nascent understanding of AI’s impact on career development, global education systems must establish policies that enhance productivity, growth, and societal well-being through the ethical use of AI [ 46 134 ]. This ensures that young people can make informed decisions based on unbiased career information and are equipped with the necessary skills to thrive in a job market increasingly exposed to job displacement and disinformation risks as AI technologies continue to advance [ 159 ].
7,120,169,170,170, At the system level, results from this review showed that AI enhances productivity but also causes job displacement, anxiety, and inequality. Effective career guidance and training programs are crucial to support career transitions and mitigate fears of job replacement, especially in regions with high automation exposure [ 154 163 ]. Globally, a succinct recurrent theme is that people who have difficulties accessing technology and re/upskilling programs are more likely to be exposed to the risks of job replacement due to automation [ 156 ]. This is an important inequality concern that policymakers can address by investing in large-scale re/upskilling programs [ 1 171 ]. A good example will be the policy reforms carried out within the European Union in which a skills framework delineated the qualities, skills, and values contemporary workers should possess [ 167 ]. These attributes were not only limited to cognitive skills development and technological competencies but also extended to psychological well-being and self-management, and how these qualities could be applied to promote sustainability and environmental protection [ 122 171 ]. The design principles adopted in these skills development frameworks can serve as a good reference for education systems worldwide for talent development. When workers are sufficiently re/upskilled, their levels of anxiety about job loss and replacement are likely to decrease as their new skill set will allow them to transition to a new job role more smoothly [ 79 ].
76,82,83, Furthermore, to help young people thrive in the future digital workplace and enjoy the benefits of AI adoption, policymakers can develop national-level skills frameworks that emphasize the development of human-centric skills, namely, language proficiency, empathy, adaptability, problem-solving, critical thinking, collaboration, and originality [ 69 152 ]. Cultivating critical thinking skills is especially important as adolescents are very likely to come across fake news and distorted information as they search for career information on the internet [ 129 ]. These skills can ensure that humans can complement and leverage technological tools rather than being replaced by them.
5,6,7,11,15,16,40,46,52, At the school level, as AI-induced job automation becomes more prominent, school administrators and managers must acknowledge its disruptive effects on students’ career planning [ 1 60 ]. Secondary school educators should recognize their crucial role in supporting students’ transition from school to work, as students are likely to experience anxiety and disappointment regarding their future career prospects due to technological disruption [ 16 44 ]. School administrators should proactively allocate sufficient resources to support school-based career guidance initiatives [ 47 ]. As influential figures, schoolteachers should stay informed about changes in the job market and communicate new career opportunities to students as they arise [ 1 53 ]. The massification of higher education has led to an increase in the number and variety of available courses. Educators must be well-versed in the career pathways these courses can lead to [ 16 ]. This can help to prevent disappointment and unemployment [ 51 ].
83,84,120,121,122,154,143, As AI’s power to automate work tasks enhances, the focus will shift to human-centric skills that AI struggles to replicate. These skills are, for example, critical thinking, originality, and emotional intelligence [ 69 161 ]. Schools will need to adjust their teaching priorities accordingly. Even though schoolteachers may not possess the resources to track the ongoing developments in the world of work, they can leverage their expertise to develop school-based career education programs focusing on human-centric skills and moral and civic values development [ 82 163 ]. Strengthening these foundational skills and values, together with educating students on the benefits of lifelong learning and the risks of using AI, are crucial for maintaining young people’s competitiveness in an AI-disrupted job market [ 44 146 ]. Additionally, mental health counseling should be offered to address anxiety and reduce stress about planning for the future [ 85 103 ]. This is essential because AI is evolving rapidly, and academic credentials alone are insufficient to assure cognitive skills proficiency [ 67 ].
| 2024-12-14T00:00:00 |
2024/12/14
|
https://www.mdpi.com/2673-8104/4/4/27
|
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Will AI Replace Jobs or Create More Opportunities?
|
Will AI Replace Jobs or Create More Opportunities?
|
https://www.harieducationalconsultancy.com
|
[] |
Routine and repetitive tasks are increasingly being handled by AI-driven systems, leading to job displacement in certain sectors. However, this shift does not ...
|
Artificial Intelligence (AI) is transforming industries at an unprecedented pace, raising a crucial question: Will AI replace jobs, or will it create more opportunities? As automation and AI-powered systems streamline operations, they also introduce new career paths and reshape the workforce. At Hari Educational Consultancy, we help students navigate this evolving job market by equipping them with the right knowledge and skills for future-proof careers.
The Impact of AI on Jobs
AI and automation are significantly affecting industries such as healthcare, finance, manufacturing, and IT. Routine and repetitive tasks are increasingly being handled by AI-driven systems, leading to job displacement in certain sectors. However, this shift does not mean a complete elimination of employment; rather, it signifies a transformation in job roles and responsibilities.
Jobs at Risk Due to AI
Some roles that involve repetitive tasks, data entry, and simple decision-making are more susceptible to automation. These include:
Data Entry Clerks
Telemarketers
Factory Workers
Basic Customer Support Roles
Cashiers and Retail Associates
Transportation and Delivery Drivers
AI-Driven Job Creation
Contrary to the fear of job loss, AI is also responsible for creating new career paths. The demand for skilled professionals in AI development, data science, cybersecurity, and AI ethics is growing rapidly. Some of the emerging roles include:
AI and Machine Learning Engineers
Data Scientists
Cybersecurity Specialists
AI Ethics Consultants
Robotics Engineers
AI-Assisted Healthcare Professionals
Digital Transformation Experts
The Need for Upskilling and Reskilling
The key to thriving in an AI-driven world is continuous learning. At Hari Educational Consultancy, we emphasize the importance of upskilling and reskilling to adapt to industry changes. Universities and institutions are now offering specialized courses in AI, robotics, and data science, ensuring students remain competitive in the job market. Some of the most in-demand skills include:
Programming and Coding (Python, R, Java, etc.)
Data Analytics and Visualization
Cloud Computing and Cybersecurity
Business Intelligence and AI-driven Decision Making
Future-Proof Career Choices
To stay relevant in the evolving job landscape, students should focus on careers that require critical thinking, creativity, and problem-solving—skills that AI cannot easily replicate. Fields such as:
Healthcare and Biomedical Engineering
Digital Marketing and AI-Enhanced Branding
Renewable Energy and Sustainable Development
Human Resources and AI-Driven Talent Management
AI-Powered Education and E-learning Development
Blockchain and Fintech Innovations
Conclusion
So, will AI replace jobs? The answer is not a simple yes or no. AI is not a threat but an opportunity for those who embrace change and innovation. Rather than replacing jobs entirely, AI is set to transform industries, creating new possibilities and roles that we have yet to fully explore. By staying informed, adapting skills, and choosing the right educational path, students can secure a bright future in the AI-driven world.
