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Effective Strategies to Bridge the AI Skills Gap - Hyland Software
|
Effective Strategies to Bridge the AI Skills Gap
|
https://www.hyland.com
|
[] |
Learn how to close the AI skills gap with targeted strategies. Explore upskilling, educational enhancements and technology to empower your workforce.
|
Industries
It's your unique digital evolution … but you don't have to face it alone. We understand the landscape of your industry and the unique needs of the people you serve.
Overview of industries
| 2023-02-01T00:00:00 |
https://www.hyland.com/en/resources/articles/ai-skills-gap
|
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Closing the AI skills gap - Financial Times - Partner Content by IBM
|
Closing the AI skills gap
|
https://www.ft.com
|
[] |
Advances in AI are being slowed by a global shortage of workers with skills and experience in areas such as deep learning, natural language processing and ...
|
This is where various technologies can oil the wheels of AI. For example, AutoAI, a capability within IBM Watson Studio, can help companies get started with AI by automating the laborious parts of a project, including preparing data and developing AI models.
For projects that are heavy on data science, IBM can provide customers with a data science team to work with the client to design and pilot an AI project.
Deutsche Lufthansa, Germany’s largest airline, recognised early on that with the right data and AI strategy, it could improve customer services, empower employees and improve operational efficiency.
The airline has worked with IBM and its cloud computing services to move from AI proof-of-concepts to scaling data science projects across the organisation. Lufthansa built a computer platform enabling its data scientists to experiment and test AI projects before rolling them out across the company.
| 2023-02-01T00:00:00 |
https://www.ft.com/partnercontent/ibm/closing-the-ai-skills-gap.html
|
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|
The AI Skills Gap: How to Identify and Address It in Your Organization
|
The AI Skills Gap: How to Identify and Address It in Your Organization
|
https://learn.credly.com
|
[] |
(429) Sep 12, 2024 · We'll provide practical tips and insights on how to assess and address these skill gaps, helping you to better prepare your team for the evolving demands of AI.
|
Artificial intelligence has already begun to transform workplaces and industries.
According to Pearson's Skills Outlook, AI can now streamline and automate many routine and repetitive tasks — freeing up employee time and energy for higher-value activities, such as:
Complex customer service
Collaboration
Creative problem solving Strategic thinking
To fully leverage AI’s potential, it’s crucial to equip your workforce with the necessary AI skills. A Salesforce survey highlights that 60% of public sector IT workers in the UK consider a shortage of skills to be the primary barrier to successful AI implementation.
In this blog post, we’ll explore why understanding the AI skills gap within your organization is essential. We’ll provide practical tips and insights on how to assess and address these skill gaps, helping you to better prepare your team for the evolving demands of AI.
Why Managing the AI Skills Gap Matters
The most competitive employees and employers of the future will be those who use AI to augment their capabilities. For successful organizations, this will include:
Navigating economic disruptions: indicates that by 2026, global tech talent shortages will cost organizations $5.5 trillion. IDC market research indicates that by 2026, global tech talent shortages will cost organizations $5.5 trillion.
While AI can help mitigate these disruptions, its effectiveness relies on proper training.
Nearly 43% of HR executives foresee AI creating a skills gap in their organizations, highlighting the need for proactive upskilling and reskilling. Organizations that address these skill gaps effectively will be better positioned to leverage AI’s benefits and minimize the financial impact of tech talent shortages.
Improving efficiency: AI can streamline and automate repetitive and routine tasks, boosting productivity. AI can streamline and automate repetitive and routine tasks, boosting productivity. Pearson Skills Outlook shows that 19 million hours of UK workers can be saved weekly with the help of Gen AI.
Retaining talent: Employers that invest in AI skills development will be more attractive to existing and future employees. As workers view their employers as willing to help them learn and advance, engagement and retention will rise.
Creating competitive advantage: AI can drive innovation, enable new products and services, and even help generate new business models. Organizations with a skilled AI workforce are more likely to outperform their competitors.
Addressing the AI skills shortage goes beyond just filling gaps; it has profound long-term impacts. By effectively managing this challenge, organizations can enhance their competitiveness, drive strategic success, and ensure that their technology investments deliver maximum returns.
Key Steps to Map and Address the AI Skills Gap
Understanding your organization's current skills landscape is the foundation for building a workforce ready to thrive alongside AI. By systematically identifying gaps and aligning talent strategies with future needs, you can ensure your employees are prepared for the evolving demands of AI-driven work. Here's how to effectively pinpoint and address the AI skills gap in your organization:
Understand Macro Trends: Use predictive insights from sources like Faethm to grasp how broader labour market trends will impact your workforce. These insights, powered by advanced AI and data science, help you anticipate challenges and opportunities, ensuring your workforce is prepared for future demands. Utilize third-party reports: Leverage trustworthy insights, such as the ' or the 'Impact AI: 2024 Workforce Skills Forecast' survey by ServiceNow and Pearson, Leverage trustworthy insights, such as the ' Pearson Skills Outlook' to gain detailed insights into skill trends and emerging needs. These reports provide valuable information on how AI and other technological advancements are shaping the skills landscape, helping you align your talent strategy with current and future requirements.
Conduct a skills inventory: Evaluate your workforce's current skills to identify strengths and gaps. Engage with powerful AI solutions like Strategic Workforce Planning by Pearson to help address AI skill gaps by pinpointing existing deficiencies and forecasting future AI-related needs. It aligns workforce development with AI advancements, ensures skills are transferable, and supports internal mobility by providing clear pathways and upskilling opportunities for future AI roles.
Snapshot Zurich Insurance, a leading global insurer, used Faethm by Pearson to analyze their workforce and address future needs. The analysis revealed 270 unfilled roles in robotics, data science, and cybersecurity by 2024, and identified £1 million in potential savings through upskilling. Result: Zurich invested £1 million in reskilling in 2020 with an established plan to retrain two-thirds of their workforce, and implemented 120 automated interventions, with 100 more in the pipeline. [Discover more]
Analyze job ontologies and requirements: Map out key roles in your organization that Map out key roles in your organization that rely on AI or could benefit from it . Skill requirements within these ontologies can highlight gaps in AI skills.
*Skills Ontologies Explained
The Pearson Ontology is a framework that captures relationships between different skill types and their connections to occupations, tasks, and technologies. By using this structured approach, you can better understand how various skills relate to each other and identify gaps in your organization’s AI capabilities.
Gather stakeholder feedback: Meet with key managers, team leaders and employees and gather input. How and where could adopting AI technologies enhance workforce productivity and business performance?
Create a plan to bridge the AI skills gap: Prioritize the skill gaps that must be addressed first for maximum impact. You can develop upskilling and reskilling programs and update recruitment strategies to address key skill gaps.
Integrating Training and Recruiting Strategies
After a clearer understanding of the existing AI skills gap and future needs, organizations can focus on building a robust talent pipeline and equipping their workforce with the right capabilities. Here’s what leaders can do.
Develop Targeted Upskilling Programs
The most efficient way to address any skills gaps — including AI skills gaps — is often upskilling and reskilling . Giving employees opportunities to learn new skills can make them more effective and productive, while also heightening engagement.
Although AI is being widely implemented in many industries and roles, it is still new. Defining learning and career pathways for employees with AI skills can motivate them to embrace training opportunities.
Focusing on AI in Skills-Based Hiring
For new and existing roles that require AI skills, clearly define the required competencies within your skills ontology. Instead of relying solely on work experience and degrees, focus on verified skills demonstrated through assessments and certifications—securely recorded with digital credentials. This approach makes skills-based hiring faster and more reliable.
This involves more than just listing AI skills for a role. By using platforms such as TalentLens , which provides data-driven insights based on your skills ontology, you can better match candidates to roles where they’ll be most successful, ensuring alignment with the defined skill requirements and competencies.
Leverage Digital Credentials to Validate AI Skills
As organizations seek to understand the potential of AI and incorporate it into the business, reliable AI skills data is critical.
Digital credentials , such as badges and certificates, provide portable, shareable and verifiable evidence of AI skills. They offer several benefits for organizations seeking to navigate into a future where AI is a strategic differentiator.
These benefits include:
Over 2 million AI badges have been issued on Credly. L&D leaders can explore relevant AI badges from a global network of training providers by searching the platform, which showcases thousands of AI courses and detailed skill development insights within their metadata.
Next steps
AI technologies are starting to disrupt industries and marketplaces. And while they will contribute greatly to productivity increases, organizations will still face talent shortages.
Understanding the current state of your workforce’s AI skills, how they compare to the rest of your industry, and what your future needs will be enables you to plan for a more competitive future where AI skills are not nice-to-haves but are must-haves for top performing employees and organizations alike.
To better identify the skills your organization needs, complete the form below to download our report on the ‘Top 10 In-Demand AI Skills in 2024 & Beyond’ and start mapping your AI skills strategy today.
| 2023-02-01T00:00:00 |
https://learn.credly.com/blog/how-to-identify-and-address-ai-skills-gap-in-your-organization
|
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Bridging the AI Skills Gap: A Guide for HR and L&D Leaders
|
Bridging the AI Skills Gap: A Guide for HR and L&D Leaders
|
https://www.leapsome.com
|
[
"Sam Abrahams",
"Sam Abrahams Is A Content Editor",
"Strategist Who Covers Enterprise Topics Including Hr Tech",
"Procurement",
"Analytics",
"Digital Systems",
"Often Working Across Teams To Shape Narratives",
"Guide Content Direction. He S Interested In How Tools Impact The Way People Work",
"Make Decisions",
"Communicate At Scale."
] |
Learn how HR teams can identify and close the AI skills gap through targeted training, adoption strategies, and role-specific capability building.
|
83% of HR leaders say AI has improved their efficiency, yet two-thirds still worry about its role in people management.* AI adoption is accelerating, but beyond leadership, uptake is uneven. Resistance, confusion, and job security fears persist — even as AI consistently tops employees’ learning wishlists.*
To close the AI skills gap, HR leaders must understand who’s using it, how, and where capability or confidence is lacking.
This article breaks down the AI skills gap’s real-world impact, five strategies to address it, and a checklist to assess where your teams stand. You’ll also see an example of what structured AI enablement looks like in practice.
📊 Get the full picture in Leapsome’s 2025 HR Insights Report
Look into the data behind AI adoption, skills gaps, and what HR teams are doing to stay ahead.
👉 Read the full report
*Leapsome’s Workforce Trends Report, 2024
What is the AI skills gap?
The AI skills gap is the mismatch between the skills employees need to effectively use AI tools and the current capabilities of the workforce. So, when 63% of decision-makers identify a critical AI skills gap in their organization, that’s to say their teams are missing the core knowledge they need to apply AI effectively.
This gap might show up as:
Lacking technical fluency when working with tools that power chatbots or data insights
Struggling to develop and manage prompts that get reliable outputs from tools like ChatGPT and Claude
Having difficulty integrating AI into daily workflows across sales, operations, and support
What this means for your organization is that automation opportunities get missed. Instead of streamlining reporting, onboarding, or content operations, teams are bogged down in manual work. Meanwhile, managers aren’t equipped to identify where processes could be redesigned. At the executive level, this weakens strategic resource allocation and scalable AI adoption.
Once you close this gap, you’ll empower your workforce to use AI with confidence, boosting productivity and efficiency.
Why the AI skills gap matters in 2025
94% of HR leaders agree that failing to train or reskill employees in AI carries multiple risks. But instead of developing internal capability, many companies bring in consultants to bridge the gap. That might work in the short term, but it creates a dependency that’s inefficient and expensive.
Other teams default to old tools and processes even when better options exist. Imagine a manager manually writing out performance reviews when the company adopts an AI performance management tool that drafts reviews from peer and self-feedback. Feedback quality suffers, and managers lose time on tasks that could be handled in seconds.
Now scale that across other roles and departments. The result is a workforce that isn’t just under-productive but also out of sync with how the business should operate. As a result, the ROI projected while procuring AI solutions doesn’t materialize in practice.
HR is uniquely positioned to lead on this. The relationship between AI and HR is becoming more central to organization-wide capability building. According to our 2025 HR Insights Report, HR leaders are:
Prepared to drive impact — 92% feel ready to generate business results.
— 92% feel ready to generate business results. Influencing strategy — Nearly half (48%) of HR leaders are already making key business decisions.
— Nearly half (48%) of HR leaders are already making key business decisions. Responsible for workforce productivity — 62% now oversee this as part of their role.
These HR statistics reflect a shift in how strategic the function has become. The fact that 60% of leaders flag employee resistance to AI implementation as an urgent challenge makes one thing clear: this isn’t just an IT problem. It’s as much a mindset and confidence issue as a skill one. Therefore, addressing the AI skills gap falls naturally within HR’s scope.
5 ways to bridge the AI skills gap
Your organization needs to treat AI capability as part of how work gets done — across levels of hierarchy, departments, tasks, and projects, all the way up to strategic planning.
The five approaches below focus on role-specific capability directly related to business outcomes. They reflect what you already drive: productivity, skills development, and adopting new tools and processes.
1. Align AI skill development with business strategy
Leapsome Goals allows users to define ownership and dependencies on the path to achievement
AI training shouldn’t happen in isolation. To close the skills gap, you must align AI capability-building with broader business priorities — like increasing operational efficiency, fostering data-driven decision-making, or improving the employee experience. That starts with making AI literacy a C-suite-level discussion. Executives should understand:
What skills are needed — Leaders should have access to data on which AI capabilities are lacking or emerging across roles and functions based on structured assessments like an AI skills gap checklist.
— Leaders should have access to data on which AI capabilities are lacking or emerging across roles and functions based on structured assessments like an AI skills gap checklist. How they connect to outcomes — They need to see how AI skills contribute to measurable impact across efficiency, speed, employee experience, and ultimately, ROI.
— They need to see how AI skills contribute to measurable impact across efficiency, speed, employee experience, and ultimately, ROI. Where the biggest opportunities lie — They should know which departments or workflows would benefit most from focused training and support.
One way to operationalize this is by tying AI skill development to goal-setting frameworks. For example, a company-wide goal to improve AI capabilities might break down into:
Team-level targets — These could include running AI training sessions or integrating AI into monthly reporting workflows.
AI-related projects — Might involve using AI to analyze engagement survey results and identify patterns or trends.
— Might involve using AI to analyze engagement survey results and identify patterns or trends. Individual goals tied to tool proficiency — Employees might be expected to develop prompts that generate actionable insights from performance data.
💡 Tools like Leapsome Goals can play a key role here by helping you visualize how each layer contributes to the broader strategy, which in turn helps keep everyone engaged and accountable.
2. Recruit for & promote AI fluency
Non-technical roles naturally don’t need deep technical expertise, but they do require a baseline comfort with AI tools, data interpretation, and experimentation.
That means updating job descriptions to reflect this shift. Add clear expectations around AI comfort level or data literacy — even for roles in HR, marketing, or operations. In interviews, ask practical questions about how candidates have used AI to improve workflows, speed up decision-making, or extract insights from data.
You should also look inward. Some of your best AI advocates are already on the team: employees who’ve found ways to integrate AI into their daily work without being told to. Identify them early, promote their methods, and build their influence into onboarding, peer learning, or cross-functional projects. The more visible and normalized AI fluency becomes, the faster it spreads across the organization.
✨ Real-world example of AI advocacy: the Leapsome AI Ambassador Program
Leapsome has established a core AI team of ambassadors with several key objectives, including:
· Enhancing efficiency by automating routine tasks to free up time for strategic work
· Upskilling the workforce through training employees to work with AI technologies
The AI Ambassadors’ operating principles include:
🌟 Embracing AI-driven change and building excitement by demonstrating AI’s capabilities
🌟 Exploring ways to radically improve processes
🌟 Sharing resources and insights via dedicated channels
🌟 Prioritizing automation with existing tools over manual processes or new tool purchases
The program focuses on identifying AI ambassadors across all teams to help formulate and implement Leapsome's AI strategy. This structured approach aims to seed AI thinking throughout the organization and drive fast, practical applications.
✨ Want to learn more about how your team, too, can automate tasks, uncover insights, and make smarter decisions? Explore Leapsome AI.
3. Build role-based AI upskilling programs
Use Leapsome Learning to provide tailored learning content for specific groups and individuals
An effective AI training program starts by identifying where AI upskilling will have the strongest impact on daily tasks — whether that’s writing effective prompts, interpreting AI-generated insights, or integrating tools into routine workflows. Generic, one-size-fits-all training won’t address these specific gaps. Different roles use AI differently, and training should reflect that.
For example, HR teams might need AI support to analyze engagement surveys, while finance might focus on AI process automation for monthly reporting. Legal teams could benefit from tools that summarize policy documents or highlight compliance risks. What matters is making the training relevant to each team’s tasks.
To build role-specific programs at scale, you can use a solution like Leapsome Learning, which allows you to build and assign tailored learning paths based on team, role, or skill focus, using a mix of internal and external content. The most effective programs emphasize hands-on application — training teams with the tools, workflows, and scenarios they work with every day. That’s how you build real familiarity and confidence.
🎯 Upskill your teams with Leapsome Learning
Create role-specific learning paths and deliver AI training that’s hands-on, relevant, and scalable.
👉 Explore Learning
4. Train teams to use AI critically & effectively
It’s easy to assume that once people know how to use AI tools, they know how to use them well. But without the ability to question outputs, spot inaccuracies, or challenge assumptions, teams risk taking AI responses at face value, which can have dangerous consequences.
For example, an HR manager might ask an unspecialized AI tool to summarize engagement survey results and present them as is — without noticing the exclusion of isolated but high-risk concerns like burnout or discrimination.
Focused, practical training on prompt engineering, bias detection, and critical thinking will help. Continuing the scenario above, you could run a workshop where teams review AI-generated summaries of engagement survey data, then use the tool to ask follow-up questions or reframe prompts to surface what might have been missed. Bringing cross-functional teams together can also accelerate learning. Creating small pods — say, HR, L&D, and product — lets teams test AI workflows in context, compare approaches, and build shared standards. When people learn from each other, not just from documentation, they develop more decisive judgment and more adaptable practices.
5. Reward experimentation & remove fear of failure
When 60% of HR leaders cite employee resistance to AI as an urgent challenge, it signals a deeper issue: people won’t adopt what they don’t feel safe trying. That resistance won’t shift through policy or training alone — people need the freedom to experiment with what does and doesn’t work.
Creating space for low-stakes testing — like AI hack weeks or opt-in sprints — helps remove pressure and encourages curiosity. One simple example: give each team a week to replace one manual process with an AI-powered alternative, then share what worked (and what didn’t) in an open team review.
Showcasing internal wins builds visibility and momentum while recognizing teams and individuals experimenting in meaningful ways reinforces that AI adoption is an evolving process, not a one-off initiative.
🎥 Learn from real AI examples in our discussion on efficiency & impact
See how leading HR teams are approaching AI training, adoption, and performance — with practical takeaways you can apply now.
👉 Watch the AI for HR webinar
AI skills gap identification checklist
Use this checklist as an assessment framework to evaluate your organization's current state of AI skills. You can consult it as part of a working session with team leads and department heads, or build it into existing performance and development planning, including discussions on how to coach your team members through their AI adoption challenges.
The goal is to highlight where teams are already strong, where adoption is lagging, and where targeted support (like training, coaching, or workflow redesign) is most needed. Revisit it regularly to track progress and adapt your learning and development approach as your teams evolve.
Close the AI skills gap with Leapsome
Use Leapsome’s learning paths to help team members develop the AI skills that’ll have the biggest impact on their role
Most teams won’t get value from AI unless it’s embedded into how they make decisions, complete tasks, and collaborate — and that will not happen through generic training or isolated tools. HR teams need a way to build real capability, track progress, and make adoption part of everyday work.
Leapsome helps you do exactly that. With tools for learning, goal setting, and performance reviews, you can create role-specific training, connect it to real outcomes, and see where support is needed.
Crucially, you can tie AI learning directly to individual growth and team priorities so adoption doesn’t happen in a vacuum. With built-in feedback and progress tracking, it’s easier to see where support is working — and get buy-in from all levels of your organization.
🔧 Make AI training stick
Use Leapsome to build relevant learning paths and understand how teams are applying AI.
👉 Bookd a demo
| 2023-02-01T00:00:00 |
https://www.leapsome.com/blog/ai-skills-gap
|
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|
AI Skills Gap - IBM
|
AI skills gap
|
https://www.ibm.com
|
[] |
Many employees believe that the AI skill gap is an AI training gap.3 ...
|
The skills shortage can be bridged with investments and initiatives around skills development. Many of the problems causing an AI skills gap are the same problems causing tech talent shortages. Several solutions for closing the AI skill gap overlap with solutions for completing the tech talent shortage.
There are several online platforms that offer teachings on AI skills. For example, IBM’s SkillsBuild and Microsoft8 offer free resources that can help anyone start to assess and develop their AI skills.
Fostering a future-ready workforce involves strategic hiring and investing in continuous learning. Most employees are amenable to more training to acclimate them to emerging technologies.
Traditional avenues for learners such as universities, PhD programs, AI camps and online academies, can still be viable for Gen Z workers to acquire skills. Training and exposure to AI technologies and tools in school curriculums, especially for younger students, is necessary. That means that keeping trainers and teachers up to date is vital.
When onboard, internal learning opportunities such as training programs, workshops with peers, office hours or sessions to practice in sandbox environments are what will help retain valuable employees, which can decrease the time needed to vet new applicants.
To streamline hiring and make the learning process efficient, companies must first thoroughly assess the benefits and limitations of AI to their organization (PDF).2
More AI is not always better.9 Businesses should carefully evaluate how they have been using it in their operations in the last year, see what’s working and what’s not, and use feedback to roadmap how they want to use AI in the next few years.
Based on this, they can test the AI readiness of their current employees in those AI topics to look for gaps in skill proficiency. Depending on how specialized a company’s AI needs are, they can then choose to either bring in new AI experts to pioneer projects or reskill their available engineers to use and apply AI tools.
To help employees be engaged to reach their personal skill-building goals, employers should consider more interactive and customizable learning programs5 that can mix online, on-demand courses with experiential opportunities and live, instructor-led training.
Importantly, companies must help ensure that their AI training approaches and initiatives are offered equitably and are inclusive of workers from different demographics.
It is easier to solve the problem collaboratively rather than having to develop strategies and in-house learning plans from scratch. Businesses can participate in partnerships with educational institutions and other organizations to provide these offerings.
| 2023-02-01T00:00:00 |
https://www.ibm.com/ae-ar/think/insights/ai-skills-gap
|
[
{
"date": "2023/02/01",
"position": 39,
"query": "AI skills gap"
},
{
"date": "2023/05/01",
"position": 38,
"query": "AI skills gap"
},
{
"date": "2023/06/01",
"position": 35,
"query": "AI skills gap"
},
{
"date": "2023/08/01",
"position": 38,
"query": "AI skills gap"
},
{
"date": "2023/10/01",
"position": 38,
"query": "AI skills gap"
},
{
"date": "2023/12/01",
"position": 42,
"query": "AI skills gap"
},
{
"date": "2024/02/01",
"position": 38,
"query": "AI skills gap"
},
{
"date": "2024/04/01",
"position": 42,
"query": "AI skills gap"
},
{
"date": "2024/07/01",
"position": 37,
"query": "AI skills gap"
},
{
"date": "2024/08/01",
"position": 39,
"query": "AI skills gap"
},
{
"date": "2024/09/01",
"position": 38,
"query": "AI skills gap"
},
{
"date": "2024/12/20",
"position": 8,
"query": "AI skills gap"
},
{
"date": "2025/01/01",
"position": 42,
"query": "AI skills gap"
},
{
"date": "2025/02/01",
"position": 43,
"query": "AI skills gap"
},
{
"date": "2025/03/01",
"position": 42,
"query": "AI skills gap"
},
{
"date": "2025/05/01",
"position": 41,
"query": "AI skills gap"
}
] |
|
New Study Reveals Only 1 in 10 Global Workers Have In-Demand ...
|
New Study Reveals Only 1 in 10 Global Workers Have In-Demand AI Skills
|
https://www.salesforce.com
|
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Yet only 1 in 10 say they have AI skills — which is cited as one of today's most in-demand digital skills. March 6, 2023. Editor's Note: AI Cloud, ...
|
Editor’s Note: AI Cloud, Einstein GPT, and other cloud GPT products are now Einstein. For the latest on Salesforce Einstein, go here.
Quick take: Salesforce’s new digital skills survey shows that the vast majority of global workers think skills are more important than education qualifications or career background. Most people leaders (98%) believe the shift to skills-based hiring provides business benefits. Yet only 1 in 10 say they have AI skills — which is cited as one of today’s most in-demand digital skills.
This article looks at the survey’s full findings, based on 11,000+ workers across 11 countries, and shares new data on how the workforce perceives the role that generative AI will play in the jobs of today and tomorrow.
Eighty-four percent of global workers consider skills-based experience more important than a degree when trying to land a job in today’s market.
However, there’s a disconnect between the skills companies are hiring for and those currently used by the workforce. While 4 in 5 global workers report using digital skills in their day-to-day work, few report skills beyond collaboration technology, digital administration, and digital project management.
In contrast, today’s fastest growing and in-demand skills as reported by workers include artificial intelligence (AI) and coding/app development — but they rank among the least used in workers’ day-to-day roles.
The good news? There appears to be less fear — and more excitement — among workers about the potential of emerging technologies to transform the jobs of the future. This, paired with workers’ reported desire to learn new skills, suggests that companies can help close the digital skills gap by providing continuous, skills-based training to their employees.
A global movement toward skills-based hiring
The shift toward skills-based hiring is evident at all levels. Most (82%) people leaders surveyed said that skills are the most important attribute when evaluating candidates. Only 18% said that relevant degrees are the most important.
Open Image Modal Image Modal
Over half of people leaders (56%) cite talent retention as a business benefit to skills-based hiring. Increased workforce diversity (48%) and knowledge sharing (46%) also ranked highly.
Many, 2 in 5, cite digital skills as the most important candidate attribute — an indication of the growing weight of these skills in today’s workforce.
Additionally, most people leaders believe that prioritizing employees’ digital skills development will have positive impacts on wider business performance, citing increased productivity (47%), better team performance (43%), and improved problem-solving capabilities (40%).
Leaders and employees agree on the need for AI skills
Workers are excited about emerging AI technologies like generative AI. Sixty percent of global workers reported excitement about the prospect of using generative AI for their job. In fact, more workers were excited about its use in their workplace (58%) than worried about it replacing them in their job (42%). This is in line with interest from management: Globally, two-thirds of people leaders(67%) say that their company is considering ways to use generative AI.
Despite this excitement, recent research shows that while over half of U.S.-based senior IT leaders say their business is currently using or experimenting with generative AI, 66% say their employees don’t have the skills to successfully leverage the technology.
IT Leaders Cite Ethics, Trust Among Top Generative AI Concerns DIVE INTO THE RESEARCH
Workers agree that AI skills are important: Nearly one-fourth of global workers rank AI skills as among the top three most important digital skills right now. This number rises when asked about the importance of these skills over the next five years.
As needs evolve, workplace skills lag
Despite its importance to their future skill set, only 1 in 10 workers say their day-to-day role currently involves AI. A mere 14% say their role involves other, related digital skills like encryption and cyber security, and a smaller 13% claim to use coding and app development skills.
The industry indexing the highest for AI skills, specifically, is the technology industry, but even for this industry, less than a third of employees (27%) use AI skills within their role today. Outside of traditional IT roles, this number drops further; less than 10% of those in healthcare (8%) and the public sector (6%) report they use AI skills in their day-to-day role.
The path forward is upskilling. Nearly all (97%) global workers believe businesses should prioritize AI skills in their employee development strategy.
Upskilling is critical across wide swaths of these emerging technologies. As a result of the rise in AI and automation, people leaders say data security skills (60%), ethical AI and automation skills (58%), and programming skills (57%) will become increasingly important in the workplace. When asked what ‘soft’ skills will likely be more important as a result, people leaders ranked creative imaginative skills (56%), customer relationship skills (53%), and leadership skills (51%) highest.
Fortunately, companies seeking to boost emerging technology skills and focus on skills-based hiring have something going for them — workers want to expand their limited set of digital skills. Nine in 10 believe that businesses should prioritize digital skills development for their employees.
Explore further:
Research Methodology
Salesforce’s Digital Skills Survey was conducted by TRUE Global Intelligence in partnership with Dynata in February 2023. The sample size was 11,035 working adults across 11 countries: Around 1,000 in Australia, France, Germany, Italy, the Netherlands, Singapore respectively; 1,005 in Spain, 1,007 in the UK, 1,013 in the USA, 1,003 in India, and 1,003 in Sweden. The figures are representative of working adult populations (ages 18+).
| 2023-03-06T00:00:00 |
2023/03/06
|
https://www.salesforce.com/news/stories/digital-skills-based-experience/
|
[
{
"date": "2023/02/01",
"position": 81,
"query": "AI skills gap"
},
{
"date": "2023/03/06",
"position": 26,
"query": "job automation statistics"
},
{
"date": "2023/03/06",
"position": 1,
"query": "AI skills gap"
},
{
"date": "2023/03/06",
"position": 96,
"query": "machine learning workforce"
},
{
"date": "2023/03/06",
"position": 9,
"query": "artificial intelligence workers"
},
{
"date": "2023/05/01",
"position": 80,
"query": "AI skills gap"
},
{
"date": "2023/06/01",
"position": 81,
"query": "AI skills gap"
},
{
"date": "2023/08/01",
"position": 81,
"query": "AI skills gap"
},
{
"date": "2023/10/01",
"position": 81,
"query": "AI skills gap"
},
{
"date": "2023/12/01",
"position": 87,
"query": "AI skills gap"
},
{
"date": "2024/02/01",
"position": 80,
"query": "AI skills gap"
},
{
"date": "2024/04/01",
"position": 88,
"query": "AI skills gap"
},
{
"date": "2024/07/01",
"position": 79,
"query": "AI skills gap"
},
{
"date": "2024/08/01",
"position": 87,
"query": "AI skills gap"
},
{
"date": "2024/09/01",
"position": 81,
"query": "AI skills gap"
},
{
"date": "2025/01/01",
"position": 88,
"query": "AI skills gap"
},
{
"date": "2025/02/01",
"position": 76,
"query": "AI skills gap"
},
{
"date": "2025/03/01",
"position": 77,
"query": "AI skills gap"
},
{
"date": "2025/05/01",
"position": 87,
"query": "AI skills gap"
}
] |
Multiverse | Upskilling platform for AI and tech adoption
|
Upskilling platform for AI and tech adoption
|
https://www.multiverse.io
|
[] |
Uncover your skills gaps. Get an expert assessment of your business goals and employee skillsets. User interface showing a skills gap diagnostic · Upskill your ...
|
Career Mobility @ Multiverse: Izzy Duffy
Next up in our Career Mobility spotlight series is Izzy Duffy. She joined Multiverse 6 years ago as an Apprentice and now is a Senior Business Operations Executive reporting to our Chief of Staff.
| 2023-02-01T00:00:00 |
https://www.multiverse.io/en-GB/
|
[
{
"date": "2023/02/01",
"position": 97,
"query": "AI skills gap"
},
{
"date": "2023/05/01",
"position": 97,
"query": "AI skills gap"
},
{
"date": "2023/06/01",
"position": 97,
"query": "AI skills gap"
},
{
"date": "2023/08/01",
"position": 98,
"query": "AI skills gap"
},
{
"date": "2023/10/01",
"position": 98,
"query": "AI skills gap"
},
{
"date": "2024/02/01",
"position": 97,
"query": "AI skills gap"
},
{
"date": "2024/07/01",
"position": 97,
"query": "AI skills gap"
},
{
"date": "2024/09/01",
"position": 99,
"query": "AI skills gap"
},
{
"date": "2025/02/01",
"position": 93,
"query": "AI skills gap"
},
{
"date": "2025/03/01",
"position": 99,
"query": "AI skills gap"
}
] |
|
HR's Role in AI Strategy Planning Is Now More Important than Ever
|
HR’s Role in AI Strategy Planning Is Now More Important than Ever
|
https://www.thehrdigest.com
|
[
"Ava Martinez",
"Diana Coker",
"Jane Harper",
"Anna Verasai",
"Priyansha Mistry"
] |
HR Plays a Role in Bridging the AI Skills Gap ...
|
Does your business utilize AI in some capacity? Was the HR team involved in the adoption of such technology? If your organization has not explored HR’s role in AI strategy, then you might be missing out on some business-critical insights.
In 2025, a large majority of businesses have begun to explore the use of AI tools across the many departments that keep the company alive, however, the adoption has not been smooth. Despite HR’s role in bridging the gap between employers and employees, it appears that this section of the organization is often left out of discussions, particularly in conversations about AI integration. The role of HR in developing an AI strategy may seem irrelevant at first glance, but it’s important to recognize that not only can HR utilize AI tools in their operations, they can also guide the organization on large-scale adoption as well.
The Importance of HR’s Role in AI Strategy Planning Must Not Be Diminished
A new study by Harvard Business Review found that only 21% of HR leaders play a role in the AI strategy of the organization. Around 30% are moderately involved, while 49% said there was little to no involvement in the AI adoption process planning. These numbers are telling but not unsurprising.
AI is seen as a technological investment, and as such, there is often room for tech and financial experts at the table when it comes to the discussion of AI adoption. HR, a people-first team, is not given credit for being the most tech-savvy. The report confirmed as much, stating scaling AI through HR was never a consideration because they were seen as lacking the necessary technological expertise. HR personnel often have to forego having a strong presence among the leadership teams, limiting how much say they have in terms of changes occurring across the organization, with AI or otherwise.
While it may be true that the HR workforce could benefit from some upskilling with regard to AI, it is also important to acknowledge that they can connect these tools with the well-being of the employees, and lead the effort to bridge the AI skill gap prevalent across the organization to ensure the transition to these tools is much smoother.
Why Should HR’s Voice Be Heard On AI Strategy?
To begin with, the nature of HR’s role and its growing familiarity with Human Resources Information Systems (HRIS) have already primed them to benefit from using technology in their operations. For an organization that is unsure of where to begin integrating AI into its functioning, introducing AI at the HR level can be a great place to start.
Instead of floundering to explore more extreme opportunities such as recruiting digital AI employees or putting them in front of customers immediately, organizations can begin cutting down on their hiring and onboarding time at once through AI-based HR tools.
HR can also help with the recruitment of AI experts and top talent who can work with such tech can level up an organization’s preparedness for AI significantly, giving them a better shot at adopting such technology.
HR Can Help Address Workforce Resistance to AI
It’s no secret that employees across organizations and industries are afraid of AI and what it will mean for their jobs. There is an obvious resistance to using AI tools, and while big businesses like Microsoft may be able to get away with forcing employees to use AI, this is not the most effective strategy. HR’s role in AI strategy extends to understanding where employees stand on the matter of artificial intelligence and communicating these concerns to leaders.
Not only can HR help with bridging the divide with information and reassurance, but they can also oversee the implementation of the changes across departments to help employees better utilize AI. Scaling AI through HR can ensure that the integration occurs smoothly, with these HR teams prepared to address the concerns that employees have, whether it’s with regard to job security or the faulty application of the technology in incorrect areas. HR’s role as the people-first department is now more important than ever.
HR teams can also continue to help the organization determine where employees are benefitting from AI tools best when the technology is adopted, as they have the most direct line of communication with workers. If the organization’s use of AI tools is less than optimal and it affects the quality of work being put out, it is up to HR to bring these concerns to the organization and ensure that it doesn’t hurt the employee’s performance reviews without cause. The spirit of collaboration and open communication is integral to the heavy investments being made in AI.
HR Plays a Role in Bridging the AI Skills Gap
HR’s own upskilling with reference to AI is near-mandatory in 2025, but for the rest of the organization to catch up to the level of familiarity needed to work with AI, HR has to take the wheel again. As investments are made into the tool, similar expenses will also need to be incurred to prepare the workforce to operate these tools. It is not enough to leave new machinery at the center of the building and assume that workers will automatically master how to operate it.
HR leaders have to play a role in designing the AI strategy so they can share their insights on how the workforce will be brought up to speed. Training programs will have to be planned and provided team by team, and HR workers will have to ensure the knowledge required to utilize these tools to their full capacity makes its way across the organization.
The HR AI Adoption Stats Need to Change Right Now
There is a prominent AI skill gap among the workforce right now, and there aren’t enough AI experts to mass hire for every role. This makes it more important than ever to prioritize HR’s role in determining an AI strategy moving forward. Every employee within an organization has a critical role to play in how the organization performs in the long run. Taking their insights and expertise into consideration can be integral to the success of the organization, capitalizing on the talent that already exists rather than looking externally to support change.
Scaling AI through HR can help ensure that the adoption of new technology is both steady and gradual, opening the organization up for a careful and planned adoption of novel tools. AI is changing how we work, but it is important to take employees along on the journey rather than isolating them from the changes that are occurring.
Subscribe to The HR Digest for more insights into the ever-evolving landscape of work and employment in 2025.
| 2025-07-05T00:00:00 |
2025/07/05
|
https://www.thehrdigest.com/hrs-role-in-ai-strategy-planning-is-now-more-important-than-ever/
|
[
{
"date": "2023/02/01",
"position": 99,
"query": "AI skills gap"
},
{
"date": "2023/05/01",
"position": 99,
"query": "AI skills gap"
},
{
"date": "2024/02/01",
"position": 99,
"query": "AI skills gap"
},
{
"date": "2024/07/01",
"position": 98,
"query": "AI skills gap"
}
] |
Future of Work with AI Agents
|
Future of Work with AI Agents
|
https://futureofwork.saltlab.stanford.edu
|
[] |
We propose a principled, survey-based and audio-enhanced auditing framework for mapping the risks and opportunities of AI agents across the full spectrum of US ...
|
NEW (July 2025) WORKBank Database is now publicly released ( github huggingface ). Explore the occupation and sector profile on our data explorer page!
1. Desire-capability landscape of AI agents at work reveals critical mismatches of current AI agent research and investment. 41.0% of Y Combinator company-task mappings are concentrated in the Low Priority Zone and Automation “Red Light” Zone.
41.0% of Y Combinator company-task mappings are concentrated in the Low Priority Zone and Automation “Red Light” Zone. 2. Many occupational tasks see a need of human-agent collaboration with equal partnership. However, workers generally prefer higher levels of human agency, potentially foreshadowing friction as AI capabilities advance.
However, workers generally prefer higher levels of human agency, potentially foreshadowing friction as AI capabilities advance. 3. Suppose AI agents start to enter the workforce, key human competencies may be shifting from information-processing skills to interpersonal and organizational skills. 70 million U.S. workers are about to face their biggest workplace transition due to AI agents, but their voices are too often missing. We address this gap by conducting a nationwide audit to understand what workers want AI agents to automate or augment, and how those desires align with the current technological capabilities. Data from 1,500 workers across 104 occupations leads to three main findings:
AI Agents are Reshaping the Workplace You may not take interest in AI, but AI will take interest in you. 80% of U.S. workers may see LLMs affect at least 10% of their tasks, with 19% facing potential disruption to over half of their responsibilities . at least 25% of tasks in 36% of occupations.
To prepare for the future of work, in collaboration with economists from Worker Outlook & Readiness Knowledge Bank (WORKBank), the first database that captures AI agent capabilities and worker preferences. This database currently consists of responses from 1,500 workers across 104 occupations and annotations from 52 AI experts, covering 844 occupational tasks. It is designed to be easily extensible to more tasks and to reflect evolving technological capabilities and worker preferences. AI is causing rapid, unexpected changes across the workforce. Studies estimate that aroundmay see LLMs affect at least 10% of their tasks, with Early-2025 LLM Usage data further indicates that AI tools are already in active use forTo prepare for the future of work, in collaboration with economists from Stanford Digital Economy Lab , we propose a principled, survey-based and audio-enhanced auditing framework for mapping the risks and opportunities of AI agents across the full spectrum of U.S. occupations. The auditing framework takes a worker-centric approach by soliciting first-hand insights from domain workers actively performing corresponding tasks. Leveraging the U.S. Department of Labor’s O*NET database as the task source, we construct the AI Agentorkerutlook &eadinessnowledge Bank (), the first database that captures AI agent capabilities and worker preferences. This database currently consists of responses fromacrossand annotations from 52 AI experts, covering. It is designed to be easily extensible to more tasks and to reflect evolving technological capabilities and worker preferences.
Overview of the auditing framework and key insights.
Understand the Fear and Desire
1. Where do workers resist AI agent automation? We analyzed worker transcripts using topic modeling, guided by the seed prompt: "The top most common fears that workers have about AI automation in their work." The three most prominent concerns identified are lack of trust (45%), fear of job replacement (23%), and the absence of human touch (16.3%). When breaking down WORKBank by sectors, in Arts, Designs, and Media, only 17.1% of tasks got positive ratings.
2. Which occupational tasks do workers desire AI agent automation? For 46.1% of tasks, workers currently performing them express a positive attitude (rating their desire above 3 on a 5-point Likert scale) toward AI agent automation, even after explicitly considering concerns such as job loss and reduced enjoyment.
3. Why do workers want AI agent automation? For pro-automation responses, we collected workers' motivations using both checkbox and free-form questions. The most cited motivation for pro-automation is “freeing up time for high-value work” (selected in 69.4% of cases). Other common reasons include task repetitiveness (46.6%), stressfulness (25.5%), and opportunities for quality improvement (46.6%).
4. Contrasting worker and AI expert perspectives delineate four task zones. Our data helps classifies occupational tasks into four zones: 1. Automation “Green Light” Zone: Tasks with both high automation desire and high capability. These are prime candidates for AI agent deployment with the potential for broad productivity and societal gains. 2. Automation “Red Light” Zone: Tasks with high capability but low desire. Deployment here warrants caution, as it may face worker resistance or pose broader negative societal implications. 3. R&D Opportunity Zone: Tasks with high desire but currently low capability. These represent promising directions for AI research. 4. Low Priority Zone: Tasks with both low desire and low capability.
5. The desire-capability landscape reveals opportunities and mismatches. We used Y Combinator (YC) companies as a proxy and mapped them to the tasks in WORKBank database. Unfortunately, the current YC investment does not skew towards Automation “Green Light” Zone and R&D Opportunity Zone. 41.0% of YC companies are mapped to Low Priority and Automation “Red Light” Zone; while many promising tasks within the “Green Light” Zone and Opportunity Zone remain under-addressed by current investments.
Opportunities for Human-Agent Collaboration Human Agency Scale (HAS), a five-level scale from H1 (no human involvement) to H5 (human involvement essential). This new scale complements the SAE L0-L5 automation levels by quantifying the degree of human involvement required for occupational task completion and quality rather than focus on an “AI-first” view. A distinctive aspect of our auditing framework is that it goes beyond the typical focus on automation. We also examine augmentation —where technology complements and enhances human capabilities. To provide a shared language for quantifying automation vs. augmentation, we introduce the, a five-level scale from H1 (no human involvement) to H5 (human involvement essential). This new scale complements the SAE L0-L5 automation levels by quantifying the degree of human involvement required for occupational task completion and quality rather than focus on an “AI-first” view.
Levels of Human Agency Scale (HAS).
6. Workers in many occupations prefer a balanced, collaborative partnership with AI. We introduce the Human Agency Scale (H1–H5) to quantify the degree of human involvement required for completing occupational tasks and ensuring their quality. This new scale centers human agency and provides a shared language to capture the spectrum between automation and augmentation. Notably, Human Agency Scale H3 (Equal Partnership) emerges as the dominant worker-desired level in 47 out of 104 occupations analyzed.
7. Workers generally prefer higher levels of human agency, potentially foreshadowing frictions as AI capabilities advance. Among 844 tasks, workers prefer higher levels of human agency than what experts deem technologically necessary on 47.5% of tasks. Notably, for 16.4% of tasks, the worker-preferred level is two levels higher than expert assessments.
8. Human Agency Scale reveals automation-vs-augmentation profile for each occupation. Tasks within the same occupation can vary significantly in their desired levels of human agency. We suggest that AI agent development should account for varying levels of human agency to enable higher-quality and more responsible adoption.
Prepare for the Future
Using the O*NET database, we match each occupational task to the specific skills it relies on. For example, the task “Approve, reject, or coordinate the approval or rejection of lines of credit or commercial, real estate, or personal loans” (performed by financial managers) will be mapped to “making decisions and solving problems” and “guiding, directing, and motivating subordinates”. For each skill, we estimate two key values:
Human agency level , based on expert assessments of the human agency level.
, based on expert assessments of the human agency level. Average Wage, using wage data from the U.S. Bureau of Labor Statistics as measure of current economic value.
By comparing skill rankings based on these two dimensions, we uncover three emerging trends that could potentially shape the future of human work:
1. Shrinking demand for information-processing skills. Skills related to analyzing data and updating knowledge—while common in today’s high-wage occupations are less prominent in tasks that demand high human agency.
Skills related to analyzing data and updating knowledge—while common in today’s high-wage occupations are less prominent in tasks that demand high human agency. 2. Greater emphasis on interpersonal and organizational skills. Skills involving human interaction, coordination, and resource monitoring are more frequently associated with high-HAS tasks, even if they are not currently prioritized in wage-based evaluations.
Skills involving human interaction, coordination, and resource monitoring are more frequently associated with high-HAS tasks, even if they are not currently prioritized in wage-based evaluations. 3. High-agency skills span diverse aspects. The top 10 skills with the highest average required human agency encompass a broad range, from interpersonal and organizational abilities to decision-making and quality judgment. Not all types of work are equally impacted by AI. To understand where the future of work is headed and what skills will be most valuable, we further use the WORKBank database to analyze human skill shift.Using the O*NET database, we match each occupational task to the specific skills it relies on. For example, the task “Approve, reject, or coordinate the approval or rejection of lines of credit or commercial, real estate, or personal loans” (performed by financial managers) will be mapped to “making decisions and solving problems” and “guiding, directing, and motivating subordinates”. For each skill, we estimate two key values:By comparing skill rankings based on these two dimensions, we uncover three emerging trends that could potentially shape the future of human work:
| 2023-02-01T00:00:00 |
https://futureofwork.saltlab.stanford.edu/
|
[
{
"date": "2023/02/01",
"position": 17,
"query": "future of work AI"
},
{
"date": "2023/04/01",
"position": 9,
"query": "future of work AI"
},
{
"date": "2023/05/01",
"position": 12,
"query": "future of work AI"
},
{
"date": "2023/09/01",
"position": 15,
"query": "future of work AI"
},
{
"date": "2023/10/01",
"position": 14,
"query": "future of work AI"
},
{
"date": "2024/03/01",
"position": 14,
"query": "future of work AI"
},
{
"date": "2024/04/01",
"position": 15,
"query": "future of work AI"
},
{
"date": "2024/07/01",
"position": 8,
"query": "future of work AI"
}
] |
|
AI and the future of work - University of Cambridge
|
AI and the future of work
|
https://www.cam.ac.uk
|
[] |
There is no future of work without AI. Whether it's automating tasks, collaborating on creativity, or redefining decision-making processes, AI is reshaping the ...
|
Some of the excitement people have for AI comes from the expectation that, by making administrative tasks more efficient, it frees up time to focus on more creative work. But what if AI can do the creative stuff too?
David Stillwell, Professor of Computational Social Science, explores this question and how it may impact organisations and workers.
“Our research shows that AI is as creative as the average human right now. It’s almost bang on the 50th percentile,” says Stillwell.
Stillwell believes the most-likely future will involve collaboration between the two, whereby AI acts as a team member (you say some things, it says some things in response, and you go back and forth), or an idea generator (you get it to list 50 ideas and then you pick the best ones), or a sounding board (you come up with the ideas and it tells you what could go wrong and what to look out for). “We don’t know yet which is the most successful approach to use – or when.”
“Another key question we are researching is, who benefits from this collaboration? Is it people who are already creative that get even better, or does it help those who struggle with creativity?" Stillwell asks.
Along with creativity, Stillwell is looking into other OECD 21st century skills – skills considered critical for the future – like critical thinking and problem solving:
"We’re doing research on teams of AI collaborating together. They are assigned different roles or expertise and you get them to talk to each other to come up with the best ideas.”
Those organisations that are embracing aspects of AI are already seeing productivity benefits.
Leanne Allen, Head of AI at KPMG UK said: "Many organisations are already designing, building and implementing AI, enhancing productivity by augmenting human capabilities such as summarising and drafting documents, drafting emails and fast information retrieval over curated knowledge bases. This is the first wave of AI implementation.”
As AI moves into areas previously thought to be exclusively human and can collaborate not only with humans but with other AI, we may need to reimagine what skills will be most valuable for the future of work.
Allen added: “Many organisations have already entered the second wave of AI agents that are bringing greater effectiveness and accuracy in addition to efficiencies such as in fraud detection, tracking customer behaviour or medical imagery.
As the effectiveness of AI grows, so will the controls for responsible use. Future waves will then see AI transform business models and operating structures, altering the skills needed for today's roles towards value-added work and critical thinking.
“However, to be successful in these future waves, the focus needs to be on adjusting workloads as replacing mundane tasks with continuous critical thinking, problem-solving and decision-making is not sustainable and could lead to cognitive overload and burnout in employees.”
| 2023-02-01T00:00:00 |
https://www.cam.ac.uk/stories/AI-and-the-future-of-work
|
[
{
"date": "2023/02/01",
"position": 28,
"query": "future of work AI"
},
{
"date": "2023/04/01",
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"query": "future of work AI"
},
{
"date": "2023/05/01",
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{
"date": "2024/03/01",
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{
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{
"date": "2025/05/14",
"position": 5,
"query": "future of work AI"
},
{
"date": "2025/05/14",
"position": 77,
"query": "workplace AI adoption"
}
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|
AI, automation and the future of work | Rotman School of Management
|
AI, automation and the future of work
|
https://www-2.rotman.utoronto.ca
|
[] |
Automation and AI are transforming the nature of work and the workplace, creating both challenges and opportunities. Society will need to grapple with ...
|
Read time:
Automation and AI are transforming the nature of work and the workplace, creating both challenges and opportunities. Society will need to grapple with significant transition, as some occupations decline, others grow, and many more undergo transformation.
Automation and artificial intelligence are transforming businesses and will contribute significantly to economic growth via contributions to productivity. These technologies will transform the very nature of work and the workplace itself. Machines will be able to carry out more of the tasks done by humans, complement the work that humans do, and even perform some tasks that go beyond what humans can do. As a result, some occupations will decline, others will grow, and many more will change.
While we believe there will be enough work to go around (barring extreme scenarios), society will need to grapple with significant workforce transitions and dislocation. Workers will need to acquire new skills and adapt to the increasingly capable machines alongside them in the workplace. They may have to move from declining occupations to growing and, in some cases, brand new ones.
In this article we will examine both the promise and the challenge of automation and AI in the workplace and outline some of the critical issues that policymakers, companies and individuals need to consider.
Opportunities ahead
Automation and AI are not new, but recent technological progress is pushing the frontier of what machines can do. Our research suggests that society needs these improvements to provide value for businesses, contribute to economic growth, and make once unimaginable progress on some of our most difficult societal challenges. Following are some of the key opportunities that lie ahead.
Rapid technological progress:
Beyond traditional industrial automation and advanced robots, new generations of more capable autonomous systems are appearing in environments ranging from autonomous vehicles to automated check-outs in grocery stores. Much of this progress has been driven by improvements in systems and components, including mechanics, sensors and software. AI has made especially big strides in recent years, as machine learning algorithms have become more sophisticated and made use of huge increases in computing power and of the exponential growth in data available to train algorithms. Spectacular breakthroughs are making headlines, many involving beyond-human capabilities in computer vision, natural language processing and complex games such as Go.
Potential to contribute to economic growth:
These technologies are already generating value in various products and services, and companies across sectors use them in an array of processes to personalize product recommendations, find anomalies in production, identify fraudulent transactions and more. The latest generation of AI advances, including techniques that address classification, estimation and clustering problems, promises significantly more value still. An analysis we conducted of several hundred AI use cases found that the most advanced deep learning techniques deploying artificial neural networks could account for as much as US$ 3.5 trillion to US$ 5.8 trillion in annual value, or 40 per cent of the value created by all analytics techniques.
At a time when aging and falling birth rates are acting as a drag on growth, the deployment of AI and automation technologies can do much to lift the global economy and increase global prosperity. Labour productivity growth — a key driver of economic growth — has slowed in many economies, but AI and automation have the potential to reverse that decline: Productivity growth could potentially reach two per cent annually over the next decade, with 60 per cent of this increase from digital opportunities.
Potential to help tackle societal moonshot challenges:
AI is also being used in areas ranging from material science to medical research and climate science. Application of the technologies in these and other disciplines could help tackle societal moonshot challenges. For example, researchers at Geisinger have developed an algorithm that could reduce diagnostic times for intracranial hemorrhaging by up to 96 per cent. Researchers at George Washington University, meanwhile, are using machine learning to more accurately weight the climate models used by the Intergovernmental Panel on Climate Change.
Challenges remain before these technologies can live up to their potential:
AI and automation still face challenges. The limitations are partly technical, such as the need for massive training data and difficulties generalizing algorithms across use cases. Recent innovations are just starting to address these issues. Other challenges are in the use of AI techniques. For example, explaining decisions made by machine learning algorithms is technically challenging, which particularly matters for use cases involving financial lending or legal applications. Potential bias in the training data and algorithms, as well as data privacy, malicious use and security are all issues that must be addressed.
Europe is leading with the new General Data Protection Regulation, which codifies more rights for users over data collection and usage. A different sort of challenge concerns the ability of organizations to adopt these technologies, where people, data availability, technology and process readiness often make it difficult. Adoption is already uneven across sectors and countries. The finance, automotive and telecommunications sectors lead AI adoption. Among countries, U.S. investment in AI ranked first at $15 billion to $23 billion in 2016, followed by Asia’s investments of $8 billion to $12 billion, with Europe lagging at $3 billion to $4 billion.
How AI and automation will affect work
Even as AI and automation bring benefits to business and society, we need to prepare for some major disruptions to work.
About half of the activities (not jobs) carried out by workers could be automated:
Our analysis of more than 2,000 work activities across more than 800 occupations shows that certain categories of activities are more easily automatable than others.
They include physical activities in highly predictable and structured environments, as well as data collection and data processing. These account for roughly half of the activities that people do across all sectors. The least susceptible categories include managing others, providing expertise, and interfacing with stakeholders.
Nearly all occupations will be affected by automation, but only about five per cent of occupations could be fully automated by currently demonstrated technologies. Many more occupations have portions of their constituent activities that are automatable: We find that about 30 per cent of the activities in 60 per cent of all occupations could be automated. This means that most workers — from welders to mortgage brokers to CEOs — will work alongside rapidly evolving machines — and the nature of these occupations will likely change as a result.
Jobs will be lost:
We have found that around 15 per cent of the global workforce, or about 400 million workers, could be displaced by automation by 2030. This reflects our mid-point scenario in projecting the pace and scope of adoption. Under the fastest scenario we have modelled, that figure rises to 30 per cent, or 800 million workers; while in our slowest-adoption scenario, only about 10 million people would be displaced — close to zero per cent of the global workforce.
This wide range underscores the multiple factors that will impact the pace and scope of AI and automation adoption. Technical feasibility of automation is only the first influencing factor. Others include the cost of deployment; labour-market dynamics, including labour supply quantity, quality, and the associated wages; the benefits beyond labour substitution that contribute to business cases for adoption; and, finally, social norms and acceptance.
Jobs will be gained:
Even as workers are displaced, there will be growth in demand for work and, consequently, jobs. We developed scenarios for labour demand to 2030 from several catalysts of demand for work, including rising incomes, increased spending on healthcare, and continuing or stepped-up investment in infrastructure, energy and technology development and deployment. These scenarios showed a range of additional labour demand of between 21 and 33 per cent of the global workforce (555 million and 890 million jobs) to 2030, more than offsetting the numbers of jobs lost. Some of the largest gains will be in emerging economies such as India, where the working-age population is already growing rapidly.
Additional economic growth, including from business dynamism and rising productivity growth, will also continue to create jobs. If history is a guide, many other new occupations that we cannot currently imagine will also emerge and may account for as much as 10 per cent of jobs created by 2030. Moreover, technology itself has historically been a net job creator. For example, the introduction of the personal computer in the 1970s and 1980s created millions of jobs, not just for semi-conductor makers, but also for software and app developers of all types, customer service representatives and information analysts.
Jobs will change:
More jobs than those lost or gained will be changed as machines complement human labour in the workplace. Partial automation will become more prevalent as machines complement human labour. For example, AI algorithms that can read diagnostic scans with a high degree of accuracy will help doctors diagnose patient cases and identify suitable treatment. In other fields, jobs with repetitive tasks could shift towards a model of managing and troubleshooting automated systems. At Amazon, employees who once lifted and stacked objects have become robot operators, monitoring the automated arms and resolving issues such as an interruption in the flow of objects.
Workforce transitions and challenges
While we expect there will be enough work to ensure full employment in 2030 based on most of our scenarios, the transitions that will accompany automation and AI adoption will be significant. The mix of occupations will change, as will skill and educational requirements. Work will need to be redesigned to ensure that humans work alongside machines most effectively.
Workers will need different skills to thrive in the workplace of the future:
Automation will accelerate the shift in required workforce skills we have seen over the past 15 years. Demand for advanced technological skills such as programming will grow rapidly. Social, emotional and higher cognitive skills, such as creativity, critical thinking and complex information processing will also see growing demand. Basic digital skills demand has already been increasing, and that trend will accelerate. Demand for physical and manual skills will decline, but will remain the single largest category of workforce skills in 2030 in many countries. This will put additional pressure on the already existing workforce skills challenge, as well as the need for new credentialing systems. While some innovative solutions are emerging, solutions that can match the scale of the challenge will be required.
Many workers will need to change occupations. Our research suggests that, in a mid-point scenario, around three per cent of the global workforce will need to change occupational category by 2030, though our scenarios range from zero to 14 per cent. Some of these shifts will happen within companies and sectors, but many will occur across sectors and even geographies. Occupations made up of physical activities in highly structured environments or in data processing or collection will see declines. 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.