At Hari Educational Consultancy, we help students make informed career decisions by providing expert guidance on university admissions, scholarships, and skill-based education. Contact us today to shape your future in the era of AI!
📞 Contact Us: +91 9966581787
🌐 Website: Hari Educational Consultancy
| 2025-04-02T00:00:00 |
2025/04/02
|
https://www.harieducationalconsultancy.com/will-ai-replace-jobs-or-create-more-opportunities/
|
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Workplace impact of artificial intelligence - Wikipedia
|
Workplace impact of artificial intelligence
|
https://en.wikipedia.org
|
[] |
Starting in the 2020s, the adoption of modern robotics has led to net employment growth. However, many businesses anticipate that automation, or employing ...
|
Impact of artificial intelligence on workers
The impact of artificial intelligence on workers includes both applications to improve worker safety and health, and potential hazards that must be controlled.
One potential application is using AI to eliminate hazards by removing humans from hazardous situations that involve risk of stress, overwork, or musculoskeletal injuries. Predictive analytics may also be used to identify conditions that may lead to hazards such as fatigue, repetitive strain injuries, or toxic substance exposure, leading to earlier interventions. Another is to streamline workplace safety and health workflows through automating repetitive tasks, enhancing safety training programs through virtual reality, or detecting and reporting near misses.
When used in the workplace, AI also presents the possibility of new hazards. These may arise from machine learning techniques leading to unpredictable behavior and inscrutability in their decision-making, or from cybersecurity and information privacy issues. Many hazards of AI are psychosocial due to its potential to cause changes in work organization. These include changes in the skills required of workers,[1] increased monitoring leading to micromanagement, algorithms unintentionally or intentionally mimicking undesirable human biases, and assigning blame for machine errors to the human operator instead. AI may also lead to physical hazards in the form of human–robot collisions, and ergonomic risks of control interfaces and human–machine interactions. Hazard controls include cybersecurity and information privacy measures, communication and transparency with workers about data usage, and limitations on collaborative robots.
From a workplace safety and health perspective, only "weak" or "narrow" AI that is tailored to a specific task is relevant, as there are many examples that are currently in use or expected to come into use in the near future. "Strong" or "general" AI is not expected to be feasible in the near future,[according to whom?] and discussion of its risks is within the purview of futurists and philosophers rather than industrial hygienists.
Certain digital technologies are predicted to result in job losses. Starting in the 2020s, the adoption of modern robotics has led to net employment growth. However, many businesses anticipate that automation, or employing robots would result in job losses in the future. This is especially true for companies in Central and Eastern Europe.[2][3][4] Other digital technologies, such as platforms or big data, are projected to have a more neutral impact on employment.[2][4] A large number of tech workers have been laid off starting in 2023;[5] many such job cuts have been attributed to artificial intelligence.[6]
Health and safety applications [ edit ]
In order for any potential AI health and safety application to be adopted, it requires acceptance by both managers and workers. For example, worker acceptance may be diminished by concerns about information privacy,[7] or from a lack of trust and acceptance of the new technology, which may arise from inadequate transparency or training.[8]: 26–28, 43–45 Alternatively, managers may emphasize increases in economic productivity rather than gains in worker safety and health when implementing AI-based systems.[9]
Eliminating hazardous tasks [ edit ]
Call centers involve significant psychosocial hazards due to surveillance and overwork. AI-enabled chatbots can remove workers from the most basic and repetitive of these tasks.
AI may increase the scope of work tasks where a worker can be removed from a situation that carries risk. In a sense, while traditional automation can replace the functions of a worker's body with a robot, AI effectively replaces the functions of their brain with a computer. Hazards that can be avoided include stress, overwork, musculoskeletal injuries, and boredom.[10]: 5–7
This can expand the range of affected job sectors into white-collar and service sector jobs such as in medicine, finance, and information technology.[11] As an example, call center workers face extensive health and safety risks due to its repetitive and demanding nature and its high rates of micro-surveillance. AI-enabled chatbots lower the need for humans to perform the most basic call center tasks.[10]: 5–7
Analytics to reduce risk [ edit ]
The NIOSH lifting equation [ 12 ] [ 13 ] is calibrated for a typical healthy worker to avoid back injuries, but AI-based methods may instead allow real-time, personalized calculation of risk.
Machine learning is used for people analytics to make predictions about worker behavior to assist management decision-making, such as hiring and performance assessment. These could also be used to improve worker health. The analytics may be based on inputs such as online activities, monitoring of communications, location tracking, and voice analysis and body language analysis of filmed interviews. For example, sentiment analysis may be used to spot fatigue to prevent overwork.[10]: 3–7 Decision support systems have a similar ability to be used to, for example, prevent industrial disasters or make disaster response more efficient.[14]
For manual material handling workers, predictive analytics and artificial intelligence may be used to reduce musculoskeletal injury. Traditional guidelines are based on statistical averages and are geared towards anthropometrically typical humans. The analysis of large amounts of data from wearable sensors may allow real-time, personalized calculation of ergonomic risk and fatigue management, as well as better analysis of the risk associated with specific job roles.[7]
Wearable sensors may also enable earlier intervention against exposure to toxic substances than is possible with area or breathing zone testing on a periodic basis. Furthermore, the large data sets generated could improve workplace health surveillance, risk assessment, and research.[14]
Streamlining safety and health workflows [ edit ]
AI can also be used to make the workplace safety and health workflow more efficient. Digital assistants, like Amazon Alexa, Google Assistant, and Apple Siri, are increasingly adopted in workplaces to enhance productivity by automating routine tasks. These AI-based tools can manage administrative duties, such as scheduling meetings, sending reminders, processing orders, and organizing travel plans. This automation can improve workflow efficiency by reducing time spent on repetitive tasks, thus supporting employees to focus on higher-priority responsibilities.[15] Digital assistants are especially valuable in streamlining customer service workflows, where they can handle basic inquiries, reducing the demand on human employees.[15] However, there remain challenges in fully integrating these assistants due to concerns over data privacy, accuracy, and organizational readiness.[15]
One example is coding of workers' compensation claims, which are submitted in a prose narrative form and must manually be assigned standardized codes. AI is being investigated to perform this task faster, more cheaply, and with fewer errors.[16][17]
AI‐enabled virtual reality systems may be useful for safety training for hazard recognition.[14]
Artificial intelligence may be used to more efficiently detect near misses. Reporting and analysis of near misses are important in reducing accident rates, but they are often underreported because they are not noticed by humans, or are not reported by workers due to social factors.[18]
Hazards [ edit ]
Some machine learning training methods are prone to unpredictabiliy and inscrutability in their decision-making, which can lead to hazards if managers or workers cannot predict or understand an AI-based system's behavior.
There are several broad aspects of AI that may give rise to specific hazards. The risks depend on implementation rather than the mere presence of AI.[10]: 2–3
Systems using sub-symbolic AI such as machine learning may behave unpredictably and are more prone to inscrutability in their decision-making. This is especially true if a situation is encountered that was not part of the AI's training dataset, and is exacerbated in environments that are less structured. Undesired behavior may also arise from flaws in the system's perception (arising either from within the software or from sensor degradation), knowledge representation and reasoning, or from software bugs.[8]: 14–18 They may arise from improper training, such as a user applying the same algorithm to two problems that do not have the same requirements.[10]: 12–13 Machine learning applied during the design phase may have different implications than that applied at runtime. Systems using symbolic AI are less prone to unpredictable behavior.[8]: 14–18
The use of AI also increases cybersecurity risks relative to platforms that do not use AI,[8]: 17 and information privacy concerns about collected data may pose a hazard to workers.[7]
Psychosocial [ edit ]
Introduction of new AI-enabled technologies may lead to changes in work practices that carry psychosocial hazards such as a need for retraining or fear of technological unemployment.
Psychosocial hazards are those that arise from the way work is designed, organized, and managed, or its economic and social contexts, rather than arising from a physical substance or object. They cause not only psychiatric and psychological outcomes such as occupational burnout, anxiety disorders, and depression, but they can also cause physical injury or illness such as cardiovascular disease or musculoskeletal injury.[19] Many hazards of AI are psychosocial in nature due to its potential to cause changes in work organization, in terms of increasing complexity and interaction between different organizational factors. However, psychosocial risks are often overlooked by designers of advanced manufacturing systems.[9]
Changes in work practices [ edit ]
AI is expected to lead to changes in the skills required of workers, requiring training of existing workers, flexibility, and openness to change.[1] The requirement for combining conventional expertise with computer skills may be challenging for existing workers.[9] Over-reliance on AI tools may lead to deskilling of some professions.[14]
While AI offers convenience and judgement-free interaction, increased reliance—particularly among Generation Z—may reduce interpersonal communication in the workplace and affect social cohesion.[20] As AI becomes a substitute for traditional peer collaboration and mentorship, there is a risk of diminishing opportunities for interpersonal skill development and team-based learning.[21] This shift could contribute to workplace isolation and changes in team dynamics.[22]
Increased monitoring may lead to micromanagement and thus to stress and anxiety. A perception of surveillance may also lead to stress. Controls for these include consultation with worker groups, extensive testing, and attention to introduced bias. Wearable sensors, activity trackers, and augmented reality may also lead to stress from micromanagement, both for assembly line workers and gig workers. Gig workers also lack the legal protections and rights of formal workers.[10]: 2–10
There is also the risk of people being forced to work at a robot's pace, or to monitor robot performance at nonstandard hours.[10]: 5–7
Bias [ edit ]
Algorithms trained on past decisions may mimic undesirable human biases, for example, past discriminatory hiring and firing practices. Information asymmetry between management and workers may lead to stress, if workers do not have access to the data or algorithms that are the basis for decision-making.[10]: 3–5
In addition to building a model with inadvertently discriminatory features, intentional discrimination may occur through designing metrics that covertly result in discrimination through correlated variables in a non-obvious way.[10]: 12–13
In complex human‐machine interactions, some approaches to accident analysis may be biased to safeguard a technological system and its developers by assigning blame to the individual human operator instead.[14]
Physical [ edit ]
Automated guided vehicles are examples of cobots currently in common use. Use of AI to operate these robots may affect the risk of physical hazards such as the robot or its moving parts colliding with workers.
Physical hazards in the form of human–robot collisions may arise from robots using AI, especially collaborative robots (cobots). Cobots are intended to operate in close proximity to humans, which makes impossible the common hazard control of isolating the robot using fences or other barriers, which is widely used for traditional industrial robots. Automated guided vehicles are a type of cobot that as of 2019 are in common use, often as forklifts or pallet jacks in warehouses or factories.[8]: 5, 29–30 For cobots, sensor malfunctions or unexpected work environment conditions can lead to unpredictable robot behavior and thus to human–robot collisions.[10]: 5–7
Self-driving cars are another example of AI-enabled robots. In addition, the ergonomics of control interfaces and human–machine interactions may give rise to hazards.[9]
Hazard controls [ edit ]
AI, in common with other computational technologies, requires cybersecurity measures to stop software breaches and intrusions,[8]: 17 as well as information privacy measures.[7] Communication and transparency with workers about data usage is a control for psychosocial hazards arising from security and privacy issues.[7] Proposed best practices for employer‐sponsored worker monitoring programs include using only validated sensor technologies; ensuring voluntary worker participation; ceasing data collection outside the workplace; disclosing all data uses; and ensuring secure data storage.[14]
For industrial cobots equipped with AI‐enabled sensors, the International Organization for Standardization (ISO) recommended: (a) safety‐related monitored stopping controls; (b) human hand guiding of the cobot; (c) speed and separation monitoring controls; and (d) power and force limitations. Networked AI-enabled cobots may share safety improvements with each other.[14] Human oversight is another general hazard control for AI.[10]: 12–13
Risk management [ edit ]
Both applications and hazards arising from AI can be considered as part of existing frameworks for occupational health and safety risk management. As with all hazards, risk identification is most effective and least costly when done in the design phase.[9]
Workplace health surveillance, the collection and analysis of health data on workers, is challenging for AI because labor data are often reported in aggregate and does not provide breakdowns between different types of work, and is focused on economic data such as wages and employment rates rather than skill content of jobs. Proxies for skill content include educational requirements and classifications of routine versus non-routine, and cognitive versus physical jobs. However, these may still not be specific enough to distinguish specific occupations that have distinct impacts from AI. The United States Department of Labor's Occupational Information Network is an example of a database with a detailed taxonomy of skills. Additionally, data are often reported on a national level, while there is much geographical variation, especially between urban and rural areas.[11]
Standards and regulation [ edit ]
As of 2019 , ISO was developing a standard on the use of metrics and dashboards, information displays presenting company metrics for managers, in workplaces. The standard is planned to include guidelines for both gathering data and displaying it in a viewable and useful manner.[10]: 11 [23][24]
In the European Union, the General Data Protection Regulation, while oriented towards consumer data, is also relevant for workplace data collection. Data subjects, including workers, have "the right not to be subject to a decision based solely on automated processing". Other relevant EU directives include the Machinery Directive (2006/42/EC), the Radio Equipment Directive (2014/53/EU), and the General Product Safety Directive (2001/95/EC).[10]: 10, 12–13
| 2022-12-01T00:00:00 |
https://en.wikipedia.org/wiki/Workplace_impact_of_artificial_intelligence
|
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|
131 AI Statistics and Trends for 2025 - National University
|
131 AI Statistics and Trends for (2024)
|
https://www.nu.edu
|
[
"Timothy Prestianni"
] |
By 2025, AI might eliminate 85 million jobs but create 97 million new ... and the evolving landscape of AI-related employment opportunities and challenges.