Workplaces and workflows will change as more people work alongside machines:
As intelligent machines and software are integrated more deeply into the workplace, workflows and workspaces will continue to evolve to enable humans and machines to work together. 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. More system-level solutions will prompt rethinking of the entire workflow and workspace. Warehouse design may change significantly as some portions are designed to accommodate primarily robots and others to facilitate safe human-machine interaction.
Automation will likely put pressure on average waged in advanced economies:
The occupational mix shifts will likely put pressure on wages. Many of the current middle-wage jobs in advanced economies are dominated by highly automatable activities, such as in manufacturing or in accounting, which are likely to decline. High-wage jobs will grow significantly, especially for high-skill medical and tech or other professionals, but a large portion of jobs expected to be created, including teachers and nursing aides, typically have lower wage structures. 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.
10 things to solve for
In the search for appropriate measures and policies to address these challenges, we should not seek to roll back or slow diffusion of the technologies. Rather, the focus should be on ways to ensure that the coming workforce transitions are as smooth as possible. This is likely to require more actionable and scalable solutions in several key areas:
1) Ensuring robust economic and productivity growth.
Strong growth is not the magic answer for all the challenges posed by automation, but it is a pre-requisite for job growth and increasing prosperity. Productivity growth is a key contributor to economic growth. Therefore, unlocking investment and demand, as well as embracing automation for its productivity contributions, is critical.
2) Fostering business dynamism.
Entrepreneurship and more rapid new business formation will not only boost productivity, but also drive job creation. A vibrant environment for small businesses as well as a competitive environment for large business fosters business dynamism and, with it, job growth. Accelerating the rate of new business formation and the growth and competitiveness of businesses, large and small, will require simpler and evolved regulations, tax and other incentives.
3) Evolving education systems and learning for a changed workplace.
Policymakers working with education providers (traditional and non-traditional) and employers themselves could do more to improve basic STEM skills through the school systems and improved on-the-job training. A new emphasis is needed on creativity, critical and systems thinking, and adaptive and lifelong learning. There will need to be solutions at scale.
4) Investing in human capital.
Reversing the trend of low, and in some countries, declining public investment in worker training is critical. Through tax benefits and other incentives, policymakers can encourage companies to invest in human capital, including job creation, learning and capability
building, and wage growth, similar to incentives for the private sector to invest in other types of capital, including R&D.
5) Improving labour market dynamism.
Information signals that enable matching of workers to work and credentialing could work better in most economies. Digital platforms can also help match people with jobs and restore vibrancy to the labour market. When more people change jobs, even within a company, evidence suggests that wages rise. As more varieties of work and income-earning opportunities emerge, including the gig economy, we will need to solve for issues such as portability of benefits, worker classification and wage variability.
6) Redesigning work.
Workflow design and workspace design will need to adapt to a new era in which people work more closely with machines. This is both an opportunity and a challenge, in terms of creating a safe and productive environment. Organizations are changing too, as work becomes more collaborative and companies seek to become increasingly agile and non-hierarchical.
7) Rethinking incomes.
If automation (full or partial) does result in a significant reduction in employment and/or greater pressure on wages, some ideas such as conditional transfers, support for mobility, universal basic income and adapted social safety nets could be considered and tested. The key will be to find solutions that are economically viable and incorporate the multiple roles that work plays for workers, including providing not only income, but also meaning, purpose, and dignity.
8) Rethinking transition support and safety nets for workers affected.
As work evolves at higher rates of change between sectors, locations, activities and skill requirements, many workers will need assistance adjusting. Many best practice approaches to transition safety nets are available, and should be adopted and adapted, while new approaches should be considered and tested.
9) Investing in drivers of demand for work.
Governments will need to consider stepping up investments that are beneficial in their own right and will also contribute to demand for work (e.g. infrastructure, climate change adaptation). These types of jobs, from construction to rewiring buildings and installing solar panels, are often middle-wage jobs — those most affected by automation.
10) Embracing AI and automation safely.
Even as we capture the productivity benefits of these rapidly evolving technologies, we need to actively guard against the risks and mitigate any dangers. The use of data must always take into account concerns, including data security, privacy, malicious use and potential issues of bias — issues that policymakers, tech and other firms and individuals will need to find effective ways to address.
In closing
There is work for everyone today and there will be work for everyone tomorrow, even in a future with automation. But that work will be different, requiring new skills and a far greater adaptability of the workforce than we have seen. Training and retraining both mid-career workers and new generations for the coming challenges will be an imperative. Government, private sector leaders and innovators all need to work together to better coordinate public and private initiatives, including creating the right incentives to invest more in human capital. The future with automation and AI will be challenging, but it will also be a much richer one if we harness the technologies with aplomb — and mitigate the negative effects.
This article originally appeared in the Winter 2019 issue of Rotman Management magazine.
| 2023-02-01T00:00:00 |
https://www-2.rotman.utoronto.ca/insightshub/ai-analytics-big-data/ai-automation-future-of-work
|
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] |
|
The future of work in the age of generative AI - Mercer
|
The future of work in the age of generative AI
|
https://www.mercer.com
|
[
"Ravin Jesuthasan",
"Global Transformation Services Leader"
] |
Charting the rapid rise of gen AI · 75% of surveyed companies expect to adopt AI. · 50% expect AI to create job growth, and 25% expect it to create job losses.
|
Every year, after the inspirational whirlwind that is the Annual Meeting of the World Economic Forum (WEF) at Davos, I sit down to reflect on the themes that emerge. Last year, we discussed the radical reassessment of how we think about work and jobs — what we called “Work’s Great Reboot”
This year, that rapid reboot has been thrown into overdrive by the tsunami that is generative AI (gen AI). It has stirred up excitement — and deep anxieties — as leaders try to understand and prepare for its impact on work, workers and society at large. Surpassing climate change, geopolitical crises and last year’s hot topic — crypto — gen AI was the hottest topic this year by a factor of 10.
This was the topic of our ninth annual Mercer breakfast, Tapping AI’s Power to Optimize Our Working World, where we collaborated with Oliver Wyman, our sister company at Marsh McLennan.
| 2023-02-01T00:00:00 |
https://www.mercer.com/insights/people-strategy/future-of-work/the-future-of-work-in-the-age-of-generative-ai/
|
[
{
"date": "2023/02/01",
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{
"date": "2023/04/01",
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},
{
"date": "2023/05/01",
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},
{
"date": "2023/09/01",
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},
{
"date": "2023/10/01",
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},
{
"date": "2024/03/01",
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},
{
"date": "2024/04/01",
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"query": "future of work AI"
},
{
"date": "2024/06/01",
"position": 37,
"query": "future of work AI"
},
{
"date": "2024/07/01",
"position": 37,
"query": "future of work AI"
},
{
"date": "2025/03/01",
"position": 33,
"query": "future of work AI"
}
] |
|
How AI and Automation Will Transform the Future of Work - SHRM
|
How AI and Automation Will Transform the Future of Work
|
https://www.shrm.org
|
[
"Tam Harbert"
] |
Explore the dual paths automation and AI could lead us to: a future facing job displacement and inequality or a utopia of less work and more personal growth ...
|
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.
| 2023-02-01T00:00:00 |
https://www.shrm.org/topics-tools/news/all-things-work/technology-future-work-way-will-go
|
[
{
"date": "2023/02/01",
"position": 35,
"query": "future of work AI"
},
{
"date": "2023/04/01",
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"query": "future of work AI"
},
{
"date": "2023/05/01",
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},
{
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},
{
"date": "2023/10/01",
"position": 36,
"query": "future of work AI"
},
{
"date": "2023/10/01",
"position": 92,
"query": "reskilling AI automation"
},
{
"date": "2024/03/01",
"position": 37,
"query": "future of work AI"
},
{
"date": "2024/04/01",
"position": 37,
"query": "future of work AI"
},
{
"date": "2024/06/01",
"position": 34,
"query": "future of work AI"
},
{
"date": "2024/07/01",
"position": 38,
"query": "future of work AI"
},
{
"date": "2025/03/01",
"position": 35,
"query": "future of work AI"
}
] |
|
Work Of The Future | IPC - MIT Industrial Performance Center
|
Work Of The Future
|
https://ipc.mit.edu
|
[
"Nathan Wilmers",
"Dylan Nelson",
"Julie Shah",
"Lindsay Sanneman",
"Christopher Fourie",
"Paul Osterman"
] |
We envision an economy where dramatic advances in automation and computation go hand in hand with improved opportunities and economic security for workers.
|
Julie Shah is Faculty Director of the Industrial Performance Center and co-leads the Work of the Future Initiative.
Lindsay Sanneman is a PhD candidate in the Department of Aeronautics and Astronautics at MIT
Christopher Fourie is a PhD candidate in the Department of Aeronautics and Astronautics, and a member of the Interactive Robotics Group.
| 2023-02-01T00:00:00 |
https://ipc.mit.edu/research/work-of-the-future/
|
[
{
"date": "2023/02/01",
"position": 36,
"query": "future of work AI"
},
{
"date": "2023/05/01",
"position": 40,
"query": "future of work AI"
},
{
"date": "2023/09/01",
"position": 39,
"query": "future of work AI"
},
{
"date": "2024/02/09",
"position": 17,
"query": "future of work AI"
},
{
"date": "2024/04/01",
"position": 36,
"query": "future of work AI"
},
{
"date": "2024/06/01",
"position": 42,
"query": "future of work AI"
},
{
"date": "2025/03/01",
"position": 38,
"query": "future of work AI"
}
] |
|
The future of working with AI: don't compete against it, lead better ...
|
The future of working with AI: don’t compete against it, lead better with its support
|
https://www.plainconcepts.com
|
[
"Elena Canorea"
] |
A very beneficial approach would be to task AI with repetitive tasks to allow workers to focus on strategic decision making, creativity, and innovation. The ...
|
¿How will AI change the future of work
We live on the threshold of a new labor era. If we have already gone through the industrial revolution, we are now in the midst of the great AI revolution. McKinsey estimates that global productivity could grow by $4.4 trillion thanks to its adoption in companies. However, only 1% of companies are considered AI mature, suggesting that the vast majority of the world’s companies are still in the early stages.
With the advent of powerful and efficient LLMs, we have entered a new era of IT, and therein lies the challenge as well, as the long-term potential of AI is huge, but the short-term benefits are more uncertain, leaving many companies hesitant to embark on this journey.
As mentioned above, over the next 3 years, 92% of companies plan to increase their investments in AI, but only a few report that AI is fully integrated into workflows and driving substantial business results.
At this point, a question arises: How can business leaders invest capital and bring their organizations closer to AI maturity?
AI is becoming much smarter than the models of just a couple of years ago, thanks, for example, to reasoning capabilities. These improve the technology when making complex decisions, allowing models to go beyond basic understanding to nuanced understanding and the ability to create step-by-step plans to achieve goals.
Companies can refine reasoning models and integrate them with domain-specific knowledge to deliver actionable information with greater accuracy.
In addition, studies show that employees are ready for AI, are familiar with it, but want more support and training, so business leaders need to step up.
In a survey conducted by McKinsey, it could be seen that 94% of employees and 99% of executives have some level of familiarity with current generative AI tools. However, business leaders underestimate their employees’ use of it: they think that only 4% of employees use AI for at least 30% of their daily work, when in reality, that percentage is three times higher.
With the impact of AI on the workplace clear, now is the time for companies to invest in the training that will help them succeed.
The survey also found that millennials are the most active generation in the use of AI, with 62% of employees having a high level of expertise, compared to 50% of Generation Z and 22% of baby boomers. By harnessing that enthusiasm and experience, leaders can help millennials play a crucial role in AI adoption
.
| 2025-06-12T00:00:00 |
2025/06/12
|
https://www.plainconcepts.com/ai-future-work/
|
[
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{
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{
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},
{
"date": "2024/06/01",
"position": 38,
"query": "future of work AI"
},
{
"date": "2024/07/01",
"position": 39,
"query": "future of work AI"
}
] |
The Future of AI: How Artificial Intelligence Will Change the World
|
The Future of AI: How AI Is Changing the World
|
https://builtin.com
|
[] |
AI is expected to improve industries like healthcare, manufacturing and customer service, leading to higher-quality experiences for both workers and customers.
|
Innovations in the field of artificial intelligence continue to shape the future of humanity across nearly every industry. AI is already the main driver of emerging technologies like big data, robotics and IoT, and generative AI has further expanded the possibilities and popularity of AI.
According to a 2023 IBM survey, 42 percent of enterprise-scale businesses integrated AI into their operations, and 40 percent are considering AI for their organizations. In addition, 38 percent of organizations have implemented generative AI into their workflows while 42 percent are considering doing so.
With so many changes coming at such a rapid pace, here’s what shifts in AI could mean for various industries and society at large.
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The Evolution of AI
AI has come a long way since 1951, when the first documented success of an AI computer program was written by Christopher Strachey, whose checkers program completed a whole game on the Ferranti Mark I computer at the University of Manchester. Thanks to developments in machine learning and deep learning, IBM’s Deep Blue defeated chess grandmaster Garry Kasparov in 1997, and the company’s IBM Watson won Jeopardy! in 2011.
Since then, generative AI has spearheaded the latest chapter in AI’s evolution, with OpenAI releasing its first GPT models in 2018. This has culminated in OpenAI developing its GPT-4o model and ChatGPT, leading to a proliferation of AI generators that can process queries to produce relevant text, audio, images and other types of content.
Other companies have followed suit with competing models of their own, including Google’s Gemini, Anthropic’s Claude and DeepSeek’s R1 and V3 models, which made headlines in early 2025 for approaching parity with competing models at a fraction of the operational cost.
AI has also been used to help sequence RNA for vaccines and model human speech, technologies that rely on model- and algorithm-based machine learning and increasingly focus on perception, reasoning and generalization.
How AI Will Impact the Future
Improved Business Automation
About 55 percent of organizations have adopted AI to varying degrees, suggesting increased automation for many businesses in the near future. With the rise of chatbots and digital assistants, companies can rely on AI to handle simple conversations with customers and answer basic queries from employees.
AI’s ability to analyze massive amounts of data and convert its findings into convenient visual formats can also accelerate the decision-making process. Company leaders don’t have to spend time parsing through the data themselves, instead using instant insights to make informed decisions.
“If [developers] understand what the technology is capable of and they understand the domain very well, they start to make connections and say, ‘Maybe this is an AI problem, maybe that’s an AI problem,’” said Mike Mendelson, a learner experience designer for NVIDIA. “That’s more often the case than, ‘I have a specific problem I want to solve.’”
More on AI75 Artificial Intelligence (AI) Companies to Know
Job Disruption
Business automation has naturally led to fears over job losses. In fact, employees believe almost one-third of their tasks could be performed by AI. Although AI has made gains in the workplace, it’s had an unequal impact on different industries and professions. For example, manual jobs like secretaries are at risk of being automated, but the demand for other jobs like machine learning specialists and information security analysts has risen.
Workers in more skilled or creative positions are more likely to have their jobs augmented by AI, rather than be replaced. Whether forcing employees to learn new tools or taking over their roles, AI is set to spur upskilling efforts at both the individual and company level.
“One of the absolute prerequisites for AI to be successful in many [areas] is that we invest tremendously in education to retrain people for new jobs,” said Klara Nahrstedt, a computer science professor at the University of Illinois at Urbana–Champaign and director of the school’s Coordinated Science Laboratory.
Data Privacy Issues
Companies require large volumes of data to train the models that power generative AI tools, and this process has come under intense scrutiny. Concerns over companies collecting consumers’ personal data have led the FTC to open an investigation into whether OpenAI has negatively impacted consumers through its data collection methods after the company potentially violated European data protection laws.
In response, the Biden-Harris administration developed an AI Bill of Rights that lists data privacy as one of its core principles. Although this legislation doesn’t carry much legal weight, it reflects the growing push to prioritize data privacy and compel AI companies to be more transparent and cautious about how they compile training data.
Increased Regulation
AI could shift the perspective on certain legal questions, depending on how generative AI lawsuits unfold in 2024. For example, the issue of intellectual property has come to the forefront in light of copyright lawsuits filed against OpenAI by writers, musicians and companies like The New York Times. These lawsuits affect how the U.S. legal system interprets what is private and public property, and a loss could spell major setbacks for OpenAI and its competitors.
Ethical issues that have surfaced in connection to generative AI have placed more pressure on the U.S. government to take a stronger stance. The Biden-Harris administration has maintained its moderate position with its latest executive order, creating rough guidelines around data privacy, civil liberties, responsible AI and other aspects of AI. However, the government could lean toward stricter regulations, depending on changes in the political climate.
Climate Change Concerns
On a far grander scale, AI is poised to have a major effect on sustainability, climate change and environmental issues. Optimists can view AI as a way to make supply chains more efficient, carrying out predictive maintenance and other procedures to reduce carbon emissions.
At the same time, AI could be seen as a key culprit in climate change. The energy and resources required to create and maintain AI models could raise carbon emissions by as much as 80 percent, dealing a devastating blow to any sustainability efforts within tech. Even if AI is applied to climate-conscious technology, the costs of building and training models could leave society in a worse environmental situation than before.
Accelerated Speed of Innovation
In an essay about the future potential of AI, Anthropic CEO Dario Amodei hypothesizes that powerful AI technology could speed up research in the biological sciences as much as tenfold, bringing about a phenomenon he coins “the compressed 21st century,” in which 50 to 100 years of innovation might happen in the span of five to 10 years. This theory builds on the idea that truly revolutionary discoveries are made at a rate of maybe once per year, with the core limitation being a shortage of talented researchers. By increasing the cognitive power devoted to developing hypotheses and testing them out, Amodei suggests, we might close the time gap between important discoveries like the 25-year delay between CRISPR’s discovery in the ‘80s and its application to gene editing.
What Industries Will AI Impact the Most?
There’s virtually no major industry that modern AI hasn’t already affected. Here are a few of the industries undergoing the greatest changes as a result of AI.
AI in Manufacturing
Manufacturing has been benefiting from AI for years. With AI-enabled robotic arms and other manufacturing bots dating back to the 1960s and 1970s, the industry has adapted well to the powers of AI. These industrial robots typically work alongside humans to perform a limited range of tasks like assembly and stacking, and predictive analysis sensors keep equipment running smoothly.
AI in Healthcare
It may seem unlikely, but AI healthcare is already changing the way humans interact with medical providers. Thanks to its big data analysis capabilities, AI helps identify diseases more quickly and accurately, speed up and streamline drug discovery and even monitor patients through virtual nursing assistants.
AI in Finance
Banks, insurers and financial institutions leverage AI for a range of applications like detecting fraud, conducting audits and evaluating customers for loans. Traders have also used machine learning’s ability to assess millions of data points at once, so they can quickly gauge risk and make smart investing decisions.
AI in Education
AI in education will change the way humans of all ages learn. AI’s use of machine learning, natural language processing and facial recognition help digitize textbooks, detect plagiarism and gauge the emotions of students to help determine who’s struggling or bored. Both presently and in the future, AI tailors the experience of learning to student’s individual needs.
AI in Media
Journalism is harnessing AI too, and will continue to benefit from it. One example can be seen in The Associated Press’ use of Automated Insights, which produces thousands of earning reports stories per year. But as generative AI writing tools, such as ChatGPT, enter the market, questions about their use in journalism abound.
AI in Customer Service
Most people dread getting a robocall, but AI in customer service can provide the industry with data-driven tools that bring meaningful insights to both the customer and the provider. AI tools powering the customer service industry come in the form of chatbots and virtual assistants.
AI in Transportation
Transportation is one industry that is certainly teed up to be drastically changed by AI. Self-driving cars and AI travel planners are just a couple of facets of how we get from point A to point B that will be influenced by AI. Even though autonomous vehicles are far from perfect, they will one day ferry us from place to place.
Sasha Luccioni discusses the real reasons why AI is dangerous. | Video: TED
Risks and Dangers of AI
Despite reshaping numerous industries in positive ways, AI still has flaws that leave room for concern. Here are a few potential risks of artificial intelligence.
Job Losses
Between 2023 and 2028, 44 percent of workers’ skills will be disrupted. Not all workers will be affected equally — women are more likely than men to be exposed to AI in their jobs. Combine this with the fact that there is a gaping AI skills gap between men and women, and women seem much more susceptible to losing their jobs. If companies don’t have steps in place to upskill their workforces, the proliferation of AI could result in higher unemployment and decreased opportunities for those of marginalized backgrounds to break into tech.
Human Biases
The reputation of AI has been tainted with a habit of reflecting the biases of the people who train the algorithmic models. For example, facial recognition technology has been known to favor lighter-skinned individuals, discriminating against people of color with darker complexions. If researchers aren’t careful in rooting out these biases early on, AI tools could reinforce these biases in the minds of users and perpetuate social inequalities.
Deepfakes and Misinformation
The spread of deepfakes threatens to blur the lines between fiction and reality, leading the general public to question what’s real and what isn’t. And if people are unable to identify deepfakes, the impact of misinformation could be dangerous to individuals and entire countries alike. Deepfakes have been used to promote political propaganda, commit financial fraud and place students in compromising positions, among other use cases.
Data Privacy
Training AI models on public data increases the chances of data security breaches that could expose consumers’ personal information. Companies contribute to these risks by adding their own data as well. A 2024 Cisco survey found that 48 percent of businesses have entered non-public company information into generative AI tools and 69 percent are worried these tools could damage their intellectual property and legal rights. A single breach could expose the information of millions of consumers and leave organizations vulnerable as a result.
Automated Weapons
The use of AI in automated weapons poses a major threat to countries and their general populations. While automated weapons systems are already deadly, they also fail to discriminate between soldiers and civilians. Letting artificial intelligence fall into the wrong hands could lead to irresponsible use and the deployment of weapons that put larger groups of people at risk.
Superior Intelligence
Nightmare scenarios depict what’s known as the technological singularity, where superintelligent machines take over and permanently alter human existence through enslavement or eradication. Even if AI systems never reach this level, they can become more complex to the point where it’s difficult to determine how AI makes decisions at times. This can lead to a lack of transparency around how to fix algorithms when mistakes or unintended behaviors occur.
“I don’t think the methods we use currently in these areas will lead to machines that decide to kill us,” said Marc Gyongyosi, founder of Onetrack.AI. “I think that maybe five or 10 years from now, I’ll have to reevaluate that statement because we’ll have different methods available and different ways to go about these things.”
Frequently Asked Questions
What does the future of AI look like? AI is expected to improve industries like healthcare, manufacturing and customer service, leading to higher-quality experiences for both workers and customers. However, it does face challenges like increased regulation, data privacy concerns and worries over job losses.
What will AI look like in 10 years? AI is on pace to become a more integral part of people’s everyday lives. The technology could be used to provide elderly care and help out in the home. In addition, workers could collaborate with AI in different settings to enhance the efficiency and safety of workplaces.
| 2023-02-01T00:00:00 |
https://builtin.com/artificial-intelligence/artificial-intelligence-future
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Future of work - Global Partnership on Artificial Intelligence - GPAI
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Future of work
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https://gpai.ai
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[] |
The Future of Work Expert Working Group (FoW EWG) examines the impact of AI on the workforce and its working environments.
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2024
Fairwork Amazon Report 2024: Transformation of the Warehouse Sector through AI (June 2024)
2023
Fairwork AI Ratings 2023 - The Workers Behind AI at Sama (December 2023)
As part of GPAI’s collaborative project with the Oxford Internet Institute, “AI for Fair Work”, GPAI Experts re-evaluated the OECD AI Principles, placing greater focus on the workplace, and applied them to two companies to study their practical implementation and effects. One such company was Sama, a data annotation firm in Kenya and Uganda. This report presents the impact of the revised AI Principles on Sama, highlighting the significant improvements their application brought to pay, working conditions, contracts, management and representation. In gathering empirical evidence of their use, Experts demonstrated measures that can be taken to ensure the security of workers in the AI supply chain and beyond.
Future of Work Working Group Report (November 2023)
The Future of Work Expert Working Group (FoW EWG) examines the impact of AI on the workforce and its working environments. Its projects explore how inclusivity can be advanced in the face of automation while encouraging healthy collaboration with AI to ensure productivity and quality of outputs. In 2023, it concentrated its efforts on generative AI due to the uncertainty it brings to the job security of many workers. This report outlines its work on AI in the workplace to ensure that it empowers workers as well as consumers.
AI Observation Platform Report (November 2023)
Since its inauguration in 2021, this project aims to improve the future of those working alongside AI, with a particular focus on variables including disability, gender, and ethnicity. Through gathering use cases across several sectors, GPAI Experts capture shifts AI is causing in the workplace and gain insights into how it can be used to empower workers. This year, it was extended to include case studies from Japan and Mexico’s student communities, as well as LaborIA, France’s centre for the impact of AI on work, as outlined in this report.
AI for Fair Work: From principles to practices (November 2023)
In partnership with Fairwork, an initiative of the Oxford Internet Institute, this project has re-assessed the OECD AI principles with a stronger emphasis on pay, working conditions, management, and representation. In applying them to two working environments (Amazon in the UK, and Sama, a data annotation firm in Kenya and Uganda), it has gathered empirical evidence of their practical use, which will be published in 2024. This report explores the methodology behind the project and highlights the multifaceted approach that must be taken to improving work in the face of AI.
CAST Constructive Approach to Smart Technologies (November 2023)
The growth of AI systems in terms of impact and complexity is making it increasingly challenging to draft frameworks that harness their potential for innovation while keeping up with their constant evolution. For AI vendors making the transition to the international market, it can be difficult to adapt to foreign demands. The CAST project proposes a model to accommodate software systems to such a fast-paced environment and navigate the nuances of constantly evolving levels of AI.
Policy Brief: Generative AI, Jobs, and Policy Response (Montreal Innovation Workshop, September 2023)
2022
Future of Work Working Group Report (November 2022)
AI Living Lab Report (November 2022)
AI Observation Platform Report (November 2022)
AI for Fair Work Report (November 2022)
2021
Future of Work Working Group Report (November 2021)
AI Observatory at the Workplace (November 2021)
2020
Future of Work Working Group Report (November 2020)
| 2023-02-01T00:00:00 |
https://gpai.ai/projects/future-of-work/
|
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How will AI change the future of work? - PwC
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AI Jobs Barometer
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https://www.pwc.com
|
[
"Contact Us",
"Matt Wood",
"Global",
"Us Commercial Technology",
"Innovation Officer",
"Ctio",
"Pwc United States",
"Joe Atkinson",
"Global Chief Ai Officer For The Pwc Network Of Firms",
"Peter Brown"
] |
AI is the biggest business opportunity of the next decade. It's already automating manual and repetitive tasks. Soon it will augment human decisions.
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Revenue growth in AI-exposed industries has accelerated sharply since 2022, the year that the launch of ChatGPT 3.5 awakened the world to AI's power. Since then, as companies have raced to leverage this technology, the value created in industries best positioned to use AI has skyrocketed.
That’s clearly great for businesses, but how is this rapid transformation affecting jobs?
To find out, PwC analysed AI’s impact on both augmentable jobs (jobs that contain many tasks in which AI can enhance or support human judgment and expertise), and automatable jobs (jobs that contain many tasks that can be autonomously completed by AI).
| 2023-02-01T00:00:00 |
https://www.pwc.com/gx/en/issues/artificial-intelligence/future-of-work.html
|
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The Future of Fulfilling Work is AI-Driven - Sponsor Content - Google
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The Future of Fulfilling Work is AI-Driven
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https://www.theatlantic.com
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[] |
As AI makes routine tasks easier to tackle, knowledge workers have an opportunity to focus on the most human aspects of their jobs.
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As AI makes routine tasks easier to tackle, knowledge workers have an opportunity to focus on the most human aspects of their jobs. But will organizations adapt to support the shift?
As AI tools become increasingly capable, they are handling routine cognitive tasks across industries—from generating marketing copy to diagnosing diseases. This rapid progress has sparked both excitement and concern, with some worried that AI could lower the demand for knowledge work jobs or even render certain roles obsolete.
Andrew McAfee, co-director of the MIT Initiative on the Digital Economy and Google’s inaugural Technology & Society Visiting Fellow, believes such fears are overblown. “Technology impacts tasks, and jobs are made up of tasks,” he explains. Just because AI can automate certain aspects of a job doesn’t mean the demand for those skills will disappear.
Take professional translation as an example. Tools like Google Translate have become remarkably good at rendering text from one language to another. Yet demand for human translators hasn’t disappeared. Instead, the Bureau of Labor Statistics projects 4 percent growth in employment of interpreters and translators between 2022 and 2032, a rate slightly faster than the average for all occupations.
As machine translation improves and becomes more affordable, McAfee posits that demand for translation services could actually increase. Human translators, meanwhile, can focus on nuanced, context-dependent tasks that are hard to automate, like localizing content, finessing creative passages, or handling sensitive communications.
A similar dynamic is unfolding in medical imaging. Consider radiology and ophthalmology, two specialties where AI is making significant inroads. In radiology, algorithms can now match the performance of human doctors in detecting diseases from scans. Meanwhile, in ophthalmology, AI tools like Google's Automated Retinal Disease Assessment can accurately interpret retinal images to identify signs of diabetic retinopathy, a complication of diabetes that, if left untreated, can lead to blindness.
The potential impact of AI on the scale and accessibility of diagnostic care is enormous. With over 420 million people living with diabetes worldwide, screening every patient for retinopathy is an overwhelming task, particularly in countries where trained eye specialists are in short supply. “I think the mistake that a lot of us made was to confuse the task with the job,” says McAfee.
Yet in neither case does the technology obviate the need for human expertise. “Analyzing a scan is only one task in a radiologist’s job,” McAfee notes. After receiving the AI-generated analysis, radiologists must double-check the results, interpret the certainty levels, calibrate their own knowledge with the AI’s findings, and make the best call on how to proceed.
The Risks of Over-Reliance
While the most dramatic predictions of AI-driven job losses may be overblown, the technology does pose more subtle risks to workers. One concern is skill atrophy, as professionals lean too heavily on AI to perform tasks they once had to learn by doing.
Ben Armstrong, a research scientist at MIT, points to the mixed attitudes some manufacturing workers had when faced with increasing automation in past decades.
“Technicians who worked on old-fashioned Bridgeport machines manually cutting parts have amazing skills for understanding what makes a part work,” Armstrong explains. “There was resistance to new technology coming in because workers felt like it would take away from their value.”
Although those fears weren’t entirely justified—the skills to understand what makes a good part are still in high demand—the worry is that a similar erosion of foundational know-how could occur with AI.
When I hear colleagues being skeptical of AI, I ask them, ‘Have you spent time learning the tools?’ Nicole Ingra
“In some cases, AI can become a crutch,” cautions Nicole Ingra, who runs a marketing agency in Barcelona and started using generative AI tools in November 2022. “We need to be hyper-aware of not just relying on it to save time but potentially losing our creative edge in the process.”
A recent study illuminates the contours of what researchers call the “jagged technological frontier”—the uneven landscape of tasks that AI can actually handle. For a set of 18 realistic consulting tasks deemed to be within the capabilities of a state-of-the-art language model, consultants using the AI significantly outperformed those without AI assistance. But for tasks that fell outside the current frontier, those using AI were less likely to arrive at correct solutions.
Navigating this jagged frontier demands a willingness to engage deeply with the tools, to kick the tires, so to speak, and develop a nuanced understanding of their capabilities and limitations.
“When I hear colleagues being skeptical of AI, I ask them, ‘Have you spent time learning the tools?’” Ingra says. “When you first started using Photoshop, did you know how to use it instinctively?”
Navigating Mixed Emotions
For many knowledge workers, particularly those in creative fields, there’s also an emotional adjustment to contend with. Some may feel a sense of unease or even guilt about using AI tools, worrying that it diminishes their own reputation or expertise.
Charlotte Cramer, who runs Plastics Studio, a healthcare UX design agency based in El Sobrante, California, has experienced this tension firsthand. “I asked ChatGPT to help me draft a bunch of briefs about AI for a client,” she recounts. “After editing the first drafts it generated, I asked myself if I should wait a few days to send it to them. I ended up sending it immediately, but my partner was like, ‘You should have waited! Now they’ll know you used AI!’”
This desire to attribute the output of the technology entirely to oneself, to maintain the illusion of effortless mastery, is a common impulse in the face of new tools.
Cramer acknowledges that deliberately waiting to send the AI-generated briefs would have been absurd. “It’s ludicrous to write about AI without using it, but even so, I felt the tension that I should wait,” she admits. This desire to attribute the output of the technology entirely to oneself, to maintain the illusion of effortless mastery, is a common impulse in the face of new tools. “But I have no doubt that, as we integrate these tools more, we’ll see them in a more similar light to spell check,” she adds.
The emotional challenges of integrating AI into knowledge work are understandable, McAfee acknowledges. “Shame is a real emotion. And pride is a real emotion.” The challenge—and opportunity—is to redirect that pride toward the uniquely human capabilities that AI cannot replicate.
Three Approaches to Implementing AI As organizations navigate the rapidly evolving landscape of generative AI, finding the right approach to implementation is critical. Deloitte’s Greg Vert and Laura Shact have identified three primary strategies companies are using. Top-Down Directive In this approach, company leadership mandates the adoption of AI tools across the organization, often with a focus on productivity gains. But it also risks alienating workers if not handled carefully. “When workers hear ‘productivity,’ they are using their imagination to fill in the white space of what that means for them,” cautions Laura Shact. Employees may worry that efficiency gains will translate into heavier workloads or even job losses. To mitigate these concerns, leaders need to communicate a clear vision for how AI will benefit both the business and its people. “AI has to be both a benefit to the enterprise and a benefit to the workforce,” stresses Greg Vert. Center of Excellence A second approach involves creating a dedicated, interdisciplinary task force to pilot AI projects and then train the rest of the organization. This “center of excellence” model can be effective in building buy-in and expertise across functions. “You have different functional leaders coming together to ensure consistency,” explains Shact. “Some parts of the organization may find value from AI faster, while others will take longer to see results.” Shact cautions, however, that a center of excellence needs to be paired with concrete action and clear communication about the goals and progress of AI initiatives in order to work. “On its own, it’s just a governance model,” she says. “1,000 Wildflowers” The third approach, which Ben Armstrong of MIT calls the “1,000 wildflowers” model, takes a more grassroots, bottom-up tack. Rather than mandating AI adoption from above, organizations create a safe sandbox environment for employees to experiment with AI tools in their own work. This decentralized approach recognizes that the most transformative use cases may emerge not from the C-suite, but from those closest to day-to-day operations. Shact has seen the power of this model firsthand through “promptathons” she’s organized at Deloitte. Styled after hackathons, these events invite employees to use generative AI to solve real business problems and share learnings with colleagues. “It’s playful, it involves people from across the organization, and it offers positive reinforcement for developing AI skills,” she says. The 1,000 wildflowers approach does require guardrails to prevent misuse or wasted efforts. But coupled with a clear strategy and ethical guidelines from leadership, it can tap into the creativity and judgment of an organization’s people to unlock AI’s full potential. Picking the Right Approach Regardless of the exact approach, the most successful implementations of generative AI will be those grounded in a clear vision of the technology as a tool for empowering rather than replacing human workers. They will combine bold leadership with deep employee engagement to navigate the challenges and opportunities ahead. “If you force people to interact with AI in a completely standardized way, you’re kind of missing out on some of the amazing upside of this technology,” argues McAfee. “The organizations that are most successful are the ones that set up sandboxes and let experimentation happen, to figure out how to make this technology helpful.”
Leveling Up
A recent study by MIT economist Erik Brynjolfsson and his colleagues provides a compelling example of how AI is magnifying the importance of skills that are fundamentally human—things like emotional intelligence, creative problem-solving, and the ability to draw connections across domains. The researchers looked at the impact of introducing a generative AI tool in a call center environment and found that productivity increased, customer satisfaction improved, and employee turnover decreased.
Importantly, the biggest gains were seen among the previously lowest-performing employees. By equipping all employees with the same knowledge base and AI-powered tools, the company was able to uplevel its customer service performance across the board. The technology helped to improve the quality and consistency of responses from all representatives while freeing up top performers to focus on developing higher-level skills and strategies. As the AI handles more routine queries, workers can focus on the kind of personalized, relationship-building interactions that set a company apart.
“Today, in-store employees can be more focused on customer engagement,” says Christian Beckner, vice president of innovation and technology at the National Retail Federation. Over his two decades in the retail sector, Beckner has seen the industry increasingly embrace AI and automation, not to replace workers but to free them up to provide differentiated value. “If you can automate some of the more mundane tasks, your workforce can spend their time building personal relationships with customers and trying to maintain and grow that trust.”
This principle extends beyond the retail floor to the realm of professional services. Cramer, the UX designer, has started experimenting with teaching her clients how to use AI tools, especially for services outside of her core offerings. By showing clients how they can create high-quality videos using AI, for instance, she demonstrates her commitment to their success, even if it means potentially reducing their reliance on her services for certain tasks.
“Agency relationships are built on trust,” Cramer explains. “And that trust comes from always doing what’s best for your client, even when it seems to go against your own short-term interests.”
For Cramer, the key is to be seen by clients as an expert in effectively leveraging AI tools. “These tools are not a secret,” she says, “but being seen by your clients as an expert in using the tools—that’s what gives you a competitive advantage.”
Rethinking Fulfillment
In the 1930s, economist John Maynard Keynes predicted that technological advancements would lead to a 15-hour workweek. Yet despite the rapid progress of AI, people today are working harder than ever.
“Does anybody think there’s a shortage of work that needs to get done out there in the world?” McAfee asks rhetorically. “Almost nobody raises their hand when I ask.”
Does anybody think there's a shortage of work that needs to get done out there in the world? Almost nobody raises their hand when I ask. Andrew McAfee
The challenge, then, is not a looming deficit of things for people to do, but rather ensuring that the work that remains is meaningful and suited to fundamentally human capabilities. That requires reimagining education and training to cultivate skills like creativity. It means redesigning jobs and workflows around a model of human-machine collaboration. And it demands a philosophical shift in how we define professional success and personal fulfillment.
The organizations that thrive in the AI era will be those that embrace this mindset—using productivity gains to invest in the growth and well-being of workers.
“There’s a false pretense that using AI means delivering more work,” says Cramer. “I’m far more interested in how we can deliver the same output and use the extra time to live our lives.”
With the right vision and values, the age of AI could be an era not of human obsolescence, but of human flourishing—a time when knowledge work becomes less rote and more creative, less transactional and more fulfilling.
| 2023-02-01T00:00:00 |
https://www.theatlantic.com/sponsored/google/ai-economics/3902/
|
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"date": "2023/02/01",
"position": 55,
"query": "future of work AI"
},
{
"date": "2023/04/01",
"position": 38,
"query": "future of work AI"
},
{
"date": "2023/05/01",
"position": 53,
"query": "future of work AI"
},
{
"date": "2023/09/01",
"position": 54,
"query": "future of work AI"
},
{
"date": "2023/10/01",
"position": 61,
"query": "future of work AI"
},
{
"date": "2024/03/01",
"position": 59,
"query": "future of work AI"
},
{
"date": "2024/04/01",
"position": 53,
"query": "future of work AI"
},
{
"date": "2024/05/29",
"position": 60,
"query": "artificial intelligence employment"
},
{
"date": "2024/06/01",
"position": 52,
"query": "future of work AI"
},
{
"date": "2024/07/01",
"position": 55,
"query": "future of work AI"
},
{
"date": "2025/03/01",
"position": 57,
"query": "future of work AI"
}
] |
|
The Future of Work: AI's impact on your workforce | Hays USA
|
The Future of Work: AI's impact on your workforce
|
https://www.hays.com
|
[] |
Discover how AI is transforming the job market and learn strategies to prepare your workforce for future challenges in tech. Get your report copy now.
|
I would like Hays to call me to discuss my current requirements.
Enter your details below to download a copy of the report:
recent Hays survey revealed that 60% of employees feel their employers are not adequately preparing them for artificial intelligence (AI) implementation. The rising demand for AI-driven solutions and the need for a digitally skilled workforce are pressing issues across many industries today.
With thousands of organizations striving to become ‘AI-ready,’ how effectively is your workforce navigating this technological shift? Download our report to understand the current tech landscape, the transformative impact of AI on workforces, and the evolving demand for jobs.
What are the true capabilities of AI, and how will it transform the job market?
Our insights, backed by extensive research and collaboration with industry experts, provide a well-rounded perspective on AI's capabilities and its effects on various roles.
In the report, you’ll discover valuable AI insights and strategies covering:
Data and key insights on job demand across vital tech specialisms, including Cyber, Cloud, and Data.
The megatrends that could derail your tech transformations—identified and debunked.
A four-part strategy for building a digitally driven organization crafted by our workforce management experts.
Find the answers you need to shape a workforce ready for tomorrow’s technological challenges. Download today.
| 2023-02-01T00:00:00 |
https://www.hays.com/market-insights/reports/future-of-work-ai-impact-on-workforce
|
[
{
"date": "2023/02/01",
"position": 59,
"query": "future of work AI"
},
{
"date": "2023/04/01",
"position": 92,
"query": "future of work AI"
},
{
"date": "2023/05/01",
"position": 59,
"query": "future of work AI"
},
{
"date": "2023/09/01",
"position": 59,
"query": "future of work AI"
},
{
"date": "2024/03/01",
"position": 66,
"query": "future of work AI"
},
{
"date": "2024/04/01",
"position": 62,
"query": "future of work AI"
},
{
"date": "2024/06/01",
"position": 60,
"query": "future of work AI"
},
{
"date": "2024/07/01",
"position": 60,
"query": "future of work AI"
},
{
"date": "2025/03/01",
"position": 59,
"query": "future of work AI"
}
] |
|
Synergy of Minds: The Future of Work in the Age of AI - Vocal Media
|
Synergy of Minds: The Future of Work in the Age of AI
|
https://vocal.media
|
[] |
Some argued that AI was undermining the essence of human work, turning people into passive consumers rather than active creators. Others ...
|
The Future of Work in the Age of AI
In the year 2040, the world had changed in ways both subtle and profound. The proliferation of artificial intelligence had transformed industries, redefined the workforce, and reshaped human life. The boundaries between humans and machines had blurred, and what it meant to work—and to be human—had been forever altered.
The cityscape of this new world was unlike any before. Neon holograms floated above towering skyscrapers, their ads shifting in real-time, responding to the moods and desires of the people passing by. On the streets, pedestrians glided along on self-driving platforms, while drones whizzed overhead delivering packages and monitoring the air for pollution levels.
But it wasn’t the technology that had most profoundly altered society—it was the evolution of work itself.
Lila, a senior strategist at a global tech firm called Epoch Dynamics, sat in her glass-walled office overlooking the shimmering city below. Her desk, sleek and minimalist, was virtually empty—save for a large holographic screen that hovered in the air. She swiped through digital documents with a simple hand gesture, her fingers flicking through reports and analyses that were generated by Epoch's cutting-edge AI systems.
As a human strategist, Lila’s job was unique in that it was less about technical expertise and more about interpretation. Her AI assistant, Aria, was capable of generating business models, analyzing data sets, and even predicting market trends, but it lacked the ability to understand the human nuances—the culture, values, and ethics—that drove decision-making in the complex world of business. That’s where Lila came in.
"Good morning, Lila," Aria's voice echoed softly in her ears through her AI-powered earpiece.
"Morning, Aria. What do we have today?"
"I've compiled an analysis of the upcoming trends in virtual reality integration into consumer markets," Aria replied, her tone calm and efficient. "There are a few emerging sectors we could focus on. Shall I prepare a presentation?"
"Yes, but add a few creative suggestions," Lila responded. "Something bold, something that could disrupt the market."
It was a task that seemed simple enough, but it was a prime example of the changing nature of work. In the old days, a strategist like Lila would have been relied upon for hard skills like market research and technical knowledge. Now, it was all about vision, creativity, and the ability to make the AI systems align with humanity’s deeper values.
AI had done much more than automate processes. It had also opened new avenues of creative collaboration. Humans no longer worked alone; they worked alongside highly intelligent machines that enhanced their capabilities, taking care of the repetitive and tedious tasks that once consumed their time. The AI, for its part, was perfectly suited for efficiency, but it lacked the ability to innovate in ways that were deeply human.
---
In another part of the world, in a small, quiet town, Jason worked in a similarly transformed job. He wasn’t a corporate strategist like Lila, but he was deeply connected to the wave of change. Jason worked as a “digital artisan,” a title that had gained popularity in the age of AI. His job was to create custom virtual worlds for clients who wanted unique, immersive experiences. These worlds could range from virtual vacations to fantasy landscapes, or even entire interactive metaverses for education, business, and entertainment.
Jason’s AI assistant, Atlas, handled most of the technical work—rendering environments, coding interactive elements, even anticipating the emotional responses of users to optimize experiences. But Jason’s role was about shaping these worlds into something meaningful. He worked closely with clients, understanding their desires and blending creativity with the power of AI to make something that was far beyond what a machine could design on its own.
“It’s not enough to just create a world,” Jason would often say to his clients. “It has to feel real. It has to evoke something in the person who experiences it.”
His philosophy was simple: the true power of AI wasn’t just in automation—it was in the synergy between human emotion and machine intelligence. And this partnership had the potential to unlock unprecedented creativity.
---
While Lila and Jason’s work had been transformed, not all workers had experienced the same smooth transition. There were, of course, those who had struggled to adapt. Many jobs had disappeared in the wake of AI’s capabilities, and not everyone had the skills to take on the new roles. For some, the future of work was a bitter pill to swallow.
But society had adapted in its own way. Universal basic income (UBI) had become a standard in many countries, ensuring that those displaced by automation would still have a safety net. Meanwhile, education systems had pivoted to focus on teaching skills that complemented AI rather than competed with it—emphasizing creativity, emotional intelligence, and collaborative problem-solving.
The debate over AI’s role in society was far from over. Some argued that AI was undermining the essence of human work, turning people into passive consumers rather than active creators. Others believed that it was the key to unlocking a new era of prosperity, where people were freed from monotonous labor and could pursue higher-level passions and goals.
---
Lila’s day was ending, but as she looked out over the city, she couldn’t help but feel optimistic. The future of work was uncertain, but one thing was clear—human ingenuity, combined with the power of AI, was creating something truly extraordinary.
She turned to Aria and smiled. “Let's disrupt the market, together.”
| 2023-02-01T00:00:00 |
https://vocal.media/futurism/synergy-of-minds-the-future-of-work-in-the-age-of-ai
|
[
{
"date": "2023/02/01",
"position": 66,
"query": "future of work AI"
},
{
"date": "2023/04/01",
"position": 46,
"query": "future of work AI"
},
{
"date": "2023/05/01",
"position": 64,
"query": "future of work AI"
},
{
"date": "2023/05/01",
"position": 98,
"query": "universal basic income AI"
},
{
"date": "2023/06/01",
"position": 95,
"query": "universal basic income AI"
},
{
"date": "2023/08/01",
"position": 98,
"query": "universal basic income AI"
},
{
"date": "2023/09/01",
"position": 63,
"query": "future of work AI"
},
{
"date": "2023/10/01",
"position": 70,
"query": "future of work AI"
},
{
"date": "2023/10/01",
"position": 94,
"query": "universal basic income AI"
},
{
"date": "2024/03/01",
"position": 70,
"query": "future of work AI"
},
{
"date": "2024/04/01",
"position": 65,
"query": "future of work AI"
},
{
"date": "2024/06/01",
"position": 65,
"query": "future of work AI"
},
{
"date": "2024/06/01",
"position": 94,
"query": "universal basic income AI"
},
{
"date": "2024/07/01",
"position": 64,
"query": "future of work AI"
},
{
"date": "2024/08/01",
"position": 96,
"query": "universal basic income AI"
},
{
"date": "2024/11/01",
"position": 98,
"query": "universal basic income AI"
}
] |
|
Every used WorkBC? Not a fan of the idea of an "AI Assistant ...
|
The heart of the internet
|
https://www.reddit.com
|
[] |
We do NOT need the already slimly available human appointments replaced by a machine, especially one that doesn't "know" anything and certainly ...
|
WorkBC has sent out this survey, seemingly with the idea of sneakily plumping up statistics to show it "makes sense" to replace human help with a predictive text machine. They have very purposefully tailored the questions to be online-forward, and slipped in one or two "AI is good!" options in the questions.
We do NOT need the already slimly available human appointments replaced by a machine, especially one that doesn't "know" anything and certainly doesn't understand the nuances of the job market. The idea that we should let AI help us make a resume which will then be read by and discarded by a different AI is absurd. Make your voice heard and fill out the survey. It only takes a few minutes.
| 2023-02-01T00:00:00 |
https://www.reddit.com/r/VictoriaBC/comments/1lurnnt/the_future_of_workbc_questionnaire_every_used/
|
[
{
"date": "2023/02/01",
"position": 68,
"query": "future of work AI"
},
{
"date": "2023/05/01",
"position": 81,
"query": "future of work AI"
}
] |
|
The Future of Work in the Age of AI and How HR Should Respond
|
The Future of Work in the Age of AI and How HR Should Respond
|
https://www.unleash.ai
|
[] |
As AI systems take on tasks once thought to require uniquely human skills, the nature of work is fundamentally changing. In this session, economist and author ...
|
UNLEASH will use your information to respond to your inquiry and share relevant marketing communications.
You can unsubscribe at anytime by clicking the unsubscribe link in the footer of any email. By clicking "Submit" you acknowledge and agree that you have read, understood and agree to be bound by our Terms and Conditions and Privacy Policy.
| 2025-06-30T00:00:00 |
2025/06/30
|
https://www.unleash.ai/unleashworld/session/the-future-of-work-in-the-age-of-ai-and-how-hr-should-respond/
|
[
{
"date": "2023/02/01",
"position": 70,
"query": "future of work AI"
},
{
"date": "2023/05/01",
"position": 69,
"query": "future of work AI"
},
{
"date": "2023/09/01",
"position": 68,
"query": "future of work AI"
},
{
"date": "2023/10/01",
"position": 73,
"query": "future of work AI"
},
{
"date": "2024/03/01",
"position": 71,
"query": "future of work AI"
},
{
"date": "2024/04/01",
"position": 67,
"query": "future of work AI"
},
{
"date": "2024/06/01",
"position": 69,
"query": "future of work AI"
},
{
"date": "2024/07/01",
"position": 70,
"query": "future of work AI"
}
] |
UNLEASH America 2025: Employees' clarion call for the future of work
|
UNLEASH America 2025: Employees’ clarion call for the future of work: ‘Skill me up so I’m ready for AI’
|
https://www.unleash.ai
|
[
"Allie Nawrat"
] |
As a result, HR leaders need to focus on getting their people ready for the AI-powered future of work – “we can't just assume everybody's going ...
|
At UNLEASH America 2025, industry analyst Josh Bersin took to the Main Stage to deliver a keynote on the HR AI revolution.
“I believe that you’re going to look back 10 years from now and you’re going to remember 2025”; it will be the year that the massive reinvention of work began, declared Bersin, CEO and Founder of The Josh Bersin Company.
He was clear that “a lot of the adoption of AI is about the people stuff; it’s not really about the technology”.
UNLEASH Editor-in-Chief Nima Sherpa Green sat down with Bersin for an exclusive video interview to dig in further in his perspectives on HR and the AI opportunity.
Bersin shares that many companies are getting it wrong when it comes to ROI with AI.
They’re too focused on cost savings, and headcount reduction, when “the bigger ROI is the result of automating this workflow with an agent or series of agents”.
As a result, HR leaders need to focus on getting their people ready for the AI-powered future of work – “we can’t just assume everybody’s going to go along for the ride unless we give them some support”.
“The number one thing they want from their employer is ‘skill me up’ so I am ready to use this stuff, and I’ll do it because I want to be part of the future too”, states Bersin.
Watch the full video to get Bersin’s advice on how to work with HR tech vendors in this AI-powered future of work, as well as his vision for The Josh Bersin Company’s own tool, Galileo, powered by Sana.
| 2025-07-03T00:00:00 |
2025/07/03
|
https://www.unleash.ai/unleash-america/unleash-america-2025-employees-clarion-call-for-the-future-of-work-skill-me-up-so-im-ready-for-ai/
|
[
{
"date": "2023/02/01",
"position": 74,
"query": "future of work AI"
},
{
"date": "2023/04/01",
"position": 43,
"query": "future of work AI"
},
{
"date": "2023/05/01",
"position": 76,
"query": "future of work AI"
},
{
"date": "2023/09/01",
"position": 75,
"query": "future of work AI"
},
{
"date": "2023/10/01",
"position": 82,
"query": "future of work AI"
},
{
"date": "2024/03/01",
"position": 82,
"query": "future of work AI"
},
{
"date": "2024/04/01",
"position": 78,
"query": "future of work AI"
},
{
"date": "2024/06/01",
"position": 76,
"query": "future of work AI"
},
{
"date": "2024/07/01",
"position": 77,
"query": "future of work AI"
}
] |
AI and the future of work - AI for Good
|
AI and the future of work
|
https://aiforgood.itu.int
|
[] |
Data and insights around the AI marketplace - trends in skills and jobs in the era of AI.
|
For important information regarding the classification, please go to the Division’s website and review the last two questions in the Q&A page. Please be advised that the utilization of this list by AI for Good is exclusively for the purpose of ticketing for the 2024 AI for Good Global Summit, unless otherwise specified
Country or Area ISO-alpha2 Code ISO-alpha3 Code Developed / Developing regions Algeria DZ DZA Developing Egypt EG EGY Developing Libya LY LBY Developing Morocco MA MAR Developing Sudan SD SDN Developing Tunisia TN TUN Developing Western Sahara EH ESH Developing British Indian Ocean Territory IO IOT Developing Burundi BI BDI Developing Comoros KM COM Developing Djibouti DJ DJI Developing Eritrea ER ERI Developing Ethiopia ET ETH Developing French Southern Territories TF ATF Developing Kenya KE KEN Developing Madagascar MG MDG Developing Malawi MW MWI Developing Mauritius MU MUS Developing Mayotte YT MYT Developing Mozambique MZ MOZ Developing Réunion RE REU Developing Rwanda RW RWA Developing Seychelles SC SYC Developing Somalia SO SOM Developing South Sudan SS SSD Developing Uganda UG UGA Developing United Republic of Tanzania TZ TZA Developing Zambia ZM ZMB Developing Zimbabwe ZW ZWE Developing Angola AO AGO Developing Cameroon CM CMR Developing Central African Republic CF CAF Developing Chad TD TCD Developing Congo CG COG Developing Democratic Republic of the Congo CD COD Developing Equatorial Guinea GQ GNQ Developing Gabon GA GAB Developing Sao Tome and Principe ST STP Developing Botswana BW BWA Developing Eswatini SZ SWZ Developing Lesotho LS LSO Developing Namibia NA NAM Developing South Africa ZA ZAF Developing Benin BJ BEN Developing Burkina Faso BF BFA Developing Cabo Verde CV CPV Developing Côte d’Ivoire CI CIV Developing Gambia GM GMB Developing Ghana GH GHA Developing Guinea GN GIN Developing Guinea-Bissau GW GNB Developing Liberia LR LBR Developing Mali ML MLI Developing Mauritania MR MRT Developing Niger NE NER Developing Nigeria NG NGA Developing Saint Helena SH SHN Developing Senegal SN SEN Developing Sierra Leone SL SLE Developing Togo TG TGO Developing Anguilla AI AIA Developing Antigua and Barbuda AG ATG Developing Aruba AW ABW Developing Bahamas BS BHS Developing Barbados BB BRB Developing Bonaire, Sint Eustatius and Saba BQ BES Developing British Virgin Islands VG VGB Developing Cayman Islands KY CYM Developing Cuba CU CUB Developing Curaçao CW CUW Developing Dominica DM DMA Developing Dominican Republic DO DOM Developing Grenada GD GRD Developing Guadeloupe GP GLP Developing Haiti HT HTI Developing Jamaica JM JAM Developing Martinique MQ MTQ Developing Montserrat MS MSR Developing Puerto Rico PR PRI Developing Saint Barthélemy BL BLM Developing Saint Kitts and Nevis KN KNA Developing Saint Lucia LC LCA Developing Saint Martin (French Part) MF MAF Developing Saint Vincent and the Grenadines VC VCT Developing Sint Maarten (Dutch part) SX SXM Developing Trinidad and Tobago TT TTO Developing Turks and Caicos Islands TC TCA Developing United States Virgin Islands VI VIR Developing Belize BZ BLZ Developing Costa Rica CR CRI Developing El Salvador SV SLV Developing Guatemala GT GTM Developing Honduras HN HND Developing Mexico MX MEX Developing Nicaragua NI NIC Developing Panama PA PAN Developing Argentina AR ARG Developing Bolivia (Plurinational State of) BO BOL Developing Bouvet Island BV BVT Developing Brazil BR BRA Developing Chile CL CHL Developing Colombia CO COL Developing Ecuador EC ECU Developing Falkland Islands (Malvinas) FK FLK Developing French Guiana GF GUF Developing Guyana GY GUY Developing Paraguay PY PRY Developing Peru PE PER Developing South Georgia and the South Sandwich Islands GS SGS Developing Suriname SR SUR Developing Uruguay UY URY Developing Venezuela (Bolivarian Republic of) VE VEN Developing Kazakhstan KZ KAZ Developing Kyrgyzstan KG KGZ Developing Tajikistan TJ TJK Developing Turkmenistan TM TKM Developing Uzbekistan UZ UZB Developing China CN CHN Developing China, Hong Kong Special Administrative Region HK HKG Developing China, Macao Special Administrative Region MO MAC Developing Democratic People’s Republic of Korea KP PRK Developing Mongolia MN MNG Developing Brunei Darussalam BN BRN Developing Cambodia KH KHM Developing Indonesia ID IDN Developing Lao People’s Democratic Republic LA LAO Developing Malaysia MY MYS Developing Myanmar MM MMR Developing Philippines PH PHL Developing Singapore SG SGP Developing Thailand TH THA Developing Timor-Leste TL TLS Developing Viet Nam VN VNM Developing Afghanistan AF AFG Developing Bangladesh BD BGD Developing Bhutan BT BTN Developing India IN IND Developing Iran (Islamic Republic of) IR IRN Developing Maldives MV MDV Developing Nepal NP NPL Developing Pakistan PK PAK Developing Sri Lanka LK LKA Developing Armenia AM ARM Developing Azerbaijan AZ AZE Developing Bahrain BH BHR Developing Georgia GE GEO Developing Iraq IQ IRQ Developing Jordan JO JOR Developing Kuwait KW KWT Developing Lebanon LB LBN Developing Oman OM OMN Developing Qatar QA QAT Developing Saudi Arabia SA SAU Developing State of Palestine PS PSE Developing Syrian Arab Republic SY SYR Developing Turkey TR TUR Developing United Arab Emirates AE ARE Developing Yemen YE YEM Developing Fiji FJ FJI Developing New Caledonia NC NCL Developing Papua New Guinea PG PNG Developing Solomon Islands SB SLB Developing Vanuatu VU VUT Developing Guam GU GUM Developing Kiribati KI KIR Developing Marshall Islands MH MHL Developing Micronesia (Federated States of) FM FSM Developing Nauru NR NRU Developing Northern Mariana Islands MP MNP Developing Palau PW PLW Developing United States Minor Outlying Islands UM UMI Developing American Samoa AS ASM Developing Cook Islands CK COK Developing French Polynesia PF PYF Developing Niue NU NIU Developing Pitcairn PN PCN Developing Samoa WS WSM Developing Tokelau TK TKL Developing Tonga TO TON Developing Tuvalu TV TUV Developing Wallis and Futuna Islands WF WLF Developing
| 2023-02-01T00:00:00 |
https://aiforgood.itu.int/event/ai-and-the-future-of-work/
|
[
{
"date": "2023/02/01",
"position": 75,
"query": "future of work AI"
},
{
"date": "2023/04/01",
"position": 51,
"query": "future of work AI"
},
{
"date": "2023/05/01",
"position": 77,
"query": "future of work AI"
},
{
"date": "2023/09/01",
"position": 74,
"query": "future of work AI"
},
{
"date": "2023/10/01",
"position": 83,
"query": "future of work AI"
},
{
"date": "2024/03/01",
"position": 81,
"query": "future of work AI"
},
{
"date": "2024/04/01",
"position": 80,
"query": "future of work AI"
},
{
"date": "2024/06/01",
"position": 77,
"query": "future of work AI"
},
{
"date": "2024/07/01",
"position": 76,
"query": "future of work AI"
},
{
"date": "2025/03/01",
"position": 72,
"query": "future of work AI"
}
] |
|
How Agentic AI is transforming the future of intelligent systems - EY
|
How Agentic AI is transforming the future of intelligent systems
|
https://www.ey.com
|
[
"Hari Balaji",
"Authorsalutation",
"Authorfirstname Hari Authorlastname Balaji Authorjobtitle Partner",
"Technology Consulting Generative Ai",
"Ai-Led Transformation Authorurl Https",
"Www.Ey.Com En_In People Hari-Balaji",
"Content Dam Content-Fragments Ey-Unified-Site Ey-Com People Local En_In H Hari-Balaji",
"Partner",
"Ai-Led Transformation",
"Nitesh Mehrotra"
] |
Agentic AI is not just innovation—it is a redefinition of modern work and a critical step toward a more autonomous, AI-powered future. Read More Read Less ...
|
The onset of the year 2025 rocked the AI world when Chinese start-up DeepSeek launched a breakthrough, cost-effective model that rattled established US tech juggernauts. The race to develop autonomous AI systems accelerated. But that was just the beginning. This year, the race to build truly autonomous AI systems—once the domain of elite labs and boardrooms—has exploded into the mainstream. At the heart of this shift lies Agentic AI : a game-changing evolution that moves beyond traditional workflows, unleashing autonomous software agents capable of making decisions and executing tasks with minimal human intervention.