|
In the rapidly evolving landscape of technology, Artificial Intelligence (AI) has emerged as a cornerstone of innovation, reshaping industries, and altering the fabric of our daily lives. The year 2025 stands as a testament to the monumental strides made in AI, with statistics and trends painting a vivid picture of its widespread adoption and impact.
In this comprehensive exploration, we delve into 131 AI Statistics and Trends (2025), offering a panoramic view of AI’s integration into businesses, its demographic reach, the burgeoning AI job market, educational initiatives, business owner perspectives, consumer usage, trust issues, and regulatory discussions. This article aims to equip readers with a deep understanding of AI’s current state and future trajectory, providing actionable insights and practical advice to navigate this transformative era.
AI Adoption in Businesses
According to research completed by Exploding Topics, 77% of companies are either using or exploring the use of AI in their businesses, and 83% of companies claim that AI is a top priority in their business plans.
Fast Facts About Artificial Intelligence
Artificial Intelligence is not just a futuristic concept but a present-day reality, deeply embedded in our everyday devices and systems. Here, we uncover some eye-opening facts about AI’s prevalence and its expected economic impact, shedding light on its exponential growth and widespread acceptance.
77% of devices being used have some form of AI.¹
9 out of 10 organizations support AI for a competitive advantage.¹
AI is projected to contribute $15.7 trillion to the global economy by 2030.¹
By 2025, AI might eliminate 85 million jobs but create 97 million new ones, resulting in a net gain of 12 million jobs.¹
63% of organizations intend to adopt AI globally within the next three years.¹
AI market size is expected to grow by at least 120% year-over-year.²
In 2024, the global AI market is projected to grow 33% year over year.²
88% of non-users are unclear how generative AI will impact their life. 10
Only a third of consumers think they are using AI platforms, while actual usage is 77%.¹
AI Demographics
Understanding the demographics behind AI awareness and usage offers valuable insights into its market penetration and acceptance across different segments of society. This section breaks down AI familiarity and utilization among various age groups, genders, and socioeconomic backgrounds.
When looking at different groups, those most aware of AI tend to be: 12 Asians (40%) Men (38%) Individuals aged 30-49 (38%) Those with a postgraduate qualification (53%) Individuals in high-income jobs (52%) Democrats (34%)
AI Demographics by Age
Across age groups, the most common AI tech used on a weekly basis was virtual assistants like Siri and Alexa. 9 30.8% of people age 61+ use this tech weekly 14% of 41-60 years olds use virtual assistants weekly 25.3% of 26-40 year olds use virtual assistants weekly 29.9% of 18-25 year olds use virtual assistants weekly
More than 4 out of 5 people ages 18-25 have never used a large language model. 9
68% of non-users are Gen X or Baby Boomers.10
AI Demographics by Sex or Gender
Men have slightly more awareness of AI than women (38% vs 23%, respectively). 12
Slightly more men use spam filters on a daily basis than do women (29.7% vs 29.2%), according to an AIPRM study. 12
Virtual assistants are slightly more popular with men than women. 12
Women use algorithmic recommendations for playlists slightly more often on a weekly basis than men do. 12
Women use AI slightly more than men when looking at tech used on a monthly basis, such as virtual assistants and wearable fitness trackers. 12
Women were less likely to have never driven an autonomous vehicle than men (85.6% vs 84.7%, respectively). 12
The same pattern was found to be true of large language models (79.4% to 78.9%) and smart cleaning machines (67.9% vs 67.5%).12
AI Jobs and Market
The AI revolution is not only transforming how businesses operate but also reshaping the job market. In this segment, we explore the most common AI applications in business and the evolving landscape of AI-related employment opportunities and challenges.
According to Forbes, the most common uses for AI in business are: 8 Customer service – 56% Cybersecurity and fraud management – 51% Digital personal assistants – 47% Customer relationship management – 46% Inventory management – 40%
52% of experts believe automation will displace jobs and also create new ones.¹
AI could increase labor productivity growth by 1.5 percentage points over the next ten years. 5
Globally, AI-driven growth could be nearly 25% higher than automation without AI. 5
Software development, marketing, and customer service are three fields that have seen the highest rate of adoption and investment.6
AI is expected to improve employee productivity by 40%.³ 60% of business owners think AI will increase their productivity. 8
83% of companies reported that using AI in their business strategies is a top priority. 4
52% of employed respondents are worried AI will replace their jobs. 7
The manufacturing sector will likely see the greatest benefit from AI, with a projected gain of $3.8 trillion by 2035. 8
63% of IT and telecom sector organizations utilize AI. 1
44% of automotive organizations implement AI. 1
The most popular job postings for AI in 2023 were: 9 Data engineer – 1898 postings Data scientist – 1692 postings Data analyst – 1206 postings Machine learning engineer – 845 postings Applied scientist – 269 postings Research scientist – 232 postings Analytics engineer- 232 postings
AI and Education
The intersection of AI and education is a critical area for future development and societal advancement. This section discusses the current state of AI in educational settings, highlighting the perspectives of educators, the preparedness of institutions, and the implications for students.
10% of educators think teaching AI should be a top priority for schools. 9 33% of educators say it’s very important to teach AI in schools. Only 13% of educators say it’s mostly unimportant or not important at all to teach AI technologies to students.
87% of educators said they have not received any AI training as part of professional development.9
54% of parents think AI could potentially have a positive effect on their child’s education. 9
80% of parents had concerns about harmful effects of AI in education. 9 The top concerns were privacy/data and accuracy and reliability of AI-generated content.
Only 35% of parents have discussed AI usage with their kids. 9
U.S. adults with higher levels of education and income demonstrate greater awareness of AI in daily life. 12 Post Graduate – 53% College Graduate – 46% Some College – 28% Highschool or less – 14%
Business Owners and AI
Business owners are at the forefront of AI adoption, making strategic decisions that will shape their companies’ future. Here, we delve into how businesses are incorporating AI technologies and the perceived benefits and challenges from a leadership perspective.
Over 50% of companies plan to incorporate AI technologies11
35% of all companies worldwide report using AI in their business.11
77% of companies are either using or exploring the use of AI.11
The most common reason for businesses not using AI was finance/cost (51%). 9
43% of businesses are concerned about technology dependence. 8
35% worry that they don’t have the technical skills to use AI.8
Nearly 2/3 of business owners think AI will improve their customer relationships. 8
Nearly 25% of business owners worry about AI negatively affecting website traffic. 8
In 2017, only 20% of companies incorporated AI into their product offerings and business operations.11
Business Owners and ChatGPT
97% of business owners think using ChatGPT will help their business. 8
Nearly 3/4 (74%) of business owners expect AI to generate responses to customers, such as chatbots. 9
7 in 10 (70%) anticipate AI to speed up content generation processes. 9
58% believe AI will be able to create personalized experiences for customers. 9
57% foresee AI increasing web traffic for their company. 9
53% expect AI to streamline job processes within their organization. 9
Another 53% believe AI will be able to summarize information effectively. 9
Half of business owners expect AI to improve decision-making processes. 9
47% anticipate AI improving their business credibility. 9
The same percentage, 47%, believe AI will be able to translate information efficiently. 9
46% of business owners expect AI to generate responses to colleagues, such as emails. 9
44% anticipate AI being able to create content in different languages. 9
41% foresee AI being used to fix coding errors effectively. 9
Nearly 1 in 3 (30%) business owners expect AI to generate website copy for their company.9
Consumer Use of AI
Consumers are increasingly interacting with AI, often in ways they may not even realize. This section examines the various ways AI has infiltrated the consumer space, from daily tasks to more complex decision-making processes.
The most common ways consumers use AI are by answering texts or emails, answering financial questions, and making travel plans. 8
The top ways consumers use AI are as follows: 8 Respond to people via text/email: 45% Answer financial questions: 43% Plan travel itinerary: 38% Craft an email: 31% Prepare for a job interview: 30% Write a social media post: 25% Summarize complex or long copy: 19%
A survey by Pew Research found that 55% of Americans said they regularly use AI, while 44% believe they do not regularly use AI. 9
According to research from AIPRM, the most common ways people use AI in the workplace are: 9 Email spam filters: 78.5% Chatbots for customer service questions: 62.2%
AIPRM also reported that consumers rely on these types of AI for personal use: 9 Virtual assistants: 61.4% Wearable fitness devices or trackers: 50.6% Algorithmic recommendations through playlists: 48.5%
AI and User Trust
Trust is a fundamental component of AI’s broader acceptance and use. In this part, we discuss the public’s trust in AI, exploring levels of comfort, skepticism, and the factors influencing these attitudes.
9 in 10 students want to learn more about AI in school. 9
More than half (54%) could tell the difference between human and AI-generated content. 9
About 2 out of 3 (67%) people would use ChatGPT instead of Google. 9
50% of consumers view AI optimistically.¹
54% of consumers think AI could improve the customer experience. 9
65% of consumers trust businesses that use AI. 8 14% of consumers do not trust businesses that use AI. 21% of consumers are neutral on businesses using AI.
78% of people polled think the benefits of generative AI outweigh the risks. 6
39% of respondents believe current AI technology is safe and secure. 7 Most said they are more concerned than excited about AI. 51% of men and 40% of women say they’re more excited than concerned about AI. 57% of Gen Z and 62% of millennials agree. Only 30% of boomers agree.
80% are concerned about AI being used for cyber attacks, 7 78% are concerned about AI being used for identity theft. 74% are concerned about AI being used to create deceptive political ads.
54% of consumers think that written content will improve with AI technology 8
4.1% of people surveyed think AI will have no benefits. 9
12.4% believe AI will not have any potential drawbacks. 9
A Pew Research study showed 39% of adults are okay with healthcare providers using AI. 9 38% also believed AI could help improve healthcare outcomes. 40% also thought AI would reduce errors. 51% thought AI would reduce instances of racial and ethnic bias.
AI Regulation
As AI becomes more integral to our lives, the call for regulation grows louder. This final section addresses the public’s stance on AI regulation, highlighting the demand for safety, transparency, and ethical considerations in AI development and deployment.
85% of respondents support a national effort to make AI safe, and secure. 7
81% of respondents think that industries should spend more on AI assurance. 7
85% of respondents want industries to be transparent about AI assurance practices before bringing AI-enhanced products to market. 7
AI assurance support is strong among Republicans, Democrats, and Independents. 7
Younger generations are more willing to use AI tech in everyday tasks than older generations. 7
There is overwhelming support for AI regulation across all generations. 7
64% say the primary purpose of AI is to assist, enhance, and empower consumers, (down 7 points from November 2022).7
Conclusion
As we navigate through the intricate web of 131 AI Statistics and Trends (2024), it becomes clear that AI’s influence is both profound and pervasive. From transforming business landscapes and job markets to reshaping educational paradigms and consumer behaviors, AI’s footprint is undeniable. However, with great power comes great responsibility, and the call for ethical practices, user trust, and regulatory frameworks cannot be ignored.
As we stand on the brink of this AI-driven era, it is imperative for businesses, consumers, and policymakers to collaborate, ensuring that principles of equity, transparency, and human-centric values guide AI’s evolution. AI’s journey is far from over, and its true potential lies in our collective hands.