Autonomy amplified
Agentic AI systems promise a radical departure from passive AI assistants. Instead of waiting for user instructions one step at a time, these autonomous software agents adapt on their own—navigating data streams, iterating on decisions, and learning from each successive outcome with minimal human oversight.
| 2025-02-19T00:00:00 |
2025/02/19
|
https://www.ey.com/en_in/insights/ai/how-agentic-ai-is-transforming-the-future-of-intelligent-systems
|
[
{
"date": "2023/02/01",
"position": 80,
"query": "future of work AI"
},
{
"date": "2023/04/01",
"position": 72,
"query": "future of work AI"
},
{
"date": "2023/05/01",
"position": 82,
"query": "future of work AI"
},
{
"date": "2023/09/01",
"position": 83,
"query": "future of work AI"
},
{
"date": "2023/10/01",
"position": 88,
"query": "future of work AI"
},
{
"date": "2024/03/01",
"position": 89,
"query": "future of work AI"
},
{
"date": "2024/04/01",
"position": 84,
"query": "future of work AI"
},
{
"date": "2024/06/01",
"position": 83,
"query": "future of work AI"
},
{
"date": "2024/07/01",
"position": 83,
"query": "future of work AI"
},
{
"date": "2024/07/01",
"position": 94,
"query": "reskilling AI automation"
}
] |
futurist on ai keynote speaker: artificial intelligence expert & consultant
|
Futurist on AI Keynote Speaker & ML Expert
|
https://www.futuristsspeakers.com
|
[
"Https",
"Www.Futuristsspeakers.Com Author Scott"
] |
AI and Human Collaboration · Ethical and Responsible AI · AI-Driven Automation and the Future of Work · AI in Everyday Life and Smart Cities · AI ...
|
Top futurist on AI keynote speakers, consulting experts and thought leaders posit that as artificial intelligence continues to transform industries and everyday life, noted names in the field have become sought-after headliners. That’s because today’s most popular and best futurist on AI presenters offer audiences a glimpse into how technology will change the future and what opportunities and challenges lie ahead. We look at emerging trends that LLM, ML and automation experts cover in their presentations.
AI and Human Collaboration
Celebrity futurist on AI emphasize how tech will not replace humans but enhance human capabilities. SMEs explore concepts like augmented intelligence, where automation assists decision-making, creativity, and productivity across sectors such as healthcare, education, and manufacturing. That collaborative future encourages new workflows blending human intuition with data power. Ethical and Responsible AI
As smart tech grows more powerful, ethical concerns become critical. Famous futurist on AI advisors discuss the importance of transparency, fairness, and accountability in intelligent systems. KOLs highlight emerging frameworks and regulations designed to prevent bias, protect privacy, and ensure that technology serves humanity’s best interests. AI-Driven Automation and the Future of Work
Keynote talks focus on how emerging innovations will automate routine tasks, transforming jobs and industries. And so leading futurist on AI headliners present strategies for workforce adaptation, emphasizing reskilling, lifelong learning, and the creation of new roles that leverage uniquely human skills like empathy and complex problem-solving. AI in Everyday Life and Smart Cities
Also a futurist on AI would showcase how automation technologies will make cities smarter and daily life more convenient. From autonomous vehicles and personalized healthcare to intelligent energy management and public safety, pros paint a picture of AI integrated seamlessly into urban infrastructure and consumer products. AI and Creativity
Apart from logic and data, the tech is increasingly involved in creative fields such as art, music, writing, and design. Thus any given futurist on AI explores how smart tools are enabling new forms of creative expression and collaboration, challenging traditional notions of authorship and innovation.
Your typical futurist on AI keynote speaker inspires audiences by revealing how technology’s ongoing evolution will redefine human potential, societal structures, and the way we live and work.
| 2025-07-08T00:00:00 |
2025/07/08
|
https://www.futuristsspeakers.com/futurist-on-ai-keynote-speaker-expert-consultant/
|
[
{
"date": "2023/02/01",
"position": 83,
"query": "future of work AI"
}
] |
Connecting the Future: How Connectivity and AI Unlock New Potential
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Connecting the Future: How Connectivity and AI Unlock New Potential
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https://blogs.cisco.com
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[
"Nicole Isaac"
] |
AI is already helping networks self-optimize, predict outages, reduce energy consumption, and respond to threats in real time—making ...
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From the early days of the internet to the rise of artificial intelligence, Cisco has helped build the digital backbone of the modern world. We’ve expanded access, secured global networks, and supported the infrastructure that powers everything from online learning to global commerce. For example, Cisco’s Networking Academy has trained over 2.6 million learners in the US since the program’s inception, empowering them with critical digital literacy and technology skills.
We’ve done that for millions worldwide. In France, for instance, we’re working with local partners to use AI and sensor technology that helps dairy farmers prevent heat stress in cows, boosting production for over 50 farms and increasing revenues by up to 20%. Meanwhile, in the City of El Paso, we partner with the city to deliver Webex and Cisco networking solutions to ensure vulnerable and at-risk residents can seamlessly connect to essential services.
Now, as AI redefines what’s possible—from breakthroughs in healthcare to smarter cities—we are once again at the center of this transformation. That’s why we are proud to sponsor this week’s ITU AI for Good Summit, the UN’s leading platform for using AI to tackle global challenges, and to launch a new white paper: Connecting the Future: How Connectivity and AI Unlock New Potential, co-developed with the ITU, Atlantic Council, and Access Partnership.
The Two-Way Relationship Between AI and Connectivity
What is the relationship between AI and connectivity? First and foremost, AI cannot succeed without strong digital infrastructure. AI also has the power to dramatically improve that infrastructure in return.
AI models—especially generative tools—require more bandwidth, ultra-low latency, and highly resilient works. Meeting these demands requires an investment in every layer of connectivity across the first, middle, and last miles and a new era of public-private collaboration.
Yet AI isn’t just a user of networks; it’s also transforming them. AI is already helping networks self-optimize, predict outages, reduce energy consumption, and respond to threats in real time—making infrastructure faster, safer, and more efficient. This is especially critical in regions where networks must scale quickly.
At Cisco, we’re building the technologies that make this possible. Take for example ThousandEyes which leverages AI to enable faster and more reliable connections, or our Cisco Nexus switches and AI-native infrastructure solutions which automate network performance and optimize data center performance.
Addressing the Readiness Gap
According to Cisco’s AI Readiness Index, 98% of global business leaders say AI adoption is more urgent than ever, yet only 13% feel fully prepared. More than half of these businesses have expressed that their infrastructure lacks the scalability to support AI, and 78% doubt they have the computing resources needed to keep up.
This isn’t surprising—with 3.7 billion people still lacking reliable internet access, the digital divide threatens to become an AI divide.
As part of our broader Partner2Connect pledge to address this gap, Cisco launched a $1 billion global AI investment fund to accelerate the development of secure, trustworthy technologies and infrastructure and we’re already seeing its impact.
In Saudi Arabia , we’ve partnered with HUMAIN AI to support national infrastructure, launch a Cisco AI Institute at KAUST , operationalize secure data centers, and upskill 500,000 learners in AI, cybersecurity, and programming.
, we’ve partnered with to support national infrastructure, launch a , operationalize secure data centers, and upskill in AI, cybersecurity, and programming. In France, we’re developing a Global AI Hub focused on energy-efficient infrastructure, startup innovation, and workforce development. We’ve also expanded our collaboration with Mistral AI and committed to establishing a center of excellence for data center innovation and train 230,000 people in AI and digital skills over the next three years.
Infrastructure Needs People
Infrastructure alone isn’t enough. AI’s full potential depends on people having the skills to build, deploy, and use it responsibly. Yet millions still lack the digital and AI skills needed to participate in the economy of tomorrow.
Through the Cisco Networking Academy, which has trained more than 20 million learners globally, and initiatives like Partner2Connect and the ITU Digital Transformation Centers, we’re helping to close that gap—equipping both technical and non-technical talent with the skills to build, manage, and apply AI responsibly and successfully.
Governments, companies, and global institutions must now work together to integrate AI literacy into national curricula, expand access to digital public infrastructure, and make upskilling a central pillar of economic development.
A Shared Responsibility
AI and connectivity are not separate agendas—they’re part of the same transformation. One cannot advance without the other. And neither will succeed without bold policy, smart investment, and public-private partnerships.
At Cisco, we’re ready to lead. We’re building secure, AI-ready networks. We’re supporting innovators and governments, and we’re focused on making sure this next chapter of technological progress includes everyone. The future is connected, intelligent, and within reach.
This is who we are—One Cisco, one community, one world, and this is how we power an inclusive future for all.
Share:
| 2025-07-08T00:00:00 |
2025/07/08
|
https://blogs.cisco.com/gov/connectivity-and-ai-unlock-new-potential-itu-ai-for-good
|
[
{
"date": "2023/02/01",
"position": 84,
"query": "future of work AI"
},
{
"date": "2023/04/01",
"position": 69,
"query": "future of work AI"
},
{
"date": "2023/05/01",
"position": 92,
"query": "future of work AI"
},
{
"date": "2023/09/01",
"position": 92,
"query": "future of work AI"
},
{
"date": "2024/03/01",
"position": 95,
"query": "future of work AI"
},
{
"date": "2024/04/01",
"position": 93,
"query": "future of work AI"
},
{
"date": "2024/06/01",
"position": 92,
"query": "future of work AI"
},
{
"date": "2024/07/01",
"position": 92,
"query": "future of work AI"
}
] |
AI agents could actually help in DevOps - Reddit
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The heart of the internet
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https://www.reddit.com
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[] |
ago. AI and the future of DevOps engineers. 119 upvotes · 121 ... Best AI For Work · Best AI Companies to Work For · Best AI Tools to Make ...
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I’ve been digging into AI agents recently .....not the general ChatGPT stuff, but how agents could actually support DevOps workflows in a practical way.
Most of what I’ve come across is still pretty early-stage, but there are a few areas where it seems like there’s real potential.
Here’s what stood out to me:
🔹 Log monitoring + triage
Some setups use agents to scan logs in real time, highlight anomalies, and even suggest likely root causes based on past patterns. Haven’t tried this myself yet, but sounds promising for reducing alert fatigue.
🔹 Terraform plan validation
One example I saw: an agent reads Terraform plan output and flags risky changes like deleting subnets or public S3 buckets. Definitely something I’d like to test more.
🔹 Pipeline tuning
Some people are experimenting with agents that watch how long your CI/CD pipeline takes and recommend tweaks (like smarter caching or splitting slow jobs). Feels like a smart assistant for your pipeline.
🔹 Incident summarization
There’s also the idea of agents generating quick incident summaries from logs and alerts ...kind of like an automated postmortem draft. Early tools here but pretty interesting concept.
All of this still feels very beta .....but I can see how this could evolve fast in the next 6–12 months.
Curious if anyone else has tried something in this space?
Would love to hear if you’ve seen any real-world use (or if it’s just hype for now).
| 2023-02-01T00:00:00 |
https://www.reddit.com/r/devops/comments/1lusb1o/ai_agents_could_actually_help_in_devops/
|
[
{
"date": "2023/02/01",
"position": 85,
"query": "future of work AI"
}
] |
|
Superhuman is being acquired by Grammarly to build the future of ...
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Superhuman is being acquired by Grammarly to build the future of work
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https://blog.superhuman.com
|
[
"Superhuman Team",
"Rahul Vohra",
"Jul"
] |
The agentic future of work ... With greater resources, we will now accelerate everything we do. We will invest even more in AI and our core ...
|
At Superhuman, we set out to build the most productive email experience ever made.
Our customers now get through their email twice as fast as before, reply 1-2 days sooner, and save more than 4 hours every single week. Together, they’ve sent over 500 million messages, triaged over 2 billion conversations, and used a whopping 6 billion shortcuts! See more on our Wall of Love.
I am beyond thrilled to announce our next chapter: Superhuman is being acquired by Grammarly. Together, we will build the AI-native productivity suite of choice!
We will now accelerate our entire roadmap. We will invest even more deeply in AI and email, build new experiences that transform how we collaborate and communicate, and create AI agents that unlock a whole new way of working. More below…
Superhuman & Grammarly 💜
I met Shishir Mehrotra — co-founder of Coda, and now the CEO of Grammarly — back in 2017.
I had just onboarded him onto Superhuman. As he closed Gmail, another tab caught my eye: an app called Krypton. I asked what it was. Shishir then gave me the best product demo I had seen in years!
Krypton became Coda, and Coda became the collaborative document of choice for tens of thousands of teams and millions of users. In late 2024, Grammarly acquired Coda, and Shishir became the new CEO.
In their acquisition announcement, Shishir wrote: “As I watched the foundational capabilities of AI change how just about every tool and surface operates, I started drafting my 2025 planning memo for the team. I titled it: the AI-native productivity suite.”
At Superhuman, our vision has always been to build the AI-native productivity suite of choice. Email is a critical part of this suite, and a much bigger problem than most people realize: there are roughly 1 billion professionals in the world, and on average we spend 3 hours a day in email. That's 3 billion hours every single day, or more than 1 trillion hours every year. In fact, we spend more time in email than any other work app.
When I read Shishir’s post, I knew we should catch up.
The AI superhighway ✨
For professionals, email turns out to be the number one Grammarly use case: the assistant helps write over 50 million emails every week. Grammarly itself is extraordinarily popular: it is used by over 40 million people and 50,000 organizations. We used the phrase “hand in glove” more than a few times!
Grammarly also has incredible potential. At first glance, it is an AI writing assistant. But unlike every other AI assistant, Grammarly works in every app. To pull this off, the team has built integrations with 500,000+ apps and websites — an impressive feat of engineering they call the “AI superhighway.” So far, that AI superhighway has delivered an AI writing assistant. Grammarly is now working on hundreds of task-specific agents, and will use this superhighway to bring these agents to users wherever they work, including — of course — email.
The agentic future of work 🧠
With greater resources, we will now accelerate everything we do. We will invest even more in AI and our core email experience. We will build calendar and tasks, connecting them beautifully together. We will reimagine chat and redefine collaboration. And we will create a new way of working with AI agents.
Email turns out to be the perfect place to work with agents. Imagine an agent triaging your inbox before you wake up; another agent drafting responses in your own voice and tone, incorporating detailed context about you and your work; while another agent surfaces insights, schedules meetings, and interacts with other agents and your systems of record.
We could not be more excited to build the future of work — an AI-native productivity suite, with agentic workflows that are deeply integrated into every part of your day!
Thank you 🙏
I want to share my heartfelt gratitude with every customer, every investor, and every teammate. This is all only possible because of you.
To our customers — thank you for trusting us every day, and for shaping the product into what it is today. Everything that we do, we do for you.
To our investors — thank you for believing in this dream, and supporting us every step of the way. We could not have asked for any more.
To every team member, past and present — thank you for living our values, and for caring so much. What we’ve built is only the beginning, and our very best work lies ahead!
Onwards 🚀
Rahul
| 2025-07-01T00:00:00 |
2025/07/01
|
https://blog.superhuman.com/superhuman-is-being-acquired-by-grammarly/
|
[
{
"date": "2023/02/01",
"position": 89,
"query": "future of work AI"
},
{
"date": "2025/07/01",
"position": 87,
"query": "future of work AI"
},
{
"date": "2025/07/01",
"position": 93,
"query": "future of work AI"
},
{
"date": "2025/07/01",
"position": 93,
"query": "future of work AI"
},
{
"date": "2025/07/01",
"position": 97,
"query": "future of work AI"
},
{
"date": "2025/07/01",
"position": 95,
"query": "future of work AI"
},
{
"date": "2025/07/01",
"position": 93,
"query": "future of work AI"
},
{
"date": "2025/07/01",
"position": 93,
"query": "future of work AI"
}
] |
Teens explore the future of AI at UB experience - University at Buffalo
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Teens explore the future of AI at UB experience
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https://www.buffalo.edu
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[] |
— As artificial intelligence tech like ChatGPT, Midjourney and Copilot have transformed how we work and create, 23 tech-savvy teens from 14 ...
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Teens explore the future of AI at UB experience
“This is the first of many K-12 experiences the School of Management and Center for AI Business Innovation will be bringing to the community. We look forward to providing platforms to foster creative, impactful and ethical engagement with these exciting new technologies.”
BUFFALO, N.Y. — As artificial intelligence tech like ChatGPT, Midjourney and Copilot have transformed how we work and create, 23 tech-savvy teens from 14 local schools stepped into the future at the University at Buffalo’s inaugural “AI Experience at UB” program.
At the free event, held June 30-July 1, students learned about the foundations of AI and machine learning, received hands-on training with a range of AI tools, used AI to develop and pitch innovative business ideas, built video games and learned about various aspects of ethics and trust in AI.
During one hands-on session, UB faculty led students through a physical simulation to help them better understand the inner workings of a neural network, which forms the backbone of AI.
“The students learned how an AI model takes an input — a picture in our case — and uses math to predict an output, which was the type of animal in our picture,” says Kevin Cleary, clinical assistant professor of management science and systems, who developed the simulation. “Throughout the process, the students each played the role of various parts of the neural network and manually calculated the math to produce a prediction at the end.”
The AI Experience at UB was hosted by the UB School of Management’s Center for AI Business Innovation and the UB School of Engineering and Applied Sciences’ Center of Excellence in Information Systems Assurance Research and Education, in collaboration with the UB Institute for Artificial Intelligence and Data Science, and was supported by the University at Buffalo AI Seed Funding Grants.
Leading the program were UB School of Management faculty members Laura Amo, assistant professor of management science and systems; Kevin Cleary, clinical assistant professor of management science and systems; Joana Gaia, clinical assistant professor of management science and systems; Celine Krzan, clinical assistant professor of entrepreneurship; David Murray, clinical professor of management science and systems; and Dominic Sellitto, clinical assistant professor of management science and systems; along with Shambhu Upadhyaya, professor of computer science and engineering in the UB School of Engineering and Applied Sciences.
“This is the first of many K-12 experiences the School of Management and Center for AI Business Innovation will be bringing to the community,” says Sellitto, who also serves as assistant faculty director of the Center for AI Business Innovation. “We look forward to providing platforms to foster creative, impactful and ethical engagement with these exciting new technologies.”
| 2023-02-01T00:00:00 |
https://www.buffalo.edu/news/news-releases.host.html/content/shared/mgt/news/teens-explore-future-ai-ub-experience.detail.html
|
[
{
"date": "2023/02/01",
"position": 90,
"query": "future of work AI"
}
] |
|
TxDOT's Blueprint for AI - HNTB
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TxDOT's Blueprint for AI
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https://www.hntb.com
|
[] |
With AI tackling repetitive tasks, TxDOT employees can focus on setting priorities, coordinating work efficiently and communicating proactively with ...
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TxDOT's Blueprint for AI
Building on a storied history of innovation, Texas is harnessing artificial intelligence’s potential to improve safety, efficiency and system performance on the state’s roadways through a highly collaborative, human-centered approach
By Erika Kemp, Director, Strategic Initiatives and Innovation Division | Texas Department of Transportation
Texas has an enduring reputation for thinking big and being big, thanks to its geographic footprint and growing population. Texas also holds claim to the country’s most extensive road network, with more than 80,000 centerline miles on the state highway system. Accordingly, our transportation agency workforce has more than 13,000 professionals committed to enhancing mobility, safety and quality of life for Texans every day.
To pursue its mission, the Texas Department of Transportation (TxDOT) has been entrusted with funding – more than $100 billion over the next 10 years – to operate, preserve and optimize our system. Innovation and continuous improvement are key to delivering measurable value for every invested dollar, which is why our leaders support us in assessing emerging technologies and techniques that improve efficiency and quality.
One high-profile outcome of this mandate was TxDOT’s public release of its first Artificial Intelligence Strategic Plan in December 2024. This 70+ page document includes strategic priorities, hundreds of possible use cases, best practices and recommendations to drive our adoption of AI through mid-2027. This document guides our pursuit of high-impact AI applications that enhance decision-making, streamline processes and unlock deeper data insights – all while keeping humans in the center of the action.
The plan’s release was the culmination of 18 months of collaborative development including the efforts of specialists throughout the agency and the creative ideas of TxDOT employees across the state. Importantly, the AI plan marked a milestone in a journey of innovation that Texas has been embarking on for decades. It sets in motion a broad set of actions that will transform how we run our operations, provide services and accelerate improvements to safety, mobility and quality of life for the state and the nation.
Innovative mindset, collaborative process
TxDOT has long supported transportation innovation. Our legislature has supported the examination of emerging technologies to gauge how they may impact mobility in our state. Early on we analyzed the potential of AI with the help of Ben McCulloch, our team’s strategic data scientist, and fast-tracked our AI strategic plan. We also engaged two critical partners – Anh Selissen, chief information officer, and Darran Anderson, director of strategy and innovation – and together agreed that AI warranted swift, but highly collaborative strategic planning to leverage it for our success. McCulloch was named the project’s champion and led the effort.
Around this same time, the Information Technology Division launched an AI Program to support, enable and educate employees on AI and machine learning technologies. Led by Kristina Miller and Michelle Brockdorf, the team developed a comprehensive policy and governance framework to guide responsible AI innovation, and the policy principles were echoed in the strategic plan. The team also began evaluating and implementing foundational technologies to support a growing portfolio of AI use cases, ensuring that TxDOT is well-positioned to harness AI in ways that are secure, scalable and impactful.
We managed the development of our AI strategic plan internally, methodically drawing out the best ideas and inputs from our teams across the state. We conducted workshops with representatives from our 25 districts, which cover a vast array of geographies, population concentrations and transportation priorities. We also engaged with our 34 divisions, which specialize in activities that impact all areas of the state.
These workshops probed for answers to an essential question: “What should TxDOT be doing with AI?” Participants were challenged to identify specific applications – tools, capabilities, applications and time-savers alike – and submit their ideas. Our team reviewed inputs, consolidated similar ideas and themes and created a compendium of more than 200 proposed AI use cases, which comprise the lion’s share of the strategic plan.
Interestingly, most proposed use cases fell into one of two categories:
1) improve process efficiencies, or
2) gain transformative insights.
| 2025-07-08T00:00:00 |
2025/07/08
|
https://www.hntb.com/think/txdots-blueprint-for-ai/
|
[
{
"date": "2023/02/01",
"position": 91,
"query": "future of work AI"
}
] |
RDW 2025: AFCEC engineers embrace AI to accelerate ...
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RDW 2025: AFCEC engineers embrace AI to accelerate infrastructure delivery and mission readiness
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https://www.afcec.af.mil
|
[] |
The encouraged adoption of AI isn't about replacing engineers or the work they do, but about giving them tools to scope better, faster, and with ...
|
ARLINGTON, Va. — Across the globe and throughout every industry and sector, the impact of artificial intelligence is evident. For Air Force Civil Engineers, the focus is shifting from exploring AI’s potential to applying it where it can make an immediate impact, including streamlining workflows, improving operational efficiency, and enabling Airmen Civil Engineers to focus on mission-critical work.
Leveraging AI in day-to-day operations was a central theme of this year’s Requirements Development Workshop, hosted by Headquarters Air Force A4C from May 6-8, with several keynote briefings focused on the topic. Two featured sessions, “Future Lab: Beyond AI, What’s Next for Air Force Civil Engineering?” and “Work Smarter, Not Harder: Unlocking Artificial Intelligence”, were led by Col. Brad Ledford, deputy director of facility engineering at the Air Force Civil Engineer Center, and Dr. Cigdem Meek, AI subject matter expert in AFCEC’s Technical Services Division. The sessions focused on how AI agents are being developed to accelerate infrastructure delivery and improve cost estimation, as well as efforts to upskill AFCEC engineers through training with MIT, Air Force Research Laboratory, and other sources to save time in daily operations.
Artificial intelligence refers to the simulation of human intelligence by machines, including the ability to learn, reason, and use language. Tools like machine learning and deep learning analyze large volumes of data to support faster and more accurate decision-making.
Ledford underscored how the need for smarter, faster solutions has never been more critical as infrastructure demands have grown in both scale and urgency. Traditional planning and programming methods, while disciplined, often take years, mismatched with mission timelines that demand greater flexibility and speed. From initial planning and requirements development through completion of construction, Ledford said the typical project lifecycle is currently seven to 10 years.
“There’re a little over 4,300 projects marching down that path right now with about a $78 billion price tag,” he shared, noting the only way to accomplish the robust pipeline of critical infrastructure projects at the speed of relevance is to employ advanced technology solutions. “If we want something we’ve never had before, we have to do something we’ve never done before.”
There is a growing emphasis to include more adaptive, technology-assisted, and data-driven approaches to project planning and delivery within the civil engineering field. AI is at the center of the evolution, with both an emphasis on automating workflows, and reshaping how engineers interact with fragmented systems and incomplete data to make faster, more informed decisions.
Ledford pointed to a small-scale pilot project already underway to develop an AI tool capable of generating project scopes and class-level cost estimates in weeks rather than months. The goal is to reach a PCR-2 level of project definition with a class 3 estimate in a fraction of the typical timeline, reducing inconsistencies and minimizing late-stage changes that often disrupt execution.
The encouraged adoption of AI isn’t about replacing engineers or the work they do, but about giving them tools to scope better, faster, and with greater clarity from the start.
“How do we free up time so engineers can get back to doing what only engineers can do?” Ledford asked during his session.
NIPRGPT, built specifically for use on government networks, can assist with drafting communications, summarizing technical documents, generating project outlines, and more. With effective prompt crafting, engineers can reduce hours of work to minutes, freeing up capacity to focus on analysis, strategy, and mission execution. Even small gains, like reducing the time spent creating slides or early project documentation, can ultimately translate into thousands of hours saved across the enterprise.
AI literacy is foundational to putting these capabilities into practice. Meek emphasized, “The future belongs to those who know how to work with AI, not against it.” To support that shift, AFCEC has launched a comprehensive AI literacy initiative, backed by DOD-wide tools and academic partners. Engineers have access to curated resources through the AFCEC AI SharePoint portal, including onboarding guides, prompt libraries, and responsible-use training modules.
“Civil Engineering is generating more data than ever before,” Meek said. “Data alone is not enough. We need intelligent tools that extract meaningful insight and transform data into actionable knowledge.”
Engineers are encouraged to explore simple, daily-use cases like writing bullets, summarizing guidance, or translating policy language. These efforts are complemented by broader work to centralize civil engineer data in the Air Force cloud, improve tool integration, and prepare the workforce to adopt more tools like Microsoft Copilot as they become available.
AI is not a silver bullet, but a powerful enabler when applied with clear intent and judgment. Its impact will come not from speculative advances, but from practical, aligned use cases that improve efficiency, reduce rework, and enhance consistency across the enterprise. As RDW shined light on, AI is already here. The next step is to master how to work with it—intentionally, strategically, and with mission in mind.
| 2023-02-01T00:00:00 |
https://www.afcec.af.mil/News/Article-Display/Article/4236988/rdw-2025-afcec-engineers-embrace-ai-to-accelerate-infrastructure-delivery-and-m/
|
[
{
"date": "2023/02/01",
"position": 92,
"query": "future of work AI"
}
] |
|
What the Google x JetBrains Hackathon Tells Us About the Future of ...
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What the Google x JetBrains Hackathon Tells Us About the Future of AI at JetBrains
|
https://blog.jetbrains.com
|
[
"Dmitriy Novakovskiy",
"Principal Architect At Google Cloud",
"Jelena Mijuskovic",
"Customer Engineer For Data",
"Ai At Google Cloud",
"Diana Nanova",
"Head Of Customer Engineering Startups",
"Scaleups",
"Emea North",
"At Google Cloud"
] |
In May, 145 JetBrains employees from across teams and time zones paused their regular work to participate in a 48-hour AI hackathon.
|
In May, 145 JetBrains employees from across teams and time zones paused their regular work to participate in a 48-hour AI hackathon.
With support from Google Cloud, the participants built a total of 41 prototypes from scratch. Some were practical, others experimental, but all explored how AI could change how developers work.
The Google Cloud team ran technical sessions ahead of the event, covering the Gemini 2.5 model’s capabilities and tool integrations, as well as AI use cases for production. These sessions gave everyone a shared foundation and helped them get straight to building. Google also provided everyone with access to some experimental features across Gemini 2.5, Imagen3, and Veo2 models.
So what came out of it? Projects that tackled debugging, local context, user interfaces, and agent collaboration, and some of these ideas are already influencing product development. Here are the winning projects, along with a variety of others that stood out.
“For the second year in a row, we are delighted to host this awesome AI hackathon with super talented teams from JetBrains. This time around, I got to spend more time reviewing the completed projects – and oh boy, how much of a profound (and often almost emotional) effect it had on me.
It was amazing to see all the ambitious ideas the teams came up with, and how many of them had such a great fit in the ecosystems of both companies. And project presentations in the end? Some of them were legit examples of work of (AI-powered) art.
One could ask – where did the teams get so much time to code the project AND do an awesome Veo2-powered video presentation? The answer? Junie does the coding now.” Dmitriy Novakovskiy Principal Architect at Google Cloud
The winning projects
Popular vote
Komponist, der
This tool for reducing repetitive prompting lets you insert predefined prompts into AI chats using buttons, hotkeys, and physical controllers like StreamDeck. You can also switch context profiles based on the task.
AI agents
Runtime-aware debugging agent for the JVM
This debugging agent helps you fix bugs by using execution traces, not just error messages. It works with a repair agent to test patches. It integrates with IntelliJ IDEA’s trace debugger and uses tools like slicers and variable inspectors.
UIGen AI
This exploration of agent-based coding uses compiler output and test results to generate code with fewer prompts. The aim was to reduce prompt repetition by providing more structured and focused instructions.
AI meets conventional frameworks
JetBar
A command bar for JetBrains IDEs that helps you find features, run actions, and open files. It introduced next action prediction, which uses AI to suggest likely next steps like running tests, generating code, or creating files with context-aware templates.
AI tool integration
Flow IDE
A tool that integrates AI into the developer workflow without taking over. It adds intelligent triggers for common IDE actions, using AI to reduce friction in everyday development tasks.
Unique concept
ZoomerCode – a vibe-code-reviewing tool
This swipe-based web app for reviewing GitHub pull requests uses emoji reactions like 👍, 🤔, 🐛, and 🚩 to speed up the review process. The design focuses on visual feedback and quick collaboration.
Local AI tools
JetMem – Local memory for AI Assistant
This tool gives JetBrains AI Assistant persistent memory on the user’s machine. It stores preferences like language or framework, applying this context to enhance code completions and chat-based interactions. It supports multiple models and works outside of chat.
ContextAI – Context management tool
A tool for capturing and indexing online content using keyboard shortcuts and browser extensions. It saves pages locally, builds an embedded database, and supports natural language search and AI-assisted writing.
“It was impressive to witness the creativity of the teams, who developed innovative solutions that address a wide range of challenges. The hackathon kicked off with amazing energy, with the packed Google Cloud pre-enablement sessions and the teams’ eagerness to start the 48-hour sprint.
It was great to see that teams built their solutions incorporating the latest AI models into scalable and future-proof architectures. The final presentations were a highlight to remember; they were polished, humorous, and market-ready pitches.
It was particularly exciting to see the use of cutting-edge models like Veo2 for video and Lyria for audio generation, elevating their presentations to a whole new level.” Jelena Mijuskovic Customer Engineer for Data & AI at Google Cloud
Google Cloud’s honorable mentions
The following projects received special recognition from Google Cloud:
Semantic search and structured summaries for PubTrends
This project upgraded pubtrends.info by providing semantic search and AI-generated summaries that connect research papers to relevant genes, datasets, and drugs using entity recognition.
Artemy – AI deployment agent
Designed to simplify final deployment steps, this proof-of-concept agent automates setup, hosting, and domain configuration for new apps, websites, and bots.
AI ad generator
Aimed at teams that need fast content generation across multiple channels, this tool turns short prompts into advertising content across formats and platforms.
“Wow, what an experience! Having been involved in a fair few hackathons, I was seriously impressed by how smoothly JetBrains ran their AI hackathon together with Google Cloud.
Getting 145 people working together for days on end without a hitch is no easy feat, and honestly, everything just worked. But what really blew me away were the creative ideas – so diverse and clever! AI agents were a big theme, but teams went wild with everything from Tinder-swipe code review agents to deep-dive research agents that could practically walk you through the latest scientific papers.
The energy and innovation were just fantastic. And the vibe during the project presentations? Super cheerful and fun. It was just awesome to see everyone diving into AI and building such cool stuff!” Diana Nanova Head of Customer Engineering Startups & Scaleups, EMEA North, at Google Cloud
What the future holds
This hackathon was not just an experiment. It created space for focused work and fast validation, and the ideas tested during those 48 hours are already shaping the future of AI at JetBrains. The time constraints helped teams focus on essential features, and participants said they appreciated the chance to work on something different and to collaborate with colleagues outside their usual teams.
Several of the hackathon projects are already on their way to becoming actual features, as ideas from JetMem, JetBar, and the debugging agent are feeding into the development of our AI products.
Learn more about JetBrains AI
AI hackathon partnerships Share
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| 2023-02-01T00:00:00 |
https://blog.jetbrains.com/ai/2025/07/what-the-google-x-jetbrains-hackathon-tells-us-about-the-future-of-ai-at-jetbrains/
|
[
{
"date": "2023/02/01",
"position": 95,
"query": "future of work AI"
},
{
"date": "2023/04/01",
"position": 59,
"query": "future of work AI"
},
{
"date": "2023/05/01",
"position": 97,
"query": "future of work AI"
},
{
"date": "2023/09/01",
"position": 96,
"query": "future of work AI"
},
{
"date": "2024/06/01",
"position": 98,
"query": "future of work AI"
},
{
"date": "2024/07/01",
"position": 96,
"query": "future of work AI"
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|
AI and the Future of Higher Education | Psychology Today
|
AI and the Future of Higher Education
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https://www.psychologytoday.com
|
[] |
AI has now introduced the ability to write articles entirely on its own. Students are using it, and faculty are using it too. I have ...
|
In 2016, I created the first Coursera course for our university. It was based on my book, The Bilingual Brain. A few years later, they asked me to come and speak to a group of faculty who were thinking about creating their own courses. Before I spoke, Jeff Morgan, the Associate Provost for Education Innovation and Technology, came to introduce the session on Coursera. The first thing he said was that Coursera was not going to replace universities. The idea that someone would learn the same thing on their own did not fit with history. Students already had that available to them. They could just pick up a book. The fact that a book had not replaced universities was evidence of their value. His view was that there was something about people gathering to learn together that was irreplaceable.
Today, people have begun to ask themselves whether AI will replace higher education. The question is most pressing in a recent article by James Walsh in New York Magazine entitled “Everyone Is Cheating Their Way Through College: ChatGPT has unraveled the entire academic project.” And once again, I turn to the point that Jeff Morgan made more than 10 years ago. If higher education were just about learning on your own, then books would have done the job long ago.
AI has now introduced the ability to write articles entirely on its own. Students are using it, and faculty are using it too. I have experimented with it to generate texts that can offer opinions that are roughly in my voice after extended Q&A sessions. It is remarkable, and as a tool, it can open up new ways of exploring ideas that I would not have explored otherwise. But after extensive use, I am sure that it is not a replacement for what I can do on my own. In the end, the thinking still has to come from me. And as many have pointed out, writing is a form of thinking.
It is the loss of the writing process where people fear the shortcuts will short-circuit our ability to create on our own. As educators, we have the ability to enhance thinking and writing by controlling the amount of technology used in the classroom. Personally, I have moved to in-person written exams. I have asked students to present in class. In the fall, I will ask them to write in class and then assemble their own writings into some form of an in-person handwritten final. Will they write less? Most likely. Could they have written more with the help of MS Word? Definitely. But I am okay with that. Last semester, some of my students came to talk to me, worried about their final presentations. They felt they were not good enough. And I assured them that they were. One in particular did not like a video she made. I told her that if I wanted a cleaned-up version of a video, I would just watch Netflix or YouTube. What I wanted to do was understand the world from their perspective.
To paraphrase Jeff Morgan again, the point is that universities are here to bring people together to learn something new. Yes, AI might change that, but it will not replace it. It is up to us as instructors to control the classroom. That is why I think it is a matter of time before we go back to what has worked for at least 100 years, paper and pencil.
The world can be as developed and sophisticated as it is. But the classroom belongs to us as humans. We no longer need to teach people using technology in the classroom. We have it readily available every day. What sets higher education apart is being together in the service of learning something new. As educators, we should work to keep it that way.
| 2023-02-01T00:00:00 |
https://www.psychologytoday.com/us/blog/the-emergence-of-skill/202507/ai-and-the-future-of-higher-education
|
[
{
"date": "2023/02/01",
"position": 96,
"query": "future of work AI"
},
{
"date": "2023/04/01",
"position": 78,
"query": "future of work AI"
},
{
"date": "2023/05/01",
"position": 95,
"query": "future of work AI"
},
{
"date": "2023/06/01",
"position": 64,
"query": "AI education"
},
{
"date": "2023/09/01",
"position": 66,
"query": "AI education"
},
{
"date": "2023/09/01",
"position": 95,
"query": "future of work AI"
},
{
"date": "2023/11/01",
"position": 66,
"query": "AI education"
},
{
"date": "2024/01/01",
"position": 67,
"query": "AI education"
},
{
"date": "2024/03/01",
"position": 67,
"query": "AI education"
},
{
"date": "2024/03/01",
"position": 98,
"query": "future of work AI"
},
{
"date": "2024/04/01",
"position": 98,
"query": "future of work AI"
},
{
"date": "2024/05/01",
"position": 68,
"query": "AI education"
},
{
"date": "2024/06/01",
"position": 97,
"query": "future of work AI"
},
{
"date": "2024/07/01",
"position": 67,
"query": "AI education"
},
{
"date": "2024/07/01",
"position": 95,
"query": "future of work AI"
},
{
"date": "2024/10/01",
"position": 67,
"query": "AI education"
},
{
"date": "2024/11/01",
"position": 66,
"query": "AI education"
},
{
"date": "2025/04/01",
"position": 78,
"query": "AI education"
},
{
"date": "2025/05/01",
"position": 74,
"query": "AI education"
}
] |
|
AI in the Workplace: The Future of Business Efficiency and ...
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AI in the Workplace: The Future of Business Efficiency and Cost Savings
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https://www.linkedin.com
|
[
"Daniel Burrus",
"Jean Ng",
"Jk Tech",
"Dick Raskopf",
"Executive Sales Strategist",
"I Help Companies Create New Revenue Streams",
"Drive Top Line Revenue Growth.",
"Sarah Resch",
"Product Manager Lxp"
] |
According to a recent survey by McKinsey, AI can improve business efficiency by up to 40% and reduce operational costs by up to 30%.
|
Artificial intelligence (AI) is revolutionizing how businesses operate, providing new opportunities for efficiency and cost savings. With the increasing availability of AI tools and platforms, businesses of all sizes and industries can benefit from this transformative technology. This article explores the benefits and challenges of using AI in the workplace and provides insights on what businesses should focus on when considering AI implementation.
Cost Savings and Efficiency
One of the most significant advantages of using AI in the workplace is the potential for cost savings and increased efficiency. According to a recent survey by McKinsey, AI can improve business efficiency by up to 40% and reduce operational costs by up to 30%. When automating repetitive tasks and streamlining business processes, AI can free up employees' time to focus on more strategic and creative tasks. In addition, AI can help businesses make data-driven decisions, leading to more accurate forecasts and reduced risk.
Implementation and Cost Considerations
Businesses should identify specific business processes that can benefit from AI when considering AI implementation. These could include time-consuming, repetitive tasks or require large amounts of data processing. It's essential to assess whether to build an in-house AI system or use an outside vendor. Using open-source code may seem cost-effective, but developing and maintaining it requires significant time and resources. In contrast, using an outside vendor may be more expensive but can save on the initial implementation time and costs.
The implementation cost of AI in the workplace can vary widely depending on a business's specific needs and processes. However, the initial investment can be significant. For example, building an in-house AI system can cost between $1 million to $10 million, while using a vendor can cost upwards of $100,000 annually. While the upfront cost may be high, the potential for cost savings and increased efficiency makes it a worthwhile investment in the future of the business' operations.
Advantages and Disadvantages
While AI has significant advantages in the workplace, there are also potential disadvantages. One of the main concerns with AI is the potential for job loss, particularly for tasks that can be automated. However, by freeing up employees' time from repetitive tasks, AI can allow them to focus on more complex tasks that require human skills, such as problem-solving and creativity.
Another potential disadvantage of AI is the need for more transparency and accountability in decision-making. As AI systems are often trained on historical data, they may reinforce biases and lead to unintended consequences. Businesses need to implement AI in a way that ensures transparency and accountability.
In conclusion, AI is a transformative technology that is driving change in a business having the potential to revolutionize the workplace, leading to significant cost savings and increased efficiency. By adopting a strategic approach to AI implementation, companies can maximize the benefits of this transformative technology and stay ahead of the competition. To successfully implement AI, companies should focus on identifying specific processes that can benefit from AI, assessing the implementation cost, and weighing the technology's advantages and disadvantages.
References
McKinsey & Company. (2017). A future that works: Automation, employment, and productivity. Retrieved from https://www.mckinsey.com/featured-insights/future-of-work/jobs-lost-jobs-gained-what-the-future-of-work-will-mean-for-jobs-skills-and-wages#
Accenture. (2017). The impact of artificial intelligence - widespread job loss or mass redeployment? Retrieved from https://www.accenture.com/_acnmedia/PDF-60/Accenture-When-Machines-Do-Everything-POV.pdf
Deloitte. (2018). Artificial intelligence and the future of work: Human-AI collaboration, not competition. Retrieved from https://www2.deloitte.com/content/dam/Deloitte/nl/Documents/innovatie/deloitte-nl-innovatie-human-ai-collaboration.pdf
PwC. (2018). Sizing the prize: What's the real value of AI for your business and how can you capitalise? Retrieved from https://www.pwc.com/gx/en/issues/analytics/assets/pwc-ai-analysis-sizing-the-prize-report.pdf
| 2023-02-01T00:00:00 |
https://www.linkedin.com/pulse/ai-workplace-future-business-efficiency-cost-savings-daniel-calciano
|
[
{
"date": "2023/02/01",
"position": 2,
"query": "workplace AI adoption"
},
{
"date": "2023/02/01",
"position": 56,
"query": "artificial intelligence layoffs"
}
] |
|
Can artificial intelligence's limitations drive innovative work ...
|
Can artificial intelligence’s limitations drive innovative work behaviour?
|
https://pmc.ncbi.nlm.nih.gov
|
[
"Araz Zirar",
"Huddersfield Business School",
"University Of Huddersfield",
"Huddersfield"
] |
by A Zirar · 2023 · Cited by 33 — Therefore, workplace AI can stimulate IWB in workers because of improved workplace technology and the uncertainty and insecurity of jobs it brings to workers ( ...
|
Artificial intelligence (AI) is deemed to increase workers’ productivity by enhancing their creative abilities and acting as a general-purpose tool for innovation. While much is known about AI’s ability to create value through innovation, less is known about how AI’s limitations drive innovative work behaviour (IWB). With AI’s limits in perspective, innovative work behaviour might serve as workarounds to compensate for AI limitations. Therefore, the guiding research question is: How will AI limitations, rather than its apparent transformational strengths, drive workers’ innovative work behaviour in a workplace? A search protocol was employed to identify 65 articles based on relevant keywords and article selection criteria using the Scopus database. The thematic analysis suggests several themes: (i) Robots make mistakes, and such mistakes stimulate workers’ IWB, (ii) AI triggers ‘fear’ in workers, and this ‘fear’ stimulates workers’ IWB, (iii) Workers are reskilled and upskilled to compensate for AI limitations, (iv) AI interface stimulates worker engagement, (v) Algorithmic bias requires IWB, and (vi) AI works as a general-purpose tool for IWB. In contrast to prior reviews, which generally focus on the apparent transformational strengths of AI in the workplace, this review primarily identifies AI limitations before suggesting that the limitations could also drive innovative work behaviour. Propositions are included after each theme to encourage future research.
There are five sections to this study. The second section offers background for the two concepts. The third section describes the research method. Section four presents the research themes and propositions. The fifth section provides the concluding remarks, including discussion and conclusion, contributions, and study limits.
The research question that guides this review is: how will AI make workers innovative in their work? Following a protocol, the analysis involved reviewing 65 articles found in the Scopus database (Tranfield et al. 2003 ). Insights in the form of themes are obtained, along with propositions and prospective research areas, to highlight gaps in current knowledge and encourage further AI-enabled IWB research.
It is reasonable to imply that AI’s limitations require human intelligence and abilities, for instance, to diagnose disease and detect fraud because current AI advancements cannot do so without humans (Klotz 2018 ; Wilson and Daugherty 2019 ). Workers, therefore, compensate for algorithmic flaws than relying on AI to enhance their creativity. While AI can influence workers’ behaviour (Malik et al. 2021 ; Yam et al. 2020 ), it is unclear how it might enable innovative work behaviour.
While the existing literature (Chuang 2020 ; Klotz 2018 ; Malik et al. 2021 ; Wilson and Daugherty 2019 ) suggests that AI enhances workers’ abilities (intuition, empathy, and imagination) by automating mundane, repetitive, and boring activities. In academic research, there is a debate on whether this enhancement in workers’ abilities results from AI augmenting human intelligence and skills (Farrow 2019 ; Klotz 2018 ). Workplace AI that helps doctors diagnose disease and helps bankers detect fraud improves workers’ detection abilities rather than innovative work behaviour. However, the use of AI in this context suggests that AI cannot act independently of human workers (Klotz 2018 ; Wilson and Daugherty 2019 ).
Artificial Intelligence (AI) is increasingly taking over physical motion and performance activities, data processing and analysis, repetitive physical equipment management, and individual effective performance in workplaces (Chuang 2020 ; Malik et al. 2021 ). One might, therefore, argue that workers engage in more innovative work behaviour (IWB) (Henkel et al. 2020 ; Jaiswal et al. 2021 ). But then, how will AI in a workplace make workers innovative? The answer seems to be more nuanced.
While workers identify potential problems and initiate behaviours that allow sharing of knowledge and insights (Chatterjee et al. 2021 ), IWB is expected to produce innovative output (Farrow 2019 ). It is, therefore, reasonable to suggest that IWB is a worker’s deliberate action of ideation and adopting new ideas, goods, processes, and procedures to their tasks, unit, department, or organisation (Jong and Hartog 2010 ). In this analysis, IWB examples include supporting the design, implementation, introduction and use of AI applications in the workplace (Desouza et al. 2020 ), implementing AI-related technologies (Choi et al. 2019 ), and proposing ways of achieving goals and executing work tasks using AI technologies (Mahroof 2019 ). However, this is not an exhaustive list; any behaviour in an organisation with an ‘innovative’ element falls under IWB (Jong and Hartog 2010 ).
The behavioural element is critical, which means the conception through completion of ideas is not enough to demonstrate IWB without workers’ engagement with others in this process (Perry-Smith and Mannucci 2017 ). A worker collaborates with others to drive new ideas and determine feasibility (Perry-Smith and Mannucci 2017 ). However, when a worker engages with others as part of IWB, groupthink is likely to kill an idea prematurely (Moorhead and Montanari 1986 ). In addition, IWB reflects organisational contexts, which means ideation and the involvement of workers with others reflect the context of an organisation (Perry-Smith and Mannucci 2017 ; Saether 2019 ).
IWB necessitates deliberate workers’ intervention in the workplace (Perry-Smith and Mannucci 2017 ). It is thus IWB is the identification of issues, together with the generation, initiation, and implementation of new and original ideas, as well as the set of behaviours required to develop, initiate, and execute ideas with the intent of enhancing personal and business performance (Jong and Hartog 2010 ).
Moreover, as per engineering literature (Anantrasirichai and Bull 2021 ; Colin et al. 2016 ), being creative is more about the creative process and an individual’s intellect than the creative outcome and behaviour. Solving a problem through a dramatic shift in viewpoint by employing technology is vital in the creative process (Colin et al. 2016 ). As per management literature, innovative behaviour manifests in the creative product (the behaviour) through idea generation, elaboration, championing, and implementation (Alter 2014 ; Perry-Smith and Mannucci 2017 ). In this review, IWB is spontaneous activity to compensate for AI flaws or make sense of AI system outputs rather than a dramatic shift in an individual’s perspective.
Further, innovative behaviour is often associated with creative problem-solving in engineering literature (Anantrasirichai and Bull 2021 ; Colin et al. 2016 ). AI technologies intend to enhance and support individuals’ creativity in problem-solving. In this literature, AI is a creative tool and can be a creator in its own right (Anantrasirichai and Bull 2021 ; Colin et al. 2016 ). On the other hand, workers can use AI applications to demonstrate ‘creativity in problem-solving’ in the workplace. They could use AI to create something new, like a unique ice cream flavour. However, IWB is generally reactive rather than proactive in management literature, such as devising workarounds (Alter 2014 ). Idea generation, elaboration, promotion, and implementation are parts of the process of developing workarounds (Alter 2014 ; Perry-Smith and Mannucci 2017 ). With AI’s limits in perspective, innovative work behaviour serves as workarounds to compensate for AI limitations.
Scholarly research combines idea conception through completion under ‘innovative behaviour’ (Baer 2012 ; Baer and Frese 2003 ; Scott and Bruce 1994 ; Somech and Drach-Zahavy 2013 ). The generation and implementation of new and original ideas into newly designed goods, services, or ways of working are examples of innovative work behaviour (Baer 2012 ; Perry-Smith and Mannucci 2017 ).
The strategy that appears to help with this dilemma is to let workers see how this advancement activates their innovative behaviour (Fügener et al. 2022 ; Klotz 2018 ; Wilson and Daugherty 2019 ). However, the reality is quite the opposite (Davenport 2019 ; Gligor et al. 2021 ; Waterson 2020a , b ). In such a technological context, while the inner workings of such systems generally remain unknown (Gligor et al. 2021 ; Klotz 2018 ), it is up to workers to upskill and reskill themselves to engage in innovative behaviour to coexist with AI systems (Afsar et al. 2014 ; Jaiswal et al. 2021 ; Sousa and Wilks 2018 ).
However, a core issue for workers with workplace AI is the loss of employment (Braganza et al. 2020 ; Rampersad 2020 ). Many workers will lose their jobs to AI applications in the workplace (Balsmeier and Woerter 2019 ). Consequently, the chances are high that the work performed by workplace AI would no longer need the workers’ involvement (Holford 2019 ; Wright and Schultz 2018 ). Workers, therefore, would not feel comfortable if they could not understand how an AI application helps or affects them.
However, the performance of intelligent systems depends on the data fed into them (Farrow 2019 ; Thesmar et al. 2019 ). Intelligent systems are unable to obtain missing parts of data. Therefore, data consistency and quantity are significant issues for AI applications in the workplace. Human intervention to support AI is required, as human intelligence and innovative behaviour are needed to find missing parts of data and categorise appropriate data for AI systems (Shute and Rahimi 2021 ). Human intervention is also necessary to override or interpret the outputs of these systems (Yam et al. 2020 ).
AI refers to computers’ ability to learn from experience and perform human-like complex tasks, such as rational decision-making (Pomerol 1997 ; Wang 2019 ). A common trend emerging from AI definitions is that machines can perform complex human-like tasks based on algorithms and data in the workplace and society. AI’s purpose is, therefore, to imitate human cognitive functions like perception, learning, reasoning, and decision-making (Holford 2019 ; Lopes de Sousa Jabbour et al. 2018 ).
Therefore, the process of thematic analysis was interpretive, intending to identify themes and highlight links (Tranfield et al. 2003 ). The analysis began with a manual text study. Themes were developed inductively (Braun and Clarke 2006 ). They were linked iteratively, meaning that the analysis process entailed continual forward and backward movement in terms of constructing and refining themes (Braun and Clarke 2006 ).
The development of a coding book, intercoder reliability, and reproducibility are inconsistent with reflexive thematic analysis (Braun and Clarke 2022 ). Furthermore, multiple analyses are possible; however, the researcher selected and developed the themes most pertinent to the research question (Terry and Hayfield 2020 ).
When reviewing the selected articles, the researcher allowed the six phases of thematic analysis (Braun et al. 2019 ; Braun and Clarke 2006 ) to guide theming and anchoring data to themes. First, the researcher immersed himself in the selected articles by re-reading them to familiarise himself with the data. During this phase, by being curious, the researcher made casual notes of interesting statements, such as “tasks robots will dominate” (Wirtz et al. 2018 ), “robot-related up-skilling” (Lu et al. 2020 ), “robots often make mistakes” (Yam et al. 2020 ), etc. In the second phase, the researcher started anchoring statements from the selected articles to interesting codes such as “robot mistake”, “replacement fear”, etc. In the third phase, constructing themes, themes were “built, molded, and given meaning” (Braun and Clarke 2019 , p. 854), and they were the analytic output of an immersion process and deep engagement (Braun and Clarke 2022 ). The researcher explored latent meanings, connections, and possible interpretations such as “tasks robots will dominate” (Wirtz et al. 2018 ) and the uncertainties around robot-human tasks distribution, or “robot-related up-skilling” (Lu et al. 2020 ) and the perpetual race of upskilling and reskilling, or “robots often make mistakes” (Yam et al. 2020 ) and the idea of worker’s finding workarounds, etc. The researcher reviewed the candidate themes in the fourth and fifth phases and revised and defined them. Themes that were substantiated using the reviewed articles and were related to one another and the research question were kept (Braun and Clarke 2022 ). The researcher also shared the themes in research circles to enhance reflexivity and interpretative depth (Braun and Clarke 2022 ; Dwivedi et al. 2019 ). After that, in the sixth phase, the researcher used an iterative approach to report the themes with supporting references from the list of selected articles and relate the analysis to the research question (Braun and Clarke 2022 ). The researcher also presented the themes at the 37th EGOS Colloquium 2021. The researcher benefited from the participants’ feedback to further enhance reflexivity and interpretative depth (Braun and Clarke 2022 ; Dwivedi et al. 2019 ).
Using reflexive thematic analysis, a researcher can offer deeper levels of meaning and significance, analyse hidden interpretations and assumptions, and explore the implications of meaning (Braun and Clarke 2022 ; Byrne 2022 ). In the reflexive thematic analysis, themes are hence patterns of meaning anchored by a shared idea or concept (Terry and Hayfield 2020 ). They are generated, explored, and refined throughout iterative rounds rather than simply emerging from the data (Braun and Clarke 2022 ; Byrne 2022 ; Terry and Hayfield 2020 ). They are meaningful entities from codes that capture the essence of meanings from data rather than clusters of data, classifications, or summaries (Braun and Clarke 2022 ; Terry and Hayfield 2020 ).
The analysis adopted ‘Reflexive Thematic Analysis’ (Braun and Clarke 2019 , 2022 ). ‘Reflexive’ in this context highlights the role of the researcher in generating the themes (Braun and Clarke 2022 ; Terry and Hayfield 2020 ). This analysis method relies on the researcher’s interpretation and active engagement with the data considering the research question (Braun and Clarke 2022 ; Byrne 2022 ; Terry and Hayfield 2020 ).
The objective of the data analysis stage was to understand the selected list of articles (Tranfield et al. 2003 ). The data were organised into themes in an Excel spreadsheet (Braun and Clarke 2006 ).
Then, in the third stage, to identify articles not returned by the Scopus search string in the second stage, a review of the reference lists of the recent articles, among the articles returned by the Scopus database, was performed (Di Vaio et al. 2020 ). Following the exclusion and inclusion criteria, 65 studies matching the criteria were combined for this analysis.
A further reduction of the list was made by reviewing the abstracts of the articles in stage 2 above (Di Vaio et al. 2020 ). Articles had to satisfy thematic requirements to be included in the review. Articles that addressed the relationship between artificial intelligence and innovative work behaviour were retained for analysis. A careful reading of the articles’ abstracts helped create an excel table highlighting the relevance of each article to the topic (Di Vaio et al. 2020 ).
Journals and disciplines of selected articles (only journals with two or more articles)
The Scopus field code EXACTSRCTITLE () was used to limit the returned results to ABS-listed journals (Table 1 ). This field code was supplied with a list of ABS-ranked journals. Then, manual checking of journal titles was performed to verify the quality of the returned data to ABS-rated journals. A graphic representation of the process is shown in Fig. 2 .
When the Scopus database was searched, the returned results were limited to the last ten years (Fig. 1 ), articles in the English language, journal as a source type, articles as a document type, articles of ABS ranking, articles in the following subject areas: Business, Management, and Accounting; Social Sciences; Psychology; Arts and Humanities; and Decision Sciences.
The final list of articles was retrieved from the Scopus database. The breadth of publications was prioritised over the depth. The Scopus database provides this, as it covers peer-reviewed journals published by major publishing houses and goes beyond only influential journals (Ballew 2009 ; Burnham 2006 ; Sharma et al. 2020 ). Further, due to the nature of ‘artificial intelligence’ and ‘innovative behaviour’ concepts, an interdisciplinary field coverage was chosen, a strength of the Scoups database compared to other databases such as the Web of Science. While Google Scholar could have provided such interdisciplinary field coverage, compared to Scopus, it has significant drawbacks, including difficulty narrowing down search results, limited sorting options, questionable content quality, and difficulties accurately extracting meta-data from PDF files, to highlight a few.
Previous literature (e.g., Bos-Nehles et al. 2017 ; Charlwood and Guenole 2021 ; Farrow 2019 ) has used these keywords. Also, Truncation and Wildcards techniques were used to optimise the search string. Truncation and Wildcards are ‘search string broadening strategies’ to include various word endings and spellings. For example, in “innovative work behavi?r”, the Truncation symbol “?” in “behavi?r” was used to force the search string to return the British and American spellings of “innovative work behaviour.“ The search string used the Scopus field code ‘ALL()’ rather than ‘TITLE-ABS-KEY()’ to search document contents for the combination of the keywords. The search string returned documents that included the combination of the keywords in the entire body of the documents and the title, abstract, and keywords.
Three stages were followed to find studies on ‘artificial intelligence’ and ‘innovative behaviour’: First, a manual search was performed in Google Scholar using a combination of the two concepts. This search aimed to delve into the potential keywords for the data collection. Second, a search was performed in the electronic and multidisciplinary database of Scopus using a combination of the keywords: ALL( ( “artificial intelligence” OR “augmented intelligence” OR robot* ) AND ( “human innovati*” OR “human creativ*” OR “innovative work behavi?r” ) ).
The researcher formulated the research question, determined the keywords, and identified, collected, analysed and synthesised the relevant literature (Klein and Potosky 2019 ; Tranfield et al. 2003 ). The research began with a review of relevant literature on ‘artificial intelligence’ and ‘innovative work behaviour.’ The review question – how will AI in a workplace make workers innovative in their work? – allowed for a ‘concept-centric’ approach to the review, as mentioned in Method 1 below (Rousseau et al. 2008 ).
Analysis and interpretation
The analysis suggested several themes in terms of how AI might drive IWB. A tabular presentation of the themes and supporting references is shown in Table 2.
These themes are discussed in this section. Propositions are also included after each theme to encourage future research into the role of AI in driving IWB.
Robots make mistakes, and such mistakes stimulate workers’ IWB In this context, ‘robots’ refers to any intelligent systems in a workplace that emulate workers’ intelligence and abilities (Yam et al. 2020). To increase efficiency, organisations rely on robots (Yam et al. 2020). For instance, robots generated non-structured agreements in greater numbers than humans (Druckman et al. 2021). Negotiators working with a robotic platform were more pleased with the results and had more favourable views of the mediation experience (Druckman et al. 2021). These algorithmic approaches reduce human knowledge and meaning in the workplace (Holford 2019). However, different cultures can have different perspectives on robots. Americans, for example, were more critical of AI-generated content than the Chinese (Wu et al. 2020). One might argue that robots in a workplace interact with workers who may not be adequately trained to interact with them. Therefore robots need to relate to inexperienced instructions and feel normal to workers even in unstructured interactions (Scheutz and Malle 2018). However, autonomy and flexibility expose robots to a plethora of ways in which they can make mistakes, disregard a worker’s expectations and the ethical code of a workplace, or create physical or psychological harm to a worker (Scheutz and Malle 2018). A robot in an Amazon warehouse mistakenly ripped a can of bear repellent spray, resulting in the hospitalisation of 24 workers (Parker 2018). Microsoft’s robot editor used a photo of the wrong mixed-race member of a band to illustrate a news article about racism (Waterson 2020b). An AI robot camera, which was meant to monitor the football during a game, instead tracked the assistant referee’s head, resulting in sudden camera movements towards the referee and repeated switching between the referee’s head and the actual football (HT Tech, 2020). Although organisations increasingly depend on robots to improve efficiency, they frequently make mistakes, adversely affecting workers and their organisations (Yam et al. 2020). Also, algorithmic approaches fail to understand the distinct characteristics of human creativity and the tacit knowledge that goes with it (Chatterjee et al. 2021; Holford 2019). Therefore, an observation in the literature (Li et al. 2019; Wilson and Daugherty 2019) is evident in favour of workers shaping an organisation’s service innovation performance more than robots. Although this can come as a surprise, since robots are supposed to make fewer errors, workers’ IWB may benefit from robots’ mistakes (Choi et al. 2019; Yam et al. 2020). Delivery drivers, for example, can drive in real traffic while predicting events for a robotic autopilot (Grahn et al. 2020). Although robotic autopilot can manage predicted events in this scenario, workers can deal with uncertainty and related adaptive and social behaviours in specific, highly congested traffic conditions and environments (Grahn et al. 2020). If robots frequently fail to provide the expected service, workers would be pushed to think outside the box to eliminate robot mistakes. In this case, the role of robots in the workplace stimulates workers’ ability to think and innovate to overcome robot limitations (Klotz 2018; Wilson and Daugherty 2019). Manual assembly, for example, would place high demands on workers’ cognitive processing in a robotic-enabled workplace (Van Acker et al. 2021). If robots make mistakes (or due to the diverse nature of services), they will likely continue to stimulate workers’ IWB. Therefore, robots in the workplace (particularly in knowledge work) must prioritise collaborative approaches in which workers and robots collaborate closely (Fügener et al. 2022; Sowa et al. 2021). The interpretation of this theme is summed up in Proposition 1. Table 3 proposes relevant questions to investigate this proposition further. Table 3. Research questions for future studies Thematic area Research questions Robots make mistakes, and such mistakes stimulate workers’ IWB • Which touchpoints of the robot-customer relationship require worker innovative behaviour? • When and where should a robot be trained to disregard a worker’s expectations and the workplace’s ethical code? • Should workers be trained to interact with robots, or should robots be trained to interact with workers who are not adequately prepared to interact with them? • How can algorithmic approaches be designed to take advantage of human creativity and intuition? • How do we determine the severity of robot mistakes, and which types of robot mistakes necessitate innovative responses? AI triggers ‘fear’ in workers, and this ‘fear’ stimulates workers’ IWB • Will workers undergo a continuous cycle of reskilling and upskilling because of AI adoption in the workplace? • What is the impact of the fear of being replaced on what is considered meaningful work? • What cultural components, aside from family support, can help to alleviate the workers’ fear of being replaced? • In the context of AI adoption, how does the ‘fear of being replaced’ affect worker morale? Workers are reskilled and upskilled to compensate for AI limitations • How effective will AI-assisted personalised guidance on what skills workers need to retrain and upskill be? • How do the workplace skills that need to coexist with AI evolve? • Beyond reskilling and upskilling, what else is required for human-AI collaboration in the workplace? AI interface stimulates worker engagement • How do AI system usability and interface requirements differ from one worker to another? • How can companies account for the needs of high- and low-skilled workers when it comes to creating the interface of AI systems? • Will high-skilled workers react differently to AI interfaces than low-skilled workers? • What should organisations consider when building AI interfaces, beyond workers’ sensory and functional needs, considering diversity and inclusion in the workforce? Algorithmic bias requires IWB • How do companies decide how much discretion workers should have when overriding AI systems? • What safeguards should organisations build into AI systems to minimise workers’ discretion to overrule AI systems in situations where nepotism and wasta are common? AI works as a general-purpose tool for IWB • Will technologies such as 3D printing improve workers’ skills or encourage them to be more innovative in the workplace? • Will ‘real-world’ case studies reveal whether AI technologies inspire workers to reach wide and deep into their knowledge bases to generate novel ideas? • What do workers think of the statement that “because AI is taking over routine activities, they will have more time to plan on more innovative aspects of their work”? • While AI technologies have the potential to free up more time and space for workers to engage in idea generation and deliberation, how should businesses foster workers’ “willingness to innovate”? Open in a new tab Proposition 1 Robots make mistakes, and such mistakes stimulate workers’ IWB.
AI triggers ‘fear’ in workers, and this ‘fear’ stimulates workers’ IWB AI technologies in the workplace can reshape tasks and the definition of work across businesses (Braganza et al. 2020). This reshaping eliminates certain jobs or parts of an automated job (Braganza et al. 2020). As a result, algorithmic approaches aim to reduce various forms of human involvement and interpretation in the workplace(Holford 2019). Self-service technology (SST) and the Wizard-of-Oz (WOZ) experiment, for example, can demonstrate that the contribution of workers to services that incorporate these technologies has been reduced (Costello and Donnellan 2007). Moreover, rather than directly influencing worker productivity, AI technologies indirectly influence the development of new, modified, or unmodified worker routines (Giudice et al. 2021). Therefore, it is generally accepted that workplace AI threatens the continuity and security of workers’ jobs (Rampersad 2020). It is also projected that AI applications will take over full-time and permanent jobs while workers will be hired for short-term assignments (Braganza et al. 2020). Therefore, considering the adoption of technological transformation, uncertainty about the employment of workers appears to be an integrated feature of AI systems (Costello and Donnellan 2007). This threat is genuine for jobs requiring repetitive motion, data management and analysis, repeated physical equipment control, and individual evaluative interaction (Chuang 2020; Lloyd and Payne 2022). This argument, however, does have limitations. Although there is a persistent fear of job loss (Sousa and Wilks 2018), the academic literature (e.g., Jaiswal et al. 2021; Wilson and Daugherty 2019) suggests a drive toward a more symbiotic synthesis of human-machine competencies. This drive takes a more proactive approach to AI adoption in the workplace, encouraging businesses to be cautious in treating their workers (Li et al. 2019). This body of literature also argues that businesses should actively protect workers’ interests and cautiously implement technology that supports rather than replaces them (Li et al. 2019). What can be suggested is that this line of thinking implies uncertainty about future employment for workers (Li et al. 2019). This uncertainty, however, may encourage workers to actively engage in service innovation (Li et al. 2019). And if an organisation can afford AI solutions, it is suggested that their workers’ IWB be enhanced by training (Klotz 2018; Wilson and Daugherty 2019). While AI systems help workers in innovation (Candi and Beltagui 2019; Verganti et al. 2020), the ‘fear’ factor may trigger IWB in workers. This factor theoretically benefits workers in their quest for a long-term career and assists organisations in surviving through the transition time by safeguarding the interests of their workers (Haefner et al. 2021; Li et al. 2019). To stay relevant in a workplace where AI is reshaping human employment, workers must enhance their IWB (Klotz 2018; Rampersad 2020; Sousa and Wilks 2018). To coexist with intelligence systems, workers can capitalise on critical thinking, problem-solving, communication and teamwork (Rampersad 2020; Sousa and Wilks 2018). Therefore, workplace AI can stimulate IWB in workers because of improved workplace technology and the uncertainty and insecurity of jobs it brings to workers (Jiang et al. 2020; Nam 2019). ‘Fear’ of being replaced by robots or artificial intelligence, or ‘aliennational psychological contacts’ may trigger IWB in workers rather than the actual technology (Braganza et al. 2020; Kim et al. 2017). This discussion may also suggest that developments in AI systems, regardless of industry or context, stimulate IWB in workers if workers perceive a threat from these systems. However, when discussing the ‘fear’ of being replaced, the human culture’s underlying position on workplace technology should be considered (Shujahat et al. 2019). How workers perceive AI in the workplace through a cultural lens could indicate how much the ‘fear’ factor is perceived (Wu et al. 2020). For example, perceived wider family support might help alleviate the ‘fear’ factor (Tu et al. 2021). Though tailored approaches to mitigate the ‘fear’ factor of AI technologies and to put workers at the centre of workplace technologies are advised (Kim et al. 2017; Palumbo 2021), artificial intelligence is increasingly being adopted by organisations without careful consideration of the workers who will be working alongside it. Uncertainty regarding the employment of workers continues to be an inherent feature of AI systems being adopted (Costello and Donnellan 2007). Proposition 2 summarises this understanding. Table 3 proposes relevant research questions to investigate this proposition further. Proposition 2 Workplace AI triggers ‘fear’ in workers, and this ‘fear’ stimulates IWB in workers as a means of staying relevant in the workplace.
Workers are reskilled and upskilled to compensate for AI shortcomings By rendering processes more scalable, broadening the scope of operations across boundaries, and enhancing workers’ learning abilities and flexibility, AI technologies enable organisations to transcend the limitations of human-intensive processes (Verganti et al. 2020). As per this interpretation, AI technologies encourage a people-centred and iterative approach (Henkel et al. 2020; Verganti et al. 2020). However, one might argue that AI supports human-based approaches to user-centred solutions by requiring workers to reskill and upskill (Verganti et al. 2020; Wilson and Daugherty 2019). Therefore, while AI technologies can potentially reshape working arrangements, they can also facilitate workers’ reskilling and upskilling (Kim et al. 2017; Palumbo 2021). Further, human oversight of AI applications can encourage reskilling and upskilling of workers (Brunetti et al. 2020; Xu and Wang 2019). Human supervision is often needed with AI applications to ensure that biases are not propagated (Sowa et al. 2021). For instance, although AI robot lawyers can perform certain tasks, such as answering legal questions, it has been argued that working with human lawyers ensures nuanced issues are adequately handled (Xu and Wang 2019). As a result, the human-AI collaboration focuses on situations in which humans and AI collaborate closely (Sowa et al. 2021; Wilson and Daugherty 2019). This interpretation explains that “algorithmic approaches,“ which aim to reduce human intervention in processes, could mean that while mundane and manual skills are delegated to robots, workers hone skills through reskilling and upskilling that robots still lack (Holford 2019; Kim et al. 2017). Workers, therefore, can reskill and upskill themselves because of the introduction of AI in the workplace (Jaiswal et al. 2021). When upskilling and reskilling combine data with human intuition to make the most of AI technological advances, algorithms can positively impact workers’ work and discretionary power (Criadoa et al. 2020; Jaiswal et al. 2021). However, not only practical and technical knowledge and skills are necessary for that purpose, but also creativity-focused skills such as managing uncertainty, critical thinking, problem-solving, exploring possibilities, tolerating ambiguity, exhibiting self-efficacy, teamwork, and effective communication (Cropley 2020; Rampersad 2020). Recent literature (Jaiswal et al. 2021) also includes data analysis, digital, complex cognitive, decision-making, continuous learning skills, and creativity-focused skills. The necessary skill set for upskilling and reskilling can fall into three categories: technical, human, and conceptual skills (Northouse 2018). Also, one might argue that upskilling and reskilling might mean workers are given AI-related short-term assignments when full-time and permanent assignments transfer to AI applications (Braganza et al. 2020). This discussion may also mean that to stay relevant, workers would go through a cycle of upskilling and reskilling, with the required skills evolving (Gratton 2020; Ransbotham 2020). Workers must also be challenged and empowered to generate and implement new ideas (Auernhammer and Hall 2014). Therefore, though workplace AI requires upskilling and reskilling (Braganza et al. 2020), workplace AI places high demands on workers’ cognitive processing (Van Acker et al. 2021). Gamification and simulations help workers understand the feasibility of ideas by collaborating in cross-functional teams and being involved in the active development and auditing processes of AI applications (Anjali and Priyanka 2020; Criadoa et al. 2020). Consequently, human-AI collaboration is needed to overcome AI limitations in the workplace. For human workers to stay relevant in such human-AI collaboration, they need to upskill and reskill. Proposition 3 summarises this understanding. Table 3 proposes relevant research questions to investigate this proposition further. Proposition 3 Workers can be reskilled and upskilled because of human-AI shortcomings in the workplace.
AI interface stimulates worker engagement When AI technologies take a people-centred approach, there are indications in the literature (e.g., Wilson and Daugherty 2019) that the interface of AI systems can stimulate IWB in workers rather than the frequent errors of service robots (Yam et al. 2020). Earlier scholars (e.g., Davis 1998) agree that the interface design of technologies provides users with a means to interact with such technologies. For instance, Wilson and Daugherty (2019) report that medical professionals became medical coders in training workplace AI when the interface was user-friendly. The software interface, in this example, allowed medical coders to work with workplace AI and be involved in the AI solution’s much-needed training (Wilson and Daugherty 2019). However, it is essential to mention that usability and interface design requirements may change from one worker to another (Massey et al. 2007). It is also possible to infer that high-skilled workers are better at interacting with AI systems (Shute and Rahimi 2021). Therefore, a high-skilled worker may have different expectations from an AI system’s interface design regarding usability and responsiveness than a low-skilled worker (Garnett 2018; Krzywdzinski 2017). While a ‘responsive’ and ‘usable’ interface is proposed to stimulate IWB in workers, the judgement of a ‘responsive’ and ‘usable’ interface is impacted by complex interactions (Garnett 2018; Massey et al. 2007). The current research (Massey et al. 2007; Wilson and Daugherty 2019) suggests that a ‘responsive’ and ‘usable’ interface, for example, to enter data into AI-systems, requires workers’ perspective on interface design. In the workplace, recent research (Beane and Brynjolfsson 2020; Yam et al. 2020) also suggests that when robots are human-like—capable of thinking and feeling—workers evaluate them more favourably. This favourable perception can be explained by the fact that workers like assisting others and sharing their knowledge (Lo and Tian 2020; Yam et al. 2020). Human-like robots may offer humans such feelings (Giudice et al. 2021; Lo and Tian 2020). However, the interface must meet workers’ sensory and functional needs (Massey et al. 2007). Therefore, designing such interfaces for workplace AI requires workers’ perspectives and understanding (Massey et al. 2007). While this extends beyond the interface design of an AI system (Massey et al. 2007), it suggests that intelligent systems can only encourage innovative behaviour if workers feel linked to them (Wilson and Daugherty 2019; Yam et al. 2020). This line of argument is extended further by proposing that a ‘responsive’ and ‘usable’ interface allows workers to conveniently enter data into an AI system and make sense of the systems’ outputs through such interface design (Verganti et al. 2020). The interface should clarify which issues need to be addressed and how the outputs should be used (Verganti et al. 2020). If workers can make informed decisions using such an interface, the interface may encourage IWB (Verganti et al. 2020). If AI systems have ‘friendly’ interface designs, responsive and usable, one may conclude that workplace AI stimulates IWB in workers. This understanding is summed up in Proposition 4. Table 3 proposes relevant research questions to investigate this proposition further. Proposition 4 When workplace AI has a user-friendly interface design, it encourages workers to generate and implement ideas.
Algorithmic bias requires innovative behaviour Algorithmic bias may come from institutionalising existing human biases or introducing new ones (Albrecht et al. 2021). Implementing a machine learning model, for example, might be pitched to improve the satisfaction of employees and consumers. However, how do you define ‘satisfaction’ specifies the algorithm’s inner workings and the desired output (Akter et al. 2021; Stahl et al. 2020). And when AI technologies inform (or make) a decision that affects a human being (i.e. a worker), such bias has a detrimental effect, resulting in discrimination and unfairness (Akter et al. 2021). For instance, based on data obtained from male CVs, Amazon’s AI recruitment system did not rate candidates gender-neutrally (BBC, 2018). Facebook’s Ad algorithm allowed advertisers to target users based on gender, race, and religion, all of which are protected classes (Hern 2018). An algorithm the UK’s Home Office used in visa decisions was dubbed “racist”; the algorithm focused on an applicant’s nationality (BBC, 2020). Hence, although organisations are increasingly using AI systems and algorithms, the accessibility and interpretability of algorithms (algorithmic transparency) have drawn attention to the organisation’s algorithmic footprint (Criadoa et al. 2020). As a result, innovative behaviour in the workplace entails addressing the ethical, social, economic, and legal aspects of AI applications (Di Vaio et al. 2020). While workers may still use discretion to override AI systems, biases in datasets and ambiguous algorithms force workers to rely on IWB to help AI systems make choices (Criadoa et al. 2020). Therefore, targeted human interventions are required to resolve the limitations of biases in a dataset and ambiguous algorithms (Palumbo 2021). Proposition 5 summarises this theme. Table 3 proposes relevant research questions to investigate this proposition further. Proposition 5 Algorithmic bias necessitates innovative behaviour to overcome the limitations of biases in a dataset and ambiguous algorithms.
| 2023-02-09T00:00:00 |
2023/02/09
|
https://pmc.ncbi.nlm.nih.gov/articles/PMC9910241/
|
[
{
"date": "2023/02/01",
"position": 3,
"query": "workplace AI adoption"
},
{
"date": "2023/02/01",
"position": 20,
"query": "AI workers"
},
{
"date": "2023/02/01",
"position": 26,
"query": "artificial intelligence layoffs"
}
] |
AI-Driven Recruitment Trends #1 | Will AI Replace Human ...
|
AI-Driven Recruitment Trends #1 | Will AI Replace Human Workforce in the Future?
|
https://www.linkedin.com
|
[
"Hind Benabbou",
"Saikat Gupta",
"Anthony M. Gonzales"
] |
5. Social impact: The widespread adoption of AI in the workplace could have a significant impact on society. Job displacement could lead to economic hardship ...
|
Artificial Intelligence (AI) has the potential to automate many tasks that are currently performed by humans, and it is possible that it could replace certain jobs in the future. However, it is important to note that the impact of AI on the workforce is a complex issue and there are a number of factors that could affect the pace and extent of job displacement.
On one hand, AI has already begun to automate many routine and repetitive tasks, such as data entry, manufacturing, and transportation. As the technology continues to improve, it is likely that it will be able to automate more complex tasks that were previously thought to be exclusively performed by humans.
At the same time, AI is also creating new jobs in areas such as data science, machine learning, and robotics. As businesses and industries adopt AI, they will need skilled workers who can design, implement, and manage these systems.
Intellectual challenges of AI
While it is possible that AI could replace certain jobs in the future, it is also important to note that there are many tasks that require the emotional intelligence, creativity, and critical thinking skills of human workers. Jobs that involve complex decision-making, interpersonal communication, and creative problem-solving are less likely to be automated by AI.
Furthermore, there are also ethical and social considerations to be taken into account when considering the impact of AI on the workforce.
Possibilities of future automation
If AI were to take the position of the human workforce in the future, it would have a significant impact on society and the economy.
Here are some possibilities, let’s take a tour of these.
1. Ethical concerns: With the increasing capabilities of AI, there are growing concerns about ethical issues related to its use. For example, biased algorithms could perpetuate existing societal inequalities, and the use of AI in surveillance could infringe on individual privacy rights.
Additionally, there are concerns about the potential misuse of AI for malicious purposes, such as cyber-attacks or the creation of autonomous weapons. As AI continues to evolve, it will be important for society to address these ethical concerns to ensure that its development and use are aligned with human values and principles.
2. Increased productivity: One potential benefit of AI in the workplace is increased productivity. AI can work faster and more accurately than humans, and it can perform certain tasks 24/7 without needing breaks or rest. This could lead to increased efficiency and output in industries such as healthcare, finance, and logistics.
3. Cost savings: Another potential benefit of AI in the workplace is cost savings. Automating tasks with AI can reduce labor costs and improve efficiency, leading to lower costs for businesses and consumers.
4. Skills gap: As certain jobs are automated, there may be a growing skills gap in the workforce. Workers who do not have the skills or education needed to work with AI may find it difficult to find employment in industries that are adopting this technology.
5. Social impact: The widespread adoption of AI in the workplace could have a significant impact on society. Job displacement could lead to economic hardship and social inequality, while increased productivity and cost savings could lead to greater prosperity for some. The impact on society will depend on how the benefits and costs of AI are distributed.
Conclusion
In conclusion, if AI were to take the position of the human workforce in the future, it would have a significant impact on society and the economy. While there are potential benefits such as increased productivity and cost savings, there are also risks such as a skills gap and social inequality. It is important to consider these possibilities and take steps to ensure that the benefits of AI are distributed fairly and that workers are not left behind as industries change.
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| 2023-02-01T00:00:00 |
https://www.linkedin.com/pulse/new-age-automation-ai-replace-human-workforce-future
|
[
{
"date": "2023/02/01",
"position": 16,
"query": "workplace AI adoption"
},
{
"date": "2023/02/01",
"position": 14,
"query": "AI labor market trends"
}
] |
|
How Society is Adopting Artificial Intelligence | by Giles Crouch
|
How Society is Adopting Artificial Intelligence
|
https://gilescrouch.medium.com
|
[
"Giles Crouch",
"Digital Anthropologist"
] |
We're also sharing how, when, where and why we are using GAI tools via social media, within workplace networks, in news media. ... adoption of AI into more ...
|
Image by Engin Akyurt from Pixabay
Artificial Intelligence (AI) as I wrote a short while ago, is having its moment in our world. AI has been lurking in the background, doing certain things, sometimes with a burst of publicity, then slouching off into the background of life. This is no longer the case.
As a technology anthropologist who’s been analysing and studying how humans, cultures and societies create, adopt and use technology, this is a fascinating and exciting time.
In today’s digital world where we are hyper-connected and mass communication almost instantaneous, we go into a phase of hype around that new technology. This happened with social media. With the rise of AI tools that we can truly understand however, we’re not in a hype phase, we’re in a hysteria phase. Why and why is this different?
Throughout human history, new technologies have taken a long time to be absorbed, adopted and adapted to various cultures. Different societies took their time to figure out the way they wanted to use a new technology.
Adoption sped up over time too. This speeding up started with the printing press and the coinciding of transportation technologies as in better ships. So what is different about these new AI tools?
A few factors have come together. First, the obvious, mass communication technology at super fast speeds. More societies are connected than even when social media burst onto the scene. More smartphones too. Secondly, we saw how these new AI tools affected us not just in one part of our society, but so many parts. This is unusual for a new technology. And thirdly, these AI tools, specifically Generative AI (GAI) slammed like a bullet train into multiple sociocultural systems all at once. This is an usual mixture.
This is also causing us to ask in a louder voice, the deeper philosophical questions around AI. Questions that have been hanging around for a while, but not with as much earnestness and anxiousness as today. What’s going on?
Usually, a new technology impacts one area of a society, such as railways did, then it moves out into broader society. Initially, railways were built to enable faster transport between coal mines and cities in the UK. Then some entrepreneur realised cattle, goods and people could also be moved. And money could be made. Clever. It lead to some social unrest as all revolutionary technologies have done to varying degrees.
What we are witnessing now is the adoption of a revolutionary technology at an unprecedented scale. We are experimenting with it in many parts of society; industry, law, politics, geopolitics, religions, art, music, literature, knowledge work, medicine, science, economics.
We’re talking about it, playing with it, experimenting and figuring out how we want to use AI at an unprecedented pace. Generative AI tools are easy to use and can be accessed almost anywhere in the world at any time on multiple devices. This too, has never happened with a revolutionary technology before.
We’re also sharing how, when, where and why we are using GAI tools via social media, within workplace networks, in news media. Capital too, is moving faster into the AI sector than it did in the first .com boom of the late 90’s and early 00's.
This is why it’s more a hysteria phase than hype. That’s not a negative. What it means is that we are likely to see a much faster adoption of AI into more societies than any previous technology. There are risks. As with any technology there are unintended consequences. We already see that ChatGPT and others can generate their own false facts. That students have used it to cheat on writing papers. But we also saw how quickly some clever people have created tools to detect AI generated content. Marketers have already generated massive amounts of marketing content that is largely meaningless. This is all part of the adoption process.
The very speed at which we are adopting and figuring out how to use these new AI tools will bring dangers but it will also result in better outcomes in the long run. The interesting thing is that we’re teaching one another, using cultural tools such as behaviours, norms and traditions, how we want AI to be part of a global society. We’ve never done this before. This is as big a game changer as the invention of stone tools, but at an almost instantaneous speed at scale.
| 2023-02-10T00:00:00 |
2023/02/10
|
https://gilescrouch.medium.com/how-society-is-adopting-artificial-intelligence-f3989e3df449
|
[
{
"date": "2023/02/01",
"position": 18,
"query": "workplace AI adoption"
}
] |
Assessing the artificially intelligent workplace: an ethical ...
|
Assessing the artificially intelligent workplace: an ethical framework for evaluating experimental technologies in workplace settings
|
https://link.springer.com
|
[
"Hosseini",
"Z.Hosseini Tue.Nl",
"Department Of Industrial Engineering",
"Innovation Sciences",
"Eindhoven University Of Technology",
"Eindhoven",
"The Netherlands",
"Nyholm",
"S.Nyholm Lmu.De",
"Philosophy"
] |
by Z Hosseini · 2024 · Cited by 27 — This paper aims to sketch a set of ethical guidelines for introducing experimental technologies into workplaces.
|
As mentioned above, we will here translate the five principles of Van de Poel's ethical framework into ethical principles for new technologies in the workplace. As a case study, we focus on logistics warehouses. Our approach to translating Van de Poel's general framework to the ethics of technology in the workplace is to relate Van de Poel's general principles to specific ideas about the distinctive harms of work and distinctive goods of work, as discussed by authors like Gheaus & Herzog [24], Anderson [3], Danaher [15] and Smids et al. [52]. Accordingly, we focus especially on the non-maleficence and beneficence of work, i.e., particularly on the first and the second of the principles Van de Poel discusses. Our two most important questions below, therefore, concern what work-related harms should be avoided when experimenting with technologies in workplaces and what goods of work should be promoted when experimenting with technologies in workplaces.
4.1 Non-Maleficence of work
4.1.1 Harms of work
The first principle of Van de Poel's framework emphasises that any harm should be prevented. To apply this idea to the context of work, we need to operate with some conception of what potential harms might be related to features of work for most workers (as opposed to harms not specifically related to work). One good way of approaching the question of what harms might be particularly related to work is to look at academic publications offering critical perspectives on work. We will here consider some different claims from the literature about the potential harms of work, with a particular focus on John Danaher's "reasons why you should hate your job", as presented in his 2019 book Automation and Utopia [15].
For example, Danaher [15] states that for many people in many sectors, work is a sometimes unjust, potentially freedom-undermining activity, with rules set by the organisation that workers in a modern corporation typically must simply follow and not question (see also [3]). As employers have control over the tasks and work schedules, employees must act within parameters that are determined by the employer(s). They must ask for permission whenever they want to do something outside those parameters. Strikingly, even though many people spend most of their time preparing for, performing, or recovering from work, only 15 percent of the workers are engaged, highly involved, and enthusiastic about their work and workplace, according to a global survey by the Gallup Institute [21]. Presumably, part of what explains this survey's finding is that people do not like being bossed around at a workplace in which they have very little freedom, which is also suggested by Parker et al. [42, 44].
Moreover, as a result of technological innovations, such as online platforms, the structure of work in contemporary society is changing. According to Danaher [15], working conditions are becoming more precarious for most workers. Instead of having permanent employment contracts, they often have less secure employment, and fewer benefits and protections. This could not only lead to an unpleasant and stressful working life, but also to increased income inequality, since economic rewards are not distributed fairly but mostly go to employers and technology suppliers [15]. An example of a digital platform is Deliveroo where people offer their services. This platform wanted to classify their employees as independent workers to avoid any duty to pay them a minimum wage or holiday pay, and they won this case [25]. This can be partly explained by technological changes being mostly ahead of legislation. For certain working conditions, roles, and values regarding employee rights, legislation may not exist, or these are not recognised yet.
New technology may also inhibit employee learning. Decisions that are made based on AI algorithms leave little room for employees to learn from mistakes and improve their work, since in most cases it is unknown how the algorithm came up with a certain decision [38]. This might lead to employees having a limited understanding of what happens in the workplace and why. Losing the skill and knowledge of making informed decisions on their own might undermine their work-related autonomy, whereas autonomy is related to positive work-related behaviour. Autonomy in the workplace is important since it enhances meaning and motivation at work, which in turn, promotes job performance, proactivity, and reduces turnover and absenteeism [8, 43, 52]. To give another example, deploying semi-autonomous robots in a real-world environment has demonstrated that these robots sometimes fail to complete their tasks by themselves. According to workplace observations by Rosenfeld et al. [50], human operators had to support robots when they could not solve a problem. This might result in a situation wherein employees have to perform both their own primary tasks and the tasks of a robot at the same time, and this implies that employees might end up with a high (cognitive) workload.
Moreover, with various tracking devices (e.g., wristbands), employers can even monitor their employees outside of work [15] since some employers ask their employees to track and share what would normally be regarded as private information, such as information about their health and sleep quality. Even if these tracking technologies are purportedly voluntary, employees may feel pressured and obliged to participate, because they do not want to stand out. Tracking and monitoring can be harmful to individuals' privacy [60]. According to a global survey among more than 250 HR leaders and employees, almost half of the employees do not trust their employers to protect their data, and these employees also worry about insecure software, a lack of transparency about how data is being protected, and whether the data is used for good or bad [30]. Furthermore, Newell and Marabelli [38] argue that surveillance might undermine employee motivation and ability to innovate.
In short, to respect the general non-maleficence principle as applied to the context of work when we introduce new technologies into the workplace, we need, among other things, to make sure that these new technologies do not (a) make people even less free at work, (b) do not significantly worsen employees’ working conditions and job security, (c) do not undermine learning and work-related autonomy, (d) do not potentially overload employees with more work than they had to do before without extra compensation, and (e) do not interfere with workers' privacy in unjustifiable ways. These are some examples of important potential harms that can be associated with work, and that the introduction of new experimental technologies needs to guard against to live up to the work-related non-maleficence principle. Having made these first suggestions about how the non-maleficence principle could be translated to the work context, let us now consider the particular case of logistics warehouses. This will also help to highlight risks of physical harms related to working with experimental technologies, which are of course also important to guard against in the ethics of experimental work technologies.
4.1.2 Potential harms of working with technologies in warehouses
Some of the leading new technologies which are being or will be adopted in today's logistics warehouses are collaborative robots, exoskeletons, wearable sensors, and virtual reality (VR)/augmented reality (AR) [8, 13]. Some of these technologies might entail risks of physical harm. For example, the malfunctioning of exoskeletons and dangerous movements of cobots in employees' vicinity could have harmful effects on employees' physical health [46], like Amazon's robots which brought products faster to employees, making them move and lift faster, without leaving much room for the workers’ muscles to rest.Footnote 5 Accordingly, while some of these technologies have the potential to enhance logistics workers' work capacity, over-extending their use can also lead to physical harm.
Moreover, some of the above-mentioned technologies in warehouses may prompt changes in tasks, such as breaking a job into subtasks or completely taking it over from employees. Autor [5] argues that between 1980 and 2015 particularly routine work (e.g., repetitive, routine, or physical tasks) was greatly affected by technology since these tasks were easy to automate. Consequently, technology could reduce employees' skill use. Especially in logistics warehouses, this might lead to employees moving from active use of skills to mostly passive monitoring. In turn, this might make work less meaningful, and insofar as reducing the meaningfulness of work is viewed as a form of harm, making work more passive can be seen as a risk of harm [24].
Other risks prominent in the logistics work context include that surveillance technologies are frequently used to control employees' performance, which may be seen as freedom-undermining or dominating, and which could in addition have negative consequences for employee morale and job satisfaction, and mixed effects on their performance [43]. An example is picking tools that use algorithms to track, analyse, and inform workers about their performance during the picking processes (e.g., the time it takes reaching a picking location, scanning, and selecting a product, and putting it in a bin). These data might furthermore encourage companies to micromanage employees and increase their work pressure [15, 60].
4.2 Beneficence of work
4.2.1 Benefits of work
The second main principle, beneficence, refers to the notion that one should not only avoid harm, but also proactively seek to do good and promote social benefits. While some authors primarily argue that work has many negative aspects associated with it (e.g., [15], other authors argue that work offers key opportunities for achieving many important goods, including goods “other than money”, as Anca Gheaus and Lisa Herzog [24] put it. These goods are often associated with what is thought of as “meaningful work” [8, 58], and with respect to some of those goods, the beneficence principle can be translated into conditions for enabling technologies to make work more meaningful for people.
A recent paper by Smids et al. [52] identifies five distinctive 'goods' of meaningful work, namely the following:
a) pursuing a purpose b) collegiality/social relationships c) exercising skills and self-development d) self-esteem and recognition, and e) work-related autonomy.
Here is a brief explanation of these five goods of meaningful work. Firstly, work may provide employees a purpose to pursue and allow them to positively contribute to their field of work (“pursuing a purpose”). Secondly, work is a place where one has the opportunity, and for many people, the most easily accessible opportunity, to build relationships with others. Interacting and collaborating with colleagues also makes work meaningful (“collegiality/social relationships”). Thirdly, work allows employees to develop, exercise, and learn (new) skills. The process of mastering skills is very rewarding for most people [15, 24] (“exercising skills and self-development”). Fourthly, people not only work to earn money, but also to contribute to society, and to attain goods such as social recognition from others [24], which in turn has positive effects on their self-esteem (“self-esteem and recognition”). Finally, work also enables employees to shape their tasks and participate in decision-making processes, which has a positive impact on meaningful work (“autonomy”).
If we accept these suggested distinctive goods of work as being markers of meaningful work for the sake of the argument, the crucial question becomes whether, and if so how, experimental technologies might be used to promote these goods of work. In other words, workplaces that strive to live up to the principle of work-related beneficence, even as they introduce experimental technologies into the workplace, need to explore ways in which the use of those technologies could be compatible with, or directly promote, the five just-reviewed goods of meaningful work.
Importantly, the extent and opportunity to achieve these benefits could differ between sectors, types of work, and the status associated with particular jobs. As noted above, working with new technologies often changes the way people work. Experimenting with new technologies in the workplace, for this reason, might ideally allow humans to outsource tedious tasks to the technologies and instead focus on more stimulating tasks. For example, Lin et al. [31, p.21] argue that when technologies 'are particularly good at highly repetitive simple motions, the replaced human worker should be moved to positions where judgment and decisions beyond the abilities of robots are required'. In other words, when new technologies help out, assist with, and take over the ‘dull, dirty, and dangerous’ tasks, human workers can focus on more challenging and stimulating tasks, which might include tasks such as the coordination or supervision of the robots and handling problems, such as breakdowns and reparation of new technologies [52]. Accordingly, handing over tedious and repetitive tasks to technologies, and instead being given more challenging tasks, might help to give employees a stronger sense of having work that involves the pursuit of a valuable purpose.
It is less clear whether experimenting with technologies in the workplace could positively promote collegiality or social relationships at work. And so when it comes to the second key good of work identified by Smids et al. [52], a main goal for the technologically experimental workplace might simply need to be to make the use of the new technologies compatible with retaining and fostering good and collegial relationships within the workforce. It is worth mentioning, however, that some people who work with robots have been observed to form what appears to be social bonds with these robots – e.g., military personnel have been observed to become attached to bomb disposal robots – and some philosophers have begun discussing whether robots could ever be considered to be good colleagues [39]. Yet, for most types of workplaces, the main goal for the beneficent employer is likely to be to not allow the new technologies they introduce into the workplace to undermine employees' opportunities to have good relationships with their team members.
Let us next consider the third key good of work, namely skills and self-development. When employees can expand their tasks and enhance their skills as a result of the introduction of new technologies into the workplace, system performance as well as employee' well-being may very well end up being enhanced. According to Smids et al. [52], employees may be able to make significant changes in their tasks with the use of AI and robots (and other technologies). Since employees may potentially have the opportunity to modify and craft their job, their modified tasks (e.g., with more responsibilities) will have a clear purpose (to refer back to the first good of work again), and will seemingly be more meaningful.
This could then also potentially promote the fourth good of meaningful work: self-esteem and recognition in the workplace. Enabling successful work with new technologies might require higher educational attainment, the development of new social and emotional skills, enhanced creativity, and the exercise of high-level cognitive capabilities and other skills which might be hard to automate. In ideal circumstances, introducing new technologies into workplaces should – to also bring up the fifth good of work – involve experimentation that seeks to boost and provide opportunities for exercising work-related autonomy. A more minimal goal that the beneficent employer might have here, though, would simply be to avoid having experimentation with new work technologies threaten the work-related autonomy of the employees in the workplace – that is, the beneficent employer should, at least, strive to not make work any less autonomous because of the introduction of new technologies that they are experimenting with in the workplace.
4.2.2 Benefits of working with new technologies in warehouses
One of the potential benefits of the most-used new technologies in warehouses, such as cobots, could be that they can perform multiple tasks (e.g., packaging) alongside human warehouse employees, instead of displacing them. Robotic exoskeletons can facilitate human employees with upper limb movements, such as reaching, grasping, or lifting objects [46]. New technologies might also help to train or otherwise support the performance of employees. For example, Virtual Reality (VR) refers to systems where 'the input from the outside world is blocked and replaced by a system-generated input' [45, p.2]. One can potentially use VR technology to train warehouse employees to decrease their grabbing times and mistakes while order picking, or, to use another example, train warehouse truck drivers on how to avoid dangerous situations in a virtual setting. Augmented Reality (AR) adds virtual elements, items, or information in real-time to the physical world. Since order picking is one of the most important tasks in the logistics warehouses, employees can be supported with additional information for faster object location by using AR technologies to avoid errors (Cirulis & Ginters, 2013) and assist with the planning of logistics systems [47].
Thus, these above-mentioned technologies may potentially benefit employees by providing them (physical) assistance and prevent them from strain when performing physically demanding tasks [36, 68] As noted earlier, however, the overuse of these kinds of technologies might put an excessive strain on employees. Yet, if used in the right way, they could boost logistics workers' performance, while putting less strain on their muscles.Footnote 6
4.3 Responsibility, respect for autonomy, and justice
The last three principles of Van de Poel's framework and how they might be translated into the work context will now be discussed much more briefly since (a) our main focus in this paper is on the potential harms and goods of work and (b) doing complete justice to the last three principles in this context would require a much longer discussion than we can fit into an article of this format. Moreover, since we identify threats to work-related autonomy as a potential harm to work and protecting and boosting work-related autonomy as a key good of work, we have in effect already briefly discussed the principle of autonomy to some extent above. Additionally, some of the potential harms of work that Danaher identifies and that we have discussed above also relate to potential (in)justice issues related to work, so we have therefore also briefly addressed justice in the workplace above. Here, however, are some brief further reflections on the principles of responsibility, autonomy, and justice as they relate to the ethics of experimental technologies in the workplace.
The third principle, responsibility, states it should be clear who (e.g., the technology developer, manager, senior employee and/or project leader) is going to be responsible for what aspects of the introduction and the use of new technologies in the workplace. This may include making clear whose responsibility it is to ensure compliance with key ethical standards when experimenting or implementing new technology in the workplace. This means that the persons who bear this responsibility should, among other things, reflect on the potential negative consequences of new technologies in the workplace and also take responsibility for their decisions when doing their tasks (taking the principles of this framework into account).
A key topic that is frequently discussed within the ethics of technology more generally that is also of relevance here is the worry that some forms of advanced technologies—particularly different forms of AI, automated systems, or advanced robots—might create so-called responsibility gaps [16, 35]. That is to say, if and when tasks that were previously performed by human beings are handed over to technologies—e.g. to AI systems—and those were tasks for which the human beings in question were responsible, this might create unclarity about who is responsible for the performance of those tasks, as well as who is responsible for any good or bad outcomes that might result from the performance of the tasks in question.
This is often discussed with an eye to who, if anybody, would bear responsibility for any negative outcomes—such as harms or damages—that might be caused by autonomously operating technologies (e.g., [35]. But as Danaher and Nyholm [16] argue, in workplaces where people seek positive recognition for the good work they do or for good outcomes that are achieved, outsourcing tasks previously performed by human beings to technologies might also give rise to worries about ‘achievement gaps’. That is, if new technologies take over what were previously human tasks, there might come to be fewer opportunities for human beings to perform work that might be seen as genuine achievements that are worthy of praise.
Accordingly, experimentation with new technologies in workplaces ought—from the point of view of the third principle—to be done in a way that does not give rise to undesirable responsibility gaps of different kinds. Instead, the use of technology in the workplace ought to be done in a way that is compatible with clarity about who is responsible for what—both in the sense of responsibility for any harms or damages that might be produced in the workplace and in the sense of recognition for good outcomes that might be achieved in the workplace.
The fourth principle—respect for autonomy—refers to the ethical requirement that human subjects' autonomy and agency should be taken into consideration. As already noted, our discussion above has already covered issues related to workplace autonomy. But here we can also very briefly relate the discussion back to Van de Poel and his sub-conditions related to the principle of autonomy as he understands it. These mainly concern that human subjects are informed about the risks and benefits of the given kinds of technology, to enable them to make well-informed work-related decisions. Accordingly, Van de Poel's first sub-condition states that human subjects have the right to be informed about how new technologies (e.g., Automated Guided Vehicles, which automatically move orders from one place to another in warehouses) create risks (e.g., like the possibility of a collision). The second sub-condition states that a democratically legitimised body should approve and give consent regarding the experiment or deployment of new technology.Footnote 7 The last two sub-conditions Van de Poel formulates state that human subjects should be able to influence any step of the experiment from the set-up to the evaluation and to withdraw from it at any time. As we see things, these general ideas about what the principle of autonomy requires with respect to experimentation with new technology carry over very naturally and seamlessly to the specific context of experimentation with new technologies in workplaces. So, here we will simply note that we endorse those general suggestions as being highly sensible also when applied to technology in the workplace in particular.
The last and fifth principle—justice—as Van de Poel [55] himself operationalises it, refers to the protection of vulnerable human subjects by a fair distribution of risks and benefits among these subjects, and the requirement that measures should be taken to protect them. This also carries over very naturally to the workplace context. For instance, in the technology-driven gig economy context, and as a result of legal systems being slow to adapt to technological change, the aforementioned platform workers are a good example of people who currently benefit less than others, who are vulnerable, and who need protection [22]. More generally, to protect employees and avoid distributive injustice in workplaces, matters such as employees' rights, obligations and working conditions clearly need to be taken into consideration when new technologies are experimented with in workplaces. Moreover, plans for how to compensate employees for any problems that may be caused for them by new technologies in the workplace should be made before the technologies are introduced. The more general topic of justice in the workplace—and how to promote it in workplaces where there is experimentation with new technologies—is a bigger topic than we can do justice to here. We aim to explore this issue further elsewhere.
In conclusion, our suggested ethical framework for experimental technologies in the context of workplaces and warehousing can be summarised with the help of Table 2, which maps our discussion in this section back onto the five general principles introduced in the previous section about Van de Poel’s framework.
| 2024-05-14T00:00:00 |
2024/05/14
|
https://link.springer.com/article/10.1007/s43681-023-00265-w
|
[
{
"date": "2023/02/01",
"position": 21,
"query": "workplace AI adoption"
},
{
"date": "2023/02/01",
"position": 65,
"query": "AI labor union"
}
] |
C.AI Adoption Bot for Microsoft Teams Review
|
C.AI Adoption Bot for Microsoft Teams Review: Bot-based Gamified Teams Learning
|
https://www.uctoday.com
|
[
"Uc Today Team"
] |
C.AI Adoption Bot (or CAI for short) is an AI-powered chatbot that helps to ease your Microsoft Teams learning curve. It was developed by an EU-based ...
|
C.AI Adoption Bot (or CAI for short) is an AI-powered chatbot that helps to ease your Microsoft Teams learning curve. It was developed by an EU-based software development company, Contexxt, an artificial intelligence startup with a longstanding partnership with Microsoft. Contexxt offers a wide range of advisory services, bot solutions, analytics capabilities, and support services for companies undergoing digital transformation. Founded in 2018, Contexxt has won several major regional customers, including Microsoft.
CAI is the company’s Microsoft Teams adoption enabling chatbot that integrates with the Teams environment. It studies your behavioural patterns, digital maturity levels, and personal learning speed to provide timely guidance. At its core, CAI is powered by artificial intelligence and natural language processing, allowing it to understand “off-topic” or non-Team-related queries and discussions. Additionally, CAI features a gamified experience, where Teams users are rewarded with badges every time they learn a new skill.
Importantly, CAI is GDPR compliant and hosts all data exclusively inside EU data centres.
Let us review the C.AI adoption bot for Teams in more detail.
Inside C.AI Adoption Bot for Teams
C.AI Adoption Bot is a paid Microsoft Teams app priced at € 1.50 per user/month. You can download the integration from Microsoft AppSource or install it directly from Contexxt’s product page. Here are the key features that it enables:
The CAI adoption chatbot – The CAI adoption chatbot allows you to access the app’s key features through simple commands. After you have installed the app, the chatbot will introduce itself and provide you with links to the Contexxt.ai website for more information. The bot will also ask you the name you would like to be addressed and start getting you up to speed with common Teams functionalities.
Conversational interface – The app enables Teams learning and adoption entirely through a conversational interface powered by natural language processing. You can ask the bot about various features through a conversational workflow – for instance, type “how to use raise hand?” to know about the feature. As part of the conversation, the bot will display a card containing the feature’s title, description, and a screenshot illustrating how it works.
Further feature exploration – You can learn more about a specific feature by clicking on the embedded button in the card. Typically, every card describing a Teams feature will have three embedded buttons: to know more about the feature, to stop the interaction, and to tell the bot that you were looking for something else.
Badges for completing Teams activities – This is one of the key capabilities enabled by C.AI Adoption Bot. It has a set of badges covering various Teams competencies and functionalities, including both hard and soft skills. For example, there are badges for attention to detail, teamwork, and work-life balance. You will also receive badges for working on document versioning, email construction, and reacting to Teams posts/messages. These badges can be accessed from the Badge Gallery tab on the C.AI Adoption Bot app.
Maturity level tracking – Right next to Badge Gallery, you will find the Maturity Level tab where you can track how your competencies have moved from starter level to professional level and, finally, to champion level. This module also tells you exactly how you can progress across levels to encourage self-competition and self-motivation.
Activity-based progress measurement – Importantly, Maturity Level is tracked based on specific Teams activities. This lets you know the areas in which you excel, topics with which you are moderately conversant, and where there is more work to be done. C.AI Adoption Bot segments Teams usage maturity based on your collaboration patterns, the frequency of @mentions, post-reactions (given and received), and adding subjects to posts, among other things.
Why the C.AI Adoption Bot App Makes a Difference
CAI is an excellent way for beginners to get used to the Microsoft Teams environment. It starts with the most basic features employees are likely to use every day and rewards active participation in Teams-based collaborative activities.
What We Think
Contexxt’s powerful AI capabilities make CAI valuable to your Microsoft 365 learning stack. Coupled with C.AI Adoption Analytics, you can further reinforce positive learning behaviour and get the most out of your Microsoft 365 investment. Note that the application is available in English, German, French and several other languages.
Download it here.
| 2023-02-01T00:00:00 |
2023/02/01
|
https://www.uctoday.com/reviews/c-ai-adoption-bot-for-microsoft-teams-review-bot-based-gamified-teams-learning/
|
[
{
"date": "2023/02/01",
"position": 42,
"query": "workplace AI adoption"
}
] |
Upscaling Employees to Maximize Company Potential and ...
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Upscaling Employees to Maximize Company Potential and Growth
|
https://marketscale.com
|
[
"Ron Stefanski",
"Feb",
"Learn More"
] |
advisory services education trends Kimo Kippen workplace education workplace training solutions ... In a workforce reshaped by automation, accelerated AI adoption ...
|
The ever-increasing role technology and innovation play in today’s workplace mean that even the most educated and trained workers will require varying degrees of upskilling, retraining, and upscaling employees to meet current and future demands. And with the talent pool getting increasingly competitive, organizations that do not provide those education and training opportunities may fall behind those who do.
Several recent workplace trends find workers looking for meaning, a desire for flexibility, and training to keep pace with technological transformation. Kimo Kippen, Founder of Aloha Learning Advisors, follows, understands, and helps organizations deploy the right strategies to capitalize on these trends.
As the talent crisis plays out today, DisruptED’s Ron J Stefanski wonders if employees are as well-trained as they need to be and if companies are as plugged into the situation as they should be in terms of upscaling their employees. Kippen joined Stefanski on the program to shed light on the problem and provide answers.
“Coming out of COVID, there were many silver linings, and there was a great deal of learning that took place,” Kippen said. “Everyone’s trying to get to where that next normal is. It’s always going to be in transition; it’s in constant flux.”
Stefanski and Kippen’s conversation includes the following…
● What an organization’s people need and want to enable success
● How educating front-line workers can be a competitive advantage
● Matching customer needs and demands with the proper hiring and training
approaches
“For today’s employers, all of our research points to one of the top things that keep CEOs awake at night is around talent,” Kippen said. “And are they going to have the talent for today and the future and tomorrow?”
Kimo Kippen is an accomplished, visionary thought leader and sought-out keynote speaker committed to making a difference in the world by inspiring business and civic leaders and employees to think outside the box and actualize what is possible. Kippen holds a B.A. from the University of Hawaii at Manoa and an M.S. in Career & Human Resources Development from RIT. In addition to founding Aloha Learning Advisors, Kippen was the V.P. of Global Workforce Initiatives and former Chief Learning Officer for Hilton and continues to teach Human Resources at The Catholic University of America in Washington D.C.
| 2023-02-01T00:00:00 |
2023/02/01
|
https://marketscale.com/industries/education-technology/upscaling-employees-to-maximize-company-potential-and-growth/
|
[
{
"date": "2023/02/01",
"position": 49,
"query": "workplace AI adoption"
}
] |
Digital Workplace Services
|
Digital Workplace Services
|
https://www.softwareone.com
|
[] |
Integrating AI, data analytics, and cloud solutions not only enhances productivity but also provides the flexibility and security needed in a dynamic work ...
|
The high-performance workplace — from SoftwareOne
Imagine a workplace where excellence, innovation, and efficiency are the norm. At SoftwareOne, we help you create a high-performance workplace that leverages the latest technology and best practices to ensure your team can work smarter, faster, and more collaboratively.
Discover how we are helping clients around the world prepare for the high-performance workplace.
| 2023-02-01T00:00:00 |
https://www.softwareone.com/en-us/digital-workplace-services
|
[
{
"date": "2023/02/01",
"position": 50,
"query": "workplace AI adoption"
}
] |
|
Almost 40% of domestic tasks could be done by robots ...
|
The heart of the internet
|
https://www.reddit.com
|
[] |
Almost 40% of domestic tasks could be done by robots 'within decade' | Artificial intelligence (AI) ... workplace. There are so many things that can be ...
|
A subreddit devoted to the field of Future(s) Studies and evidence-based speculation about the development of humanity, technology, and civilization. -------- You can also find us in the fediverse at - https://futurology.today
Members Online
| 2023-02-01T00:00:00 |
https://www.reddit.com/r/Futurology/comments/11ar0ll/almost_40_of_domestic_tasks_could_be_done_by/
|
[
{
"date": "2023/02/01",
"position": 57,
"query": "workplace AI adoption"
}
] |
|
AI and Neurotechnology - Communications of the ACM
|
AI and Neurotechnology – Communications of the ACM
|
https://cacm.acm.org
|
[
"Sara Berger",
"Francesca Rossi",
"R. Colin Johnson",
"Samuel Greengard",
"About The Authors",
"Anusha Musunuri"
] |
Companies of every size and business model, all over the world, are adopting AI solutions to optimize their operations, create new services and work modalities, ...
|
Artificial intelligence (AI) is a scientific field and a technology that is supported by multiple techniques—such as machine learning, reasoning, knowledge representation, and optimization—and has applications in almost every aspect of everyday life. We use some form of AI when we swipe a credit card, search the Web, take a picture with our cameras, give vocal commands to our phone or other device, and interact with many apps and social media platforms. Companies of every size and business model, all over the world, are adopting AI solutions to optimize their operations, create new services and work modalities, and help their professionals to make more informed and better decisions.