Sources
| 2024-03-01T00:00:00 |
2024/03/01
|
https://www.nu.edu/blog/ai-statistics-trends/
|
[
{
"date": "2022/12/01",
"position": 92,
"query": "AI job creation vs elimination"
},
{
"date": "2022/12/01",
"position": 95,
"query": "AI labor market trends"
},
{
"date": "2023/01/01",
"position": 76,
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},
{
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{
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{
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{
"date": "2023/11/01",
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},
{
"date": "2023/12/01",
"position": 73,
"query": "artificial intelligence employers"
},
{
"date": "2024/02/01",
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"query": "AI job creation vs elimination"
},
{
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{
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{
"date": "2024/03/01",
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{
"date": "2024/03/22",
"position": 75,
"query": "ChatGPT employment impact"
},
{
"date": "2024/03/22",
"position": 43,
"query": "artificial intelligence business leaders"
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{
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{
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{
"date": "2024/09/01",
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{
"date": "2024/11/01",
"position": 48,
"query": "AI employers"
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{
"date": "2024/11/01",
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{
"date": "2024/12/01",
"position": 50,
"query": "AI employers"
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{
"date": "2025/03/01",
"position": 91,
"query": "artificial intelligence employers"
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{
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{
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] |
25 Jobs AI Can't Replace (Yet): Safe Careers for the Future - Paybump
|
25 Jobs AI Can’t Replace (Yet): Safe Careers for the Future
|
https://www.paybump.com
|
[] |
These jobs require traits AI simply doesn't have: creativity, emotional intelligence, judgment, and that “human touch” you can't program into an algorithm. In ...
|
Discover 25 jobs that AI can’t replace. Explore AI-proof careers that require human skills like emotional intelligence and creativity, keeping you future-proof.
Because Siri can’t do it all (yet).
AI is Everywhere, But So Are Humans
Artificial Intelligence (AI) has become our modern-day Genie.
It predicts your Spotify playlist, finishes your sentences in emails, and might even help you find your next Netflix binge (*yes, I watched the entire series in one weekend, no shame*).
But as helpful as AI is, it’s also shaking up industries and automating jobs left, right and center.
Job displacement is a real concern of the 21st century, with 3 in 10 (30%) US workers concerned that they may be replaced by AI.
It’s enough to make anyone ask: “Will my job still exist in five years?”
Here’s the good news—some careers are as AI-resistant as that Tupperware lid you can’t open. These jobs require traits AI simply doesn’t have: creativity, emotional intelligence, judgment, and that “human touch” you can’t program into an algorithm.
In this guide, we’ll explore 25 AI resistant jobs and future proof careers, as well as why these roles will keep thriving and why you should consider them in your career path.
Why AI Can’t Replace Certain Jobs
Let’s break it down: AI can process data faster than we can say "ChatGPT," but what it lacks is a soul. That’s right - AI isn’t pulling out tissues to comfort someone during a tough moment or improvising solutions to unexpected problems.
Here’s where AI struggles:
Emotional Intelligence: AI can recognize patterns but can’t empathize with someone who’s nervous before their wedding or grieving a loved one.
AI can recognize patterns but can’t empathize with someone who’s nervous before their wedding or grieving a loved one. Creativity: Sure, AI can generate art or write copy, but it’s only as good as the prompts it gets. It doesn’t have the imagination to create Beyoncé-level magic.
Sure, AI can generate art or write copy, but it’s only as good as the prompts it gets. It doesn’t have the imagination to create Beyoncé-level magic. Judgment and Ethics: Robots don’t have intuition. They can analyze risks but can’t make gut decisions, especially in gray areas where ethics come into play.
Robots don’t have intuition. They can analyze risks but can’t make gut decisions, especially in gray areas where ethics come into play. Adaptability: AI thrives on predictable outcomes, but throw it into an unexpected scenario (like a toddler’s tantrum), and it’ll short-circuit.
These limitations mean that many industries and roles requiring human insight, judgment, and care remain untouched by automation; these are jobs that AI can’t replace.
25 Jobs That Are Safe from AI
Here’s a deeper dive into the careers where humans still reign supreme—and tips on how to break into these future-proof roles:
1. Healthcare Professionals (Doctors, Nurses, Therapists)
Why it’s safe: Healthcare demands empathy, split-second decision-making, and the ability to adapt to unique patient needs. An AI might suggest treatment options, but it can’t hold a patient’s hand and offer reassurance during tough times. Employment in healthcare occupations is projected to grow by 12.6% from 2021 to 2031, adding about 2 million new jobs.
How to break in: Start by gaining experience in caregiving roles or volunteering at clinics to understand patient needs.
Key skills to succeed:
Strong communication skills are essential to explain complex medical information and provide emotional support to patients and their families.
Critical thinking and problem-solving abilities are crucial for diagnosing and creating tailored treatment plans.
Emotional resilience helps professionals cope with high-pressure situations and maintain their well-being.
According to AIPRM, “of the six industries evaluated for their rate of generative AI adoption by Fishbowl, the healthcare industry has the lowest uptake, at 15%. This is less than half the adoption rate of marketing and advertising (37%).”
To get started on job hunting in the healthcare industry, join the Paybump Careers Hub here.
2. Teachers and Educators
Why it’s safe: AI can’t replace the patience, encouragement, and connection a good teacher provides. Teaching is about more than delivering knowledge; it’s about inspiring students to learn and grow.
How to break in: Volunteer as a tutor or work in educational support roles to gain experience.
Key skills to succeed:
Exceptional communication and public speaking skills are necessary to convey ideas clearly.
Patience and adaptability enable teachers to handle diverse student needs and classroom dynamics.
Creativity is vital for crafting engaging lessons and fostering a love for learning.
3. Social Workers
Why it’s safe: Social work relies on deep emotional intelligence and the ability to navigate complex interpersonal situations. AI can’t replicate the trust and empathy needed for this role.
How to break in: Volunteer with local organizations or assist in community programs to understand the challenges people face.
Key skills to succeed:
Empathy and active listening are essential for building trust and understanding clients’ needs.
Problem-solving skills help navigate complex family or social issues.
Organization and time management are crucial to balance heavy caseloads and ensure effective support.
4. Creative Professionals (Writers, Designers, Artists)
Why it’s safe: Creativity is inherently human. While AI can create, it lacks the emotional storytelling and originality of human artists.
How to break in: Build a portfolio of your work and share it on platforms like Behance or Medium.
Key skills to succeed:
Creativity and originality are at the heart of this field, ensuring fresh and engaging content or designs.
Strong communication skills are needed to understand client needs and collaborate effectively.
Time management is vital for meeting deadlines while maintaining quality.
5. Psychologists and Counselors
Why it’s safe: Mental health support requires empathy, trust, and personalized care—qualities that are uniquely human.
How to break in: Volunteer with crisis lines or mental health organizations to understand the needs of individuals.