Back to Top
Key Insights AI ethics aims to identify and address several issues concerning the use of AI in our society, such as privacy, inclusion, robustness, transparency, fairness, and explainability, via technical, social, and sociotechnical methods.
AI ethics has delivered principles, guidelines, tools, playbooks, educational modules, corporate policies, governance frameworks, standards, and regulations.
Neurotechnologies collect and/or modify data from our nervous system and are rapidly being used in combination with AI.
Lessons learned from AI ethics may offer useful insights to address neuroethical issues that may expand upon or introduce new concerns compared to AI.
Back to Top
Current Issues in AI Ethics
There is no doubt that AI is a powerful technology that has already imprinted itself positively on our ways of living and will continue to do so for years to come. At the same time, the transformations it brings to our personal and professional lives are often significant, fast, and not always transparent or easily foreseen. This raises questions and concerns about the impact of AI on our society. AI systems must be designed to be aware of, and to follow, important human values so that the technology can help us make better, wiser decisions. Let us consider the main AI ethics issues and how they relate to AI technology:
Data issues. AI often needs a lot of data, so questions about data privacy, storage, sharing, and governance are central for this technology. Some regions of the world, such as Europe, have specific regulations to state fundamental rights for the data subject—the human releasing personal data to an AI system that can then use it to make decisions affecting that person’s life.15
Explainability and trust. Often the most successful AI techniques, such as those based on machine learning, are opaque in terms of allowing humans to understand how they reach their conclusions from the input data. This does not help when trying to build a system of trust between humans and machines, so it is important to adequately address concerns related to transparency and explicability (see the online appendix at http://bit.ly/3CIgJ42 for examples of tools that provide solutions to these issues). For example, without trust, a doctor will not follow the recommendation of a decision-support system that can help in making better decisions for patients.
Accountability. Machine learning is based on statistics, so it always has a percentage of error, even if small. This happens even if no programmer actually made a mistake in developing the AI system. So, when an error occurs, who is responsible? To whom should we ask for redress or compensation? This raises questions related to responsibility and accountability.
Fairness. Based on huge amounts of data that surround every human activity, AI can derive insights and knowledge on which to make decisions about humans or recommend decisions to humans. However, we need to ensure that the AI system understands and follows the relevant human values for the context in which such decisions are made. A very important human value is fairness: We do not want AI systems to make (or recommend) decisions that could discriminate against or perpetuate harm across groups of people—for example, based on race, gender, class, or ability. How do we ensure that AI can act according to the most appropriate notion of fairness (or any other human value) in each scenario in which it is applied? See the online appendix for an example of an open-source library as well as for descriptions of multiple (though not exhaustive) dimensions of AI fairness. Like all ethics issues, fairness is a complex, socially influenced value that can neither be defined nor addressed by technologies alone.17
Profiling and manipulation. AI can interpret our actions and the data we share online to make a profile of us, a sort of abstract characterization of some of our traits, preferences, and values to be used to personalize services—for example, to show us posts or ads that we most likely will appreciate. Without appropriate guardrails, this approach can twist the relationship between humans and online service providers by designing the service to more clearly characterize our preferences and make the personalization easier to compute. This raises issues of human agency: Are we really in control of our actions, or is AI being used to nudge us to the point of manipulating us?
Impact on jobs and larger society. Since AI permeates our workplace functioning, it obviously has an impact on jobs, since it can perform some cognitive tasks that usually are done by humans. These impacts need to be better understood and addressed4 to ensure humans are not disadvantaged. As mentioned earlier, AI is pervasive and its applicability expands rapidly; any negative impacts of this technology could be extremely detrimental for individuals and society. The pace at which AI is being applied within (and outside) the workplace also brings concerns about people and institutions having enough time to understand the real consequences of its use and to avoid possible negative impacts.
Control and value alignment. Although AI has many applications, it is still very far from achieving forms of intelligence close to humans (or even animals). However, the fact that this technology is mostly unknown to the general public raises concerns about being able to control it and align it with larger and sometimes disparate societal values, should it achieve a higher form of intelligence.34 Figure 1 lists these issues and links them to AI capabilities and methodologies.
Figure 1. Main AI ethics issues.
No single organization can address and solve all these issues alone, which is why AI ethics includes experts from many scientific and technological disciplines. Indeed, the AI ethics community includes AI experts, philosophers, sociologists, psychologists, lawyers, policymakers, civil society organizations, and more. Only by including all the voices—those that produce AI, those that use it, those that regulate it, those that are impacted by its decisions, and those that understand how to evaluate the impact of a technology on people and society—can we understand how to identify and address AI ethics concerns.
Technical solutions to AI ethics concerns, such as software tools and algorithms to detect and mitigate bias, or to embed explicability into an AI system, are certainly necessary. But they are not enough. Non-technical solutions, such as guidelines, principles, best practices, educational and reskilling activities,12 standards,18 audits, and laws,15 are also being considered and adopted. On top of these, there is the need to specify methodologies that operationalize AI ethics principles and create appropriate governance around them.
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AI and Neuroscience
As we learn more about the nervous system and untangle the embodied and bidirectional interactions between our external environments and internal milieus, the need for new tools and capabilities increases. This is not only to meet the demands of basic bench, translational, and clinical neuroscience research by creating more advanced methods and materials for capturing neural signals and computing neural features, but also to provide novel therapies, develop new ways to restore or generate human functions, and create resources to augment and enrich our existing skills and experiences. At the same time, AI’s capabilities are continuously expanding, becoming more complex, efficient, and faster due to numerous advances in computing power.
AI, and especially machine learning, is increasingly being used in neuroscience applications. But the conceptual links between AI and neuroscience have been strong since the emergence of AI as a research field. The intertwined trajectories of the two fields are exemplified by goals of emulating and augmenting human intelligence via machines that can “learn,” common and often contentious brain-is-a-computer and computer-is-a-brain tropes (and associated colloquialisms such as, “I don’t have the bandwidth” or “my computer is out of memory”), and more recent computing techniques such as neural nets, neuromorphic algorithms, and deep learning. The associations between our minds and machines are only strengthening as AI becomes embedded into nearly every aspect of our lives—from our “smart” phones to our “smart” fridges and from our shopping habits to our social media. Neuro- or brain-inspired metaphors permeate our jobs, our homes, our transportation, our healthcare, and our interactions. Neuroscience-influenced AI applications are likely to increase as big tech and startup companies invest in neuroscientific research and insights to improve algorithms and associated capabilities.
The associations between our minds and machines are only strengthening as AI becomes embedded into nearly every aspect of our lives.
The inevitable movement beyond conjectural linkages into real-world interactions between computation and neuroscience has begun. Albeit indirectly, AI already pervasively interacts with our nervous systems by influencing, reinforcing, and changing our behaviors and cognitive processes. While the concept of an “extended mind” is not new (see Clark and Chalmers8), until relatively recently, humans were largely limited to extending their thoughts into the physical realm via representations such as symbols, writings, art, or spatial markers—stored on the walls of caves, on canvases, within books and diaries, or as signs in the environment. These tools and relics functioned as repositories of ideas, memories, directional aids, and external expressions of our internal selves. Now, however, we are extending the neural into the digital,19 and the more pervasive AI and digital technologies become, the more intertwined and almost inseparable they are with our nervous systems and associated abilities and psychology.20 Numerous studies now indicate that our use of smartphones, social media, and GPS has not only made us more dependent on these technologies but has also significantly influenced our attention,39 spatial navigation,10 memory capabilities,13 and even underlying neurophysiology.21
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Neurotechnology
Simultaneously, the indirect and often theoretical links between AI and neuroscience are transforming into direct and tangible ones, from one-way extensions of our minds into digital spaces to bidirectional connections between nervous systems and computers. Over the last few decades, we have seen a rise in the development and deployment of devices—called neuro-technologies (neurotech)—that exploit advances in computing and the pervasiveness of AI to collect, interpret, infer, learn from, and even modify various signals generated throughout the entire nervous system (called neuro-data or neuroinformation). Neurotechnologies can interact with neurodata either invasively and directly through different kinds of surgical implants, such as electrodes or devices implanted into or near neuronal tissues, or they can interact non-invasively and indirectly through wearable devices sitting on the surface of the skin, picking up signals or proxies of those signals from the head, body, or limbs. Generally, neurotech is divided into three categories:
Neurosensing, which essentially “reads” neurodata by collecting, monitoring, or interpreting it.
Neuromodulating, which “writes” data by changing the electrical activity, chemical makeup, and/or structure of the nervous system.
Combinatorial or bidirectional, which can read and write neurodata, so to speak.
Neuromodulatory and combinatorial/bidirectional neurotech devices are areas where AI will be increasingly used and relied on to interpret neuro-data, infer and replicate proxies of neural signals in real time, and contribute to closed-loop systems for automatic and autonomous control of devices.
Figure 2 shows examples of invasive and non-invasive neurotechnology applications across neurosensing, neuromodulation, and combinatorial applications. These and other devices are being used for a gamut of applications spanning basic research, medicine, gaming, and more. For example, many of the invasive neurotechnologies are being applied in healthcare for neurological disorders including epilepsy (invasive EEG) and Parkinson’s disease (invasive neuromodulation and combinatorial). In contrast, many noninvasive technologies (including TMS and neuroimaging techniques such as fMRI) are being used to answer fundamental neuroscience questions. Noninvasive bidirectional neurotechnologies are being developed to be integrated into everyday processes, such as work and video games. In the example in Figure 2, a wrist-worn device senses peripheral neural signals, uses them to infer intended actions, and provides haptic sensory feedback to improve the experience and make it more intuitive. For an overview and to learn about many other examples, see the Royal Society’s 2019 report referenced in the online appendix.
Figure 2. Examples of invasive and non-invasive neurotechnology applications across three categories of neurosensing, neuromodulation, and combinatorial.
As neurotechnology is still emerging, the state of the art is constantly evolving, and the kinds of neurotech devices and applications present today run the gamut in terms of technological maturity, robustness, and scalability—from basic science and early translational or clinical research all the way to currently available consumer products. At present, scientists can invasively record from hundreds of neurons simultaneously. That is likely to soon become thousands with the advent and increasing adoption of interfaces such as neuralace, neural dust, and neural threads. Improvements in these interface capabilities, along with advancements in the materials used, will vastly change the future landscapes of computing and neuroscience. They will enable more accurate and specific recordings, enhanced signal-to-noise ratio, better efficacy and precision of targeted interventions, longer-lasting device functionality, and improvements to important safety considerations, including minimizing tissue damage and resisting corrosion from the internal corporeal environment, various bodily fluids, and device stimulation parameters.
Today, neurotech is most often developed or used within the clinical sciences and healthcare spaces to monitor and treat a gamut of chronic illnesses or injuries spanning neurological and psychological ailments, including Parkinson’s disease (deep brain stimulation16), chronic pain (spinal cord stimulation35), epilepsy (neuropace), depression (transcranial direct current stimulation25), and more. Some neurotech devices are beginning to restore movement and sensation in people with missing or damaged limbs;38 spinal cord severation;14 or those with sensory loss, impairments, or differences that individuals want to improve—for example, cochlear implants for deafness7 or retinal implants for certain kinds of visual impairments.5
Neurotechnologies can decode and project very specific and often rudimentary forms of thought, such as imagined handwriting,40 typing,27 and other kinds of intended and directed movements.28 They can also very crudely reconstruct conscious and unconscious mental imagery.24 These are being investigated for use primarily in medical contexts for people with communicative difficulties2 and functional movement issues28 but also more recently for the commercial space and to improve everyday work-life environments.32 Neuroscientists have also demonstrated the technological capability of brain-to-brain communication; that is, the ability to transfer sensory and perceptual experiences26 and memories11 directly between animals using invasive techniques, as well as to manipulate31 and control them.6 This capability is slowly and rudimentarily being developed and tested non-invasively between people for use in applications such as gaming and augmented reality (AR)/virtual reality (VR). Although applications are still emerging, there is evidence for widespread interest in neurotechnologies and/or neurodata collection for a variety of market sectors outside those described here, including but not limited to education, work, marketing, and military uses.
While once relegated to the realm of science fiction, the merging of machine, body, and psyche is on the horizon due to the technological advancements enabled by neuroscience and AI. However, in most of the state-of-the-art examples summarized above, additional research and an extensive amount of work is needed before these neurotechnologies can be reliably or ethically implemented. For instance, considerable improvements are required in terms of the ease and speed of acquisition and analysis of neurodata (for future scalability), the standardization of methods, the feasibility and accessibility of neurodevices (for example, to make them more intuitive, less cumbersome, more affordable, and more adaptable to differences across human bodies), the size and diversity of neuro datasets to build future representative models,30 and the validation of existing results to establish robustness and replication.
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Neuroethics
Given the important implications of neurotech on society, the relative immaturity of their techniques and inferences, the increasing hype and misinformation around their abilities, and the growing direct-to-consumer push of their capabilities, there are concerns that the commercialization of neurotech and the commodification of neurodata is moving at a speed and scale that could proceed without proper policies and regulations in place to protect future consumers. Likewise, if history is any indication of the future, the increase in cautionary tales from AI applications resulting in community harms and reactionary mitigation strategies only strengthens the need to develop proactive guardrails within an AI-enabled neurotech space.
However, for agreed-upon standards and best practices to be put in place, we must first understand the ethical concerns associated with neurotechnologies and how they compare to those seen in AI. The study of ethical principles and implications related to the development, deployment, and use of neurotechnologies (and associated neuroscience research and neurodata) is commonly referred to as neuroethics, a relatively nascent but growing field of inquiry emerging in the late 1990s and early 2000s out of medical and bioethics.22 Neuroethics is critical of the assumptions and intentions underlying neurotech and neuroscientific findings. It is also concerned with questions about neurotech’s impact on human self-understanding and the downstream effects of changes in this fundamental understanding on our biology, our psychology, and our society.
AI already pervasively interacts with our nervous systems by influencing, reinforcing, and changing our behaviors and cognitive processes.
The ethical considerations surrounding neurotech are still being researched; there is yet so much to learn about the nervous system and about how, and the extent to which, neuro-technology will influence humanity. However, there are at least eight core neuroethics issues that consistently emerge which could pose significant societal, technical, and legal challenges. These are briefly defined in the following list of concepts, rights, and values that could be impacted by using neurotechnology:
Mental Privacy: A condition met when one’s neurodata is free from unconsented observation, intrusion, interpretation, collection, or disturbance by third parties or unauthorized neuro-tech devices.
A condition met when one’s neurodata is free from unconsented observation, intrusion, interpretation, collection, or disturbance by third parties or unauthorized neuro-tech devices. Human Agency and Autonomy: The ability to act or think with intention, in the absence of coercion or manipulation, with sufficient information to make rational decisions regarding one’s mind and body.
The ability to act or think with intention, in the absence of coercion or manipulation, with sufficient information to make rational decisions regarding one’s mind and body. Human Identity: The subjective, complex, and dynamic embodiment of various aspects of human reality, including but not limited to biology, culture, ecology, lived experiences, and historic socio-political situations—which together give rise to each person’s unique ideas of meaning, relatedness to others and the world, and conceptions of self and ownership of life. This phenomenon is simultaneously unique and literally inscribed within the nervous system while also being influenced and constructed by external forces, such as communal/societal needs.
The subjective, complex, and dynamic embodiment of various aspects of human reality, including but not limited to biology, culture, ecology, lived experiences, and historic socio-political situations—which together give rise to each person’s unique ideas of meaning, relatedness to others and the world, and conceptions of self and ownership of life. This phenomenon is simultaneously unique and literally inscribed within the nervous system while also being influenced and constructed by external forces, such as communal/societal needs. Fairness (including issues of access): Equitable and just treatment of individuals or communities irrespective of their choice or ability to use neurotech or not or to participate in neurodata collection or not, as well as in manners regarding availability of neurotech; access to neurotech benefits; and/or participation in neurotech design influence, neurotech solutions, and neurodata interpretations.
Equitable and just treatment of individuals or communities irrespective of their choice or ability to use neurotech or not or to participate in neurodata collection or not, as well as in manners regarding availability of neurotech; access to neurotech benefits; and/or participation in neurotech design influence, neurotech solutions, and neurodata interpretations. Accuracy: The correctness of neurodata measurements, interpretations provided by neurotech, or code generated by neurotech for the purpose of modifying the nervous system.
The correctness of neurodata measurements, interpretations provided by neurotech, or code generated by neurotech for the purpose of modifying the nervous system. Transparency: The quality of being clear and open about the capabilities of neurotech, the use of neurodata, and any inferences drawn from either (related to explicability as well as informed consent).
The quality of being clear and open about the capabilities of neurotech, the use of neurodata, and any inferences drawn from either (related to explicability as well as informed consent). Security: A set of technologies, standards, and procedures which protect neurodata and data inferred from neurotech against access, disclosure, modification, or destruction by unauthorized users.
A set of technologies, standards, and procedures which protect neurodata and data inferred from neurotech against access, disclosure, modification, or destruction by unauthorized users. Well-being: A prioritized state of physical and mental satisfaction (including the health, safety, happiness, and comfort of individuals and/or communities) achieved through both the avoidance of negligence and the prevention of harm (broadly defined), injury, or unreasonable risk of either in the design or implementation of neurotech (or the usage of associated neurodata). This also includes elements of psychological safety as well as larger societal and environmental considerations, such as cultural or community conservation or avoidance of toxic or non-degradable waste.
Importantly, these concerns are not mutually exclusive and are considerably interrelated. For example, obtaining informed consent to collect neurodata would involve mental privacy, human autonomy and agency, transparency, and data security assurance. Likewise, well-being is met when all other concerns are sufficiently addressed. Many of these concepts also invoke previously established bioethical and medical principles related to beneficence, nonmaleficence, dignity, and justice, indicating that they are relevant to a broader range of applications outside of neurotech. In practice, responding to these concerns will often mean answering challenging questions on a contextual, socially and historically informed, case-by-case basis, as the extent of risk changes depending on:
The neurodata of interest
How the neurodata is being treated—for example, is it being read, written, or both?
Whose neurodata is being collected (by whom and for what purposes)
The overall literacy of participants and end users in the space—for instance, do they understand the neurotech’s capabilities or how sensitive their neurodata is?
The location of the discussion or application—for example, the specific impacted community, cultural norms and values, societal expectations, associated politics and regulations, economic or financial contexts, or any environmental considerations if applicable.
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Ethics in the Age of AI and Neurotech
When considering the previous list in the specific context of AI ethics, it’s clear that neurotech poses familiar ethical challenges, and both largely converge along issues of value alignment and transparency. Areas such as these, where the fields’ ethical issues overlap, are important to highlight because they suggest that some of the existing solutions or strategies (technological and otherwise) used by AI might be applied to neurotech applications to mitigate these specific concerns. However, neurotech also poses risks that may not be sufficiently covered by existing AI regulations, governance frameworks, best practices, or company policies, and this may indicate the need to update or develop new preventative strategies, policies, and solutions. The remaining neuroethics considerations fall under this kind of divergence, as they highlight challenges that while potentially shared with those posed by AI, may also be appreciably different, magnified, or expanded given the potential capabilities of neurotech and the sensitivity of neurodata.
For example, addressing mental privacy, human identity, and human agency and autonomy may be considerably more challenging in neurotech than in AI. These are of particular concern, given that neurotech could one day both directly collect neurodata and write new information into our nervous systems, all potentially without being detected. This contrasts with the current AI technologies humans interact with, which may only indirectly influence our nervous systems and associated thoughts or behaviors—or do so more slowly or at a level that is multiple steps removed. Additionally, the majority of our nervous system’s signals are unconscious and outside our awareness or control, making it technically challenging to precisely pinpoint the kinds of data neurotech collects, interprets, or modulates in the first place. Data choice, collection, and curation, however, are likely less difficult to determine or control in an AI application.
Likewise, it may also be difficult to establish what kind of neurodata we consent to share, and it might be plausible to provide neurotech with private information unknowingly or unintentionally. This is also true with most behavioral data that AI systems have access to today, but we call attention to this because there still exists a presumption of privacy within one’s own mind that seems inaccessible to others or technology. Yet, this assumption may no longer be a certainty with advances in neurotechnology. Furthermore, the fact that some neurotech can directly modify ongoing neural activity and directly feed (or write) data into the nervous system in real time raises questions about how we can better protect and ensure bodily/mental autonomy and decision capacity. This includes the potential for changing (purposefully or not, quantifiably or not) the integrity of our mental processes, including our conceptions of identity. Because neurotech may one day be able to directly influence a person’s behavior, thoughts, emotions, memories, perceptions, or relationships between these phenomena, it poses challenging questions about free will, cognitive liberty, agency, and notions of self-hood that AI may not yet have had to truly address to this extent, although there is a legacy of bioethical research showing that identity has been a fundamental concern in other biotech spaces, such as organ transplants and pharmaceutical enhancements.
Likewise, fairness is a substantial component of AI ethics and some of the same issues surrounding equitable access and inclusion in design and interpretation are also in neuroethics. But neurotech may one day allow us to directly infer and act on neurodata that we are unaware of (for example, unconscious biases or repressed memories) or cannot control, as well as significantly augment or change our mental and physical abilities. Thus, the risk to fairness is greater with neurotech, as these kinds of capabilities could perpetuate existing inequities and biases or create new avenues for discrimination or malicious targeting that are even harder to see because they are quite literally hidden inside us. Additionally, underlying and unchallenged assumptions about what constitutes “normative” neurodata or which neurotech outcomes are considered desirable may also be biased against people with hidden disabilities or neurological differences (a concern that has also been put forth when discussing genetic technologies and biometrics, among others. Normative assumptions may also contribute to complex cultural and intra-community dilemmas for similar reasons.
Moreover, neurotech interfaces may exacerbate or compound issues we currently see with AI: Device sensors do not adequately account for different hair types or skin colors, instructions and interfaces are designed in a way that widens instead of bridges the current digital or technological divides, devices are not available to or affordable for all, or neurotech benefits are not equitably distributed when appropriate. Issues of fairness are at stake not only when we consider neurotech devices that might purposely replace abilities/sensations and restore baseline functionality, but also at extremes when we consider the capability of neurotechnology to augment functions or abilities beyond those available in humans. Fairness also underlies the very conceptualizations used in this paper, as many of the ethical concerns and values listed are heavily influenced by Western norms and pedagogy, and neurodata is often produced and curated within Western European origins.30 This means that some concepts may not resonate well with or apply to different cultures, social standards, or global contexts. To make neurotech governance fairer, there needs to be a better understanding of how the social and technical concerns pertaining to neurotechnology differ, are redefined, or get reprioritized across local, national, and international communities.
Commercialization of neurotech and the commodification of neurodata are moving at a speed and scale that could proceed without proper policies and regulations in place.
At the very least, many of the issues outlined here regarding neurotech will likely be compounded with those of other technologies, including AI, due to their overlap and societal relevance. For example, AI and data science regularly contend with data privacy and de-identification practices. Depending upon the technology and data format, neurodata can be used to reasonably identify someone9 in ways that go beyond traditional PII or demographic aggregates. While this property is not unique to neurodata (it also applies to genetics and other bio-metric data types), it presents a challenge that many AI methods are not equipped to handle. It remains to be seen whether neurodata may be more technically challenging to de-identify than other data forms. Similarly, existing technological harms to well-being are complicated by neurotech applications. Like many of the technologies we use today, neurotech will also require elements that impact materials sourcing, resource allocation, energy consumption, and supply-chain operations.
However, some neurotechnologies may also have understudied interactions with the environment that AI may not have to face. For example, many medical neurotechnologies (positron emission tomography and some CT or MRI scans, for instance) require contrast agents to be able to visualize certain kinds of neurodata. Some of these contrasts, such as flourine-18, are radioactive and do not degrade in the environment while other agents, such as gadolinium, are toxic to the environment and humans over time as concentrations build. Unfortunately, due to poor waste management practices and overt pollution, they are increasingly being found in wastewater, groundwater, rivers, and oceans,33 eventually making their way into our drinking water and the food we consume—for example crops, livestock, aquatic life. This has obvious implications for the general well-being and health of our planet and our communities and only adds to the list of existing negative environmental impacts caused by technology.
Research regarding ethical concerns elicited by neurotech and AI as well as the need for action in this space is not new nor are discussions illustrating the intersections of AI with neurotech in methods and applications.36,37 However, to our knowledge, we are the first to summarize and compare the core ethical issues between both technologies, as well as offer guidance and lessons learned from the specific perspective of a tech company that actively participates in both spaces. The initial comparison of ethical concerns between neurotech and AI is summarized in the Table. Note, however, that this table may not be exhaustive; additional considerations and differences may emerge as both AI and neurotech unfold.
Figure 3 provides an example of the kinds of ethical concerns that might arise in a bidirectional neuro-tech application that also relies on AI. Specifically, the two application scenarios refer to a system that aims to prevent epileptic seizures in one example or stop Parkinson’s tremors in a different example. A neurotech component is used to read and deliver signals from/to the brain, and an AI system provides classification and prediction capabilities based on incoming data to influence the outgoing neurotech action. These types of closed-loop adaptive neurotechnologies, combined with AI, are not too far off; they are currently being developed and honed for both Parkinson’s disease3 and intractable epilepsy.23 Importantly, Figure 3 illustrates that ethical considerations are associated with both the technology capabilities (on the right) and the context in which they are embedded and used (on the left). For example, issues of fairness can be found throughout the entirety of the application, whether that is:
Figure 3. Example of a closed-loop combined neurotech and AI application scenario, and the relevant ethical concerns across contextual and technical considerations.
Ensuring there is a representative group of patients enrolled in the trial and involving them in co-creation of treatment goals.
Including a diverse set of engineers and designers in creating the technology and associated methods.
Testing hardware and software on a representative set of patients to make sure neural signals are collected in the same way across individuals in future trials.
Mitigating potential bias in the AI algorithms that learn the signals and adapt the stimulation.
Verifying that associated neuro-modulatory effects are equitable across different groups of patients and that certain populations are not disproportionately impacted by its side effects.
Obtaining feedback and input from a diverse set of patients, caretakers, and clinicians as part of one’s assessment of the technology’s impact.
Additionally, some ethical issues are not common throughout the process but appear only within certain contexts. For example, neuroethics concerns around human identity might be most likely to appear later in the application, after prolonged neuromodulation, and seen only through interactions with patients (see the online appendix for a real-world example). All these considerations highlight the importance and necessity of methods such as participatory design and the associated inclusion of multi-stakeholder considerations from the start—not only to help create neuro-tech that is useful for and wanted by individuals, communities, and societies at large but also to aid in identifying issues and problems before and as they arise.
Once we identify the issues around the combined use of AI and neuro-tech, how can we address them? As explained, some issues are or could potentially be greatly expanded compared to AI, so we may need to deploy updated or even new solutions and mitigation or prevention strategies to address them, both technical and not—for example, social, political, institutional, and economic approaches. However, the good news is that we do not need to start from scratch. A lot of foundational work has been completed over the past five or so years to begin addressing many AI ethics issues. We’ve constructed and used multi-stakeholder approaches to identify the concerning issues and their impacts; specified best practices, principles, and guidelines; built technical solutions which may be considered, re-used, or updated for neurotech or neurodata (for example, federated learning practices or de-biasing techniques); adopted educational/training methodologies; created governance frameworks and international standards; and even defined hard laws based on AI ethics considerations, such as the very recent one by the European Commission15). While doing all this, we have learned several lessons, identified challenges, listed the failures, and reported on successful approaches. By “we,” we mean the whole society, not just AI experts: experts from many scientific disciplines, business leaders, policymakers, and civil society organizations. Additionally, the international neuroethics community has created more than 20 ethical guidelines, principles, and best practices—compiled by the Institute of Neuroethics (IoNx)—from which we can and should draw.
Therefore, we can and should exploit this knowledge and the developed capabilities to accelerate the path toward addressing the issues raised by the combination of AI and neurotech. The first step is to clearly map the relationship between common and magnified issues, which we started doing in the previous sections and have summarized in the Table. Then, we will be able to update and augment current AI ethics frameworks and actions to cover many if not all the expanded issues. To fully understand the current state of the art in neurotech, the real implications on humans and society, and places of intersection with existing regulatory and governance strategies, we need to involve experts from other disciplines, such as neuroscience and neuroethics, that are now rarely found in existing AI ethics initiatives.19 What is considered to be “multi-stakeholder” must be greatly expanded if we want to correctly identify the issues in this broader technological/scientific context, define the relevant principles and values, and then build the necessary concrete actions. If we are to understand the breadth of neurotech applications and potential issues, conversations and considerations will need to not only include experts from neuroscience, ethics, and computer science/AI disciplines but also experts from sociology and anthropology, medicine, science and technology studies, law, business, human-computer interaction, international politics, and more. Moreover, these efforts will also require the expertise of individuals and communities most likely to use or be affected by neurotechnology, as well as those who may be excluded from use or access to the tech or who have been historically disadvantaged by technology or under-considered in tech discussions. Their needs and lived experiences should be considered as additional forms of expertise that are essential to understanding and measuring impacts of neurotechnology, as well as creating better technology and reducing harms.
Table. AI vs. neurotech ethics issues.
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Conclusion
With this article, we wanted to point out the co-evolution of AI and neurotech and their potential convergence points to those who are actively thinking and working in AI and AI ethics. Our intention has been to initiate a more general conversation and clear a path to addressing the identified neuroethics issues. We hope that by identifying the core ethical issues at stake in neurotech, comparing these issues with those in AI, and highlighting places where existing AI ethics initiatives and tools may suffice or may warrant new or different approaches, we can encourage interdisciplinary and cross-stakeholder collaborations between multiple fields and communities to engender timely and concrete actions that minimize the negative impact of this emerging technology.
AI is already a powerful and often positive technology in our lives. Combined with neurotech, it will bring huge new benefits in healthcare, work, leisure, and more. But as we know, greater power comes with greater responsibilities. Knowledge should advance at the same pace as wisdom and awareness of human values and societal forces, so that technological progress can benefit all of us. Given that neurotechnologies are still emerging, there is an opportunity to continue to learn from the past, think proactively about potential issues, and develop preventative technical, legal, societal, and educational solutions before problems arise.
Figure. Watch the authors discuss this work in the exclusive Communications video. https://cacm.acm.org/videos/ai-and-neurotechnology
| 2023-02-01T00:00:00 |
https://cacm.acm.org/research/ai-and-neurotechnology/
|
[
{
"date": "2023/02/01",
"position": 60,
"query": "workplace AI adoption"
}
] |
|
Introducing AI to the modern CX team
|
Introducing AI to the modern CX team
|
https://boldrimpact.com
|
[
"James Fouche"
] |
Simply looking at the evolution of chatbots and how AI has improved it, makes a convincing argument for AI adoption. ... workplace. Gone are the days of ...
|
In the last couple of months social media platforms have flared up with news about ChatGPT, artificial intelligence (AI), and the potential horrors it could pose in the wrong hands. The truth is, AI is no new friend. It’s been around for years. And no, it is not here to take your job. It could, however, soon become your coworker.
What is ChatGPT?
With all the discussion about AI, it is extremely important to consider how the idea will be introduced to working teams. But first, what exactly is ChatGPT? And what is all the huff about?
It might be best to let ChatGPT introduce itself here. By typing this question into a new chat, it provides this answer:
In layman’s terms, it’s a computer program that is trained on a large amount of data that can understand and respond to human language. Not only can it generate text that is similar to what a human would write or say, but it can be used to perform numerous tasks in a conversational tone and generate rapid responses and tasks, which will automate customer experiences (CX).
This immediately sets off alarm bells for those in customer service positions, because the core functions in their job description often relates to engaging with customers. It is natural to assume that an advanced program could soon replace much of the human component. Simply looking at the evolution of chatbots and how AI has improved it, makes a convincing argument for AI adoption. Keep in mind, though, that the first chatbot was called ELIZA, and was created in 1960. Also, if you have ever used Netflix, Amazon, or any social media platforms, then you’ve unknowingly encountered and engaged with AI in some form. This moment has been 60 years in the making.
Innovations and advancements in the artificial intelligence field are coming in terrifying leaps and bounds, but there is still ample time to take it all in. In a recent interview, Sam Altman, CEO of OpenAI, spoke openly about the slowness with which AI will be introduced when he said: “I think in general we are going to release technology much more slowly than people will like. We are going to sit on it much longer than people will like. Eventually people will be happy with our approach to this, but at the time I realize people want the shiny toy and it is frustrating.”
ChatGPT is not perfect. In fact, Altman himself has called it “deeply imperfect”. It often delivers incorrect information and is, at times, tragically limited in its responses. After testing ChatGPT with some of their more common customer service queries, Mathew Patterson from Help Scout shared his thoughts in a recent article. He writes:
“ChatGPT is not a knowledge base or an encyclopedia. It always sounds confident about its answers, even when they are utterly wrong, and it cannot differentiate between “facts” and made up information. That’s why Stack Overflow temporarily banned the use of ChatGPT-generated answers.”
The way forward
While we believe that AI will not completely replace the human element of the outsourcing industry, it is important to look at the future of AI in BPO, especially with regards to occupations where customer interaction is a daily requirement. However, as HR and CX managers contemplate the possibilities of ChatGPT with a glint in their eyes, they often fail to notice the uneasy stares and hushed conversations within their teams.
Should I be concerned about my position?
Am I in danger of being replaced by a machine?
Is this the end of CX or CS as we know it?
These are some of the questions team members are mulling over when the topic of AI comes up. How can one possibly begin to assuage these concerns, or even dispel them completely?
Introduction: Tech revolutionaries like Musk and Gates have warned us that AI would at some point arrive in the modern workspace. Instead of denying its existence or worth, try to create immediate awareness. Introducing AI to the team might feel like welcoming a new team member, but it is also not dissimilar to onboarding the team to a new CRM platform, though a bit more complex.
Discussion: Open discussions and transparency can guide the process, whether through webinars, training sessions, or effective 1-on-1 meetings. Each team member should have the freedom to voice their views, be it their fears about losing their job, or their concerns about data security.
Training: Proper training on the uses of AI, as well as access to the resources to better understand it, will become a necessity in the workplace. Gone are the days of learning how to use the new office printer. Technology demands that teams stay current and informed about the latest innovations. AI, or CX and CS for that matter, is no different. What is the use of having the latest tech, without having the know-how to apply it properly? If you don’t know where to start, then try to familiarize the team with common AI terms. Check out the list of resources below.
Implementation: Leaders, managers, and fellow team members, can apply a proactive, more pragmatic, approach to the adoption of AI, one that involves the entire team in its implementation. A team-led application of AI will create excitement and stimulate a curiosity about the other benefits of AI. If everyone is invested in the project, then everyone will have a vital role in its success.
These are only some of the next steps to consider as we enter this unknown terrain. While widespread adoption of AI is likely to become the norm, the great differentiator will remain real human interaction. If a valued customer has a real problem, they want a real person to come up with a real solution.
Current use of AI in CX and CS
That said, AI platforms have a lot to offer and can support teams in effective ways. Like all office tools, ChatGPT will go through the rigmarole of updates and improvements in coming months and years, as does all new software or equipment. It is, therefore, important to ensure that everyone in an organization is on the same page.
Showcasing real-world examples of AI in action, can provide an opportunity to better understand the benefits of AI. In its current form, AI is already being used to streamline business operations, to prompt workflows, to automate digital processes, and to scour through massive amounts of data in milliseconds. While this can increase efficiency and productivity, it can also reduce unnecessary or unforeseen costs.
Here are some examples and practical applications of how AI is currently being utilized by CX or CS teams:
Automation of ticket creation and tagging
Automation of initial chatbot and email responses
Conversational AI
24-hour self-service
Data processing
Practical applications of AI for businesses
Boldr‘s e-commerce clients, as well as those who work with sensitive customer information, will find benefit in the fact that AI is being used to automate repetitive tasks, as well as to bolster security systems and processes, which could reduce the occurrence of fraud. Its ability to analyze large amounts of customer data in real-time, enables it to “understand” customer behavior and needs faster than any human being. This will greatly improve and personalize the customer experience journey, not to mention the fact that it can use that data to generate a customer sentiment analysis.
Practical applications of AI in a CX team
The most valuable part of AI, is its ability to make light of the menial tasks that tie up CX teams. Besides streamlining internal functions and processes, AI is invaluable when it comes to repeat inquiries from customers.
Conversational AI is probably the biggest game-changer for customer service. This is the ability for a program to learn how to respond in a more personal or conversational tone, instead of just generating standardized responses based on the parameters of the question. AI programs can be taught to be more personable and give real-time human-sounding responses to customers, which can subsequently be automated.
Where automated AI responses and data processing fail to soothe customer concerns, it will be escalated to a team member, who will have substantial information at hand before making contact with the customer. AI is therefore not a substitute for a functional CX team, but rather a sophisticated digital tool that can refine the team’s capacity to fix problems, as well as their efficiency or their output. With access to AI assistance, each team member can reduce the average handling time of queries or complaints, which will improve their CSAT and KPI reports.
AI RESOURCES
For those who are eager to learn a bit more about how AI can be utilized in a CX team, here are some resources and articles that dig deeper into the many uses of AI in CX and CS:
Every business wants to make a profit and to grow. To do more with what resources it has, not to do the same with less. At this stage, companies should focus on how to leverage AI to do exactly that. They must look for areas where AI can improve the way they run their business, or at least to enhance the experience for their customers. Those who fail to include the use of AI in this part of their business strategy, do so at their own peril.
James Fouche is the Content Manager at Boldr, as well as an author and a columnist. He is passionate about sharing his love of reading and writing with others.
| 2023-02-01T00:00:00 |
https://boldrimpact.com/learn/introducing-ai-to-the-modern-cx-team
|
[
{
"date": "2023/02/01",
"position": 62,
"query": "workplace AI adoption"
}
] |
|
WorkplaceBuddy for Microsoft Teams Review: A Simple ...
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WorkplaceBuddy for Microsoft Teams Review: A Simple and Effective Microsoft 365 Learning Bot
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https://www.uctoday.com
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[
"Uc Today Team"
] |
WorkplaceBuddy eases your Teams adoption journey through helpful tips and a personalised learning pathway. The company was founded in 2020 and is based out ...
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WorkplaceBuddy eases your Teams adoption journey through helpful tips and a personalised learning pathway. The company was founded in 2020 and is based out of Amsterdam. WorkplaceBuddy helps in Microsoft 365 adoption through smart training interventions via Microsoft Teams. It uses a chatbot-based interface to interact with end-users, learn about their unique goals and needs, and personalise the learning experience accordingly. To that end, WorkplaceBuddy is a Microsoft Silver Partner.
WorkplaceBuddy comes with a robust library of pre-built Microsoft 365 content, covering Yammer, Outlook, Teams, and SharePoint, among others. It also uses gamification principles to encourage sustained participation and steady progress. Learning experiences are tailored as per persona as well.
WorkplaceBuddy targets two types of customer audiences – end-users and resellers. The latter can leverage the platform as part of their delivery services, enabling Microsoft 365 adoption through the WorkplaceBuddy tool. Also, while WorkplaceBuddy has its content library, you can always customise and extend it further to support your e-learning requirements.
Let us review the WorkplaceBuddy app for Teams in detail.
Inside WorkplaceBuddy for Microsoft Teams
The WorkplaceBuddy app is free for the first five days, so you can easily get started without any preliminary subscription or licensing hassles. Keep in mind that WorkplaceBuddy costs €4.74 per user per month from the sixth day onwards, with special discounts for non-profits and WorkplaceBuddy in Dutch.
You can download WorkplaceBuddy directly from the company’s pricing page. You could also visit Microsoft AppSource or the application store in Teams.
WorkplaceBuddy for Teams offers the following core features:
The WorkplaceBuddy chatbot – The intelligent chatbot is among the critical features of WorkplaceBuddy, and it greets you immediately after you log into the app. New users must complete a bot-based interview session, where the WorkplaceBuddy chatbot asks you several questions about your job role, skill requirements, and learning objectives. Throughout the learning experience, you can ask the WorkplaceBuddy chatbot for tips on common Microsoft 365 tasks, like adding apps, creating documents, conducting meetings, etc.
Learning content library – The WorkplaceBuddy chatbot is one way to find and access learning content – the other alternative is to browse the learning content library. When you open the WorkplaceBuddy app, you will find the Learnings tab right next to the Chat tab on the top. Here, all the available content and learning pathways are listed as per specific categories – for example, learnings that match your profile, learning for a particular product, etc. every learning pathway is tagged as per the functionality it covers, the skill level of the learner, the role, and the product. Also, you can search for content using the search bar on the top-right.
Personal profile manager – As mentioned, WorkplaceBuddy provides Teams users with a personalised learning experience. To deliver this, the app ingests, stores, and dynamically updates your personal learning profile, which will determine the recommended pathways. Click on the Your Profile tab next to the Learnings tab on the top of the WorkplaceBuddy app. Here, you will find a history of your completed learnings, knowledge score, learnings in progress, and the number of hours you have potentially saved by learning new Microsoft 365 skills.
Content customisation – It should be noted that WorkplaceBuddy is a turnkey product, which means that you can start using the app right out of the box without any customisation or organisation-specific configurations. However, WorkplaceBuddy allows you to create a tailored learning experience. You can add your homegrown Microsoft 365 content to the library so that Teams users can search it. You can also design customised conversation flows that are accessible when a Teams user speaks with the WorkplaceBuddy chatbot. Keep in mind that this content is in addition to WorkplaceBuddy’s regularly updated repository.
Gamification – Gamification is a key feature of WorkplaceBuddy that encourages adoption. Every learner is assigned a persona name and a skill level, progressing across various levels as they complete the recommended learning pathways. You can also win badges when you complete specific milestones.
Why the WorkplaceBuddy App Makes a Difference
WorkplaceBuddy is among the few Microsoft 365 learning and adoption tools that provide a personalised and gamified experience without requiring a standalone app. Also, it is affordable and rich in customisation.
What We Think
WorkplaceBuddy is an excellent option for small, mid-sized and large companies looking to deliver personalised Microsoft 365 content under a budget. You can download it here.
| 2023-02-01T00:00:00 |
2023/02/01
|
https://www.uctoday.com/reviews/workplacebuddy-for-microsoft-teams-review-a-simple-and-effective-microsoft-365-learning-bot/
|
[
{
"date": "2023/02/01",
"position": 64,
"query": "workplace AI adoption"
}
] |
Taskforce searches for AI, drone and wearable tech to ...
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Taskforce searches for AI, drone and wearable tech to make construction safer
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https://www.imeche.org
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[] |
Artificial Intelligence (AI), drones, wearable devices and other technologies could improve safety and risk management in industrial workplaces, said the Health ...
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Engineering news
A new taskforce is searching for companies with potentially ‘game-changing’ technology that could improve safety in construction and industry.
Artificial Intelligence (AI), drones, wearable devices and other technologies could improve safety and risk management in industrial workplaces, said the Health and Safety Executive (HSE).
The regulator is inviting UK technology companies to join the Industrial Safetytech Regulatory Sandbox, a partnership with non-profit Safetytech Accelerator. The sandbox will bring technology innovators and industrial companies together to explore ‘significant opportunities’ to improve workplace health and safety.
The project has three main aims: explore ways to undertake assessment and compliance activities more effectively; help accelerate the adoption of proven safetytech products in industry; understand and reduce barriers that might delay the development of new life-saving technologies.
The sandbox aims to recruit six technology companies with high-potential products by the end of the month. It will focus initially on innovation around significant areas of risk in construction, including falls from height, vehicle collisions, crane operations and manual handling.
Dr Maurizio Pilu, managing director at Safetytech Accelerator, said: “Thanks to advances in areas such as AI, analytics, augmented reality, wearables, drones and robotics, there is now huge potential to make industry safer. This groundbreaking sandbox presents safetytech companies of all sizes with an opportunity to understand and help shape the regulatory landscape to accelerate life-saving innovation for all.”
Dr Helen Balmforth, head of data analytics at HSE and the leader of Discovering Safety, said: “We are committed to supporting health and safety innovation, and also to exploring ways that we can be innovative in how we approach regulation. We are looking to the safetytech community to help identify the best opportunities for progress and how we can collectively overcome the barriers that limit progress.”
The project is open to UK-based companies with ‘market-ready or pilot-ready’ products that could improve safety and risk management in construction. Visit the website for more information and to apply.
Want the best engineering stories delivered straight to your inbox? The Professional Engineering newsletter gives you vital updates on the most cutting-edge engineering and exciting new job opportunities. To sign up, click here.
Content published by Professional Engineering does not necessarily represent the views of the Institution of Mechanical Engineers.
| 2023-02-01T00:00:00 |
https://www.imeche.org/news/news-article/taskforce-searches-for-ai-drone-and-wearable-tech-to-make-construction-safer
|
[
{
"date": "2023/02/01",
"position": 91,
"query": "workplace AI adoption"
}
] |
|
Gartner's Intelligent IT Automation Trends for 2023 - Comidor
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Gartner’s Intelligent IT Automation Trends for 2023
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https://www.comidor.com
|
[
"Comidor Team"
] |
... Workplace. Enterprise Collaboration · Files and Content · People Management ... Organizations are now aggressively adopting AI-aided decision-making and RPA ...
|
The COVID-19 pandemic has single-handedly changed the way we used to work, shop, or avail services. We have seen how emerging technologies and automation are being used to implement digital transformation initiatives across industries. Businesses are investing heavily in IT Automation to make their move towards hybrid models and cloud-native operations.
Gartner Inc. is known for releasing research papers, which foresee the trends that will influence technology, IT, and businesses in the coming years. These automation trends reflect the changes that have been brought in by IT automation to enhance business productivity and customer experience. Businesses are looking for much-needed innovation and agility so that they can optimize their IT operations to deliver value-based KPIs while improving their overall profitability.
In this blog, we have covered Gartner’s 2023 technology trends, most relevant to intelligent IT automation. If you are an entrepreneur or an IT professional, then these trends will help you accelerate your digital business initiatives.
Here we have some exciting stats about the IT automation trends:
By 2024 , the improvements in digital workplaces and processes driven by IT automation will help organizations spend 30% of their time on IT operations management, and the rest on engineering and designing .
By 2024 , IT automation will assist digital resources to spend 30% more time on engineering, as maintenance and endpoint support will be taken care of by automation tools .
By 2023 , 40% of platform and product teams will utilize AIOps to reduce unplanned downtime by 20% .
More than 80% of survey respondents maintained that the need for innovative digital services and products will increase in 2022-23.
Top IT automation Trends
1. LNCA (Low and No Code Automation)
This technology will be a game changer for enterprise-wide automation initiatives. Low-code/No-code Automation platforms allow business owners and users to develop and deploy enterprise apps with little or no coding expertise.
As per Gartner, we may witness an almost 50% rise in enterprise automation using Low and No-Code Automation, as more organizations are poised to adopt this efficient and cost-effective method of automating applications and business processes.
Gartner also predicts that by 2025, approximately 70% of enterprise applications will be developed using low-code or no-code technologies.
It is a massive increase of 25% if we compare it with 2020. Furthermore, the Google engineering team maintains that by 2025, more than 50% of enterprise or business applications will be built by those who don’t consider themselves conventional developers (mostly known as citizen developers).
2. Hyperautomation
It is a business-driven and disciplined approach to swiftly determine, validate, and automate IT and business processes with ease.
It will continue to be a hot trend in enterprise automation in 2023. We may witness the rising adoption of Hyperautomation due to its wide range of use cases, which enables organizations to automate complex and industry-specific tasks and processes.
Hyperautomation offers an unprecedented capability to learn from human behavior, which allows consultants to gain momentum due to its increasing capability to learn from humans and automate a large number of monotonous manual tasks and processes.
The usage of Hyperautomation will only increase further as businesses will leverage its capabilities to reduce the overall cost and enhance their process efficiency.
3. Workflow Automation, Management, and Orchestration
In an increasingly competitive world, most enterprises are automating their workflows to extend the speed, efficiency, and agility of their business. Organizations are now aggressively adopting AI-aided decision-making and RPA (robotic process automation) to avail the benefits of automation.
Furthermore, workflow orchestration and automation technologies are more in demand than ever before. There is a plethora of standalone low-code or No-code workflow tools in the market, which offer many exciting features and capabilities.
For example, Comidor’s workflow automation engine is fully compliant with BPMN 2.0 and comes with AI and RPA capabilities, ready-to-use integrators, and more. Get rid of manual tasks and endless paperwork, and streamline your business processes, automate data manipulation, and get instant AI capabilities – no coding required. Take control of your workflows and start working smarter today.
4. Intelligent Document Processing
Intelligent Document Processing (IDP) is another enterprise automation technology that has evolved dramatically in recent times. It enables users to understand the context and intent of the documents, and ensure better accuracy while automating the document-based processes. IDP improves document processing time, which ultimately enhances customer experiences.
We are pretty much sure that most of the IDP service providers will extend their value proposition to incorporate Unstructured Data Processing (UDP) as a whole.
While we already have innovative applications of Artificial Intelligence such as NLP, video analytics, and computer vision, UDP-driven solutions can help fetch unstructured data from different sources and process them. It can work on a plethora of data types such as text content, social media post, videos, and even phone calls.
5. API Management
An application Programming Interface (API) is a code-based intermediary that allows two different apps to communicate with each other to perform a specific function. API plays an important role in the implementation of digital transformation and hyper-automation.
APIs can automate the backend workflows and also expedite data transfer between numerous independent systems.
API management tools and technologies have gradually become more valuable for enterprise automation, as they offer higher visibility into enterprise-wide functions and enable organizations to manage their APIs effectively and securely.
This trend will only increase in 2023, as more organizations are poised to leverage the capabilities of APIs to kickstart their digital transformation initiatives.
Three major takeaways from Gartner’s IT automation trend predictions:
The adaption of IT automation is quickening at an unprecedented pace, with more enterprises and start-ups building completely automated value chains. The surge in data diversity will only push enterprises to adopt innovative computing and storage technologies. More IT leadership positions are emerging in the IT operations and Customer experience domain.
Final Words
We are living in an era where automation is playing a pivotal role in the IT-led transformation of businesses across all industries. Enterprises are now embracing scaled automation solutions to avail the benefits of digital transformation to enhance their customer experiences while improving efficiency.
We are pretty much sure that IT automation will be a critical factor for enterprises to drive innovation, devise competitive strategies, and get a niche edge ahead of their competitors.
As an entrepreneur, leader, or IT professional, it is crucial for you to stay up to date on the latest technologies, IT automation trends, and tools. It will help you to leverage the capabilities of automation to avail the intended outcomes for your business.
Author bio:
Gourav Sharma is a Digital Marketing Strategist at Arka Softwares, a leading Web, and Mobile app development company. He has 4 years of experience in the Information Technology industry. He spends his time reading about new trends in Digital Marketing and the latest app development technologies.
| 2023-02-01T00:00:00 |
2023/02/01
|
https://www.comidor.com/news/industry-news/it-automation-trends/
|
[
{
"date": "2023/02/01",
"position": 92,
"query": "workplace AI adoption"
}
] |
The impact of artificial intelligence on labor markets in ...
|
The impact of artificial intelligence on labor markets in developing countries: a new method with an illustration for Lao PDR and urban Viet Nam
|
https://pmc.ncbi.nlm.nih.gov
|
[
"Francesco Carbonero",
"University Of Turin",
"Turin",
"Jeremy Davies",
"East Village Software Consultants",
"London",
"Ekkehard Ernst",
"Ilo Research Department",
"Geneva",
"Frank M Fossen"
] |
by F Carbonero · 2023 · Cited by 56 — AI is transforming labor markets around the world. Existing research has focused on advanced economies but has neglected developing economies.
|
Abstract AI is transforming labor markets around the world. Existing research has focused on advanced economies but has neglected developing economies. Different impacts of AI on labor markets in different countries arise not only from heterogeneous occupational structures, but also from the fact that occupations vary across countries in their composition of tasks. We propose a new methodology to translate existing measures of AI impacts that were developed for the US to countries at various levels of economic development. Our method assesses semantic similarities between textual descriptions of work activities in the US and workers’ skills elicited in surveys for other countries. We implement the approach using the measure of suitability of work activities for machine learning provided by Brynjolfsson et al. (Am Econ Assoc Pap Proc 108:43-47, 2018) for the US and the World Bank’s STEP survey for Lao PDR and Viet Nam. Our approach allows characterizing the extent to which workers and occupations in a given country are subject to destructive digitalization, which puts workers at risk of being displaced, in contrast to transformative digitalization, which tends to benefit workers. We find that workers in urban Viet Nam, in comparison to Lao PDR, are more concentrated in occupations affected by AI, which requires them to adapt or puts them at risk of being partially displaced. Our method based on semantic textual similarities using SBERT is advantageous compared to approaches transferring AI impact scores across countries using crosswalks of occupational codes. Keywords: Artificial intelligence, Machine learning, Digitalization, Labor, Skills, Developing countries
Introduction The impacts of digitalization and artificial intelligence (AI) technologies on labor markets are multifaceted. Workers performing predominantly work activities that can be automated are at risk of being displaced by digital machines. However, occupations combining activities that cannot be automated with those that can are likely to be transformed. Workers in these occupations may benefit from working closely with new digital technologies rather than being displaced by machines (Acemoglu and Restrepo 2018; Lane and Saint-Martin 2021). Prior research has investigated the impact of new digital technologies on occupations primarily in the United States (Frey and Osborne 2017; Brynjolfsson et al. 2018; Felten et al. 2019; Acemoglu et al. 2020; Fossen and Sorgner 2021, 2022) and in some cases in other developed countries (Arntz et al. 2016, 2017; Georgieff and Hyee 2021). These papers develop measures of the impact of digitalization on occupations in these countries and proceed by testing effects on wages and unemployment (Felten et al. 2019; Fossen and Sorgner 2022). Few papers in the literature investigate the impacts of digitalization in developing countries. Carbonero et al. (2020) evaluate the impacts of robotization on employment in supply chains in developing countries. Aly (2022) looks at various digitalization indices in developing countries and their associations with macroeconomic variables including employment. Although many developing countries, including some of the world’s poorest, are already using basic AI technologies, for instance, in smart farming, credit scoring and targeted advertising, advanced AI technologies are not yet widely adopted there. Yet, there exists a substantial potential for adoption of such technologies to leapfrog traditional development models (IFC 2020; Soto 2020). The use of digital technologies has accelerated in developing and even the poorest countries, not least due to lockdown measures that governments implemented during the COVID-19 crisis. In the service sector in Lao PDR, for example, the lockdowns have led many enterprises to switch to digital processes (Homsombath 2020). Similar efforts were made in the education sectors in which many activities were held online. These developments may have been a trigger for further digitalization efforts in the near future. Research applying occupation-level data for the United States to other countries typically points to a substantial risk of job destruction and an imminent job crisis, especially when analyzing developing countries (Balliester and Elsheiki 2018). There are several issues that need to be considered when analyzing the impacts of digitalization in the context of developing countries. Applying the occupational digitalization scores computed for the United States in the context of developing countries might lead to significantly biased results, since the occupational tasks in developing countries might differ considerably from the occupational tasks of a similarly coded occupation in the United States (Arntz et al. 2017).1 Alternatively, reproducing approaches that assign AI impact scores to occupations based on extensive surveys of AI experts (Frey and Osborne 2017; Brynjolfsson et al. 2018) in developing countries would be very costly. Several studies have therefore relied on correction procedures. In particular, Arntz et al. (2016, 2017) adjust occupation-level computerization risk calculated for the US occupations (Frey and Osborne 2017) by regressing them on individual- and job-specific characteristics from the OECD’s Survey of Adult Skills (PIAAC) or other national surveys available in the US and the country of interest. Then they use the estimated coefficients to make predictions of computerization risk for individual jobs and occupations in other countries. While the correction procedure partly accounts for peculiarities of national labor markets, it has several drawbacks. First, before the regression can be estimated, the occupational codes used in O*NET (6-digit level of SOC) must be translated to the occupational codes in PIAAC (ISCO) using a crosswalk, and the latter codes are only available at the imprecise 2-digit level. Arntz et al. (2016, 2017) use a multiple imputation method to deal with this issue. Second, the approach starts with digitalization scores at the occupation level, whereas we suggest starting with scores directly attributed to the much finer level of detailed work activities to enhance accuracy and precision. Third, predictions from a regression have a lower variance than the original data, which is likely to be reflected in the results. In this paper, we rely on the main advantage of previous cross-country adjustment methods, namely the use of individual-level survey data, but aim to overcome the drawbacks of prior approaches mentioned above. We develop a methodology that allows translating existing scores of AI impacts, most of which were developed using data for the U.S., to the contexts of other countries at the level of work activities. Our method allows comparing AI impacts on workers in countries at vastly different levels of development, including low-income and least-developed economies. In a nutshell, we propose to use individual-level surveys of workers’ skills, such as the World Bank's Skills Measurement Program (STEP) for developing countries or PIAAC (for OECD countries). We use the state-of-the-art method SBERT to assess semantic similarities between textual descriptions of detailed work activities (DWA) from the O*NET occupational database for the US,2 for which AI impact scores are available, and the textual descriptions of workers’ skills elicited in surveys available for developing countries, in particular the World Bank’s STEP Skills Measurement Program. We then use the matrix of relatedness to translate the AI impact scores to the level of individual workers’ skills in a given country. In this way, an additional advantage of our method is that it supports different levels of analysis of AI impact on labor markets: at the individual level distinguishing by workers’ characteristics such as age or gender, at the skill level, or at the occupation level. We illustrate the method using the cases of two neighboring Asian countries: Lao PDR, a least developed country according to the United Nations classification,3 and urban areas in Viet Nam, a developing country that has transformed from one of the poorest countries in the 1980s into a lower middle-income country today. Among the digitalization measures available, we choose the suitability of work activities for machine learning as reported by Brynjolfsson et al. (2018). The picture that emerges from our approach is insightful and shows that the impact of AI on individual workers is more heterogeneous in urban Viet Nam than in Lao PDR. While most respondents in urban Viet Nam are moderately affected, a significant number of workers are at high risk of being displaced by digital technologies; in Lao PDR, the impact is more evenly distributed. The most common occupation reported by STEP respondents in Lao PDR, subsistence crop farming, has a comparably low suitability for machine learning, presumably due to the importance of non-routine manual tasks in this occupation. The most common occupations in urban Viet Nam are more suitable for machine learning, in particular the occupations of shop salespersons and textile machine operators, but also of crop growers (according to the tasks they perform in Viet Nam). At the same time, workers in these occupations perform a relatively large variety of tasks in Viet Nam, some of which cannot be automated; this makes it likely that these occupations will be transformed rather than completely automated. It should be noted that these results only make an assessment regarding the impact of machine learning on jobs, not about the overall risk of automation due to other types of technologies, such as non-AI software and robots. Non-digital mechanization, for instance, might affect occupations such as subsistence crop farming in Lao PDR more immediately than digitalization and AI. We also compare results obtained with the proposed method to the results from a naïve approach when the AI impact scores are transferred from the United States to Lao PDR and Viet Nam at the level of occupations. In comparison to our proposed method based on semantic textual similarity matching, the naïve approach seems to produce too much noise to derive meaningful insights.