Key skills to succeed:
Empathy and active listening are essential to create a safe space for clients.
Analytical thinking helps in understanding behaviors and crafting personalized treatment plans.
Communication skills are vital for conveying complex ideas and building trust.
Over 61% of professionals say soft skills are just as important as hard skills in the workplace.
6. Human Resources Managers
Why it’s safe: Seven in 10 (71%) US workers are worried that AI may impact human resources decision-making. HR is about understanding people and resolving conflicts. It’s true that AI can assist with data analysis but can’t manage interpersonal dynamics, making it difficult to replace humans altogether!
How to break in: Look for roles in recruiting or administrative support to develop people management skills.
Key skills to succeed:
Strong interpersonal and conflict-resolution skills help manage diverse employee needs.
Decision-making and organizational skills are necessary for handling recruitment and policy implementation.
Emotional intelligence is key for fostering a positive workplace culture.
7. Sales and Account Managers
Why it’s safe: Closing deals is about building relationships and understanding client needs. AI can provide insights, but humans seal the deal. There’s a level of rapport and customer service which just can’t be replicated by AI.
How to break in: Start in entry-level sales roles and focus on developing negotiation skills.
Key skills to succeed:
Persuasion and negotiation skills are critical for closing deals and managing client relationships.
Emotional intelligence helps understand client needs and build trust.
Time management ensures you can juggle multiple clients and deadlines effectively.
8. Lawyers and Legal Professionals
Why it’s safe: Legal work involves reasoning, ethics, and decision-making that AI cannot replicate.
How to break in: Support legal teams as a paralegal or assistant to gain exposure to the field.
Key skills to succeed:
Critical thinking and analytical skills are vital for interpreting laws and building strong cases.
Communication skills ensure you can present arguments persuasively.
Attention to detail is essential to avoid errors in legal documents.
9. Marketing Strategists
Why it’s safe: Marketing requires connecting emotionally with audiences and crafting compelling campaigns.
How to break in: Offer marketing support for small businesses or nonprofits to build experience.
Key skills to succeed:
Creativity and storytelling skills are crucial for crafting compelling campaigns.
Analytical thinking helps interpret data to refine strategies.
Collaboration and teamwork are vital for working with designers, writers, and other stakeholders.
10. Business Leaders
Why it’s safe: Leadership is about vision, ethics, and inspiring teams to achieve goals - qualities no AI can mimic.
How to break in: Volunteer to lead projects in your current role and build leadership skills.
Key skills to succeed:
Vision and strategic thinking are essential for driving a company forward.
Leadership skills, including motivating and inspiring teams, are crucial in ensuring you can pivot effectively in response to challenges.
11. Event Planners
Why it’s safe: Planning events requires flexibility, quick thinking, and creativity—skills that AI lacks.
How to break in: Start small by organizing events for your community or family.
Key skills to succeed: Organization and multitasking skills are crucial for managing multiple vendors and timelines. Problem-solving abilities help adapt to last-minute changes. Creativity ensures events are engaging and memorable.
12. Public Relations Specialists
Why it’s safe: PR is all about managing perceptions and navigating crises with tact and intuition.
How to break in: Gain experience by managing social media or communications for local organizations.
Key skills to succeed:
Strong communication and interpersonal skills are vital for managing public perception and building relationships.
Crisis management skills help navigate difficult situations.
Writing skills are essential for crafting press releases and other materials.
13. Project Managers
Why it’s safe: Project managers lead teams, solve problems, and ensure goals are met—human leadership at its core.
How to break in: Volunteer to manage small projects and learn tools like Trello or Asana.
Key skills to succeed:
Leadership skills are crucial for motivating and guiding teams.
Organizational abilities ensure timelines and budgets are met.
Problem-solving skills are necessary to address challenges as they arise.
14. Personal Trainers and Fitness Coaches
Why it’s safe: Fitness coaching requires encouragement and flexibility to client needs.
How to break in: Help friends or family with fitness goals to gain hands-on experience.
Key skills to succeed:
Communication and motivational skills help clients stay on track.
Adaptability is needed to create personalized fitness plans.
Knowledge of anatomy and exercise science ensures safe and effective training.
15. Journalists and News Analysts
Why it’s safe: Investigative reporting and storytelling require human curiosity and critical thinking.
How to break in: Start a blog or contribute articles to local outlets to build a portfolio.
Key skills to succeed:
Curiosity and critical thinking drive investigative reporting.
Strong writing and storytelling skills ensure compelling content.
Time management is essential for meeting deadlines without compromising accuracy.
16. Chefs and Culinary Experts
Why it’s safe: Cooking is an art. AI might suggest recipes, but it can’t create flavors like a master chef.
How to break in: Experiment in the kitchen and share your creations online.
Key skills to succeed:
Creativity is vital for developing unique dishes and flavors.
Attention to detail ensures consistency and quality.
Time management and organization are critical in high-pressure kitchen environments.
17. Occupational Therapists
Why it’s safe: Therapy involves personal interaction and tailored approaches to recovery.
How to break in: Volunteer with rehabilitation programs to understand patient needs.
Key skills to succeed:
Empathy and active listening are essential for understanding patient needs.
Problem-solving skills help craft personalized recovery plans.
Patience ensures you can guide clients through long-term rehabilitation.
18. Childcare Workers
Why it’s safe: Caring for children requires patience, creativity, and emotional connection.
How to break in: Babysit or volunteer at daycare centers to gain experience.
Key skills to succeed:
Patience and empathy are crucial for managing children’s needs and emotions.
Creativity helps in planning engaging and educational activities.
Strong communication skills ensure effective collaboration with parents and colleagues.
19. Financial Advisors
Why it’s safe: Financial planning involves trust and understanding personal goals.
How to break in: Offer budgeting advice to friends or community members.
Key skills to succeed:
Analytical thinking and problem-solving skills are vital for crafting effective financial plans.
Interpersonal skills help build trust with clients.
Time management ensures you can balance multiple client portfolios.
20. Speech-Language Pathologists
Why it’s safe: Helping people communicate requires a personal touch and creative strategies.
How to break in: Volunteer with organizations supporting communication challenges.
Key skills to succeed:
Patience and empathy are crucial for working with individuals facing communication challenges.
Problem-solving skills help tailor therapy approaches.
Strong interpersonal skills foster trust with clients and families.
21. Veterinarians
Why it’s safe: Caring for animals requires empathy and hands-on expertise.
How to break in: Volunteer at animal shelters to gain experience with animal care.
Key skills to succeed:
Empathy and communication skills are essential for understanding pet owners’ concerns.
Problem-solving and diagnostic skills help identify and treat health issues.
Attention to detail ensures accurate treatment and care.
22. Firefighters and Emergency Responders
Why it’s safe: Responding to emergencies requires quick decision-making and courage—traits no AI can replace.
How to break in: Join community safety programs or volunteer with local departments.
Key skills to succeed:
Quick decision-making and problem-solving skills are vital in emergencies.
Physical fitness ensures you can handle physically demanding situations.
Teamwork and communication are critical for coordinating effectively.
23. Clergy and Religious Leaders
Why it’s safe: Providing spiritual guidance and emotional support is deeply human.
How to break in: Volunteer with community or faith-based organizations to build leadership skills.
Key skills to succeed:
Empathy and active listening are essential for providing spiritual guidance.
Public speaking skills are necessary for delivering sermons or speeches.
Leadership and interpersonal skills help build strong community connections.
24. Politicians and Public Servants
Why it’s safe: Representing people and negotiating policies requires empathy, ethics, and human understanding.
How to break in: Attend local meetings and get involved in community initiatives.
Key skills to succeed:
Communication and public speaking skills are vital for engaging with constituents.
Ethical decision-making ensures trust and credibility.
Negotiation skills help navigate complex policy discussions.
25. Tradespeople (Electricians, Plumbers, Carpenters)
Why it’s safe: Trades require hands-on skills and creative problem-solving, making them AI-resistant.
How to break in: Start with small DIY projects or apprentice with professionals.
Key skills to succeed:
Problem-solving skills are crucial for troubleshooting and repairs.
Manual dexterity and attention to detail ensure high-quality work.
Communication skills help in understanding client needs and explaining solutions.
What Makes a Job AI-Proof?
Jobs that rely on emotional intelligence, creativity, adaptability, or physical dexterity are safe from automation. To stay ahead, and not get swallowed up like the roles replaced by AI in more than a third (37%) of companies in 2023, focus on honing these skills, learning new tools, and staying adaptable in the face of change. Remember, AI can assist, but it’s humans who lead, connect, and innovate.
Embrace Your Humanity
AI might be taking over certain industries, but there’s one thing it can’t replicate: you.
By focusing on careers that emphasize human-centered skills, you can future-proof your job and thrive in a tech-driven world.
Ready to explore AI-proof careers? Paybump has the tools, tips, and resources to help you shine. Click here to get started today!
FAQs
What jobs are safe from AI?
Jobs requiring emotional intelligence, creativity, leadership, and complex problem-solving—such as healthcare, education, and management roles—are less likely to be replaced by AI.
What makes a job AI-proof?
AI-proof jobs typically involve tasks that require human intuition, emotional intelligence, creativity, and manual skills, which are difficult for AI to replicate.
Can AI replace creative jobs?
While AI can assist with repetitive tasks, creative jobs like writing, design, and marketing still require human imagination and emotional storytelling, making them harder to automate.
Are healthcare jobs safe from AI?
Yes, healthcare jobs such as doctors, nurses, and therapists are generally AI-resistant due to the need for human empathy, decision-making, and personalized care.
Will AI take over legal jobs?
While AI can assist with data processing and legal research, the complexities of legal reasoning, argumentation, and ethical decision-making make many legal roles AI-resistant.
How can I future-proof my job from AI?
Focus on developing skills AI cannot easily replicate, such as critical thinking, creativity, emotional intelligence, problem-solving, and leadership. Upskilling and staying adaptable are key to staying relevant in an AI-driven workforce.
What industries are least affected by AI?
Industries like healthcare, education, creative arts, human resources, and trades (e.g., electricians, plumbers) are less affected by AI because they require human judgment, creativity, and physical dexterity.
Can AI replace sales jobs?
While AI can assist with data analysis and customer insights, sales jobs that involve relationship-building, negotiation, and understanding client needs still rely on human intuition and empathy.
What are the best careers to pursue in the age of AI?
Careers that require creativity, emotional intelligence, leadership, and complex decision-making, such as marketing, healthcare, project management, and legal professions, are good choices in an AI-driven future.
Which jobs are most at risk from AI?
Jobs involving repetitive tasks, data entry, and routine analysis—such as bookkeeping, basic customer service, and telemarketing—are at higher risk of being replaced by AI.
| 2022-12-01T00:00:00 |
https://www.paybump.com/resources/25-jobs-ai-cant-replace-yet-the-safest-careers-for-the-future
|
[
{
"date": "2022/12/01",
"position": 93,
"query": "AI job creation vs elimination"
},
{
"date": "2024/02/01",
"position": 92,
"query": "AI job creation vs elimination"
},
{
"date": "2024/12/29",
"position": 4,
"query": "AI employment"
},
{
"date": "2024/12/29",
"position": 6,
"query": "AI impact jobs"
},
{
"date": "2024/12/29",
"position": 5,
"query": "artificial intelligence employment"
},
{
"date": "2024/12/29",
"position": 67,
"query": "artificial intelligence hiring"
},
{
"date": "2024/12/29",
"position": 77,
"query": "job automation statistics"
}
] |
|
Reskilling in the Age of AI - Harvard Business Review
|
Reskilling in the Age of AI
|
https://hbr.org
|
[
"Jorge Tamayo",
"Leila Doumi",
"Sagar Goel",
"Orsolya Kovács-Ondrejkovic",
"Raffaella Sadun",
"Is A Managing Director",
"Partner At Boston Consulting Group",
"Singapore",
"A Fellow At The Bcg Henderson Institute.",
"Is An Associate Director At Boston Consulting Group"
] |
Back in 2019 the Organisation for Economic Co-operation and Development made a bold forecast. Within 15 to 20 years, it predicted, new automation ...
|
Back in 2019 the Organisation for Economic Co-operation and Development made a bold forecast. Within 15 to 20 years, it predicted, new automation technologies were likely to eliminate 14% of the world’s jobs and radically transform another 32%. Those were sobering numbers, involving more than 1 billion people globally—and they didn’t even factor in ChatGPT and the new wave of generative AI that has recently taken the market by storm.
| 2023-09-01T00:00:00 |
2023/09/01
|
https://hbr.org/2023/09/reskilling-in-the-age-of-ai
|
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"position": 88,
"query": "AI job creation vs elimination"
},
{
"date": "2025/06/01",
"position": 1,
"query": "reskilling AI automation"
}
] |
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