Data and methodology AI impact measures Several measures of AI impacts on occupations in the United States have been suggested by recent literature. To illustrate our method, among the available digitalization measures that we will briefly discuss below, we choose the suitability of work activities for machine learning (ML) provided by Brynjolfsson et al. (2018). The main reason for this choice is that this measure is available at the very detailed level of work activities, while other measures are usually available at the less disaggregated level of workers’ abilities, work tasks or occupations. Brynjolfsson and Mitchell (2017) identify eight key criteria that specify conditions under which ML techniques can be employed as substitutes or complements to human labor.4 The authors emphasize that these criteria are developed solely on the basis of technical feasibility, and that other factors, such as the elasticity of labor supply, price and income elasticities, determine the economic feasibility of implementation of ML applications. Brynjolfsson et al. (2018) create a rubric of 23 questions that aim at estimating the degree to which a detailed work activity (DWA) as defined in the O*NET database (compiled by the US Department of Labor) falls under the eight above criteria, and hence, is “suitable for machine learning” (SML). Corresponding to the eight criteria, this rubric also only concentrates on technical feasibility, not on the economic, organizational, legal, cultural, and societal factors influencing ML adoption. Based on a survey, the authors evaluate the potential for applying machine learning to the 2,069 DWAs, 18,156 tasks, and 964 occupations in the O*NET database. The authors use Crowdflower, a Human Intelligence Task (HIT) crowdsourcing platform, where each DWA is scored by 7 to 10 respondents with knowledge in the area. Through the 23 questions respondents are asked to evaluate each DWA based on the eight criteria. Brynjolfsson et al. (2018) then aggregate their scores from the DWA level to the task level and further to the occupation level in the United States weighted by importance as recorded in O*NET. The result is an average SML score for each US occupation. Since the SML scores reported by these authors focus on the possibility of automation of activities currently performed by human workers, the average SML of the work activities performed in an occupation can be interpreted as destructive digitalization in the sense of putting workers at risk of being displaced (see also Fossen and Sorgner 2022). In contrast, the standard deviation of SML scores across work activities performed within an occupation reflects transformative digitalization, because occupations combining activities that can be automated with activities that cannot be automated are likely to be reorganized (Brynjolfsson et al. 2018) and transformed rather than to displace workers. Workers in these occupations are more likely to benefit from their close interaction with new digital technologies than to lose their jobs. The SML scores have the advantage that they are first generated at the level of DWAs in O*NET. These DWAs resemble the skills and work activities elicited in surveys like STEP or PIAAC, which facilitates the translation of these scores to other countries. We elaborate further on the conceptual differences and similarities between the DWAs from O*NET and the skills questions from STEP in Section 2. 3. Alternative currently available AI impact measures could also be applied within our methodological framework, but some adaption would be necessary. A second option are the AI Occupational Impact (AIOI) scores provided by Felten et al. (2018), potentially as a measure of transformative digitalization (as argued by Fossen and Sorgner 2021, 2022). These scores are constructed at the ability level in O*NET. Although our approach could be suitable to use the AIOI scores in combination with individual-level surveys measuring workers’ abilities, there are only 52 abilities in O*NET, much less than DWAs. Moreover, the textual descriptions of abilities in O*NET seem to be quite dissimilar to the textual descriptions of skills provided in STEP, reflecting different concepts underlying these measures and, therefore, making the AIOI scores less suitable for applying our approach in combination with the STEP surveys.5 A third option are the computerization probability scores provided by Frey and Osborne (2017) as a measure of destructive digitalization. However, these probability scores are only available at the occupation level, so one would have to break these down to the level of work activities, implying imprecision. One way to do so could be to regress the computerization probabilities at the occupation level on the nine bottleneck skills from O*NET identified by Frey and Osborne (2017). This would allow the prediction of computerization risk at the occupation level in countries where data on occupations linked to the bottleneck skills are available. Arntz et al. (2016, 2017) pursue a similar approach by regressing the automation probability as provided by Frey and Osborne (2017) on a set of individual job-related characteristics (including tasks and skills) from the PIAAC survey. Yet, the assessment of which tasks are automatable is ultimately derived from the expert opinions assembled by Frey and Osborne (2017) on the occupational level. Alternatively, one would have to resort to the simple approach of transferring the measure to other countries at the occupation level, which does not seem to be accurate, as argued above. A fourth option is provided by Webb (2020). He develops a measure of exposure of occupations to AI technology by matching descriptions of work tasks in O*NET to the text of patents using text similarity measures. This procedure generates AI exposure scores at the O*NET task level; however, the author currently only provides the data aggregated to the occupation level. It should be noted that the different measures capture different technologies within digitalization and AI; Fossen and Sorgner (2022) provide a detailed discussion. In particular, machine learning is a subfield of AI from a technological perspective. Therefore, the rankings and relative positions of occupationsare not necessarily expected to be similar when using the different scores. Table 3 in the Appendix shows the mean SML score and its within-occupation standard deviation provided by Brynjolfsson et al. (2018), the computerization probability provided by Frey and Osborne (2017), and the AIOI scores provided by Felten et al. (2018), which were all developed for the United States, for the 10 largest occupations in the United States in terms of employment. Cashiers have the highest SML score among these occupations, and also the highest computerization probability, but a moderate AIOI score. Laborers and freight, stock and material movers (by hand) have the lowest SML score and AIOI score, but a high computerization probability. Therefore, analyses using different scores would be interesting as they would answer different research questions, but they are not suitable as robustness checks. Table 3. Measures of AI Impact on the Largest Occupations in the United States SOC 2010 Code Occupation Employment in the US Mean SML Std. dev
SML AI Occ. Impact Comput. prob 41–2031 Retail Salespersons 4,448,120 3.574 0.486 0.666 0.92 35–3021 Combined Food Preparation & Serving Workers, Including Fast Food 3,676,180 3.451 0.593 0.642 0.92 41–2011 Cashiers 3,635,550 3.670 0.465 0.649 0.97 43–9061 Office Clerks, General 2,972,930 3.613 0.574 0.699 0.96 29–1141 Registered Nurses 2,951,960 3.428 0.556 0.661 0.009 53–7062 Laborers and Freight, Stock, & Material Movers, Hand 2,893,180 3.314 0.627 0.622 0.85 43–4051 Customer Service Representatives 2,871,400 3.597 0.558 0.713 0.55 35–3031 Waiters & Waitresses 2,582,410 3.447 0.703 0.626 0.94 11–1021 General & Operations Managers 2,289,770 3.473 0.508 0.683 0.16 39–9021 Personal Care Aides 2,211,950 3.483 0.510 0.650 0.74 Open in a new tab Individual-level data on skills in developing countries: STEP survey The STEP skills measurement program is provided by the World Bank. The goal of the survey is to provide representative individual-level data on the skills of the workforce and the usage of these skills in the individuals’ jobs that can be compared across countries. STEP is based on the adult population aged between 15 and 64 residing in urban municipalities6 in developing countries and is comparable to the PIAAC survey by the OECD. While the focus of PIAAC is primarily on high-income developed countries, the STEP survey focuses on developing and transition economies. So far, STEP has been administered in two waves, in 2012 and 2013, in 13 countries, including Lao PDR and Viet Nam (surveys in these two countries were conducted in 2012). STEP surveys provide detailed information on individuals’ socio-demographic characteristics (e.g., age, gender, formal education level) and job characteristics. The STEP survey specifically targets the measurement of skills of the workforce, broadly defined as “abilities to do certain things”. STEP distinguishes three types of skills: cognitive skills (e.g., reading and writing proficiency), socio-emotional skills (referring to social and emotional behaviors, personality, and attitudes), and job-relevant (technical) skills (see Pierre et al. 2014, for more details). For the purpose of our study, we use a subsection of STEP questions that attempt to measure cognitive skills and job-relevant skills through self-reported information on respondents’ use of these skills in work-related activities (see Table 4 in the Appendix). These questions therefore link the relevant skills to typical work activities. These activities in the STEP questions resemble direct work activities (DWA) as defined in O*NET. We call these 44 activities “STEP skills” throughout the paper, even though, strictly speaking, these questions mostly relate to certain activities that are supposed to reveal information about underlying skills of the respondents in the three categories mentioned above (cognitive skills, socio-emotional skills, and job-relevant skills). We exclude respondents from the sample who did not work during the last 12 months before the interview because they are not asked about their work-related skills. Table 4. STEP Questions to Measure Workers’ Skills and SML Scores Question SML Do you (did you) read anything at this work, including very short notes or instructions that are only a few sentences long? -0.31 As a regular part of this work, do you (did you) have to read forms? 0.80 As a regular part of this work, do you (did you) have to read BILLS OR FINANCIAL STATEMENTS? 1.07 As a regular part of this work, do you (did you) have to read INSTRUCTION MANUALS/ OPERATING MANUALS -0.74 As a regular part of this work, do you (did you) have to read REPORTS? 1.24 As a regular part of this work, do you (did you) have to read NEWSPAPERS, MAGAZINES, OR BOOKS? 0.57 As part of this work, do you (did you) fill out bills or forms? 0.40 Do you (did you) ever have to write anything (else) at work, including very short notes, lists, or instructions that are only a few sentences long? 0.07 As a normal part of this work, do you (did you) MEASURE OR ESTIMATE SIZES, WEIGHTS, DISTANCES, ETC -0.40 As a normal part of this work, do you (did you) CALCULATE PRICES OR COSTS 0.96 As a normal part of this work, do you (did you) PERFORM ANY OTHER MULTIPLICATION OR DIVISION -1.20 As a normal part of this work, do you (did you) USE OR CALCULATE FRACTIONS, DECIMALS OR PERCENTAGES 0.59 As a normal part of this work, do you (did you) USE MORE ADVANCED MATH, SUCH AS ALGEBRA, GEOMETRY, TRIGONOMETRY, ETC -0.27 As part of this work, do you regularly have to lift or pull anything weighing at least 50 pounds [25 kilos]? -1.75 Using any number from 1 to 10 where 1 is not at all physically demanding (such as sitting at a desk answering a telephone) and 10 is extremely physically demanding (such as carrying heavy loads, construction worker, etc.), what number would you use to rate how physically demanding your work is? -2.00 As part of this work, do you (did you) have any contact with people other than co-workers, for example with customers, clients, students, or the public? -0.63 Using any number from 1 to 10, where 1 is little involvement or short routine involvements, and 10 means much of the work involves meeting or interacting for at least 10–15 min at a time with a customer, client, student or the public, what number would you use to rate this work? -0.92 As part of this work, do you drive a car, truck or three-wheeler? -1.12 As part of this work, do you (did you) repair/maintain electronic equipment? (cell phones, computers, printers, other electronic equipment…) -0.68 As part of this work, do you (did you) operate or work with any heavy machines or industrial equipment? For example, machines/equipment in factories, construction sites, warehouses, repair shops or machine shops, industrial kitchens, some farming (tractors, harvesters, milking machine) -1.49 As part of this work, how often do you have to undertake tasks that require at least 30 min of thinking (examples: mechanic figuring out a car problem, budgeting for a business, teacher making a lesson plan, restaurant owner creating a new menu/dish for restaurant, dress maker designing a new dress) -0.13 As part of this work, do you (did you) have to make formal presentations to clients or colleagues to provide information or persuade them of your point of view? -1.07 As a normal part of this work do you direct and check the work of other workers (supervise)? -0.90 Still thinking of your work, how much freedom do you (did you) have to decide how to do your work in your own way, rather than following a fixed procedure or a supervisor's instructions? Use any number from 1 to 10 where 1 is no freedom and 10 is complete freedom -0.91 How often does (did) this work involve carrying out short, repetitive tasks? -1.41 How often does (did) this work involve learning new things? -0.53 As part of this work do you (did you) regularly use A TELEPHONE, MOBILE PHONE, PAGER OR OTHER COMMUNICATION DEVICE? -0.78 As a part of your work do you (did you) use a computer? 0.70 As part of this work do you (did you) regularly use A BAR CODE READER? 0.38 Does (did) your work as [OCCUPATION] require the use of EMAIL 0.81 Does (did) your work as [OCCUPATION] require the use of SEARCHING FOR INFORMATION ON THE INTERNET 2.12 Does (did) your work as [OCCUPATION] require the use of DATA ENTRY 1.50 Does (did) your work as [OCCUPATION] require the use of WORD PROCESSING 0.99 Does (did) your work as [OCCUPATION] require the use of SPREADSHEETS (such as EXCEL) 0.81 Does (did) your work as [OCCUPATION] require the use of DATABASES (such as ACCESS) 2.36 Does (did) your work as [OCCUPATION] require the use of other software packages, designing websites or doing programming or managing networks? -0.10 Does (did) your work require the use of ADVANCED FUNCTIONS IN SPREADSHEETS SUCH AS MACROS AND COMPLEX EQUATIONS 0.21 Does (did) your work require the use of BOOK-KEEPING, ACCOUNTING OR FINANCIAL SOFTWARE 1.13 Does (did) your work require the use of PRESENTATION, GRAPHICS SOFTWARE (such as POWERPOINT) -0.32 Does (did) your work require the use of DESIGNING WEBSITES 0.10 Does (did) your work require the use of CAD SOFTWARE (computer aided design) -0.06 Does (did) your work require the use of STATISTICAL ANALYSIS OR OTHER ANALYSIS 0.97 Does (did) your work require the use of SOFTWARE PROGRAMMING -0.19 Does (did) your work require the use of MANAGING COMPUTER NETWORKS 0.14 Open in a new tab Matching O*NET work activities to skills in STEP A major challenge regards matching the descriptions of work activities in O*NET, for which we have AI impact scores such as the SML scores, to skills in STEP. Even at the level of abilities, which is more aggregated than the level of DWAs, a manual approach seems infeasible. For example, there are 52 O*NET abilities and 44 skills in STEP, so a translation matrix would require determining 2,288 weighting scores. Furthermore, this approach would be entirely subjective. Alternatively, one could conduct a new expert survey specific to a country of interest, similar to the approach of Brynjolfsson et al. (2018) or Frey and Osborne (2017), to produce new digitalization scores instead of using the existing scores developed for the United States. Although we consider this approach as a possible avenue for further research, a disadvantage is that it requires substantial resources (e.g., conducting a survey and collecting expert judgments), and it would be limited to a single country or region. In this paper, we suggest and illustrate a third approach. We directly match 2,069 detailed work activities (DWAs) in O*NET to the 44 STEP skills creating a matrix of relatedness. The PIAAC survey could also be used instead of the STEP to target a different set of countries. O*NET uses its “Content Model” as its conceptual foundation and provides clear definitions for abilities (“enduring attributes of the individual that influence performance”), for skills (“developed or acquired attributes of an individual that may be related to work performance”), and for detailed work activities (“specific work activities that are performed across a small to moderate number of occupations within a job family”). The O*NET model defines a set of generic skills, for example, basic skills like “active listening”, “mathematics”, or cross-functional skills like “social skills” or “technical skills”, which can be further broken down into a total of 35 more detailed skills. Workers then need some of these 35 skills to successfully carry out tasks or activities in their occupations. These activities are described in detail as 2069 DWAs, which are then linked to the 1014 U.S. occupations. As we explained more elaborately in the previous section, the STEP survey collects a wide range of variables including questions about performed activities at work. It does not provide a detailed typology and rather asks the interviewee about actual activities he or she has performed recently (which may allow to draw conclusions on the skills of the surveyed person). The 44 “STEP skills” from the utilized questions resemble more the DWAs than the generic skills in O*NET. Thus, our approach works better at the DWA level than the abilities or skills level. This has affected the choice of the AI impact measure that we use to illustrate our method: since the SML scores of Brynjolfsson et al. (2018) are available at the work activities level, the application of our approach at the work activities level using the SML scores is straightforward. To find semantic similarities between the textual descriptions of O*NET work activities and the STEP skill measures, we apply automated semantic textual similarity matching techniques (SBERT). By using SBERT, we avoid a manual assignment of similarity as discussed above. The main advantages of this approach are the following: it is systematic rather than subjective; it is automated; there is no need to conduct new surveys; and the same method can be used with different data sources such as STEP and PIAAC for many countries. As our method is based on activities performed within the occupation, it has the additional advantage that occupations not included in the original set of occupations with AI impact scores can be examined as well, including new or reorganized occupations. A new method based on semantic textual similarity matching using SBERT In this section, we describe our method in detailed steps. The first step involves processing the textual descriptions of the DWAs in O*NET and the descriptions of the skills used by employed STEP respondents in their main job. The latter are the questions from the STEP questionnaire that aim at assessing the skills of employed respondents (see Table 4 in the Appendix). We combine the textual descriptions to a single string vector. Then we preprocess the string data stored in this vector. This includes removal of accents, consecutive whitespaces, substitutions of various text characters (e.g., “- “, “,” and “.”), and text conversion to lowercase. In the next step, word (semantic) embeddings are created for both DWAs and STEP questions using the Sentence-BERT (SBERT) method (Reimers & Gurevych 2019).7 The model we apply is provided by MS Marco, which is pre-trained with real user search queries from the Bing search engine, a corpus that consists of 8.8 million passages. In the second step, we create a similarity matrix that contains cosine measures of similarity8 between all documents in the sample using the semantic word embeddings created in the previous step. These similarity measures account for semantic similarity between the textual descriptions of 2069 DWAs from O*NET and 44 STEP questions.9 O*NET also provides broader, less occupation-specific activity descriptions in a hierarchy. General work activities (GWAs) are the broadest category, followed by intermediate work activities (IWAs), and DWAs are the finest categories. In addition to the first cosine similarity matrix using the DWAs, we create a second cosine similarity matrix using the GWAs to add more information on the nature of each work activity. For example, consider the DWA “Prepare forms or applications.” We improve similarity matching results by adding information that this DWA belongs to the broader GWA category “Documenting/Recording Information”. This way we distinguish this DWA clearly from the DWA “Position construction forms or molds”, which also contains the word “form”, but belongs to the different GWA category “Handling and moving objects”. Our final similarity measure is built as the average between the two similarity measures: between STEP skills and DWAs on the one hand and STEP skills and GWAs on the other hand. The overall patterns of results are not very sensitive to the choice of whether the similarity scores of the DWAs are averaged with any similarity scores of higher-level categories: with the GWAs as done here, with the IWAs, with both the GWAs and IWAs, or with none of these higher-level categories. After this second step, we have for each of the 44 STEP skills 2069 similarity scores that link the particular STEP skill to the DWAs. In the third step, we use these final similarity measures as weights to create SML scores at the level of STEP skills. We do so by calculating for each of the 44 STEP skills a weighted average of the SML scores at the O*NET DWA activity level: SML skill = ∑ activities similarity a c t i v i t y , s k i l l SML activity / ∑ activities similarity a c t i v i t y , s k i l l 1 While larger SML scores signify better suitability of the skills for machine learning, the units of the original SML scores provided by Brynjolfsson et al. (2018) do not have a direct interpretation. Therefore, we standardize the SML scores at this level of STEP skills, with each skill receiving the same weight. Table 4 in the Appendix shows the standardized SML score for each of the 44 skills in STEP. For example, ‘using databases’ and ‘searching for information on the internet’ are the skills most suitable for machine learning, as indicated by the largest SML scores, which seems very plausible. In contrast, ‘physically demanding work’ has the lowest SML score. An example for physically demanding work from the STEP questionnaire is ‘construction’ and one for physically not demanding work is ‘sitting at a desk answering a phone’; it is plausible that the latter task is much more suitable for machine learning (an example would be automated call centers using AI) than the former. Fourth, we merge the SML scores calculated at the level of STEP skills with the individual-level STEP surveys for Lao PDR and Viet Nam conducted in 2012, the latest available year for both countries. There are three types of questions in STEP that are used to measure the skills respondents use in their jobs: yes/no questions about whether a certain skill is relevant in one’s job (e.g., if a job requires reading books); cards questions that measure on a 10-point Likert scale the extent to which a particular job characteristic is relevant for one’s main job (e.g., the extent to which a job is physically demanding); and frequency questions that measure (on a 4- or 5-point Likert scale) the time that a person dedicates to a particular skill or task in his or her main job. In order to make the responses to the different types of questions comparable, we normalize them such that the responses can take values within an interval between 0 and 1. Now we use the normalized individual responses to create a score capturing the SML of the skills each individual uses in his or her job. More precisely, we create an SML score for each individual i averaged over the skills and weighted by the normalized individual responses to the questions on the usage of these skills. This is our measure of labor-displacing (destructive) AI technology at the level of the individuals’ jobs: SML i = ∑ skills usage i , s k i l l SML skill / ∑ skills usage i , s k i l l 2 Fifth, we create mean SML scores at the occupation level and the within-occupation standard deviation of the SML scores. We follow the method by Brynjolfsson et al. (2018) as closely as possible. These authors start with SML scores for each DWA in O*NET, then they aggregate them to a broader level of tasks and then to the level of occupations by building weighted averages (they call this mSML). In addition, they calculate the standard deviation of SML across tasks within each occupation (sdmSML). Both mSML and sdmSML are weighted by the importance of the tasks in the occupation as provided in O*NET. Since detailed occupation databases like O*NET are unavailable for most countries, including Lao PDR and Viet Nam, we use the STEP survey to derive the task composition of occupations in these countries. To do so, we calculate the average of the usage of each skill, obtained from questions in STEP, over individuals i in each occupation occ in a country: usage o c c , s k i l l = usage i , s k i l l ¯ occ i = o c c 3 Then we create an SML score for each occupation as the average SML score over the skills, weighted by the average usage of the skills in the occupation. This is our measure of labor-displacing (destructive) AI technology at the level of occupations: mSML occ = ∑ skills usage o c c , s k i l l SML skill / ∑ skills usage o c c , s k i l l 4 Finally, we calculate the standard deviation of the SML scores across the skills in each occupation, weighted by the average usage of the skills in the occupation in the country ( usage o c c , s k i l l ): sdmSML occ = σ SML skill 5 A large standard deviation of the SML scores within an occupations indicates that an occupation combines work activities that are suitable for machine learning with work activities that are not suitable for machine learning. This suggests that human workers will still be needed in the occupation but could closely collaborate with AI technologies in reorganized occupations (Brynjolfsson et al. 2018). Therefore, we interpret this measure as transformative AI technology at the level of occupations.
Discussion We proposed a methodology that allows meaningfully assessing AI impacts on individuals, jobs, and occupations in different countries. So far, the analysis of AI impacts on labor markets in countries other than the United States has been rather limited, particularly so in developing countries. While the implementation of AI technologies is still rather low in developing countries, basic AI technologies are already in use in these countries, and substantial potential for adoption of more advanced AI technologies has been identified (IFC 2020). Pronounced interest in enhancing the implementation rate of AI technologies in developing countries is further driven by the promise of these technologies to help leapfrog development.12 Hence, understanding the impacts of AI on labor markets in developing countries, including in least developed countries, is crucial, but is dependent on the availability of appropriate methods. Previous methods that we discussed in this paper do not sufficiently account for the fact that occupations are organized in different ways and comprise different work activities across countries. This has been the main challenge to the study of impacts of digitalization on occupations in various countries. The novel method we propose in this paper relies on the assessment of the suitability for machine learning of 2,069 detailed work activities that constitute occupations. These detailed work activities are reasonably universal activities that can be considered relevant in all labor markets including those in developing and least developed countries. This highly disaggregated level of analysis allows us to overcome the main challenge described above. In a nutshell, our method is based on the SBERT assessment of semantic similarities between textual descriptions of detailed work activities in the occupational database O*NET in the United States, for which digitalization measures are available, and skills elicited in household surveys available in a wide range of countries, such as STEP or PIAAC. This makes it possible to translate measures of digitalization to other countries at the level of work activities and to compare the impact of digitalization across countries and for various groups of individual workers within countries. This method builds on and advances prior approaches such as that suggested by Arntz et al. (2016, 2017), which starts from occupation-level digitalization scores instead of detailed work activities and relies on a crosswalk to 2-digit-level occupational scores. We illustrate our approach using the suitability of work activities for machine learning (SML) provided by Brynjolfsson et al. (2018) as the AI impact measure, STEP as the survey of individual skills used at work, and the country cases of Lao PDR, a least developed country, and its neighbor Viet Nam, a developing country. Our methodology allows calculating AI impact scores at the level of individuals rather than at the level of occupations, and it provides less noisy and more insightful results than the naïve approach when digitalization measures are translated to other countries at the occupation level. While the mean of the suitability of work activities for machine learning in an occupation reflects destructive (potentially labor-displacing) AI technology, we also calculate the within-occupational variation of this measure to account for transformative effects of AI technology or the extent to which an occupation can be reorganized rather than replaced by technology. The main insights from our analysis for Lao PDR and Viet Nam can be summarized as follows. First, we find that a larger share of individuals and occupations in urban areas in Viet Nam are exposed to labor-displacing machine learning technologies than in Lao PDR (where the data covers both urban and rural areas). This observation might reflect the differences in skill use between the two countries but also the fact that Viet Nam has already seen a larger transformation of its labor market through previous waves of mechanization, thus, making implementation of machine learning technologies easier. A significant share of workers in Lao PDR are employed in subsistence crop farming where the immediate implementation of AI technologies is challenging given the current state of technology and human capital in the country. This reduces the threat of rising unemployment due to this specific type of technology, but at the same time casts doubt on the feasibility of leapfrogging the current development path by means of AI technologies in Lao PDR. In Viet Nam, where the potential for labor-displacing automation is greater, policy responses could consist, for instance, in implementing measures to support job creation in less affected sectors or supporting workers in obtaining skills that will allow them to make transitions to jobs in these sectors. Second, the urban labor market in Viet Nam is pronouncedly more heterogeneous with respect to the impacts of AI on individual workers, as compared to the labor market in Lao PDR. Both countries have a rather high share of workers in occupations that are characterized by high suitability of work activities for machine learning technologies, and, at the same time, have a high potential for re-organization of tasks within occupations. However, in Viet Nam there are some relatively highly populated occupations, such as building frame workers, that mainly consist of work activities that are not very suitable for machine learning technologies. While these occupations can be considered as safe in terms of labor-displacing effects of AI on them, there are not many opportunities for workers employed in these occupations to improve their productivity by means of AI. Thus, policy makers should monitor the aspects of inequality that may be due to unequally distributed opportunities for productive work using AI technologies across occupations. Third, the results of gender-disaggregated analysis indicate that in both countries female workers are slightly more affected by labor-displacing AI technologies than their male counterparts. This is in line with previous research on the impacts of digital technologies on women in the context of developing countries (e.g., Sorgner 2019). We further show that heterogeneity of AI impact on occupations in urban Viet Nam does not seem to be driven by male or female workers, but that it is a rather general phenomenon in this country. Given that the digital gender gap is particularly pronounced in developing countries (Mariscal et al. 2019), policy makers should design and promote educational programs designed for girls and women, to increase their participation in STEM fields and prevent the aggravation of the digital gender gap. Fourth, several insights emerge from our analysis disaggregated by workers in different age cohorts. We find substantial differences in both countries regarding the impact of AI technologies on younger workers. In Lao PDR, younger workers appear to be least affected by suitability of their work activities for machine learning technologies, while in urban Viet Nam younger workers seem to be among the most affected by this type of AI technology. This suggests that there are large differences in skill use among young workers in both countries, which deserves a more in-depth analysis given that particularly in Lao PDR the share of young individuals in the population is substantial. Our analysis is not without limitations. Some limitations can be attributed to the methodology, while others are due to the data used in our analysis. In terms of methodology, we were able to improve earlier methods by significantly disaggregating the level of analysis and breaking it down to the level of detailed work activities. Still, one may wonder in how far the detailed work activities are comparable across countries, given different stages of economic development. We argue that using more than 2,000 detailed work activities is currently the most disaggregated level of analysis used in the literature, which represents an important advantage of our method. The highly disaggregated level of work activities makes them rather universal and applicable in various contexts. Moreover, our methodology is based on the application of semantic similarity matching techniques with textual data. We rely on the state-of-the-art Natural Language Processing technique, namely SBERT, to create semantic word embeddings to be used later for finding similar textual descriptions of work activities and skills. Should more advanced methods become available in the future, the method can be adjusted accordingly. There are several limitations in terms of data used in the analysis. First, surveys like STEP and PIAAC elicit a rather restricted number of skills, which might lead to imprecise results of similarity matching with work activities, as some of the latter might be relevant for one’s job but corresponding information is missing in the survey. Therefore, household survey programs should ensure to include comprehensive information about skills and tasks that do not miss important areas. Second, for illustration purposes we used the measure of suitability of work activities for machine learning provided by Brynjolfsson et al. (2018). If other measures, for instance, of other types of AI technologies will be developed in the future that are available at this narrow level of analysis, they can be adopted with our methodology in a straightforward way. In addition, future surveys should also attempt to distinguish between work activities, tasks, and abilities in a more systematic way, because some existing AI measures are available at the level of abilities (e.g., Felten et al. 2018), which were not measured in the STEP survey and therefore could not be analyzed with our method. Moreover, considering the speed at which new AI technologies are being developed to automate tasks hitherto not feasible, a more forward-looking approach could be to translate patent data on AI to identify tasks and skills susceptible to be replaced in the future, similar to the approach undertaken by Webb (2020). In addition, it would be desirable to have measures of technology adoption in addition to the task suitability measures to assess the actual impact of digital technologies on job tasks. The actual impact of machine learning technologies on jobs in developing countries could be diminished by many barriers to automation, such as the availability of a young and relatively cheap labor force, the presence of tariffs on digital goods, a lack of high-quality human capital that is needed to adopt new digital technologies, and a relatively high cost of technology adoption given a high share of SMEs and informal businesses, among others (World Bank 2016). Third, the STEP surveys for Lao PDR and Viet Nam are only available for the year 2012. It would be very useful to have similar surveys of adult’s skills in developing countries that are more recent, representative and include a sufficient number of respondents to allow for a meaningful analysis of different categories of workers. In addition, the measure of suitability of job tasks to machine learning technologies (Brynjolfsson et al. 2018) that we use in our analysis is slightly more recent than the STEP data. Thus, our results show how the occupations of individuals, captured in the structure that existed in Lao PDR and Viet Nam in 2012, were expected to become suitable for machine learning in subsequent years. If in the relatively short period between the collection of the STEP data and the construction of the SML measure the adoption of machine learning technologies in developing countries already affected the composition of job tasks individuals performed, our estimation would still be relevant because it demonstrates the potential impact of machine learning technologies on the structures that existed in 2012. Availability of more recent data on job tasks in developing countries would allow to estimate the extent to which job tasks have changed over the last decade and to relate these changes to the availability of machine learning technologies. In addition, the STEP survey was mainly conducted in urban areas of developing countries but given a strong urban–rural regional divide in these countries, it would be desirable to have data that also includes respondents residing in rural areas. In this paper, only data for Lao PDR covered population residing both in urban and rural areas.
Conclusion Our proposed methodology opens avenues for future research by allowing the estimation of digitalization impact measures of choice for a wide range of different countries, both developing and developed countries. While our illustrative example focuses on SML scores, the STEP survey and the cases of Lao PDR and Viet Nam, other digitalization measures, other surveys such as PIAAC, and other countries should be investigated in the future. The full value of our approach will become visible when applying it to various countries, because the methodology allows using the same digitalization measures across countries, which makes the results comparable. This research will inform policymakers about challenges and opportunities that new digital technologies deliver to different labor markets outside of the United States in a more targeted and precise way than current approaches do. Comparing the impact of digitalization between developed and developing countries will allow adjusting economic development strategies in a timely manner. Future research will also be able to apply our methodology to regions within countries as far as representative surveys with sufficient sample sizes are available. This research will reveal regional digital divides due to digitalization and AI and allow policymakers to develop mitigating and enabling labor market policies such as targeted training programs.
Appendix Table 3 Table 4 Table 5 Figure 11 Fig. 11. Open in a new tab Destructive and Transformative Digitalization in Viet Nam (Urban and Rural Areas). Notes: Each bubble represents an occupation in Viet Nam. mSML denotes the mean suitability for machine learning of skills used in an occupation (standardized at the level of STEP skills) and is a measure for destructive digitalization. sdmSML denotes the standard deviation of the SML of skills used within each occupation and is a measure of transformative digitalization. The size of the bubbles represents employment in the occupations based on the 2016 Labour Force Survey for Viet Nam (both urban and rural areas). The largest occupations are labeled
Acknowledgements We thank Fabrizio Colella, Rafael Lalive, and participants at the 2021 International Joseph A. Schumpeter Society Conference in Rome, the 2021 United Nations “Least Developed Countries Future Forum” in Helsinki, the 2022 International Centre for Economic Analysis virtual “Future of Work” conference, the 2022 “AI in Strategic Management” workshop at the Center for the Future of Management at the NYU Stern School of Business, and seminar participants at the International Labor Organization and ESAI Business School, for valuable comments.
Data Availability The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
Declarations Conflict of interest The authors have no relevant or material financial or non-financial interests that relate to the research described in this paper. The authors have no competing interests to declare that are relevant to the content of this article. Any view expressed or conclusions drawn represent the views of the authors and do not necessarily represent ILO views or ILO policy. The views expressed herein should be attributed to the authors and not to the ILO, its management or its constituents.
Footnotes 1 Consider the following examples for differences between countries: Teaching is an important part of the occupation of craftspeople in Germany because they teach apprentices, whereas teaching crafts is performed by teachers in schools in other countries. Another example is farmers: A large share of a farmer’s work in a developing country may be manual field labor, whereas a farmer’s workday in the United States is filled to a larger extent with accounting work. Therefore, the impact of AI on farmers may be different across countries. 2 O*NET is a database of quantitative indicators about a variety of attributes for 1016 occupations in the United States. Based on expert opinions or worker surveys, these indicators cover various job-oriented attributes (occupational requirements, workforce characteristics, occupation-specific information) and worker-oriented attributes (worker characteristics, worker requirements and experience requirements). 3 https://www.un.org/development/desa/dpad/least-developed-country-category.html. 4 The following eight criteria are mentioned by the authors: (i) Learning a function that maps well-defined inputs to well-defined outputs, (ii) large (digital) data sets exist or can be created containing input–output pairs, (iii) the task provides clear feedback with clearly definable goals and metrics, (iv) no long chains of logic or reasoning that depend on diverse background knowledge or common sense, (v) no need for detailed explanation of how the decision was made, (vi) a tolerance for error and no need for provably correct or optimal solutions, (vii) the phenomenon or function being learned should not change rapidly over time, (viii) no specialized dexterity, physical skills, or mobility required. 5 In a related study, Tolan et al. (2021) map 59 generic tasks from worker surveys, such as PIAAC, to 14 cognitive abilities, and then to 328 AI evaluation tasks that they identify from the literature. They also rely on experts’ judgements to relate tasks to abilities and abilities to AI evaluation tasks. 6 For Lao PDR, the survey covered also rural areas. 7 SBERT is a state-of-the-art method in Natural Language Processing (NLP). It performs significantly better than alternative methods, such as averaging over a sentence’s individual word embeddings and BERT (Reimers and Gurevych 2019). The method has been applied, for instance, in the context of patent applications (Jansson and Navrozidis 2020) and gender differences in Covid-19 discourse on online discussion platforms (Aggarwal et al. 2020). 8 Cosine similarity measure can take values between -1 and 1, where 1 means that two vectors of word embeddings point in exactly the same direction, -1 means that the vectors point in opposite directions, and 0 means that the. vectors are perpendicular. We normalize the cosine similarity measures to take values between 0 and 1, which allows us to use them as weights later when translating the SML scores from the level of DWAs to the level of STEP skills. 9 Consider the example of the final similarity scores for the STEP question “Do you (did you) read anything at this work, including very short notes or instructions that are only a few sentences long?” The highest similarity score (0.749) is obtained for the DWA “Receive information or instructions for performing work assignments” and the lowest similarity score (0.181) is obtained for the DWA “Drive passenger vehicles.” 10 Workers in this age cohort in Lao PDR are more likely to be in physically demanding jobs that are less suitable for machine learning than workers in this age cohort in urban Viet Nam. Young workers in urban Viet Nam are more likely to be in service occupations where they perform tasks that are more suitable for machine learning, for example involving various mathematical calculations. 11 Merging the LFS to the STEP is unproblematic because both datasets use the ISCO occupational codes. We make one manual adjustment: In Lao PDR, we merge the occupation “market gardeners and crop farmers” in the LFS to the occupation “subsistence crop farmers” in STEP. 12 See Ernst et al. (2019) for a discussion of the potential for AI technologies to support developing countries in their quest to catch up. Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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| 2023-02-17T00:00:00 |
2023/02/17
|
https://pmc.ncbi.nlm.nih.gov/articles/PMC9936490/
|
[
{
"date": "2023/02/01",
"position": 1,
"query": "AI labor market trends"
},
{
"date": "2023/02/01",
"position": 37,
"query": "universal basic income AI"
},
{
"date": "2023/02/01",
"position": 3,
"query": "AI wages"
}
] |
Does Artificial Intelligence Promote or Inhibit On-the-Job ...
|
Does Artificial Intelligence Promote or Inhibit On-the-Job Learning? Human Reactions to AI at Work
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https://www.mdpi.com
|
[
"Li",
"Zhang",
"Niu",
"Chen",
"Zhou",
"Chao Li",
"Yuhan Zhang",
"Xiaoru Niu",
"Feier Chen",
"Hongyan Zhou"
] |
by C Li · 2023 · Cited by 35 — This paper examines how AI at work impacts on-the-job learning, shedding light on workers' reactions to the groundbreaking AI technology.
|
The contributions of this paper include the following aspects. First, this paper deepens our understanding of human reactions to artificial intelligence at work from an on-the-job learning perspective and expands the research concerning AI’s impact on workers. Existing studies about the impacts of AI on workers have mainly focused on its direct effects on employment [ 8 25 ], income [ 10 ], and well-being [ 11 26 ]. However, there is a lack of literature on how workers react to AI. This paper provides a valuable exploration in this regard. The findings of this paper reveal that workers passively accept the effect of AI and reduce on-the-job learning. This can help companies to better understand employee preferences and behaviors while taking advantage of AI for technological upgrades. Second, this paper examines factors influencing employees’ on-the-job learning from a novel perspective of technological change and clarifies the impact mechanism of technological progress on on-the-job learning. There are numerous elements affecting on-the-job learning, and the related literature has mainly emphasized factors with respect to organizational environment [ 27 ], job attributes [ 28 29 ], demographic characteristics [ 30 31 ], as well as personal traits [ 32 ]. Nevertheless, the effects of technological advances on on-the-job learning have been relatively neglected. Therefore, this study contributes to a more comprehensive understanding of the elements influencing on-the-job learning from a new view. In addition, this study indicates that by creating more pessimistic expectations, reducing income, and extending work hours through its deskilling effect, AI inhibits workers’ on-the-job learning. Thus, this paper enlightens enterprises to optimize their management strategy from the perspectives of workers’ expectations, earnings, and working hours, so as to motivate workers to learn furth, improve human capital, and promote innovation. Third, this paper also identifies vulnerable subgroups whose on-the-job learning is more adversely affected in the era of AI, such as older, female and less-educated workers, as well as those with less job autonomy and work experience. This contributes to encouraging enterprises and governments to pay more attention to these employees and consider providing them with more training opportunities and labor protection.
Therefore, based on a theoretical analysis, it is unclear whether artificial intelligence promotes or inhibits on-the-job learning. In the current context of the booming AI technology and digital transformation of industries, promoting employees to learn new skills that can facilitate collaborating with AI is crucial for enterprises to improve their competitiveness [ 23 24 ]. In light of this, this paper empirically examines the impact of AI on on-the-job learning using the data from the Chinese general social survey (CGSS). In addition, this study conducts a series of robustness and endogeneity tests by applying different AI and on-the-job learning measures, ordered response models, instrumental variables approach, penalized regressions, placebo tests, etc. We also systematically explore the future expectation, economic income, and working-time mechanisms through which AI affects on-the-job learning. Furthermore, the heterogeneities of AI’s influence in terms of demographic, working, and regional characteristics are further explored.
On the one hand, the stimulation effect of artificial intelligence may motivate workers to further improve skills so as to avoid being replaced by the new technology [ 12 ]. AI’s complementarity and cost-saving effects can also increase people’s income [ 13 14 ]. In addition, the productivity and replacement effects of AI may reduce employees’ working hours [ 15 17 ]. All of these factors can help to promote people’s on-the-job learning. However, on the other hand, AI may also lead people to have more pessimistic expectations about the future by substituting their jobs, as well as bringing about a burnout effect [ 18 19 ], which undermines their motivation for further learning. In addition, AI has the replacement effect [ 8 ], mismatch effect [ 20 ], and deskilling effect [ 21 22 ] on people’s jobs, decreasing their financial resources and time available for on-the-job learning.
Artificial intelligence, as the core driving force of the fourth technological revolution, has grown by leaps and bounds in recent years. According to 2022 International Federation of Robotics (IFR) statistics, the number of new industrial robots installed worldwide reached an all-time high of 517,385 in 2021, with a yearly growth rate of 31%. Over the past six years, annual robot installations have more than doubled ( https://ifr.org/worldrobotics/ accessed on 16 January 2023). The rapid development of AI brings immense economic benefits [ 1 3 ] and profound changes in people’s preferences and behaviors [ 4 ]. From an enterprise perspective, the new work pattern applying AI contributes to wiser organizational decisions [ 5 ], better innovation management [ 6 ], and performance improvement [ 7 ]. From the viewpoint of workers, the new work style teaming with AI deeply influences their employment [ 8 9 ], income [ 10 ], and well-being [ 11 ]. However, how workers react to the AI technological revolution at work remains an unanswered question. It is yet to be determined whether workers tend to further improve their skills to enhance competitiveness against AI, or whether they passively accept AI’s replacement and deskilling effects on their jobs, resulting in a decrease in further learning.
Second, the impact of artificial intelligence may differ based on working characteristics. Labor contracts are the legal basis for workers to protect their rights and interests. They can raise social insurance coverage and reduce the likelihood of wage arrears, thus improving the welfare of underprivileged labor [ 61 62 ]. All these factors contribute to mitigating the impact of AI on workers. Therefore, labor contracts may reduce the impact of AI at work on on-the-job learning. With regard to job autonomy, related studies have shown that granting workers more autonomy to determine their work content increases their willingness to learn [ 63 ]. Conversely, less job autonomy not only decreases willingness to learn but also increases turnover intention [ 64 65 ]. In addition, work experience can enhance employability [ 66 ], which helps workers better switch jobs when they are replaced by AI. Consequently, the effect of AI on on-the-job learning may be smaller for those with higher job autonomy and more work experience. Third, macro-level regional factors may also play a role in the impact of AI on workers’ on-the-job learning. In regions with faster technological development, workers often develop more negative emotions, such as anxiety, insecurity, and aversion to new technologies [ 67 ]. As a result, they may react more passively to technological progress, reducing on-the-job learning to a greater extent. In contrast, in regions with stronger labor protection, workers tend to have a more positive attitude toward new technology. They are also more likely to adapt to rapidly evolving skill demands through reeducation and retraining [ 68 ]. From the above analysis, the impact of AI on workers’ on-the-job learning can vary in regions with different levels of technological development, AI application, and labor protection.
From the existing literature, there may exist heterogeneities in the effects of artificial intelligence on on-the-job learning across different groups. First, the impact of AI on on-the-job learning may vary depending on demographic characteristics. In terms of age, younger workers are more willing to acquire new knowledge and skills, whereas the older labor force is less able to adapt to technological changes, and thus, more likely to be replaced by AI [ 55 ]. Moreover, the accelerating pace of technological progress and innovation increases the demand for work-related learning. However, as people age, their motivation to learn diminishes. As a consequence, employers are more willing to provide more training and development opportunities to younger workers [ 56 57 ]. For these reasons, older employees’ willingness to learn on the job may decrease to a greater extent when their jobs are impacted by AI. Regarding gender, studies find that women tend to perform routine tasks more often than men even within the same occupational category, making them more at risk of being substituted [ 58 ]. Furthermore, women are more vulnerable to labor market discrimination in terms of job search, promotion opportunities, and payment for labor [ 59 ]. Therefore, it is more difficult for them to switch jobs by updating skills when being replaced by new technology. In terms of education, those with higher levels of education are less likely to be displaced by AI [ 25 ]. Zhou et al. [ 60 ] have discovered that for those with college and above educational attainment, the substitution probability is only half of that of low-education groups. Furthermore, educational level is usually positively correlated with a willingness to participate in work-related learning [ 31 ]. Hence, in the face of AI’s replacement effect, workers with lower levels of education may be less inclined to learn on the job.
However, on the other hand, some research demonstrates that artificial intelligence may reduce working time. First, AI has the productivity effect. It can support workers in performing repetitive and heavy tasks and the human–machine cooperation saves working time [ 52 ]. Therefore, the application of AI directly improves productivity [ 2 53 ]. Moreover, technological upgrading promotes labor reallocation, for which AI also indirectly promotes productivity [ 15 ]. Owing to the lifted productivity, people’s working hours may decrease. Second, AI has the replacement effect discussed earlier, which can decrease people’s work on certain tasks, thereby shortening working hours [ 16 17 ]. For example, Cho and Kim [ 54 ] find that AI application in some Korean firms helps to reduce overwork. According to the above literature, AI may either decrease or increase working hours. If AI increases working time due to its deskilling effect, workers would have less time available for learning, and thus, their on-the-job learning would be less frequent. This leads to Hypothesis 3a: AI’s deskilling effect increases working hours and thus inhibits on-the-job learning. Conversely, if AI reduces working hours thanks to its productivity or substitution effects, then workers can spare more time to learn new skills. From this, Hypothesis 3b can be put forward: AI’s productivity and replacement effects reduce working hours and thus promote on-the-job learning.
On-the-job learning, which is a form of human capital investment, not only requires financial expenses but also involves investment in time. Therefore, exploring the impact of artificial intelligence on further learning also requires clarifying the relationship between AI application and working time. There are two opposite viewpoints on this issue in the existing literature. On the one hand, some studies suggest that AI deskills workers and reduces their bargaining power [ 46 ]. As a consequence, AI may extend working hours. To be specific, with the standardization of work tasks realized by advanced technologies, it is no longer necessary for experienced and skilled workers to perform complicated tasks at work. As a result, their skills could not automatically give them bargaining power in the workplace [ 21 22 ]. Therefore, people’s working hours may increase because of their reduced negotiating power. Moreover, employees’ control over their work also decreases due to AI-driven deskilling [ 47 ]. Machines can work at more intense rhythms and increase human workload and working time [ 48 49 ]. In this process, AI can also manage and monitor work processes in real time [ 50 ], resulting in employees experiencing more work stress. In addition, from a macroeconomic perspective, technological advances may also increase working hours. This is due to the fact that, based on the real business cycle theory, favorable technological advances can drive investment, which requires more labor input [ 51 ].
However, another stream of research holds the view that artificial intelligence reduces workers’ income, mainly due to the following two reasons. First, AI has a capital-deepening effect and brings down labor demand, which leads to job replacement and lowers earnings for workers [ 8 ]. For example, it has been found that in the human–machine competition, automation brought by AI displaces labor and decreases its wage [ 16 ]. Second, AI can also exert a mismatch effect on the labor market. Despite the aforementioned complementarity effect, skills in the workforce may not suit the requirements of the new technology in the short term. Consequently, AI can lead to a mismatch between labor skills and the demands of new technologies, thus reducing economic gains from AI application [ 20 ]. Furthermore, the AI-induced mismatch creates a higher risk of frictional unemployment and lowers workers’ income [ 10 25 ]. Therefore, from the theoretical analysis, the impact of AI on people’s income is unclear. On-the-job learning entails financial resources as well as opportunity costs in lost income. If AI raises employees’ income, they can shoulder higher costs of on-the-job learning, and thus increase the frequency of their on-the-job learning. In view of this, Hypothesis 2a can be proposed: AI’s complementarity and cost-saving effects increase income and thus promote on-the-job learning. On the contrary, if AI decreases people’s income, there will be less financial resources available for training, resulting in reduced on-the-job learning. So, Hypothesis 2b, opposite to Hypothesis 2a, can be proposed: AI’s replacement and mismatch effects reduce income and thus inhibit on-the-job learning.
In addition to affecting future expectations, artificial intelligence can also impact workers’ income. Nevertheless, studies have not reached a unanimous conclusion as to whether AI increases or decreases income. A stream of research argues that AI is labor-friendly and raises income. First, AI has a complementarity effect on labor demand. When AI is applied in production, it requires close collaboration with human cognitive and interpersonal skills, thus promoting the employment and income of workers with such skills [ 13 14 ]. Moreover, this will motivate workers to seek further learning and training to acquire these skills to achieve better human–AI collaboration, and therefore, increase wages [ 44 ]. In addition, AI also has a cost-saving effect. The application of automation can save production costs, lower the price of products and increase total demand, creating more demand for labor and thus resulting in increased incomes [ 45 ]. Therefore, on the grounds of its complementarity and cost-saving effects, AI may exert a positive effect on income.
When workers perceive their roles as vulnerable to being replaced by artificial intelligence and have pessimistic expectations, the existing literature does not provide a definitive answer as to whether they are stimulated to further improve their skills to enhance competitiveness or whether they become burned out and reduce on-the-job learning. A strand of literature believes that the threat brought by new technologies stimulates workers to learn more to update their knowledge. Ivanov et al. [ 12 ] find that when faced with a potential threat of substitution, employees prefer the “fight strategy”, in which they learn how to use new technologies to be more productive to stay competitive. In addition, it is also shown that people with job insecurity are more willing to undertake training to strengthen their ability to find jobs outside the organization [ 32 ]. At the same time, the improvement of skills enables workers to build confidence in their employability, thus increasing job satisfaction and security [ 42 ]. Therefore, Hypothesis 1a can be proposed: the pessimistic expectations caused by AI have a stimulation effect and thus promote on-the-job learning. However, on the other hand, when employees feel fear of unemployment and the negative emotions dominate, AI may lead to burnout, thus discouraging further learning [ 18 19 ]. This is because people may perceive themselves as no longer competitive against AI and that human capital has been totally replaced by AI. Hence, people have a sense of hopelessness in achieving better performance at work than AI even if they engage in further learning [ 43 ]. Based on this, Hypothesis 1b, which is the opposite of Hypothesis 1a, can be put forward: the pessimistic expectations caused by AI have a burnout effect and thus inhibit on-the-job learning.
The previous literature has shown that the growing power of artificial intelligence and its increasing status in the workplace may make workers more pessimistic about their future. According to Huang and Rust [ 33 ], both humans and AI have four intelligences: mechanical, analytical, intuitive, and empathetic intelligences. Mechanical intelligence is responsible for standardized and repetitive tasks. Analytical intelligence is the ability to process information and to perform tasks requiring logical thinking in decision-making. Intuitive intelligence completes tasks needing intuitive, holistic, experiential, and contextual interactions. Empathetic intelligence is applied for tasks that involve empathy and emotional analytics. Although currently humans have the advantage in the “softer” intuitive and empathetic skills [ 33 34 ], with the rapid development of enhanced computing power and machine learning algorithms, AI can learn and accumulate knowledge on its own, just like human capital [ 35 ]. Thus, it is suggested that AI will replace people to perform complex tasks requiring the latter two intelligences [ 33 ]. In addition, the power of AI is also reflected in its increasing importance and status at work [ 4 6 ], where the role of AI gradually changes from follower to manager [ 36 37 ] or even leader [ 38 39 ], leaving workers with less room for further promotion. Because of the above reasons, humans may perceive AI as a threat to their jobs, leading to increased fear of unemployment, anxiety [ 40 ], and job insecurity [ 26 ]. Lan et al. [ 41 ] find that when human employees feel threatened, they may develop robot-phobia and have more pessimistic expectations about the future due to AI-induced displacement of their jobs.
Referring to the literature on factors influencing on-the-job learning, e.g., [ 18 69 ], this paper comprehensively controls for variables in six aspects to avoid omitted variable bias: (1) Demographic characteristics include gender, age, and the quadratic term of age; (2) Work characteristics include personal income, whether one works in the system and whether one has pension and medical insurance; (3) Human capital characteristics include educational status and health condition; (4) Social identity characteristics include whether one belongs to an ethnic minority, whether one has religious beliefs, and whether one is a Communist Party of China (CPC) member; (5) Family characteristics include whether one is married, family size, and number of children; (6) Time and province dummies. Table 1 presents the descriptive statistics of the above variables.
The most commonly used indicator to characterize artificial intelligence’s impact on workers is the routine task intensity constructed by Autor and Dorn [ 71 ]. They have found that the new technological progress led by AI is task-biased rather than skill-biased. The greater degree of routine cognitive and routine manual intensity a task has, the more codable it is, the easier it is to be performed by machine learning algorithms, and the greater the impact of AI on it [ 72 73 ]. Therefore, based on the task characteristics of different occupations in theof the United States Department of Labor, they measure occupations’ routine intensity from the cognitive and operational dimensions to characterize AI’s impact on workers. This indicator is calculated based on the standard occupational classification 2009 version (SOC-2009). We use the occupational crosswalk system from the United States Department of Labor to convert this index into the ISCO-2008 standard indicator, and then match it to CGSS data, denoted as AI. It is worth pointing out that in the crosswalk from SOC-2009 to ISCO-2008, a certain ISCO-2008 occupation may correspond to multiple SOC-2009 occupations. In this regard, we calculate the mean value as the AI indicator of the ISCO-2008 occupation. In addition, other measures are also used for robustness checks [ 25 ].
This paper examines the impact of artificial intelligence on employees’ on-the-job learning using data from the Chinese general social survey (CGSS). CGSS is the first nationwide, comprehensive, and continuous large-scale survey project in China, aiming to systematically collect multi-dimensional information at the individual level and monitor changes in society. Compared with other data sources, CGSS has two advantages for this study: first, CGSS investigates respondents’ on-the-job learning frequencies and comprehensive factors influencing further learning, facilitating the construction of the dependent and control variables of this research. Second, CGSS provides people’s occupational codes of the international standard classification of occupations 2008 (ISCO-2008), which enables us to measure AI’s impact on respondents’ work, so as to construct the explanatory variables of this paper. CGSS data have been updated to 2018; however, since the ISCO-2008 standard occupational codes are not available in the data before 2017, this paper uses the 2017–2018 wave data in CGSS.
We have examined the impact of artificial intelligence on on-the-job learning from different perspectives, but there is still a concern that the statistical significance of AI’s estimates may be due to uncontrolled random factors. If this speculation is true, the results of this paper would not be valid. To address this concern, placebo tests were conducted. Specifically, we randomly assigned the AI indicator in the sample 1000 times to obtain the placebo explanatory variable, and then used these 1000 new samples for regression to estimate the impact ofon on-the-job learning. The estimated results are plotted in Figure 3 , where the six sub-figures are the placebo test results performing the benchmark regressions in Columns (2)–(7) of Table 2 , with the stepwise addition of control variables. The black solid lines are the probability density curves of’s estimated coefficients in regressions with the 1000 new samples. The colored scatters are the P values corresponding to the estimates of. The red vertical dashed lines signify the estimates of benchmark regressions in Columns (2)–(7) of Table 2 . It is clear that the distributions ofcoefficients are all basically centered on 0, showing a near-normal distribution in all regression models. The colored scatters, which are the P values corresponding to the estimated coefficients of, are almost all higher than 0.1. More importantly, the estimated coefficients of benchmark regressions in Columns (2)–(7) of Table 2 are all much smaller than the 1000 placebo estimates. This proves that the magnitude of the relationship between AI and on-the-job learning owing to chance factors is far from the results obtained in benchmark regressions. Therefore, this provides evidence that the effect of AI on further learning is not due to uncontrolled random factors.
Given that the existing literature has not yet focused on the impact of artificial intelligence on on-the-job learning, we further test the predictive and explanatory power of AI on further learning in comparison to other factors. To achieve this, this paper first utilizes the lasso model of machine learning for analysis. We calculate the optimal penaltiesusing both 10-fold and 20-fold cross-validation methods and obtain the two optimal penalty parameters of 0.00025 and 0.00039, respectively. As shown in Columns (1) and (2) of Table 9 , under the optimal penalties, AI is among the non-zero independent variables in both models with negative estimates. This implies that AI is an important predictor of workers’ on-the-job learning frequencies. Subsequently, we use the Ridge and Elastic net models for estimation. The optimalobtained in the Elastic net model is 1, indicating that it is equivalent to the lasso model. The results demonstrate that AI is a necessary predictor of on-the-job learning in all the penalty models. To visually and intuitively analyze the changes in the coefficients of different independent variables as the penalty parameter increases, we further plot the change paths of independent variables’ coefficients for each penalized machine learning model, as shown in Figure 2 , where the coefficient paths of AI are the black bolded lines. It is demonstrated that, first, none of the AI’s estimates converge to 0 at the optimal penalties in these models, consistent with the estimation results in Table 9 . Second, the estimated coefficients of AI converge to 0 only when penalty parameters are very large when few variables are not penalized to 0. This suggests that the explanatory power of AI for on-the-job learning is very robust compared to other factors.
In Model (9),is the instrumental variable. This is the first stage regression of 2SLS, in whichis used to estimate. In Model (10), the predicted value ofis used to test its effect on on-the-job learning. The instrumental variableis the routine intensity index constructed by Marcolin et al. [ 74 ]. The higher the, the higher the routine intensity of the occupation, and the easier it is to be influenced by artificial intelligence [ 74 ]. Therefore, this instrumental variable satisfies the correlation prerequisite. In addition, this indicator also satisfies exogeneity due to the following two reasons. First, from a reverse causality perspective,is an indicator measured based on the 2011–2012 OECD survey of adult skills, whereas the data used in this paper are from the 2017–2018 CGSS, making it unlikely that Chinese respondents in 2017 and 2018 reversely affect the earlier. Second,is an indicator at the sectoral level, which is based on three-digit ISCO-2008, whereasis an individual-level four-digit ISCO2008 measure. Consequently,is exogenous to respondents’ individual characteristics. Based on the above two points,satisfies the exogeneity condition of instrumental variables. Column (1) of Table 8 exhibits the estimation results of the first stage of 2SLS, indicating thatsignificantly increasesat the 1% level, proving the above proposition. Additionally, the F value of this regression is 74.127, much larger than the empirical criterion of 10, confirming that this instrumental variable is valid for the estimation. The results of the second-stage regression of 2SLS are listed in Column (2) of Table 8 . After addressing endogeneity, the effect of AI on on-the-job learning is still significantly negative at the 1% level, demonstrating that AI robustly inhibits on-the-job learning. Furthermore, as presented in Columns (3)–(5) of Table 8 , when employing different instrumental variable models, including the limited information maximum likelihood estimation (LIML), two-step optimal generalized method of moments (GMM) and iterative GMM (IGMM), AI’s impact on on-the-job learning remains significantly negative at the 1% level. This supports the notion that the findings of this paper are not affected by the endogeneity issue and are highly reliable.
In benchmark regressions, the impact of artificial intelligence on on-the-job learning may be subject to the endogeneity problem for two reasons. On the one hand, the reverse causality problem may arise from the concern that workers who learn more frequently may be less likely to be replaced by AI because of their high human capital, and thus, AI has less impact on the jobs they perform. On the other hand, there may exist other unobserved factors that could affect on-the-job learning in the random disturbance term, resulting in biased estimates of AI’s coefficient. To address potential endogeneity issues, the following two-stage least squares (2SLS) model is constructed:
The estimation results obtained from the ordered probit and ordered logit models are displayed in Table 6 and Table 7 , respectively. In all regressions, the estimated coefficients of artificial intelligence’s impact onare significantly negative at the 1% level, consistent with the conclusions derived from the benchmark regressions. This indicates that the application of AI decreases workers’ further learning. Additionally, with the inclusion of different control variables, the estimated coefficients of AI are stable around −0.06, suggesting that the conclusions of this paper are robust and that AI’s effects on on-the-job learning are not altered by model selection or the inclusion of other factors.
The dependent variableis an ordered variable because it takes the values of 1, 2, 3, 4, and 5. This means that although further learning frequencies are ordered from “never” to “frequently”, the spacing between the values ofmay not be the same across different levels. The linear models, such as OLS, assume that these categories are equally spaced, which may not be the case. Therefore, to check the robustness of the conclusions obtained from the linear model, ordered response models are further applied, which assume that the explained variable is ordinal and different categories are not equally spaced. Specifically, based onthe sample is divided into five subgroups, denoted= 1 to 5, representing those that never, seldom, sometimes, often, and frequently engage in on-the-job learning, respectively. The probabilityof a given observationin groupisor
Considering that different respondents may have varied understandings of the options presented in the questionnaire, the dependent variable may suffer from measurement errors. To check the robustness of the benchmark regression results, this paper further constructs the dummy variable Whe_Learning, according to whether ratings of on-the-job learning frequencies are greater than 3. Therefore, if the respondents seldom or never participate in on-the-job learning, Whe_Learning is coded as 0 and as 1 if otherwise. This dummy variable can mitigate measurement errors caused by differences in respondents’ understandings of question options to a large extent. The reason lies in the fact that although different respondents may have divergent understandings of the frequency of “sometimes”, “often”, and “frequently”, there is hardly any difference in the judgment of whether the further learning frequency belongs to Whe_Learning = 0 (never or seldom) or Whe_Learning = 1 (sometimes, often or frequently). Since this explained variable is a binary variable, the predicted values of the OLS model may be outside [0, 1], so probit and logit models based on the maximum likelihood estimation are applied. The log-likelihood function is of the following form:or
In addition, we are also concerned about whether the conclusions of this paper still hold if artificial intelligence indicators constructed by other scholars are exploited. Another AI indicator newly developed by Frey and Osborne [ 25 ] has also received attention recently. So, we performed a robustness test using this indicator, denoted as AI_Frey. This indicator, based on the characteristics of different occupations in the occupational information network (O*NET), uses machine learning algorithms to measure the degree to which different occupations are affected by AI. It takes values in the interval [0, 1], with higher values indicating AI’s greater impact on people. Robustness tests results using the above different AI indexes are exhibited in Table 3 . It is shown that the impact of AI on on-the-job learning is significantly negative at the 1% level, regardless of which indicator is applied. Furthermore, AI’s estimated coefficients obtained using the above indicators are largely consistent, confirming the robustness of the findings in this paper.
Moreover, to more comprehensively characterize working features, considering that the higher the non-routine intensity, the lower the influence of AI on the occupation, this research subtracts the non-routine cognitive analytic and non-routine interpersonal intensities of the occupation from its routine cognitive and routine manual intensities to construct the AI_2 index. Based on this, we also calculate the AI_3 indicator that further subtracts the non-routine manual physical and non-routine manual interpersonal intensities from AI_2. Then, this paper utilizes these different AI indicators based on Autor and Dorn [ 71 ] to perform robustness tests.
In converting the artificial intelligence indicator constructed by Autor and Dorn [ 71 ] from SOC-2009 to ISCO-2008 occupations, when an ISCO-2008 occupation has more than one corresponding occupations in SOC-2009, the mean measure of SOC-2009’s AI indexes is used in the benchmark analysis. However, this approach fails to take into account the scale of employment in different SOC-2009 occupations. To address this, this paper constructs a weighted average AI indicator, denoted as AI_weighted, by taking the employment scale of different occupations in the US occupational employment survey (OES) as the weight to check the robustness of the above findings. In addition, to avoid the interference of outliers to which the mean and weighted values are subject to, the median AI index of SOC-2009 occupations is also constructed for ISCO-2008, denoted as AI_median. Furthermore, to check the robustness of findings, a more radical conversion is applied, in which the maximum value of different SOC-2009 occupations’ AI indicators is used.
Estimation results of Model (1) are shown in Table 2 . Column (1), which does not include any control variables, indicates that the effect of artificial intelligence is significantly negatively related to on-the-job learning frequencies. Columns (2)–(7) sequentially include the six types of control variables mentioned above. Results display that no matter which controls are included in the regression, the estimated coefficients of AI, which are −0.075, −0.06, −0.049, −0.048, −0.05, and −0.05, are all significantly negative at the 1% level, implying that AI significantly reduces on-the-job learning. In addition, as different aspects of characteristics are added, estimates of the relationship between AI and on-the-job learning decrease slightly but basically stabilize at around −0.050, suggesting that the negative relationship between the two factors is robust and not interfered by other elements. The estimated results of controls are generally in line with theoretical expectations and the existing literature. For example, younger workers participate in more on-the-job learning because they have more opportunities of career development and acquire new skills more quickly [ 56 57 ]. Higher-educated employees have a greater willingness to engage in further learning [ 31 ]. Those with higher incomes can afford higher learning costs, and therefore, learn on the job more frequently.
On-the-job learning involves not only financial costs but also investment in time. In this respect, H3a and H3b are proposed in theoretical analysis. On the one hand, the deskilling effect of artificial intelligence increases working time, and thus, may inhibit on-the-job learning. However, on the other hand, its productivity and replacement effect can reduce working time, which may enhance on-the-job learning. To test the mechanism, the number of hours worked per week is firstly used as the mediator for analysis. Results in Column (2) of Table 12 exhibit that AI significantly increases working hours at the 1% level. This indicates that the application of AI does not reduce workers’ workload, while extending their working time because of its deskilling effect as discussed in the theoretical analysis, which is consistent with the existing literature [ 22 49 ]. In Column (3), working hours significantly reduce people’s further learning, demonstrating that the longer the working time, the less time they can spend on learning. Furthermore, the absolute value of AI’s estimated coefficient in Column (3), which is 0.220, drops compared to 0.227 in Column (1). This implies that AI inhibits on-the-job learning through increasing working hours, proving H3a. Furthermore, to test the robustness of this mechanism, a dummy variable of whether one overworks is generated. Referring to [ 76 77 ], working more than 50 h per week is defined as overworking. Since the explained variable here is a binary variable, the IV Probit model is applied for estimation. As illustrated in Columns (4) and (5), AI also discourages further learning by increasing the probability of overworking, again verifying H3a. Therefore, it is concluded that the application of AI results in longer working hours, and thus, inhibits on-the-job learning.
In addition to the future expectation mechanism, artificial intelligence may also influence on-the-job learning through its impact on income. In this regard, H2a and H2b are put forward in the second part of the theoretical analysis. It is shown that the complementarity and cost-saving effects of AI may increase income, and therefore, promote on-the-job learning. However, its replacement and mismatch effects can reduce earnings, which may inhibit further learning. To test the hypotheses, this paper firstly uses workers’ personal income for mechanism analysis. The results in Column (2) of Table 11 demonstrate that AI significantly reduces workers’ personal income at the 1% level. In Column (3), personal income significantly increases the frequency of on-the-job learning. Moreover, after controlling for income, the absolute value of AI’s estimated coefficient decreases from 0.230 to 0.227, while still being significant at the 1% level, compared with that in Column (1). This means that personal income mediates the negative effect of AI on on-the-job learning. Therefore, H2b is supported. Furthermore, we perform a robustness test for this mechanism using household income. To avoid multicollinearity, personal income is not controlled in Columns (4) and (5). The results show that the economic income mechanism still holds when household income is used as the mediating variable. Therefore, by cutting down people’s income, AI decreases the financial resources they can spend on human capital investment, thus reducing on-the-job learning.
In the first part of theoretical analysis, two hypotheses concerning the future expectation mechanism (H1a and H1b) are proposed. It has been discussed that the pessimistic expectations caused by AI may produce a stimulation effect and thus promote on-the-job learning. At the same time, the pessimistic expectations can also bring a burnout effect, which may inhibit further learning. In light of this, this paper tests the future expectation mechanism using the above mechanism test approach. To directly measure people’s future expectations, we construct a variable Optimism about future , based on respondents’ answers to the CGSS question “I am optimistic about my future”. This variable, based on the five-point Likert scale, classifies the level of agreement to the above question from 1–5 in the order of “strongly disagree”, “disagree”, “neither agree nor disagree”, “agree”, and “strongly agree”. Obviously, higher scores represent more optimistic future expectations. Because this question comes from the extension module of the CGSS questionnaire in the 2017 wave, the number of observations in regressions with this variable is smaller. Additionally, a variable indirectly characterizing future expectations, Anticipated social status , is utilized, which is based on the question “what social class do you think you will belong to in the next 10 years?” The options for this question are on a scale from 1 to 10, with 10 representing the top social class and 1 being the bottom class. Based on the above two variables that directly and indirectly measure workers’ future expectations, this paper conducts the following mechanism analysis.
The preceding sections have confirmed the negative effect of artificial intelligence on on-the-job learning. We next examine the mechanisms by which AI influences workers’ on-the-job learning. Based on the above theoretical analysis, this paper further tests the future expectation mechanism, income mechanism, and working-time mechanism. Referring to Alesina et al. [ 75 ], the following model is constructed to conduct the mechanism analysis based on dealing with endogeneity:whereis the mediating variable.is the fitted value of AI in the first stage regression of 2SLS in Model (9). Models (11) and (12) use the fitted values of AI obtained from Model (9) for the second stage regression of 2SLS. If bothin Model (11) andin Model (12) are estimated to be significant,is proven to mediate AI’s effect on on-the-job learning.
This paper further investigates the regional variations in the impact of artificial intelligence. First, we carry out an analysis from the perspective of human–AI competition. Considering that more technologically developed regions have a wider application of AI and the human–AI competition there is more intense, subsample analysis is carried out from the perspective of regional variations in technological development. Specifically, according to the median of the proportion of high-technology industry output (=high-technology industry output/GDP) in the, provinces are divided into high-technology and low-technology regions. The results in Table 15 , Columns (1) and (2) show that AI has a greater negative effect on on-the-job learning for workers in high-technology regions, indicating that intense human–AI competition reduces workers’ willingness to learn further to a greater extent. Moreover, this paper investigates AI’s heterogeneous effects in regions with different numbers of labor disputes and unemployment rates. Based on the median number of labor disputes and unemployment rate in the, provinces are divided into areas with fewer and more disputes, as well as lower and higher unemployment rates. Results demonstrate that the negative effect of AI on on-the-job learning is more pronounced in regions with more labor disputes and higher unemployment rates. This may be due to the fact that in regions with stronger labor protection, workers tend to have a more positive attitude toward new technology. They are also more likely to adapt to rapidly evolving skill demands through reeducation and retraining [ 68 ]. Therefore, more harmonious labor relations and better job security contribute to weakening AI’s adverse consequences.
We have examined the variations in artificial intelligence’s effect on on-the-job learning from the perspective of demographic characteristics. Next, the heterogeneities of AI’s impact on workers’ on-the-job learning in terms of working characteristics are explored. In detail, we conducted analysis from the three aspects of labor contract, job autonomy, and work experience. First, considering that labor contract provides the legal basis for employees to protect their rights, we performed the subsample regressions based on whether one has a labor contract. The results are reported in Columns (1) and (2) of Table 14 , where the effect of AI on further learning is greater for workers without a labor contract. This indicates that strengthening labor protection through labor contracts can mitigate AI’s negative effect on on-the-job learning. This echoes the findings of Li and Freeman [ 61 ] and Zhao and Tang [ 62 ] that labor contracts help improve the welfare of vulnerable workers. Second, with regard to job autonomy, according to respondents’ answers to the CGSS question, “In your current job, how much autonomy do you have to decide your work pattern?”, the sample is divided into lower and higher job autonomy subgroups. Specifically, the workers reporting “having no autonomy” and “having little autonomy” are classified in the lower autonomy group, while those answering “have full autonomy” and “have some autonomy” are categorized as in higher autonomy jobs. Columns (3) and (4) of Table 14 present that the inhibitory effect of AI on on-the-job learning is more prominent for workers in low-autonomy jobs. On the contrary, those with more job autonomy are less affected by AI, since they can spare more time on further learning. This also supports the previous analysis on the working-time mechanism from another angle. Third, in terms of work experience, it is generally believed that the more the working years, the more work experience is accumulated. So, based on the average working time in the current job of respondents in the CGSS sample, which is 6.120 years, we classify respondents into subgroups with less and more working experience. The results in Columns (5) and (6) show that AI has a greater impact on further learning for workers with less work experience. This can be explained by the fact that work experience can enhance employability [ 66 ].
Artificial intelligence has significantly negative effects on on-the-job learning, but this consequence may vary with different workers’ characteristics. So, we perform subgroup regressions to test heterogeneities in terms of different demographic characteristics. Results in Columns (1) and (2) of Table 13 show that AI’s negative impacts on further learning are greater for workers over 45 years of age. This may be attributed to the fact that older people have more difficulty in acquiring new skills and are less willing to learn further [ 55 ]. Consequently, faced with the new AI technology, they have lower on-the-job learning frequencies. In terms of gender, findings demonstrate that AI’s inhibitory effect on on-the-job learning is more salient for female workers. This may be because women are at a disadvantaged position in the labor market [ 59 ] and it is harder for them to switch jobs by updating skills when being displaced by new technologies. In addition, women may tend to perform routine tasks more often than men even within the same occupational category, making them more at risk of being substituted [ 58 ]. Hence, AI negatively affects their on-the-job learning to a greater extent. In addition, with respect to educational attainment, Columns (5) and (6) display that AI causes a greater reduction in on-the-job learning for workers with lower education levels. By contrast, since highly educated workers who have a college degree or higher are more competitive in the labor market, they are less affected by technological shocks [ 25 60 ]. Moreover, because employers tend to provide those who are higher educated with more training opportunities, they are easier to engage in further learning [ 31 ]. Thus, AI has a less negative impact on on-the-job learning for highly educated workers.
7. Conclusions and Implications
This paper systematically explores the impact of artificial intelligence on on-the-job learning. Based on the literature review and theoretical analysis, six hypotheses are proposed relating to three aspects of AI’s consequences on workers’ further learning. On this basis, empirical tests using CGSS data are performed. The findings demonstrate that: First, the application of AI in the workplace significantly inhibits workers’ on-the-job learning. This conclusion holds in a series of robustness and endogeneity checks, including using different AI and on-the-job learning measures, ordered response models, instrumental variable approach, penalized regressions, placebo tests, etc. Second, AI’s adverse impact on on-the-job learning is mediated by future expectation, economic income and working-time mechanisms. To be specific, AI makes people more pessimistic about their future, leading to burnout, and thus, less motivation for on-the-job learning. At the same time, AI decreases workers’ income and lengthens working hours, for which their available financial resources and disposable time for further learning are cut down, thus inhibiting on-the-job learning. Third, this paper explores the variations in AI’s impact in the aspects of demographic, working, and regional characteristics. It has been found that AI has greater negative impacts on on-the-job learning for older, female, and less-educated employees. Moreover, its effect is more conspicuous for those without a labor contract, as well as with less job autonomy and work experience. In terms of regional heterogeneities, results show that in regions with more intense human–AI competition, more labor-management conflicts, and poorer labor protection, the negative effect of AI on on-the-job learning is more pronounced. This highlights that harmonious labor relations and better employment protection are conducive to mitigating AI’s adverse impact on on-the-job learning.
In the current context of the fourth technological revolution driving the intelligent transformation, the findings of this paper have important implications for enterprises to better understand employee preferences and behaviors, and accordingly optimize their management strategy in the era of AI. First, managers should attach great importance to AI’s negative effect on employees’ on-the-job learning. The findings of this paper reveal that AI does not necessarily stimulate employees to further improve their skills and increase human capital investment, but rather makes them more pessimistic about the future and thus discourages on-the-job learning. This is not favorable in terms of promoting employees to acquire new skills to achieve human–AI teaming and cooperation, and thus, is detrimental to future innovation. So, in the process of taking advantage of AI for technological upgrades, on the one hand, enterprises should bolster workers’ positive psychological expectations and motivate them to improve their skills. On the other hand, more training opportunities should be provided for workers to master new skills that are compatible with the new technology, which can help enhance their human capital and foster long-term innovation. Second, enterprises should be aware of AI’s adverse impact on employees’ income and working time. It has been shown that AI reduces workers’ income while extending work hours through its deskilling effect, leading them to have less money and time available on on-the-job learning. For this reason, for enterprises aiming to motivate employees to learn new skills, they can consider offering subsidies for skill training. At the same time, for those who have further learning motivations, managers need to optimize their working time arrangement to better support them to participate in on-the-job learning. Third, more attention should be paid to employees who have fewer learning opportunities or are less willing to learn, such as older, female and less-educated workers, as well as those with less job autonomy and work experience. It is more critical for companies to provide more training opportunities for them. Fourth, in the process of applying AI technology, governments and relevant public agencies should strengthen labor protection for workers. Measures such as improving employment contracting systems, setting up efficient labor dispute resolution mechanisms and providing better job security should be emphasized to help workers develop more stable and positive employment expectations, thus mitigating the negative effects of AI on on-the-job learning.
| 2023-03-14T00:00:00 |
2023/03/14
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https://www.mdpi.com/2079-8954/11/3/114
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[
{
"date": "2023/02/01",
"position": 18,
"query": "AI labor market trends"
},
{
"date": "2023/02/01",
"position": 23,
"query": "AI workers"
},
{
"date": "2023/02/01",
"position": 21,
"query": "AI wages"
}
] |
What will the next 10 years look like for the average worker?
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The heart of the internet
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https://www.reddit.com
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[] |
Already so many jobs are being automated and done by robots. This trend will only increase as corporations continue to put profits above people. Some would ...
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I’ve been following tech and AI for a while. Been playing around with Chatgpt and midJourney lately. For me I feel like the next 5-10 years will be revolutionary in the tech world and will continue to expand at a blistering pace.
But that brings up the thoughts about us not evolving fast enough with it and the impact it will have one the work force. Already so many jobs are being automated and done by robots. This trend will only increase as corporations continue to put profits above people.
Some would argue AI could help us get to that “Star Trek utopia” but it will certainly have some huge problems for humanity to figure out.
Are there any trends in the workforce people are already thinking about? New jobs that will come up, jobs now that will never be done by a human again?
Are you doing anything to position yourself in a spot to gain instead of loose from the future that is upon us?
| 2023-02-01T00:00:00 |
https://www.reddit.com/r/Futurology/comments/10sz64d/what_will_the_next_10_years_look_like_for_the/
|
[
{
"date": "2023/02/01",
"position": 23,
"query": "AI labor market trends"
}
] |
|
Human vs. Artificial Intelligence: Employing Both and How ...
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Human vs. Artificial Intelligence: Employing Both and How One May or May Not Displace the Other
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https://www.linkedin.com
|
[
"Lionel Sim",
"Gopinath Polavarapu",
"Md. Ashikur Rahman"
] |
However, the adoption of AI is also expected to have a significant impact on the job market. As more tasks are automated, specific job roles will become ...
|
Artificial intelligence (AI) is rapidly changing the consumer and business landscape. As more and more companies adopt AI technologies to increase efficiency, reduce costs, and drive innovation while benefiting the consumer, it will significantly impact jobs, the labor market, and training.
Benefits to Companies
Companies are looking to leverage AI to drive revenue growth by optimizing their operations: optimizing internal processes, reducing costs, and increasing efficiency. By automating routine tasks and improving decision-making, companies can increase profitability.
AI is one of today's most hyped technologies and is already the primary driver of emerging technologies like big data, robotics, and the Internet of Things (IoT). It is clear from the meteoric attention and impact ChatGPT has had since its release. As such, it will continue to act as a technological innovator for the foreseeable future, shaping the future of humanity across nearly every industry. By employing machine learning and computer vision, companies can extract more value from their data and create new opportunities for operational efficiency and revenue growth.
AI can help companies expand their market share and identify new revenue streams. By analyzing customer data and predicting future trends, companies can develop new products and services that meet the changing needs of their customers. This is especially important as customer expectations evolve and the competition intensifies.
Another area where AI can significantly impact revenue growth is through customer experience. AI-powered chatbots and personalization engines can help companies deliver more personalized experiences and engage customers more meaningfully. Companies are already deploying AI to replace people and reduce costs, as well as using the technology to predict and catch instances of fraud in the financial industry.
AI has the potential to revolutionize a variety of industries, including healthcare, law, journalism, aerospace, and manufacturing, to name just a few. The technology is already being used to perform tasks that generally require human understanding, which will likely continue to increase as AI systems become more sophisticated and capable.
Companies such as Amazon, Google, Microsoft, and IBM are investing heavily in AI, providing services to their customers, and using it to improve their services. Amazon, for example, launched Amazon Fresh and Amazon Go stores that use AI. As the global AI market continues to grow, it is projected to expand at a compound annual growth rate (CAGR) of 37.3% from 2023 to 2030, with automotive, healthcare, retail, finance, and manufacturing among the industries adopting the technology [1]. This might even be greater as momentum can increase in unpredictable ways through innovation.
However, the adoption of AI is also expected to have a significant impact on the job market. As more tasks are automated, specific job roles will become redundant, leading to job losses in some industries.
Job Gains and Losses
There are many inventions throughout history that have caused significant job losses. For example, the advent of the automobile led to the decline of the horse and carriage industry, which had previously employed many people. The invention of the shipping container led to the displacement of thousands of dockers who used to load and unload cargo manually. In addition, the rise of e-commerce and online shopping has resulted in the closure of many brick-and-mortar retail stores, leading to significant job losses in the retail industry. Similarly, In the 1980s, the widespread adoption of ATMs led to a reduction of bank teller jobs.
Similarly, the shift from film to digital photography caused significant job losses in the photographic industry. Many workers in film processing labs and film camera manufacturing plants were no longer needed. The decline of the newspaper industry due to the rise of online news sources has also led to significant job losses for journalists, editors, and other newspaper employees. Other examples include the introduction and agriculture's mechanization. This list can keep going.
Introducing new technologies and inventions has often led to the displacement of workers in industries that are no longer in high demand. However, it is worth noting that new technologies create new jobs and industries. For example, e-commerce has created many logistics, warehousing, and online marketing jobs.
AI also creates new job roles requiring specialized training and expertise, such as data scientists, machine learning engineers, AI ethicists, and information security jobs specializing in AI. As a result, companies will need to train their employees in these new skill sets, and individuals will need to seek further training opportunities to remain competitive in the job market.
AI has also been creating new opportunities in other sectors, such as semiconductor companies, allowing them to capture 40 to 50 percent of the total value from the technology stack. Storage will experience the highest growth, but semiconductor companies will capture the most value in compute, memory, and networking [2].
The impact of AI on jobs, the labor market, and training are significant, and it will only increase in the coming years as AI technology becomes more sophisticated. However, while AI has the potential to create new job opportunities and increase efficiency, it is also likely to lead to job losses in some industries, making retraining and upskilling essential for both individuals and companies.
However, not all inventions lead to job losses or affect one set of skills more than another. New technologies sometimes create jobs and industries requiring different or complementary skills. For example, the rise of the computer and software industry in the late 20th century created millions of jobs for programmers, engineers, designers, and analysts, who developed and maintained the hardware and software that powered the digital revolution.
Challenges
Recently It’s been found that ChatGPT is susceptible to prompt injection attacks. The concept of prompt injection attacks involves adding a few words to a prompt to manipulate the output of an AI language model. This has also shown me that we are just at the cusp of the AI revolution; there will be a need for ongoing scientific inquiry and analysis into intelligence and its relationship to reasoning and logic, and the insights gained from studying AI and machine learning could have potential implications for our understanding of human cognition and intelligence. Can all intelligence be tricked?
Artificial Intelligence currently has other limitations as well:
1. Limited training data: The AI model may have been trained on a vast amount of text data, but it is still limited by the quality and quantity of the data it has been exposed to. If it has not been trained on a specific topic or has not seen a particular type of question before, it may not be able to respond accurately.
2. Ambiguity: Some questions or phrases may have multiple interpretations, leading the model to provide a technically correct answer but different from what the user intended. This is particularly true for questions with multiple meanings or that rely on context.
3. Outdated information: The model is only as up-to-date as the data it has been trained on, and it may need to be made aware of recent developments or changes in the world. This is particularly true for fast-changing fields like technology, medicine, and politics.
4. Bias: The model may be influenced by biases in the data it has been trained on. For example, if the training data contains more information about one culture or demographic group than another, it may be more likely to provide answers that reflect that bias.
5. Intuition: One of the significant limitations is the lack of intuition and judgment in machines, which makes them unable to predict circumstances as perfectly as humans. A minor change in test cases can create a significant error in the results.
6. Small Scope: AI algorithms are designed to work on defined algorithms; AI’s inability to think outside the box is a limitation. Current AI systems lack the ability to learn and reason outside the scope of the data they are trained on
What Next
The potential of AI is vast, and companies are taking innovative approaches to overcome these constraints. One of the significant challenges facing AI today is its need for explaining ability. AI will need to evolve where possible to provide a more transparent and explainable decision-making process, allowing people to understand why an AI system made a particular decision in a human-digestible manner. Conversational AI has been promising because it introduced a natural way for people to introduce and request information through conversation.
The potential of AI to boost productivity and innovation in economies must be considered. Deploying AI and automation technologies can do much to lift the global economy and increase global prosperity. At a time of aging and falling birth rates, productivity growth becomes critical for long-term economic growth. Understanding this technology's nature, context, and distributional effects is crucial in developing policies and programs that foster innovation, productivity, and social inclusion and ensuring AI deficiencies are supplemented for continued growth.
1- Artificial Intelligence Market Size, Share & Trends Analysis Report by Solution, By Technology (Deep Learning, Machine Learning), By End-use, By Region, And Segment Forecasts, 2023 – 2030
| 2023-02-01T00:00:00 |
https://www.linkedin.com/pulse/human-vs-artificial-intelligence-employing-both-how-one-kovshilovsky
|
[
{
"date": "2023/02/01",
"position": 49,
"query": "AI labor market trends"
}
] |
|
Key concepts in artificial intelligence and technologies 4.0 ...
|
Key concepts in artificial intelligence and technologies 4.0 in services
|
https://link.springer.com
|
[
"Belk",
"Russell W.",
"Rbelk Schulich.Yorku.Ca",
"Russell W. Belk Schulich School Of Business-York University",
"York University",
"Toronto",
"Belanche",
"Belan Unizar.Es",
"Department Of Marketing Management",
"Market Research"
] |
by RW Belk · 2023 · Cited by 72 — AI is being implemented globally disrupting labor markets, with some countries leading this race. In particular, PwC (2022) foresee that the impact of AI on ...
|
The emerging Industry 4.0 technologies (also known as Technologies 4.0) represent a great opportunity to increase customer value in the service sector (Lee and Lee 2020). These advanced technologies incorporate disruptive analytical systems and hardware such as: Artificial Intelligence (AI), autonomous robots, virtual and augmented reality (VR/AR), Big Data analytics, cloud computing and the Internet of Things (IoT). The incorporation of Technologies 4.0 in business operations is fast and unstoppable, with AI playing a crucial role in many industries including services. The actual statistics indicate that global corporate investment on AI technologies grew a 38.9% in 2020 and a 37.8% in 2021 (Statista 2022a). AI is being implemented globally disrupting labor markets, with some countries leading this race. In particular, PwC (2022) foresee that the impact of AI on the GDP of the USA and China will be a 14.5% and a 26% boost respectively by 2030. For instance, recent studies suggest that financial services will be totally automatized in seven years, surpassing the level of automation in manufacturing (PwC 2022). However, previous research on Technologies 4.0 has been mostly focused on their impact on manufacturing and supply chain operations (Alcácer and Cruz-Machado 2019), ignoring their tremendous potential to shape current and future service interactions with customers.
Over the history of industry, the introduction of technology has shaken the established status quo of companies’ operations and the whole economy. The first three technological revolutions changed business, labor policies and quality of life. However, the Fourth Industrial Revolution that has already started is moving at an unprecedented rate to alter these standards. The advent of the Fourth Industrial Revolution goes several steps further and differs from previous technologies in three main ways: (1) technological developments are overcoming humans’ capabilities such that humans or even companies are no longer controlling technology, but technology itself starts to set the rules; (2) customers embrace life in new technology-made environments (e.g. social media, virtual worlds, smart devices around personal space), and (3) the boundaries between human and technology become to be blurred (e.g. robots with human skills, humans with integrated technologies).
Therefore, our work explains these novel insights, devoting one section to each of the three distinctive aspects of the Fourth Industrial revolution from a service business approach. Given the jigsaw puzzle of technologies and concepts investigated under this research approach, we clarify and define the key concepts related to AI and Technologies 4.0 linked to each section. Figure 1 depicts this framework and the key concepts defined in this work.
| 2023-03-14T00:00:00 |
2023/03/14
|
https://link.springer.com/article/10.1007/s11628-023-00528-w
|
[
{
"date": "2023/02/01",
"position": 100,
"query": "AI labor market trends"
}
] |
The Singularity and the Future of Work: AI Will Not Replace ...
|
The Singularity and the Future of Work: AI Will Not Replace the Human Workforce
|
https://medium.com
|
[
"John Wafula"
] |
... AI is used in a responsible and ethical manner. This will likely lead to new job opportunities in the fields of AI ethics, AI regulation, and AI oversight. “AI ...
|
The Singularity and the Future of Work: AI Will Not Replace the Human Workforce John Wafula 4 min read · Feb 11, 2023 -- Listen Share
Are we cornered towards Oblivion?
The Singularity is a term used to describe the hypothetical future point at which artificial intelligence surpasses human intelligence. This idea has captured the imagination of many, leading to concerns about a future in which robots and machines take over jobs traditionally performed by humans. The Singularity represents a theoretical future point at which artificial intelligence, human biology, and technology have advanced to the extent that society and humanity undergo a profound transformation. While the concept is still highly speculative and debated, many experts in the field of artificial intelligence believe that it represents an inevitable and transformative shift in the history of human progress. However, despite significant advancements in the field of artificial intelligence, the Singularity is still far from becoming a reality and there are several reasons why AI will not take your job anytime soon.
First and foremost, it is important to understand that AI is still in its infancy. While AI has made significant progress in recent years, it is still far from being able to match the complexity of the human brain. For example, AI still struggles with tasks that require common sense reasoning, creativity, and emotional intelligence. These are all traits that are essential for many jobs, and which AI is unlikely to be able to replicate in the near future.
Additionally, the widespread use of AI will likely lead to the creation of new jobs, rather than the elimination of existing jobs. For example, as AI becomes more advanced, new jobs will be created in areas such as data management, AI maintenance, and ethical AI design. Furthermore, the use of AI will also likely lead to the creation of new industries and businesses, providing new job opportunities for people.
Another important factor to consider is the cost of developing and deploying AI. Despite recent advancements in AI, developing and deploying AI systems can still be a complex and costly process. In many cases, it is simply not cost-effective for companies to automate jobs that require a low level of skill, particularly in light of the cost of developing and deploying AI systems.
Another point to consider is that AI is not a single monolithic entity, but rather a collection of various technologies and systems. Each AI system is designed to perform a specific task, and is not capable of doing everything that a human can do. This means that there will always be a role for humans in the workforce, even as AI becomes more advanced.
Moreover, the development of AI is not a linear process, but is rather characterized by fits and starts, with progress being made in some areas while progress in other areas is slow. This means that it will likely be many years before AI reaches the point where it is capable of performing all tasks that humans can do.
Additionally, it’s important to consider the ethical and societal implications of AI. As AI becomes more advanced, there will be a growing need for ethical AI design and oversight, to ensure that AI is used in a responsible and ethical manner. This will likely lead to new job opportunities in the fields of AI ethics, AI regulation, and AI oversight.
“AI is not a single monolithic entity, but rather a collection of various technologies and systems. Each AI system is designed to perform a specific task, and is not capable of doing everything that a human can do.” — John Wafula
Finally, it’s important to remember that human skills are essential for many jobs, and that these skills are unlikely to be replaced by AI. For example, jobs in the service sector, such as sales, marketing, and customer service, require a high degree of emotional intelligence, empathy, and interpersonal skills that are difficult to automate. Additionally, many jobs also require a level of creativity and problem-solving ability that is not easily replicable with AI.
In conclusion, while the Singularity may eventually become a reality, it is not something that we need to worry about in the near term. AI will continue to play an important role in many industries, but it will be some time before it reaches the point where it is able to replace the majority of human jobs. In the meantime, it will be important for individuals to embrace AI and continue to develop their skills, so that they are well-positioned for the future. Those who are able to work effectively with AI will be the ones who will thrive in the new, AI-driven economy.
| 2023-02-11T00:00:00 |
2023/02/11
|
https://medium.com/@johnwakks2/the-singularity-and-the-future-of-work-ai-will-not-replace-the-human-workforce-4275ee13f728
|
[
{
"date": "2023/02/01",
"position": 8,
"query": "AI regulation employment"
}
] |
What are the ethical impacts of automation and artificial ...
|
What are the ethical impacts of automation and artificial intelligence on employment and human skills?
|
https://www.linkedin.com
|
[
"David Edelman",
"Axel Schwanke"
] |
Automation and AI will lead to job displacement in several ways yet to be fully identified. As machines and algorithms become more and more advanced and are ...
|
Automation and artificial intelligence (AI) are disruptive technologies, with a very high innovation ration and speed, with the potential to revolutionize many industries, and change in the process, the way we live and work. As machines and algorithms become more capable of performing tasks, once thought to be the exclusive domain of humans, it becomes important to consider the impact on employment and human skills.
In this article, I will try tao analyze and provide an overview of the potential impact that automation and AI could have on employment, including job displacement, changes in the demand for certain types of skills, and its more than potential impact on more vulnerable populations with less access to education and technology.
I will also discuss steps that can be taken to mitigate negative impacts and the importance of considering these impacts as society moves forward with the development and deployment of these technologies.
I. Defining Automation and AI
Automation refers to the use of technology, such as computers and robots, to perform tasks previously performed by humans, while Artificial intelligence (AI) is a subset of automation that involves the development of computer systems able to perform tasks that normally require human intelligence, such as learning, problem solving, and decision making.
II. Overview of Potential Impacts on Employment and Human Skills
Automation and AI have the potential to have a significant impact on employment in the very near future.We have seen in the last year, we have seen an explosion of application of AI technologies into several fields like images, text and music generation.
On one hand, automation and AI could lead to job displacement as machines and algorithms are able to perform tasks that were previously done by humans, leading to a decrease in demand for certain types of jobs and a decrease in wages for workers in those fields.
On the other hand, AI could lead to a decrease in demand for certain types of human skills, such as repetitive or routine work, and an high increase in demand for other skills, such as programming, data analysis, and AI/machine learning.
There are also ethical concerns about its impact on human skills such as creativity, critical thinking, and problem solving, which could lead to a decline in the overall level of skills needed in different fields traditionally associated with humans.
In addition, vulnerable populations, such as low-skilled workers and those in developing countries, may be disproportionately affected by job displacement and may find it difficult to acquire new skills to adapt to the changing labor market.
III. Job displacement
Automation and artificial intelligence (AI) will most certainly impact significantl the labor market that will lead to job displacement for certain types of workers which tasks are more pruned(repetitive) to be replaced by .AI.
In this section, I will try to examine on how automation and AI could lead to job displacement, and which industries and jobs are likely to be most affected.
In parallel, I will explore some possible and potential solutions to mitigate the negative effects of job displacement caused by this set of technologies.
i. How automation and AI could lead to job displacement
Automation and AI will lead to job displacement in several ways yet to be fully identified. As machines and algorithms become more and more advanced and are becoming able to perform tasks previously done by humans, we can expect a progressive decrease in demand for certain types of jobs. This will happen horizontally across industries such as manufacturing, where robots and automation technology are increasingly being used to perform tasks such as assembly and welding. In addition, automation and AI could lead to job displacement in service industries such as transportation, where self-driving cars and drones could potentially replace human drivers and pilots.
Another way, that automation and AI could lead to job displacement, is through the automation of certain tasks within some jobs. For example, a customer service representative's job could be partially automated through the use of AI-powered chatbots, leading to a decrease in demand for customer service representatives.
As a result of these changes, there could several impacts on jobs like a decrease in wages for workers in fields affected by automation and AI. This could lead to financial hardship for individuals who are displaced from their jobs, and who may struggle to find new employment or acquire new skills that would allow them to adapt to the changing labor market.
ii. Potential Impact on Wages and Jobs Opportunities in Specific Sectors
Manufacturing: Automation technologies, such as robots and automation systems, are increasingly being used in manufacturing to perform tasks such as assembly, welding, and packaging. This could eventually lead to job displacement for workers in these fields of activities.
Transportation: Self-driving cars and drones can have the potential to replace human drivers and pilots, leading to a reduction of the number of job opportunities in these fields.
Retail: Automation technologies, such as self-checkout systems and inventory management systems, could eventually affect retail workers.
Customer service: The use of AI-powered chatbots and virtual assistants could affect jon opportunities for customer service representatives.
Data entry: Automation and AI-powered software could impact people working on data entry and data processing.
iii. Potential Solutions.
Government policies, such as retraining and education programs for workers, and policies to support workers and communities affected by job displacement.
Investing in research and development to improve automation and AI technology in ways that create new jobs and opportunities
Encouraging companies to invest in their workforces and prepare for the changes that automation and AI will bring..
IV. Impact on Human Skills
As automation and AI continues to advance, it becomes more important to consider the potential impact on demand for human skills. In this section, I will try to explore how AI could lead to a decrease in demand for certain types of human skills and the potential increase in demand for other types of skills.
I will also consider the potential ethical concerns associated with the potential decline in the use of human skills such as creativity and critical thinking as they start to be replaced by more machine-based functions.
i. How AI could lead to a decrease in the demand for certain types of human skills
AI is expected to lead to a gradual (also fast but not 100% as it still lacks some quality and fiability) decline in the demand for certain types of human skills.
For example, as machines become better at performing certain tasks requiring repetitive or routine work, the demand for people with those skills could eventually decline. Another example would be in industries such as manufacturing, transportation, and customer service which are already seeing an increase in automation, that has already lead to an important decrease in the demand for those workers performing repetitive tasks and low added value, such as assembly line work and call center operations.
In addition, automation could also reduce the need for certain types of manual labor, such as data entry and accounting, which could affect the jobs of many low-skilled workers.
In transportation, although not yet directly affected, the rise of self autonomous vehicles, could eventually lead to high decrease of human drivers.
ii. The Potential Increase in Demand for Other Types of Skills
Although I have approached firstly the negative impact of these technologies in the jobs offering, it is also expected that an increase in demand for other types of skills, such as those related to programming, data analysis, and machine learning, could occur driven by employers' need for a new set of skills and knowledge.
As machines and algorithms become more advanced overtime, there will be a growing need for professionales who can design, program, and maintain these type of systems and technologies.
In addition, others many jobs will be requiring the use of data analytics, machine learning, and artificial intelligence,that will require people with the technical skills and knowledge to manipulate and analyze how to apply these technologies to provide added value to the companies.
iii. Ethical concerns about the impact on human capabilities such as creativity and critical thinking
The existing application of AI in the last year, has already generated a lot of ethical concerns and suspitions, most of them associated with intellectual propriety rights, but that will certainly expand as more and more AI is applied in more sectors of activities.
For example, as machines and algorithms become better at performing tasks, the need for human skills such as creativity, critical thinking, and problem solving can eventually decrease, which in turn could lead to a decline in the overall level of skills needed, that ultimatly would affect our society's ability to innovate and adapt to new challenges, or by decreasing the motivation to pursue more creative educative courses and trainings associated with creative activities.
Furthermore, if human skills such as creativity, critical thinking and problem solving are not valued or rewarded in the labour market, people may be discouraged from developing these skills, which could have negative long-term consequences for society.
Conclusion
In conclusion, automation and AI are rapidly evolving technologies that have the potential to significantly impact employment and human skills. On one hand, they could lead to job displacement and decrease in demand for certain types of jobs, particularly in industries such as manufacturing, transportation, retail, customer service, and data entry. On the other hand, AI could lead to an increase in demand for certain skills, such as programming, data analysis, and AI/machine learning. Vulnerable populations, including low-skilled workers and those in developing countries, may be disproportionately affected. It is important to consider the potential impact on human skills, such as creativity, critical thinking, and problem solving. To mitigate the negative effects of job displacement, government policies, investment in R&D, and workforce development by companies could play a crucial role.
The major highlights are:
Artificial intelligence is playing an increasingly important role in various industries, including healthcare, finance, and retail.
AI is able to analyze large amounts of data and make predictions or decisions based on that data, making it a valuable tool for businesses looking to gain a competitive advantage.
However, AI also raises ethical concerns, such as privacy and accountability.
Governments and organizations are taking steps to regulate AI and ensure that it is used in a responsible and ethical manner.
One example of such regulation is the European Union's General Data Protection Regulation (GDPR), which sets guidelines for how data should be collected, stored, and used by companies.
Another example is the Partnership on AI, a non-profit organization that brings together companies, academics, and advocates to develop ethical guidelines for AI.
Despite these efforts, there are still challenges to regulating AI, such as determining who is responsible for the actions of an AI system and how to balance innovation with protection.
As AI continues to play a larger role in our lives, it is important to ensure that it is used in a responsible and ethical manner, and that the benefits of this technology are balanced with the potential risks.
| 2023-02-01T00:00:00 |
https://www.linkedin.com/pulse/what-ethical-impacts-automation-artificial-employment-rodriguez
|
[
{
"date": "2023/02/01",
"position": 10,
"query": "AI regulation employment"
},
{
"date": "2023/02/01",
"position": 85,
"query": "AI labor union"
}
] |
|
Is UBI inevitable irrespective of A.I? : r/Futurology
|
The heart of the internet
|
https://www.reddit.com
|
[] |
UBI will probably never happen. My theory about what a world will look like where AI has taken over every job is: the working class will become something ...
|
A subreddit devoted to the field of Future(s) Studies and evidence-based speculation about the development of humanity, technology, and civilization. -------- You can also find us in the fediverse at - https://futurology.today
Members Online
| 2023-02-01T00:00:00 |
https://www.reddit.com/r/Futurology/comments/114ah4q/is_ubi_inevitable_irrespective_of_ai/
|
[
{
"date": "2023/02/01",
"position": 2,
"query": "universal basic income AI"
}
] |
|
Universal basic income: An idea whose time has come or ...
|
Universal basic income: An idea whose time has come or just another mechanism to grease the wheels of capitalism?
|
https://www.scienceopen.com
|
[
"Fitzpatrick",
"Pamela J.",
"Pamela Fitzpatrick"
] |
by PJ Fitzpatrick · 2023 · Cited by 5 — The basic concept of UBI is that every person is entitled to a fixed amount of money from the state regardless of their income or need, and the payment is free ...
|
In this article the author examines the UK’s crumbling social security system, the growth of extreme poverty, and whether UBI is the solution to these problems or just another opportunist mechanism to further fuel the insatiable hunger of capitalism.
Introduction The welfare benefit system in the United Kingdom has long been criticized for failing to protect people from poverty. Over a decade of austerity has had a devastating impact across the UK. Savage cuts to welfare benefits, the rise of precarious work, the dismantling of legal aid, and the starving of funding to public services have all resulted in obscene levels of poverty and inequality. The introduction of universal credit, the benefit cap, the bedroom tax, 1 two-child limit, 2 the abolition of disability living allowance, and the localization of council tax benefit 3 has created a level of poverty not witnessed in the UK since the 1930s. The harsh conditionality rules such as the work capability assessment and benefit sanctions has led to thousands of unnecessary deaths. 4 Those in power suggest austerity is simply about living within your means. The reality is that it is a cruel dismantling of social protection. A system which prevents people from having the basic means to survive, and which punishes the already disenfranchised. The onset of the COVID-19 pandemic cast a stark spotlight on poverty in the UK and the inadequacies of our social security system. The disease does discriminate, disproportionately affecting working-class communities and in particular black and minority ethnic communities. The impacts of COVID-19 were exacerbated for those living in poverty. They were more likely to be living in overcrowded, poor-quality accommodation. Many would be in low-paid precarious work in front-line jobs such as shop work, social care, and cleaning. This meant prolonged daily contact with others, which puts them more at risk of the virus. Many people who had never had to rely on benefits found that it was not as generous as some in the media had portrayed. There were often long delays before payment and when it did finally arrive it was inadequate to meet basic needs. It is clear that, if we are to tackle such poverty and inequality, we need change on a revolutionary scale. Universal Basic Income (UBI) is hailed by some as an idea whose time has come. A guaranteed regular payment for every person that would ensure they did not fall below the poverty line even if not in work. No one would go hungry or be homeless and the indignity of means testing and conditionality would be but a distant memory. Although UBI might seem to be a radical idea born out of the COVID-19 pandemic, it is not new. A version of UBI was put forward as far back as the eighteenth century by Thomas Paine. 5 Nor is it something favored solely by those on the left. UBI has drawn support across the political spectrum, including from economists such as Milton Friedman, Friedrich Hayek, and Sam Bowman when at the Adam Smith Institute. In one survey 49% of Conservative voters favoured a UBI. 6 At the height of the pandemic a letter was sent to the Financial Times calling upon the government to establish a universal basic income as part of its economic response to the coronavirus crisis. The letter was signed by over 170 MPs and members of the Lords and signatories were drawn from Labour, Liberal, SNP, Green, and Plaid Cymru.
What is UBI? The basic concept of UBI is that every person is entitled to a fixed amount of money from the state regardless of their income or need, and the payment is free of any conditionality. The same fixed amount is paid no matter how wealthy or how poor the person may be. It is not linked to any life event or risk such as unemployment, sickness, or old age. Instead, it is a payment made to everyone for life. Without any conditionality, it would allow those who choose to work to do so but others may choose to do something other than take up employment. There are variations to this model. Some are intended to replace all other welfare benefits and others simply to provide an additional layer to existing welfare schemes. Some paid to a defined group rather than the entire population.
Trial schemes Several countries have trialed UBI schemes including the United States, Brazil, Canada, Finland, and Uganda. UBI was under serious consideration by the UK parliament, but the Work and Pensions Committee recommended in 2017 that parliament reject the idea. 7 The committee stated that the cost of introducing a UBI at a level that would be beneficial for the poor would be prohibitive, as equal benefits would go to the whole population irrespective of their income. It would require rises in taxes to a level not considered acceptable by any political party. It stated that, if parliament were to opt for a more affordable system, there would need to be a retention of the existing benefit system and a UBI layered on top. This would not achieve the aim of reducing complexity. The committee concluded that such a scheme would be scarcely distinguishable from universal credit 8 and that UBI would be an unhelpful distraction. Notwithstanding, the parliamentary report the devolved government in Wales is to undertake a pilot of UBI. 9 In Uganda, a Belgian charity funded a two-year project with payments to a limited number of households and, in Kenya, a scheme is to make a payment to people for a twelve-year period. Four years into the scheme it does seem to be making some inroads into alleviating poverty. 10 A trial in Finland found that recipients were happier and healthier than when they were on unemployment benefit. But the UBI had little impact on their employment prospects. A scheme in Brazil was paid at a low rate but, nonetheless, did reduce poverty.
What is the rationale for UBI? Two main reasons are offered in support of UBI. Surprisingly, the broad rationale is largely the same for both the left and the right but perhaps for ultimately different reasons: the failure of existing welfare benefit schemes;
and the increasing role of automation in the workplace. There appears to be universal agreement that the existing benefits system is not achieving its aims; yet is vastly costly to the public purse. In addition, some critics say the system is unwieldly and the stigma of means testing deters many people from claiming what they are entitled to. Furthermore, the conditionality attached to most benefits ensures that large numbers of people simply do not qualify. The work capability assessment, for example, has been widely criticized and at times ridiculed for frequently assessing seriously ill people as being fit to work. Even when a person manages to navigate the system, there are often long delays in processing payments, particularly if a person’s circumstances change. Poverty has soared in the UK and we have seen a huge increase in people using food banks. Despite its failures, spending on social security is large. In 2021–2022, spending was £298.70 billion, representing approximately a quarter of all public expenditure. 11 The cost to the public purse may explain why so many politicians adopt a rhetoric hostile to benefit claimants. However, the greater part of spending on social security is on the state retirement pension. According to the Office of National Statistics in 2016, 42% of all social security expenditure was on pensions and only 1% on unemployment benefits. Approximately 16% was spent on disability and incapacity benefits, 10% on housing benefits, 18% on family benefits, and 13% on other benefits. 12 As the baby boom generation reaches pension age, the amount spent on pensions will increase further. According to the government, in 2018 spending on state retirement pensions had risen to 55% of the social security budget. 13 The government also spends around £22 billion a year on housing benefit and the housing element within universal credit. Spending on housing costs has doubled since the early 2000s despite attempts by successive governments to reduce this bill. The problem, however, is that the measures designed to reduce expenditure have focused on restricting those benefits helping claimants pay their rent, rather than tackling the spiraling costs of private rented housing. This has created a shortfall between the benefit a person gets to help with housing costs and the actual rent. Increasingly, tenants are having to choose between eating or paying their rent. Inevitably many fall into rent arrears and then struggle to find alternative accommodation. The cost of living may have hit crisis level in 2022, but most people have been struggling to keep up with rising costs, particularly in housing, for at least a decade. During the Thatcher period of government two key things happened that affected housing. The right to buy council homes and the deregulation of private sector housing. The sale of council stock under the right to buy and the restrictions placed on local councils building new homes reduced available social housing (council housing plus housing owned by housing associations). Those on modest and low incomes were forced into the private housing sector, whereas once they would have relied on council housing. Private rented housing was regulated, making it relatively affordable and secure. A landlord wanting to increase the rent had to apply to a rent officer and the level of increase had to be reasonable. In 1989 the Thatcher government deregulated the private rental sector. New tenancies generally were granted for only twelve months at a time and there were no longer any restrictions on what a landlord could charge. It was left purely to the market. Since wages did not increase to match rents, more and more people claimed housing benefit. This led to a huge increase in government spending on housing benefit. 14 At this time, housing benefit would usually cover the whole rent. A narrative began that this meant there was little incentive for tenants to resist landlords who wanted to raise rents or to shop around for cheaper accommodation. Politicians started to talk of the need to reduce the housing benefit bill. However, the reality for most low-income households is that they cannot shop around for cheaper rents. The shortage of housing means that landlords can pick and choose who to rent to and many of them exclude people on benefits. Despite this, restrictions were introduced on housing benefit intended to force to tenants to shop around for cheaper accommodation. A Labour government was the first to introduce what has become known as the “bedroom tax”. This initially only applied to private rented accommodation. A person living in a property deemed to be too big for their needs or too expensive would have their housing benefit capped at a level lower than the rent they were paying. In 2013, the coalition government, led by David Cameron, extended the bedroom tax to social housing. The restrictions have not reduced the cost to the government of housing benefit. It has caused a housing crisis. Families now frequently live in overcrowded, poor-quality accommodation because they cannot afford the rent on a property that is of adequate size. The days of slum accommodation in the UK have returned. Socialist supporters of UBI say that it offers a solution to an overly complex and bureaucratic social protection system. It would alleviate poverty, reduce inequality, and offer protection to people as work is increasingly automated. And providing people with a UBI throughout their lives would empower workers, who would not be dependent on a job for survival, and help to increase pay and improve workplace conditions. It is perhaps not immediately obvious why those on the right, the cheerleaders for free markets and small government, would also favor UBI. Libertarian and Conservative supporters of UBI offer a freedom of choice argument – people would be given money to spend on what they want. But it would also cut the cost of administration. Everyone would be added to the system at birth and removed on death. No staff needed to check whether a person meets the conditions, no need to help them into work or apply sanctions if they do not comply. No need for expensive judges at tribunals or large fraud teams. For the right, far from giving employees greater power in the workplace, it is viewed as a mechanism to keep wages low and employment contracts flexible. Employees may be less inclined to demand higher wages if they already receive a UBI. And subsidized services such as council housing, free education, and free health could be scrapped. Everyone would have sufficient income to pay for whatever they needed when they needed it. A long-standing reason promoted in support of a UBI is the prospect that increasing automation will render many unemployed. Our work will be done by robots as technology develops. UBI would free up people to have more leisure time and devote themselves to useful activities. Almost a century ago, John Maynard Keynes predicted his grandchildren would enjoy a shorter working week of 15 hours along with a better quality of life, better health care, and a better standing of living. Keynes prediction was wrong. Despite a financial collapse in 2008, we have not witnessed high levels of unemployment, such as those of above 20% in the 1930s. The UK Conservative government has been able to claim that their austerity policies were working. The reality is that poverty has extended to include those in work due to the increase in insecure and informal work. It is now common for people to be working at two or three jobs and still be unable to manage. The explosion of food banks across the UK is a stark reminder of this. Successive governments have supported a low-pay economy, subsidizing employers’ low pay by offering in-work benefits such as family credit, housing benefit, working tax credits, and now universal credit. Informal, insecure, non-unionized work benefits employers. Mass unemployment – a reserve army of labor – enables employers to keep wages down. The eroding of employment rights and weaker trade unions have seen a downward spiral of wages and an increase of people working on zero hours or short-term contracts. Workers are dismissed before they gain employment rights, or, like so many delivery drivers, are forced to accept self-employed status.
Current social security system Our current system of welfare is rooted in the Beveridge report of 1942. William Beveridge, an economist working in the East End of London, wanted to address the vast social inequality in the Britain of his day. He was commissioned by the government during World War II to lead an inquiry into social services. His vision was to battle against what he called the five great evils: idleness, ignorance, disease, squalor, and want. His “cradle to the grave” social program included a call for a free national health service. The plan was taken up by Clement Atlee and formed part of the Labour Party manifesto of 1945 which secured a huge parliamentary victory and the first majority Labour government. The Beveridge plan, implemented by Labour, was an ambitious and radical program. However, it was based largely on the traditional Western family model of a working man supporting his wife and children. It has been criticized by feminists as being a system which puts money in a wallet than a purse. 15 There was a less gendered model on offer at the time, a type of UBI, put forward by Lady Rhys-Williams. Rhys-Williams was a leading campaigner in the maternity and child welfare movement. She stood for election as a Liberal National candidate in 1938 advocating family allowances and cheap milk. She also served on a committee which investigated unemployment in South Wales. She was concerned with poverty and inequality. In her paper “Something to Look Forward to” she proposed a form of UBI. It was not taken up by the Labour Government. 16 The model based on the Beveridge report remained largely intact until the 1980s, when Thatcher promised to roll back the state. But successive UK governments, of all colors, have turned the benefit system into a political football and sought to penalize and demonize benefit claimants. The promise of simplification and targeting to those most in need means making life for those claiming benefits as difficult as possible. The most recent iteration of these reforms has been the introduction of universal credit. Simplification has proved a pipedream. Universal credit, and before them tax credits, 17 have made social security even more unwieldly and punitive. There is no denying the complexity of the current UK social security system. It is comprised of a variety of benefits each with complex rules and conditions for claimants. They largely fall within three categories: Means-tested benefits require an assessment of a person’s income and capital, and are usually only paid to those with a very low income or no income at all. If you have capital, such as savings, depending on their size, you may be excluded completely from benefit, or your benefit may be reduced. The main means-tested benefits are universal credit and council tax support. Some people remain on what are referred to as legacy benefits, 18 including housing benefit, income-related employment and support allowance, and income-based job seeker’s allowance.
Contribution-based benefits are conditional on you having paid an appropriate amount of national insurance contributions over a defined period to qualify for the particular benefit. In 1948 most benefits were contributory. But increasingly such benefits have been abolished and replaced with means testing on the premise of targeting those most in need. Remaining contribution-based benefits include job seeker’s allowance and state retirement pension.
Non-contributory benefits are neither means-tested nor dependent on national insurance contributions. They are paid mainly for disability and include personal independence payment and attendance allowance. All three categories of benefit come with many complex rules and conditions that a person must meet before qualifying. Some are linked to a specific issue such as disability, housing, industrial injury, or raising children. In the past, some benefits had a degree of universality. For example, child benefit was paid irrespective of income to all those responsible for a child. Most people knew about it and it was cheap to administer. This was changed in 2013 as part of the UK coalition government’s cuts to welfare benefits. 19 Those with incomes above £50,000 were no longer automatically entitled to child benefit. Adding to the confusion, there are a variety of agencies that administer benefits. Some are administered by the Department for Work and Pensions (DWP) or its agencies the Job Centre or Benefits Agency. Others are administered by the Inland Revenue and some by local authorities. Given the complexity of the system and rising poverty in the UK, it is not surprising that the system is subject to considerable criticism. UBI is seen as a potential answer. Surely a single benefit would cut through the complexity. And would not a benefit paid to cover basic needs end serious poverty and inequality? But is UBI really the answer?
Would UBI fix the problems of our existing system? UBI may offer a solution to some of the problems of poverty in the UK. But this depends entirely on the type of UBI scheme that is introduced. It is only likely to end poverty if it is paid at a sufficiently high rate to cover the basic needs of a person and is paid to everyone. But there has been a reluctance to do this on the basis that such a scheme would be completely unaffordable. It is difficult to see how a UBI of any form would solve the problem of poverty and inequality without looking at issues such as the costs of housing and other services. If everyone were to receive an increase in income, landlords would likely see an opportunity to increase rents. Consider, for example, how the housing benefit bill has soared since the deregulation of the housing market. 20 However, the true cost of a comprehensive UBI scheme in the UK has never been fully assessed. The current benefit system causes enormous stress resulting in poor mental and physical health. If people had decent housing, sufficient food, and less money worries it might well be the case that there would less poor health and consequently less demand on mental health services, social services, general practitioners, and hospitals. People with higher incomes might decide to do more in their community or help with caring for extended family or friends. So as public expenditure goes up in some areas, there is a good chance it will reduce in others. There is also a danger that, as UBI is supported by both left and right, it is a concept that means all things to all people. A UBI introduced by a right-wing government for example might decide that the UBI replaces all other universal services, such as free education and health or social housing. In such circumstances, how much better off would we be as individuals and as a society in the UK?
Public attitude toward benefit claimants UBI might help in dispelling some of the myths about benefit claimants but would only do so it if were universal. A system that is ring-fenced for a defined group or one that is layered on top of existing benefits schemes will not rid us of the problems we have identified. The major problem we have is that our benefit system in the UK is largely set up to deter people from getting benefits. Consider the harsh benefit sanctions, the ever-more-onerous conditionality, and the means testing designed to impoverish. Our system bears more resemblance to the poor law provision of the 19th century than what should be achievable in a modern wealthy economy. Over the last three decades, the public have been fed a daily diet by the media and most politicians that those on benefits were other than everyone else: they were the something-for-nothing brigade; “skivers” rather than “strivers”. Each political party has sought to show they will be tougher on benefit claimants than anyone else. It was the Liberal Democrats in coalition with the Conservatives who introduced massive cuts to welfare benefits. This included a freeze on the uprating of benefits for five years as well as excluding advice on welfare benefits from the scope of legal aid. 21 Such attitudes were not confined to the political right in the UK. It was Tony Blair’s Labour government that cut payments to single parents, removed asylum seekers from accessing the benefit system at all, and introduced the work capability test. In 2013 Rachel Reeves 22 in her first interview as Labour’s shadow work and pensions secretary promised that Labour would be tougher than the Conservative on benefit claimants. She said that under Labour the long-term unemployed would not be able to “linger on benefits” for long periods. In 2015 Reeves again gave the clear message that Labour viewed benefit claimants as somehow bad when she stated “We are not the party of people on benefits. We don’t want to be seen as, and we are not, the party to represent those who are out of work. Labour are a party of working people, formed for and by working people.” Of course, this is a complete misrepresentation of the formation of the Labour Party but the message was loud and clear. Research by the national centre for social research shows 23 that the hardening in public attitudes towards welfare and welfare recipients took place over a 30-year period between 1983 and 2011. The decline in support for welfare and the recipients was particularly pronounced amongst Labour Party supporters and those aged between 18 and 34. This is not surprising. For decades social security has been used as a political football by all main political parties and most of the media. Barely a week goes by without talk of getting tough on benefit “scroungers”, ending the “something for nothing” culture, and encouraging the public to report their neighbors for benefit “fraud”. However, the reality is there are very few people in the UK who will not be a recipient of a social security benefit at some point in their lives. From child benefit, job seeker’s or maternity allowance, to the state pension for the retired, most people during their lifetime will benefit from the social security systems. UK governments could choose to give positive messaging about welfare benefits, but they do not. Forcing people into low-paid work is the preferred option for parties of all colors. Indeed, when the COVID-19 pandemic forced more people into the benefit system, attitudes began to change. However, research by the University of Kent 24 shows that COVID-19 welfare claimants were perceived as more deserving and less worthy of blame than other claimants.
| 2023-02-01T00:00:00 |
https://www.scienceopen.com/hosted-document?doi=10.13169/jglobfaul.9.2.0225
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[
{
"date": "2023/02/01",
"position": 7,
"query": "universal basic income AI"
}
] |
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AI's biggest impact? - Robert Reich
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AI’s biggest impact?
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https://robertreich.substack.com
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[
"Robert Reich"
] |
A universal basic income could be a potential solution to ensure that individuals have a basic income to support themselves and their families. UBI is a system ...
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Friends,
Artificial intelligence (AI) is finally hitting the economy and society big time. Bing’s chatbot (Microsoft plans a wide release soon) is capable of long, open-ended text conversations on virtually any topic.
It’s caused a Times columnist to become “deeply unsettled, even frightened.” Google engineer Blake Lemoine was fired after claiming that the firm’s AI model, LaMDA, is “sentient.”
It’s causing professors like me to wonder how to distinguish between student writing on exams and AI writing.
It’s causing people who track online misinformation to worry it will undermine democracy. “This is going to be the most powerful tool for spreading misinformation that has ever been on the internet,” warned Gordon Crovitz, co-chief executive of NewsGuard, which tracks online misinformation.
It’s causing philosophers and biologists to fret that it will eventually destroy human beings and take over the world (Hal? You still there?).
But one aspect we’re not talking about enough is AI’s effect on work.
We all know what happened when complex machines first began taking over jobs. Then mechanization replaced skilled artisans. Then automation replaced repetitive jobs that could be put into software code. Numerically controlled machine tools and robotics replaced assembly lines. More recently, big data processing has replaced much analytic work.
Now comes AI — which will replace almost all professional work.
At every stage, productivity (output per worker) has increased dramatically, so fewer workers have been needed to accomplish what came before. This has reduced the bargaining power of less-skilled workers to obtain high wages, while fueling the compensation of people who produce the labor-replacing technologies.
We’re now approaching an inflection point when the financial returns to AI’s producers are heading into the stratosphere, even as professional jobs disappear.
Wall Street is going nuts over AI. Venture capitalists are pouring hundreds of billions into it, driving up startup valuations. Microsoft’s rally on Bing pushed its market capitalization to above $2 trillion. Alphabet’s stock is expected to soar more than 20 percent on its AI investments.
But after AI takes over almost all remaining jobs (including those of the venture capitalists who finance AI and the engineers who design it), what exactly will human beings be doing to make money?
Or to put the matter more baldly, who will be able to afford any of the wondrous goods and services powered by AI if we no longer have incomes?
My prediction: It will be the high-level professional class, including top business executives and the wizards of finance, who push for the most obvious solution: A guaranteed universal basic minimum income for everyone, financed by a tax on AI.
A universal basic income could be a potential solution to ensure that individuals have a basic income to support themselves and their families. UBI is a system in which every citizen or resident of a country receives a regular, unconditional sum of money from the government, regardless of their employment status. The goal of UBI is to provide individuals with enough income to meet their basic needs, such as food, shelter, and health care.
(The last paragraph, above, was generated entirely by ChatGPT. The rest of this letter came from me. Promise.)
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| 2023-02-01T00:00:00 |
https://robertreich.substack.com/p/ais-biggest-consequence-that-nobodys
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[
{
"date": "2023/02/01",
"position": 9,
"query": "universal basic income AI"
}
] |
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Why Leftists Should Oppose Universal Basic Income (UBI)
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Why Leftists Should Oppose Universal Basic Income (UBI)
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https://medium.com
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[
"Matt Drabek"
] |
... AI' art and writing, and other technologies. Third, UBI puts money directly into people's hands without offering public services or options. It provides ...
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Why Leftists Should Oppose Universal Basic Income (UBI)
It might sound like a good idea, but it’s not Matt Drabek 8 min read · Feb 21, 2023 -- 9 Share
Universal Basic Income (UBI) is a curious idea with an even more curious history. It entered mainstream U.S. politics from many directions. Not only does it enjoy strong support among segments of both the political right and left, but much of the tech industry also backs it.
How does that happen? What are the politics here? What unites right, left, and tech support for UBI? More fundamentally, what is UBI, anyway?
What is UBI?
I’m going to start with the last of those questions. Why? Because I think it’s the least clear. And I suspect confusion over the nature of UBI drives much of the politics.
A genuine UBI must be both universal and basic. By ‘universal,’ I mean that it goes out to everyone, or at least to every adult. It’s not means tested or targeted only at low income people. Everyone gets it, even the wealthy. And by ‘basic,’ I mean that it pays enough to cover necessary living expenses like housing, food, utilities, and transportation. It can’t be a tiny monthly or yearly check. It has to pay enough to enable people to live without working.
Most so-called ‘UBI’ proposals fail to meet one or both of these criteria. Some UBI advocates cite the Alaska Permanent Fund as an example, even though it pays nowhere near enough to cover basic living expenses. And so-called ‘UBI experiments’ are rarely, if ever…
| 2023-02-21T00:00:00 |
2023/02/21
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https://medium.com/@matt.drabek/why-leftists-should-oppose-universal-basic-income-ubi-3fb0329c8f7d
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[
{
"date": "2023/02/01",
"position": 15,
"query": "universal basic income AI"
}
] |
Does Frequency or Amount Matter? An Exploratory ...
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Does Frequency or Amount Matter? An Exploratory Analysis the Perceptions of Four Universal Basic Income Proposals
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https://www.mdpi.com
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[
"Hamilton",
"Despard",
"Roll",
"Bellisle",
"Hall",
"Wright",
"Leah Hamilton",
"Mathieu Despard",
"Stephen Roll",
"Dylan Bellisle"
] |
by L Hamilton · 2023 · Cited by 4 — Advocates for a Universal Basic Income (UBI) argue that it would provide citizens with a basic foundation for financial security, boost the economy, ...
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Because voters tend to make decisions based on heuristics or mental shortcuts ( Lau and Redlawsk 2001 ), the ongoing UBI policy debate would benefit from a better understanding of American assumptions about the hypothetical impacts of a UBI. To that end, in this study, we surveyed more than 800 adult Americans regarding their expectations concerning how recipients would respond to various models of unconditional cash transfers (including various work and spending behaviors) and the degree to which these expectations vary based on the generosity and allocation schedule. We also examined whether respondent demographics influenced their perceptions of recipient behavior. Ultimately, we find that the amount of transfer did little to influence behavioral expectations. Still, monthly disbursements were more likely to elicit predictions of work disincentives, while lump sum transfers were more often associated with debt repayment. These findings may help to inform differing support between tax credits, pandemic relief, and UBI policy proposals.
Though basic income proposals are gaining more consideration and attention at the local and state levels, recent polls indicate that support for a UBI among the American public is split, with roughly half in support and half opposed ( Reinhart 2018 Gilberstadt 2020 ). Meanwhile, other existent “lump sum” cash transfer policies in the United States, such as the Earned Income Tax Credit (EITC), the more recent 2021 COVID relief package and enhanced Child Tax Credit, and targeted tax relief to low- and moderate-income families enjoy much higher approval ratings ( Data for Progress n.d. Rendleman and Yoder 2019 ). One factor underlying these differing positions may be the expectations concerning how Americans would use a UBI benefit and the cultural meaning ascribed to these uses. While there is extensive research on how individuals themselves respond to different payment structures and amounts in the context of public benefits such as tax credits ( Mendenhall et al. 2012 Smeeding et al. 2000 ) and social welfare programs such as unemployment insurance ( Ganong and Noel 2016 ), little research exists concerning how individuals perceive that other people will use their payments and, to our knowledge, there is no research concerning the perceived uses of a UBI.
Economic volatility since the 2008 Great Recession and, more recently, the COVID-19 pandemic has renewed American public interest to an old idea: universal basic income (UBI). Andrew Yang, a technology entrepreneur who ran for the Democratic Party’s nomination for presidential candidate in 2020, is credited with raising awareness of UBI as a current policy option, proposing that all Americans receive1000 monthly. Several cities and some states have recently proposed or enacted basic income pilots. Most modern UBI proposals include three conditions: the payment should be distributed directly to individuals (rather than households), should not be means-tested, and hold no mandatory conditions (i.e., workforce or educational participation) to receive the payment ( Johnson and Roberto 2020 ). Put simply, UBI is “a periodic cash payment unconditionally delivered to all on an individual basis, without means-test or work requirement” ( Basic Income Earth Network 2020 ). However, basic income programs currently being piloted in cities such as Stockton, CA, and St. Paul, MN, are sometimes referred to as “guaranteed income” rather than basic income because they are means-tested to target low-income households. Whether universal or means-tested, basic income programs have no requirements for how the money is spent. That is, they may be conditional on income or not (universal), yet are unrestricted concerning benefit usage.
The NIT’s effects on workforce participation differed for men and women. While men worked 20–130 h less per year, women (mostly mothers) worked zero to 166 h less per year ( Widerquist 2005 ). One might argue that an NIT giving mothers more choice in whether to stay home with young children or work outside the home is positive. Further, while women contribute significantly more unpaid care to household and national economies ( Johnson and Wiener 2016 ), this labor is rarely counted as “work” in American discourse. This lack of a nuanced conversation around a UBI or NIT’s effects on the workforce soured early supporters of the idea, such as Senator Daniel Patrick Moynihan, who wrote, “But were we wrong about a guaranteed income! Seemingly it is calamitous” ( Widerquist 2005, p. 24 ).
The defining difference between UBI/NIT, the EITC, and SSI is that the EITC and SSI require a recipient to either work or have a documented inability to work. Widerquist 2005 ) argues that the media and public misunderstanding of an NIT’s effects on labor force participation were the primary reasons for its defeat in the 1970s. While overall work did slightly decline among recipients of the four NIT pilots, several important nuances were not captured in the public discussion. First, there was no measurement of whether external workforce demand declined during the period under observation. Further, the pilots only included participants with low incomes. Moffitt 1979 ) estimates that NIT would reduce work among low-income recipients by 4.5% but only 1.6% for mid and high-income earners (presumably because NIT makes low-wage jobs less attractive), an overall effect which might be offset by the other benefits and efficiencies described above. Additionally, the fact that unconditional cash transfers give workers greater bargaining power against poverty-level wages is now seen as a benefit among many basic income proponents ( Lowrey 2018a ).
Ultimately, this political impasse led to a compromise that launched the Earned Income Tax Credit (EITC) and transformed the former Aid to the Blind into the modern-day Supplemental Security Income (SSI) program ( Social Security Administration 2018 ), but interest in UBI and NIT fizzled as national politics moved to the right. Therefore, while the EITC is conditional on work, its political history provides important insight into the political challenges of adopting a UBI in the U.S. Currently, the EITC serves as one of the primary anti-poverty mechanisms for families with children in the United States. The credit is delivered via an annual lump sum as part of a taxpayer’s tax refund and research suggests families use their EITC to catch up on bills, save for emergencies, and reduce debt ( Halpern-Meekin et al. 2014 Shaefer et al. 2013 ). However, while the lump-sum delivery of the refundable credit gives households a sizable income boost, waiting until tax filing to receive it may induce families to accumulate unsecured debt and defer necessary health care in anticipation of their refund ( Farrell et al. 2018 Weber 2016 ). Furthermore, unlike other stigmatizing means-tested programs such as Temporary Assistance to Needy Families (TANF), Sykes et al. 2015 ) provide evidence that EITC recipients imbue the EITC with social meaning as a reward for work, an opportunity for mobility and to treat their children as “ordinary kids” and therefore promotes social inclusion and offers access to social rights of citizenship. Therefore, while still conditional on work, the EITC may at least be perceived by recipients as offering some aspirational citizenship rights many believe a UBI may offer.
In 1972, President Nixon proposed the Family Assistance Plan (FAP). This policyhad some similarities to an NIT, except that it did not cover unemployed households and was to be disbursed at a household level. The plan was ultimately defeated in Congress by forces on both the political left and right. Welfare rights organizers viewed it as insufficient ( Withorn 2006 ) and as an effort to reinforce male dominance over women, as the FAP explicitly sought to support men, particularly Black men, as household heads through job-training programs and wage supplements to support low-wage employment ( Quadagno 1990 ). Southern Congressmen and business elites also opposed it because they believed it threatened the local low-wage labor market that depended upon the labor of economically marginalized Black people, particularly Black women, who were largely excluded from locally controlled public assistance programs ( Ventry 2000 Quadagno 1990 ).
While versions of the concept first appeared in the 16th century, UBI did not truly gain traction in the United States until the civil rights and welfare rights movements of the 1960s. With proponents ranging from Martin Luther King Jr. to conservative economist Milton Friedman, federally funded Negative Income Tax (NIT) pilots launched at multiple sites throughout the United States with thousands of participants ( Hamilton 2020 ). NIT has several similarities to a UBI (there are no work requirements or expectations on how the money is spent), but instead of being dispersed universally, it is a means-tested refundable tax credit that brings low-income households up to a designated income floor. However, because most UBI proposals include a “taxing back” of the benefit for higher-income individuals, the distributional effects of a UBI and NIT are functionally similar ( Groot 2004 ).
However, the national UBI discourse also includes several ethical, practical, and political objections. For example, ethicists argue that via the principle of reciprocity, people who receive benefits should make societal contributions if able ( Richards and Steiger 2021 ). Practical objections include the high cost of implementation, efficiency distortions from tax increases, and benefits to non-poor people. Political objections to UBI include the possibility that it would require the elimination of other safety-net programs and would lead to incentives for immigration ( Richards and Steiger 2021 ).
Meanwhile, critics of the existent American welfare state, which comprises a patchwork of conditional and/or means-tested fungible and non-fungible benefits, argue that it disincentivizes work and savings through restrictive income and asset limits and stigmatizes poor and minority women ( Hamilton 2020 ). Further, welfare “sanctions” and lost benefits are associated with comparatively poor school performance, increased child abuse, and decreased family preservation ( Kortenkamp et al. 2004 Slack et al. 2007 ). Therefore, supplementing or replacing means-tested assistance with UCTs can better support low-income families and improve the well-being of their children. A UBI also has potential policy advantages over more traditional means-tested assistance, as it minimizes administrative costs and increases efficiency since there is no need to determine eligibility ( Fouksman and Klein 2019 ). Finally, advocates argue that UBI would be less vulnerable to future budget cuts, as more universal programs such as Social Security for retired persons and tax credits enjoy broad political support ( Kasy 2018 ).
Advocates argue that implementinga basic income would provide every citizen with a basic level of financial security, boost the economy, alleviate poverty, encourage entrepreneurship, reduce crime, and help compensate for jobs lost due to technological advances. A Stanford University meta-analysis of 16 other systematic reviews of worldwide UBI pilot test data with thousands of participants, dating from the early 1970s to today, reveals several consistent themes. UBIs, a type of unconditional cash transfer (UCT), decrease poverty, increase consumption, have minimal effects on labor force participation, improve school attendance and achievement of the children of recipients, and improve physical and mental health ( Hasdell 2020 ).
To our knowledge, there is little to no existing literature on the perceived uses of unconditional cash transfers. The following exploratory analysis examines whether the perceived uses of unconditional cash transfers differ across various frequencies and amounts and therefore helps to explain its uneven public support. We then seek to learn whether these behavioral expectations differ by demographic characteristics of the respondent to add to previous research findings that respondent’s positionality can influence UBI support. Specifically, we examine age, gender, and employment status as young people, women, and persons with low incomes have been found to have higher favorability towards UBI proposals ( Freeland 2019 Gilberstadt 2020 ).
These hypothetical suppositions would be consistent with behavioral economics research, which has found that individuals tend to treat money viewed as windfalls differently than money viewed as regular income ( Epley and Gneezy 2007 ) and are more likely to consume income distributed through regular payments than an equivalent amount of income distributed in a single lump sum (e.g., Shefrin and Thaler 2004 ). Similarly, Friedman ’s ( 1957 ) permanent income hypothesis argues that consumer behavior is more influenced by permanent changes in income than one-time infusions. Alternatively, sociological research suggests that individuals imbue different sources of money with social meaning, shaping how they spend and allocate the money ( Zelizer 1989 2017 ). Indeed, several scholars have illustrated how individuals who receive the EITC regard their tax refund as “family money”, and this guides their decisions to spend the money in ways to support their and their children’s general well-being ( Bellisle 2022 Sykes et al. 2015 ).
A monthly allocation that represents a consistent cash inflow may be perceived as having a substitution effect for earned income, which may trigger concerns (which can also be racialized) about work disincentives. The expected impact of UBI on labor force participation, especially among economically and racially marginalized communities, may also be an important factor that shapes these differing positions. Indeed, several politicians and lobbying entities helped derail past efforts to establish a guaranteed income through a negative income tax (NIT) by invoking fears of lazy and undeserving individuals, particularly Black women, that will choose not to work and become a drain on society ( Ventry 2000 Soss et al. 2011 ).
Still, little public opinion polling thus far has examined whether the design and expected use of unconditional cash transfers might influence these perceptions. For example, is the amount or frequency influential? While UBI is potentially a more effective and efficient method of remediating economic insecurity, public support lags behind other annual or one-time cash transfer programs. A UBI may be perceived as frivolous if Americans use their benefit on non-essential goods and services and decrease labor force participation, while annual or one-off benefits may be perceived as addressing important financial challenges and goals.
Much evidence suggests that racist, sexist, and classist heuristics such as these play an important role in voter perceptions of policy proposals ( Bernhard and Freeder 2020 Kahneman 2013 ). This is especially true when examining public attitudes towards “welfare” policy. When asked to evaluate policies aimed at low income individuals, respondents often rely on the age-old heuristic of “deservingness”, with those willing to work but unable labeled as “deserving” and those able to but unwilling to work labeled as “undeserving” ( Petersen et al. 2012 ). When asked what they might do with a basic income, Americans state that they would use the money to pay down debt or save for education and homeownership, but when asked what they think others would do, respondents fall back on debunked stereotypes of work disincentives and negative consumption ( Evans and Popova 2016 Hamilton et al. 2021 ). When asked to explain these reactions, qualitative responses include statements such as “people nowadays [sic] will take everything they can get and then some like they are owed” and “Americans can be lazy, so they’ll do anything to reduce time at work” ( Hamilton et al. 2021 ).
Similarly, “welfare queen” rhetoric amidst the passage of the Personal Responsibility and Work Opportunity Reconciliation Act of 1996, which created TANF, was concerned with ensuring that recipient mothers return to work as quickly as possible. Kandaswamy pointedly argues that “the implicit message of [TANF] is that impoverished children do not need care as much as they need role models in labor discipline ( Kandaswamy 2021, p. 3 ).” Today, this racialized context and the state-level control afforded by policy devolution means that African Americans are more likely to live in states with harsher eligibility criteria and lower benefits than White Americans ( Soss et al. 2008 ). This has created a patchwork of welfare policies with greater interstate variation than between many European countries ( Bruch et al. 2018 ).
Disparities in support for UBI and existent cash transfer programs such as the EITC and CTC may partially stem from long-standing welfare narratives suggesting that generous social policy breeds dependence. Furthermore, these attitudes towards welfare policy often have racist undertones, playing into prejudicial stereotypes ( Gilens 2000 ). Indeed, instead of adopting principles of universality and equity as in some European countries, the American social safety net has been deeply altered by the racist political rhetoric of “welfare queens” and intergenerational dependence ( Hamilton 2020 ). Conservative and neoliberal skepticism of the welfare state reached its zenith in 1996 when the 60-year-old Aid to Families with Dependent Children program was replaced by Temporary Assistance to Needy Families (TANF), ushering in new work requirements and lifetime limits on assistance. Furthermore, critical race theorists argue that the neoliberal establishment of TANF was simply a replication of Reconstruction-Era efforts to control the lives and reproduction of newly freed Black women ( Kandaswamy 2021 ). Many Freedman’s Bureau policies after the American Civil War were concerned with ensuring that Black women worked outside the home, often in domestic service, caring for the children of White families while their own children were cared for by others ( Kandaswamy 2021 ).
The expanded CTC is argued by some to be a form of guaranteed income for families with children ( DeParle 2021 ). It contains no work requirements and begins phasing out at150,000 in annual income for two-parent families. While the EITC does include a work requirement, it and the 2021 CTC both lack other more intrusive eligibility requirements present in traditional welfare programs such as TANF, such as asset limits, drug testing, paternity establishment rules, and extremely low income limits. They also lack restrictions on how the money is spent, arguably making them more akin to an unconditional cash transfer than in-kind benefits such as food stamps and housing assistance.
Meanwhile, existing cash transfer programs such as the EITC and CARES Act economic impact payments enjoy much broader public support. The EITC began as a small tax credit to help offset payroll taxes of low-income parents ( Hotz and Scholz 2003 ), and program eligibility and generosity have expanded under both Democratic and Republican administrations ( Mendenhall 2006 ). Governors implementing state-level EITCs have higher approval ratings and vote shares ( Rendleman and Yoder 2019 ). Similarly, President Biden’s 20211.9 trillion COVID-19 relief package enjoyed a 70% approval rating, although Republicans are still less likely to support it than Democrats (41% vs. 94%) ( Pew Research Center 2021 ). Within this package, most Americans received a1400 one-time cash payment, phasing out after the first75,000 in income for individuals and150,000 for married households. The Child Tax Credit (CTC) was also temporarily expanded to3000 for school-age children and3600 for children under six, removing previous earnings requirements and making the credit fully refundable so that even those who are unemployed can receive the full benefit. Half of the child credit was issued monthly between July and December 2021 ( Taylor 2021 ).
Still, growing inequality and economic instability since the Great Recession of 2008 and the COVID-19 pandemic have brought renewed attention to UBI as a legitimate policy alternative, with at least 11 pilots launched in cities across the country in 2021 ( Holder 2021 ). Public opinion surveys conducted in recent years find that roughly half of Americans favor the idea ( Reinhart 2018 Gilberstadt 2020 ). However, demographics seem to play an important role in UBI support. Two-thirds (67%) of young people under 30 support a UBI ( Gilberstadt 2020 ). Race also seems to play a factor in UBI favorability, as 45% and 35% of Black and Hispanic respondents strongly favor a UBI, compared to only 16% of White respondents in a survey by the Pew Research Center ( Gilberstadt 2020 ). Further, only 5% of self-described Conservatives strongly support a UBI compared to 38% of Liberals ( Gilberstadt 2020 ).
Chi-squared tests were used to examine whether differences in the amount and allocation frequency of hypothetical UCTs might influence perceptions of how the general public might use those benefits. Associations between hypothetical benefit and expected uses were tested across all four benefit configurations and between amount (3000 vs.6000) and allocation frequency (monthly vs. lump-sum allocation) groups. Finally, z-tests were used to directly compare the proportions of expected benefit uses by hypothetical scenario groups using Bonferroni corrected-values to adjust for the large number of comparisons made across analyses. The Bonferroni approach is a conservative one allowing us to minimize the chance of Type I errors ( VanderWeele and Mathur 2019 ).
Use of the phrases “no strings attached” and “no matter their situation” were meant to characterize the benefit as unrestricted and unconditional, respectively. These questions were designed to assess variation in opinions concerning how participants thought recipients might use the benefit relative to the amount and allocation frequency. After a random assignment in Qualtrics, 227 participants responded to Q1 (500 per month), 198 to Q2 (250 per month), 200 to Q3 (one-time payment of6000), and 211 to Q4 (one-time payment of3000). For each of the questions, respondents were allowed to choose one of the following responses: (A) Quit working or seeking work, (B) Reduce working hours, (C) Continue working as they do now, (D) Put the money in savings, (E) Pay down debt, (F) Apply the money towards education or student loans, (G) Apply the money towards homeownership, (H) Apply the money towards small business development, (I) Apply the money towards regular expenses (housing, groceries, utilities, etc.), (J) Apply the money towards childcare, (K) Apply the money towards healthcare expenses, (L) Apply the money towards a major consumer purchase, such as a vehicle, television, or appliance, (M) Spend it on small luxuries or non-essentials (e.g., eating out, travel, gifts, alcohol, clothes), and (N) Other (please explain). These categories were informed by existent research on expenditures and usages of other cash transfer and UBI programs ( West et al. 2021 Shaefer et al. 2013 ). While these categories are not necessarily mutually exclusive, we asked respondents to choose only one to gauge their first reaction to the questions. As discussed above, these “knee-jerk” heuristics are important in voter perceptions and decision-making.
Just under a third (31.2%) of the sample worked part-time, 31.6% worked 40+ hours per week, 13.2% were unemployed and looking for work, 12.3% were unemployed and not seeking work, 6.1% were retired, and 5.6% were unable to work due to a disability. By comparison, in 2019, 70.2% of Americans aged 16+ worked full time ( Bureau of Labor Statistics 2019 ). Finally, 16.4% of respondents had an annual household income (in 2019) of less than20,000, 32.4% earned20,000–49,999, 34.7% earned50,000-99,000, and 16.5% earned100,000 or more. In 2019, the median US household income was65,712 ( Guzman 2020 ).
The sample was racially and ethnically comparable to the total US population ( US Census Bureau 2019a ), with 74.0% reporting that they were White, 12.9% Black, 0.6% American Indian or Alaska Native, 5.4% Asian, 0.4% Native Hawaiian or Pacific Islander, and 6.7% either multiracial or some other race. Additionally, 14.6% of participants identified as either Hispanic or Latino. The sample also varied in terms of education, employment, and income. Only 1.7% of the sample had less than a high school diploma, 23.0% had a high school diploma or GED, 24.8% had some college, 14.8% had an Associate’s degree, 25.4% had a Bachelor’s degree, and 10.4% held a graduate degree. Educational attainment was similar to that for the U.S. population, in which 32.1% have a Bachelor’s degree or higher compared to 35.8% in the study sample ( US Census Bureau 2019b ).
Initially, 877 participants were recruited; however, 41 cases were excluded because participants either did not either (a) provide consent ( n = 5), (b) report US citizenship ( n = 35), or (c) respond to the full survey ( n = 1). The final sample of 836 participants was limited to adults (18+) who were also US citizens. As a randomization check, Chi-squared tests of independence were conducted to ensure that demographic variables did not differ significantly based on the UBI plan condition. As expected, group assignment was not significantly associated with participant age ( p = 0.696), gender ( p = 0.563), race/ethnicity ( p = 0.716), Hispanic/Latino status ( p = 0.681), marriage status ( p = 0.212), education ( p = 0.609), employment status ( p = 0.729), or annual household income ( p = 0.643). In addition, minimum detectable effect size (MDES) calculations indicated that we were able to detect effect sizes of 0.097, 0.146, and 0.181 with the final sample of N = 836 for chi-square tests with 1, 13, and 39 degrees of freedom, respectively, setting alpha at 0.05 and power at 0.80.
Amazon’s Prime Panels platform is an increasingly popular recruitment tool in social science research, one which can provide diverse respondent samples and high-quality data ( Chandler et al. 2019 ). Prime Panels was used to recruit adult Americans randomly assigned to one of four hypothetical UBI scenarios. The four scenarios reflected two dimensions of an unconditional cash transfer: amount (a3000 or6000 total benefit) and allocation frequency (monthly vs. lump-sum). These amounts were chosen as they are similar to existing cash transfer programs and modern UBI pilots. For example, the 2021 CTC provides250 per month for children over age 5, and the EITC includes a maximum credit of6660 for 2020. Meanwhile, UBI pilots such as those in Stockton, CA, and Hudson, NY, provide recipients with500 per month. Respondents in each condition were then asked how they thought most people would spend these funds. A university Institutional Review Board approved the study in August 2018, as it involved minimal risk to participants.
We also examined differences in responses by the yearly amount offered through the plans (i.e., $ 3000 vs. $ 6000), but we did not observe any significant differences based on the payment amount. This likely indicates that the bulk of the differences we observe in this study is due to the frequency of the payments rather than the amount.
Another set of Chi-squared tests were conducted between outcome and plan type, collapsed by frequency of benefits (i.e., monthly vs. one-time payments). These tests are reported in Table 3 , and responses to each outcome by benefit frequency are displayed in Figure 1 . The association between benefit frequency (monthly vs. one-time) and outcomes was statistically significant, Χ(13, 836) = 40.6,< 0.001, with a small effect size (Cramer’s= 0.220). Consistent with previous analyses, respondents believed that most people would reduce their working hours more on monthly plans (7.8%) than one-time plans (2.4%), Χ(1, 836) = 12.2,< 0.001, and pay down debt more on one-time plans (28.0%) than on monthly plans (17.2%), Χ(1, 836) = 14.0,< 0.001. Additionally, a statistically significant difference in smaller purchases (e.g., luxury items, non-essentials) emerged, Χ(1, 836) = 7.99,= 0.005, such that respondents thought that people would use monthly benefits (18.1%) more than one-time benefits (11.2%) to make these purchases.
Chi-squared tests comparing perceived uses of the four types of benefits are reported in Table 2 . A statistically significant association between plan type and the outcome was observed, X(39, 836) = 69.4,= 0.002, though the effect size for this association was small (Cramer’s= 0.166). Individual comparisons of plan types by outcome revealed statistically significant differences in perceptions that UBI benefits would lead to a reduction in working hours, Χ(3, 836) = 14.0,= 0.003, debt repayment, Χ(3, 836) = 15.4,= 0.002, major consumer purchases, Χ(3, 836) = 12.2,= 0.007, and spending on small luxuries, Χ(3, 836) = 8.1,= 0.045. In addition to examining differences across all treatment conditions, we also examined the extent to which individual treatment conditions led to significantly different responses compared to other conditions. This analysis shows that respondents believed that the average person would reduce their working hours more when provided with a500/month (8.8%) than when provided with a one-time payment of6000 (1.5%), pay down their debts more when given3000 (28.4%) or6000 (27.5%) one-time payments than when given500/month (15.0%) and spend more on major purchases with500/month (5.7%) or6000 (7.5%) one-time payments than the250/month plan (0.5%). No other statistically significant differences in outcomes by plan types emerged.
Across all hypothetical conditions, the majority of respondents believed that the average person receiving unconditional benefits of any amount or allocation frequency would most likely use benefits on regular expenses (27.5%), paying down debt (22.5%), and purchasing small luxuries or non-essentials (14.7%). Nearly twice as many respondents believed that the average benefit recipient would continue to work as they do now (10.2%) than reduce their working hours (5.1%), and even fewer believed that the average recipient would quit working or seeking work (2.5%).
Chi-squared tests were also used to compare these collapsed outcome groups with dichotomized age (under 30 vs. 30 or older), gender (male vs. female), and employment (working part-/full-time vs. not working) variables, regardless of plan assignment. While outcomes did not statistically significantly differ by gender ( p = 0.273) or employment ( p = 0.083), significant differences in outcomes were observed between participants over/under 30 years of age, Χ 2 (4, 713) = 30.6, p < 0.001. Compared to respondents older than 30, younger respondents more frequently believed that the average person would use benefits for quitting or reducing work (13.9% vs. 6.4%) and saving or investing (18.0% vs. 9.2%), but not for necessary/regular expenditures (25.0% vs. 38.8%; p’s < 0.05). No other statistically significant differences appeared between these demographic variables and outcomes ( p’s > 0.05)
Once more, significant differences emerged in comparisons of outcome frequency by benefit frequency, Χ 2 (4, 713) = 24.6, p < 0.001. Compared to one-time plans, monthly plans were significantly associated with perceptions that the average person would use benefits to quit or reduce work (10.4% vs. 4.9%; p = 0.003) and that the average person would spend on major or minor consumer purchases (18.6% vs. 12.7%; p = 0.018); conversely, one-time plans were significantly associated ( p < 0.001) with perceptions that the average person would pay off debt with their benefits (28.0%) relative to monthly plans (17.2%). No statistically significant differences were observed by benefit frequency in perceptions that people would save/invest benefits ( p = 0.338) or spend benefits on regular/necessary expenses ( p = 0.943).
Since some response options were selected less frequently than others regardless of cash transfer plan, outcomes were collapsed into theoretically similar categories and compared once more with benefit frequency. Specifically, new categories were created for quitting or reducing work hours (outcomes A and B), saving or investing the money (outcomes D, F, G, and H), paying off debt (outcome E), spending on necessities (outcomes I, J, and K), and spending on major or minor consumer purchases (outcomes L and M). If participants had responded in the affirmative to any of these outcomes, they were added to the associated response class.
7. Discussion
Overall, we did not discover much variation in how respondents projected that the money would be spent. Across all four questions, a plurality (25–30%) of respondents assumed that recipients would apply the money towards regular expenses (housing, groceries, utilities, etc.), the intensive consumption margin. It is also notable that the second most frequent behavioral expectation was debt reduction. Far smaller proportions of respondents expected a response on the extensive margin of consumption concerning non-essentials and an income effect on labor supply; only 5.1% said recipients would reduce their work hours. An even smaller proportion expected movement on the extensive margin of labor supply, perhaps because the magnitude of the benefit was too low to facilitate labor market exits. Thus, the overall picture that emerges is that respondents mostly expect recipients to use the additional money from a UBI to smooth consumption (inclusive of debt) rather than adjust labor force participation. Clearly, the idea that a UBI might discourage work—a chief concern among many policymakers when considering income support programs—was not a widespread issue among respondents. Instead, they considered how recipients could use a UBI to optimize household resource management.
Two areas that did create significant variations with respect to benefit configuration were the reduction of work hours and repayment of debt. While 8.8% and 6.6% of those predicting the effects of $ 500 and $ 250 per month (respectively) believed that recipients would reduce working hours, this was only the case for 1.5% and 3.3% of those responding to questions regarding lump-sum payments ( $ 6000 and $ 3000, respectively). Thus, the overall expectation of an income effect on labor supply was low, but greater when considering a monthly versus lump-sum benefit. This may be because a monthly benefit makes it easier for individuals to consider reducing work hours based on X fewer hours and X hourly pay relative to a $ 250 or $ 500 monthly payment.
This relatively small prediction of labor force reduction adds nuance to similar previous studies. For example, Richards and Steiger 2021 ) presented a basic income-like scenario and then asked respondents for their agreement with the statement “(T)he number of people not working would increase”, to which 47.8% agreed. Similarly, in an IPSOS poll, 63% of Americans agreed that “Basic income will discourage people from being in or seeking paid employment” ( Colledge and Martyn 2017 ). However, the difference between these findings and ours mostly likely lies in the question-framing. In these previous studies, respondents were asked about overall labor force participation. They believed it would decline but were not asked by how much it would decline. We asked what “most people would do.” Taken together, it is reasonable to surmise that American respondents believe that some people would work less (resulting in an overall decline in labor force participation) but that most people would continue working.
Those considering the effects of lump-sum payments thought that the repayment of debt would be a likely outcome more often than those considering a monthly benefit. A lump-sum allocation could make it easier for individuals to pay down if not eliminate debt—an important consideration given record levels of student debt, that 17% of student loan borrowers are having trouble repaying their loans, and that over a quarter of U.S. households carry credit card balances most or all of the time ( Board of Governors 2020 ). Conversely, expectations concerning using the benefit to put money in savings did not differ with respect to benefit amount or allocation method. Because so many Americans struggle to save—less than half have the recommended amount of emergency savings, and only 58% have retirement accounts ( FINRA Investor Education Foundation 2019 ), any additional income in any form may be expected to make a positive difference in household balance sheets.
While informative, the research design includes several important limitations. First, because we asked about perceived uses of an unconditional cash transfer rather than the strict favorability of the various proposals, inferences regarding favorability cannot be made and require follow-up research. However, because previous research has linked UBI proposals with welfare narratives of work disincentives and dependence ( Hamilton et al. 2021 ), one could reasonably hypothesize that expectations of reduced work hours in the context of monthly benefit proposals were seen as a negative outcome by participants. At the same time, because no more than 8.8% of respondents predicted reduced work in any scenario, it may be that these attitudes are changing. Interestingly, those under 30 were more likely to predict reduced work hours, and previous research has found young people are more supportive of UBI proposals ( Gilberstadt 2020 ), suggesting that the conception of work and UBI among young people requires further study. For example, could Generation Z be less likely to intertwine the concepts of work and deservedness than Baby Boomers? The fact that our sample was younger and more heavily female than the general population both complicates this dynamic and provides fodder for further study. Similarly, we did not ask about the respondents’ political leanings, which could arguably influence their perceptions. Levay et al. 2016 ) recommend using MTurk’s available statistical controls to ensure demographically representative samples.
Further, we did not ask respondents about annual payment scenarios in addition to lump-sum and monthly payments. In the lump-sum condition, we referred to the UBI allocation as one-time rather than specifying it as a recurring annual payment. In contrast, the monthly allocation condition might have been construed as a recurring payment over an indefinite period. Thus, differently inferred time horizons between the lump-sum and monthly conditions might have influenced responses. We recommend this for future research in this area. Future research might also investigate a wider array of disbursement amounts beyond the four examined here. Finally, because we surveyed American respondents, all of whom operate in the uniquely American capitalist, neoliberal paradigm, we expect our findings to be primarily useful to researchers and advocates in the same context.
| 2023-03-14T00:00:00 |
2023/03/14
|
https://www.mdpi.com/2076-0760/12/3/133
|
[
{
"date": "2023/02/01",
"position": 16,
"query": "universal basic income AI"
}
] |
Money for Nothing: The Pros and Cons of Universal Basic ...
|
Money for Nothing: The Pros and Cons of Universal Basic Income (UBI)
|
https://medium.com
|
[
"Kopfkino Fm"
] |
UBI is a government-funded program that provides a regular, unconditional cash payment to every citizen.
|
Money for Nothing: The Pros and Cons of Universal Basic Income (UBI) Kopfkino FM 7 min read · Feb 4, 2023 -- 2 Listen Share
Welcome to the world of Universal Basic Income (UBI) — a topic that’s been gaining traction in recent years as a potential solution to poverty and inequality. But what exactly is UBI and why is it so appealing?
Imagine a world where everyone gets paid just for being alive. Sounds too good to be true? Well, it’s not as far-fetched as you may think. UBI is a government-funded program that provides a regular, unconditional cash payment to every citizen. The idea is that with this guaranteed income, people would have the financial stability to pursue their dreams and live their lives with dignity and freedom.
Did you know that the idea of UBI dates back to the 16th century? Thomas More proposed it in his book “Utopia” as a way to eliminate poverty and create a more equal society. Fast forward to the 21st century, and UBI is being tested and implemented in several countries around the world, such as Finland, Canada, and India.
But UBI isn’t just a pipe dream — it’s a real policy being considered by governments and economists. And the idea is gaining momentum. According to a survey by the Basic Income Earth Network, over 70% of people in the US and Europe support the idea of UBI.
So, are you ready to dive into the world of UBI? Let’s take a closer look at the pros, cons, and everything in between.
The Basics of UBI
So, how exactly would UBI work in practice? The specifics can vary depending on the country or region, but the basic concept is the same: every citizen would receive a regular, unconditional cash payment from the government.
This payment could be set at different levels — for example, it could be enough to cover basic necessities like food and housing, or it could be set at a higher amount to provide more financial freedom. And unlike traditional welfare programs, UBI would not have any means-testing or work requirements. Everyone would receive the payment, regardless of their income, employment status, or other factors.
There have been a number of experiments and pilot programs involving Universal Basic Income (UBI) in various countries around the world.
Finland: In 2017, Finland conducted a two-year pilot program in which 2,000 unemployed individuals were given a monthly unconditional payment of €560. The results of the pilot showed that while UBI did not significantly reduce unemployment, it did improve recipients’ overall well-being and reduced their stress levels. Canada: In the 1970s, the Canadian province of Dauphin conducted a “mincome” pilot program in which residents of the town received a guaranteed annual income. Results from the pilot suggested that it had positive effects on health, education, and work incentives. United States: In 2016, a pilot program in the city of Oakland, California provided a group of low-income residents with a basic income of $1,500 per month. A report from the Economic Security Project, which funded the pilot, found that recipients used the money to pay for housing, food, and transportation, and that the program helped to reduce financial stress and increase recipients’ sense of security.
There have also been several UBI experiments and pilot programs that have been discontinued due to various reasons. Here are some notable examples.
Netherlands: In 2018, the Dutch city of Utrecht launched a pilot program that would have provided a basic income to some welfare recipients. However, the program was discontinued after a change in the national government led to a shift in priorities and lack of funding for the program. United States: In 2019, the city of Stockton, California began a pilot program that would have provided $500 a month to a select group of residents. However, the program was discontinued after 18 months due to lack of funding. India: The Indian state of Madhya Pradesh ran a pilot program in 2010–11, where 6,000 people in eight villages were given an unconditional cash transfer of 200 rupees (US$2.8) per person per month. The program was discontinued after the pilot phase, due to lack of political support, and lack of funds to scale it up. Kenya: GiveDirectly, a non-profit organization, ran a 12-year basic income experiment in Kenya, where they gave cash transfers to people in Kenya. But the program was discontinued in 2019 because the cost of running the program was not sustainable.
It’s important to note that the discontinuation of these pilot programs does not necessarily mean that UBI is not a viable solution. These pilot programs were not long enough to draw definitive conclusions, and were not implemented on a large scale to be able to measure their true potential impact, whether positive or negative.
The Pros of UBI
Now that we’ve got the basics of UBI down, let’s take a look at some of the potential benefits of this program.
First and foremost, UBI could provide increased economic stability for individuals and families. With a guaranteed income, people would have the means to pay for basic necessities like food, housing, and healthcare. This could help to reduce poverty and inequality, as well as improve overall health and well-being.
But UBI isn’t just about survival — it’s also about thriving. With a guaranteed income, people would have more freedom and opportunity to pursue their passions and dreams. They could start their own businesses, go back to school, or take time off to care for a loved one without worrying about how they’ll pay the bills. In other words, UBI could give people the freedom to live their lives on their own terms.
And let’s not forget about the potential economic benefits of UBI. Some economists argue that UBI could boost economic growth by increasing demand for goods and services. It could also encourage entrepreneurship and innovation, as people would have the financial security to take risks and start their own businesses.
The Cons of UBI
As with any policy, there are also potential downsides to UBI. And it’s important to consider these when evaluating the viability of this program.
One of the main concerns about UBI is the potential for increased government spending and taxes. Funding a UBI program would require a significant investment from the government, which could lead to higher taxes for citizens. Additionally, some critics argue that UBI could lead to inflation, as the increased spending from citizens could drive up prices.
Another concern is that UBI could disincentivize work. If people have a guaranteed income, some may choose not to work, which could have negative consequences for the economy and society. It’s also important to note that UBI alone won’t solve the problem of unemployment caused by automation and technology, other measures should be taken to accompany it.
Finally, there’s the issue of implementation and maintenance. Establishing a UBI program would require a significant amount of bureaucracy and administration, which could be costly and time-consuming. Additionally, there are concerns about how to ensure that UBI payments are distributed fairly and how to prevent fraud or abuse of the system.
It’s important to weigh the pros and cons of UBI and consider the feasibility of implementing such a program. While UBI has the potential to provide financial stability and greater freedom for individuals, it’s also important to consider the potential downsides and challenges that would come with it.
In conclusion
UBI may be the hot topic of the moment, but it’s not a one-size-fits-all solution. It’s like trying to fit a square peg into a round hole, it may work in some cases, but not in others. The idea of providing a basic income to citizens has its merits, but it also has its challenges. From funding to implementation, there are plenty of obstacles to overcome. But just because it’s not easy, doesn’t mean it’s not worth exploring. It’s like climbing a mountain, it’s tough, but the view from the top is worth it. Whether UBI is the answer or not, it’s worth considering as one of the tools in the toolbox to combat poverty and inequality.
| 2023-02-04T00:00:00 |
2023/02/04
|
https://medium.com/kopfkino/money-for-nothing-the-pros-and-cons-of-universal-basic-income-ubi-f40705d91b6e
|
[
{
"date": "2023/02/01",
"position": 24,
"query": "universal basic income AI"
}
] |
Implementing artificial intelligence in Canadian primary care
|
Implementing artificial intelligence in Canadian primary care: Barriers and strategies identified through a national deliberative dialogue
|
https://journals.plos.org
|
[
"Katrina Darcel",
"Upstream Lab",
"Map Centre For Urban Health Solutions",
"Unity Health Toronto",
"Toronto",
"Ontario",
"Undergraduate Medical Education",
"Temerty Faculty Of Medicine",
"University Of Toronto",
"Tara Upshaw"
] |
by K Darcel · 2023 · Cited by 25 — These findings provide insight into the barriers and facilitators associated with implementing AI in primary care settings from different perspectives.
|
The barriers that emerged from the deliberative dialogue sessions were grouped into four themes: (1) system and data readiness, (2) the potential for bias and inequity, (3) the regulation of AI and big data, and (4) the importance of people as technology enablers. Strategies to overcome the barriers in each of these themes were highlighted, where participatory co-design and iterative implementation were voiced most strongly by participants.
With large volumes of longitudinal data in electronic medical records from diverse patients, primary care is primed for disruption by artificial intelligence (AI) technology. With AI applications in primary care still at an early stage in Canada and most countries, there is a unique opportunity to engage key stakeholders in exploring how AI would be used and what implementation would look like.
Funding: This project was supported by the Canadian Institutes for Health Research (FRN 156885), with ADP as Principal Investigator. TLU's time was supported by a CIHR Frederick Banting and Charles Best Canada Graduate Scholarship. ADP is supported by the Department of Family and Community Medicine, Temerty Faculty of Medicine at the University of Toronto and at St. Michael’s Hospital, and by the Li Ka Shing Knowledge Institute, Unity Health Toronto. He is also supported by a CIHR Applied Public Health Chair and a fellowship from the Physicians’ Services Incorporated Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
To effectively implement AI in primary care settings, it is critical that these barriers are explored from multiple perspectives, as emphasized by Singh et al. above. Thus, we engaged patients, providers, and healthcare leaders in a deliberative dialogue series to gain insight into perceived challenges associated with AI. These findings will help assist those that are being called upon to make pivotal decisions regarding the future of AI in primary healthcare.
Other researchers including Lovejoy et al. and Singh et al. have conducted reviews to explore the challenges of implementing AI in healthcare settings. The recent review by Lovejoy et al. explores the challenges of implementing AI to healthcare by breaking down the AI innovation process into three stages–invention, development, and implementation. Specifically with respect to the implementation stage, they highlight three main barriers of focus (generalizability, regulation, and deployment). Generalizability refers to the need for diversity within the training set, regulation refers to the need for ongoing monitoring of an algorithm or tool throughout its lifetime to ensure continued safety, and deployment refers to the need to minimize disruption to workflow and account for interoperability between healthcare technologies [ 13 ]. Although this review is not specific to the primary care space, it provides insights into barriers that are commonly encountered in the healthcare AI space, all of which will help inform our study. Another review by Singh et al. outlines the barriers in relation to the perspective of healthcare organizations, healthcare providers, and patients. Interestingly, from the healthcare organization perspective, they discuss the role of “company culture” as important when considering the use of embracing AI in their day-to-day practice. The provider perspective outlined concerns for the consideration of racial, ethnic, gender, and other sociodemographic characteristics within the algorithm. The perspective of patients was centered on trust, including the concern for physician-patient confidentiality if AI were to be used in their care [ 14 ]. This review provides vital insight from various stakeholder perspectives and emphasizes the importance of doing so.
To identify and ease these barriers, researchers have developed models related to technology implementation including the ‘Technology Acceptance Model’ (TAM) and the ‘Unified Theory of Acceptance and the Use of Technology’ (UTAUT) model [ 10 , 11 ]. These are well studied models that explore the reasons that users accept or reject a given technology and how acceptance can be improved, but unfortunately are not specific to primary healthcare or even to the healthcare at all. Another model, Sittig and Singh’s sociotechnological model for studying health information technology includes eight dimensions (hardware and software computing infrastructure, clinical content, human-computer interface, people, workflow and communication, internal organizational features, external rules and regulations, and measurement and monitoring [ 12 ]. Sittig and Singh’s model focuses specifically on implementation of technology in complex adaptive healthcare systems and will be used as a basis of comparison for the findings of this study.
Applying AI in primary care settings poses unique challenges as this field of medicine encompasses a wide variation of health concerns in diverse patient populations [ 3 ]. Primary care EHR data is unique in that it is longitudinal, it covers undifferentiated and broad health concerns, it serves populations of differing socioeconomic statuses, it contributes to public health records, and it contains data to inform preventative care practices. Despite these distinct qualities, AI is beginning to be used in primary care in the areas of clinical decision making, risk prediction, care management, and proactive detection [ 4 ]. Examples of risk prediction AI tools applicable to the primary care space include machine learning algorithms that identify patients at risk for cancer [ 5 ], predict which patients may experience post-partum depression [ 6 , 7 ], and estimate those who may be at risk of multiple chronic diseases including diabetes, osteoarthritis, and hypertension [ 8 , 9 ]. Although a flurry of research is focused on the development of AI tools, implementation into practice remains one of the most challenging barriers to overcome.
With advancements in artificial intelligence (AI) and vast volumes of health data produced through electronic health records (EHRs), we are in an opportune time to explore ways in which AI can be applied in healthcare settings. Although many of the advancements have taken place in the field of radiology [ 1 ], AI applications such as online scheduling tools, drug dosing algorithms, and digitization of medical records have been implemented in many other areas of healthcare, including primary care [ 2 ].
Dialogue guides for rounds 2 and 3 were not pilot tested because of the study’s emergent nature. These guides were iterated upon and further developed as the study progressed. Investigator triangulation was achieved through the involvement of multiple researchers in data collection and analysis [ 29 ]. Data triangulation was achieved through careful comparison of field notes, primary transcripts, researcher interpretive reflections, and participant activities throughout the study. We created multiple opportunities for participants to member-check researcher interpretations. Eighteen participants provided reflections on preliminary findings at the study’s conclusion.
Audiovisual recordings and fieldnotes were generated for each session by one of the observers (ACN, JM). Following a session, TLU and the designated observer met after each session to analyze fieldnotes using interpretive description methods, coding for concerns, perceived barriers, and facilitators of AI implementation in primary care [ 25 – 28 ]. This analysis guided the emergent nature of the study (e.g., participant-facing summaries, use cases, etc.) and generated an initial code list. Early codes were later confirmed and expanded upon through dual-coder thematic analysis of verbatim transcripts (TLU, KD). Transcript analysis occurred in two passes, the first being entirely inductive and the second using agreed upon codes. TLU, KD, and AP then met to review coded transcripts and agree on final interpretations. The final interpretations were compiled into four major themes and then compared to Sittig and Singh’s sociotechnical model for studying health information technology (HIT) in complex adaptive health systems [ 12 ]. Data was managed using Microsoft Excel and Word. Saturation was reached for most concepts after nine sessions.
Patients and primary care providers were recruited under distinct purposive variation sampling frames from various online advertisement platforms and email distribution lists. This recruitment method is outlined further by Patton et al. [ 24 ], and was chosen in order to optimize variation in age, gender, race and ethnicity, education level, income, province of residence for patients and gender, race and ethnicity, provider type, country of health professions education, years in practice, practice size, and province of practice for primary care providers. Specifically, patients were recruited using Kijiji and social media channels across 10 provinces as well as patient advisor distribution lists (patient advisory network in British Columbia, patient-family advisory council of downtown Toronto hospitals). Primary care physicians were recruited through social media channels, primary care research network news channels, and through email invitations from AP to practice colleagues and those in his network interested in digital health and health informatics. To be eligible, patients must have seen their primary care provider once within the last year and providers had to work at least one day in the clinic per week. To promote variation, we collected sociodemographic information during consent interviews and then iteratively adjusted recruitment strategies. We used a critical case frame for system leaders, seeking those involved in digital health, health informatics, or primary care governance [ 24 ]. Patients and providers were invited to take part in up to three rounds, while system leaders were invited to join one final-round session or to provide feedback on the dialogue from the first two sessions by survey. The sessions were designed this way to allow for interactive dialogue between the different perspectives of patients, providers, and health system leaders in real-time. An honorarium of $32.50 USD was offered to participants for every session attended. TLU was acquainted with two patient participants. AP was known to most providers in the study and observed three sessions.
To adhere to COVID-19 public health restrictions, we held twelve 90-minute dialogues by video conference between September 8 th and October 15 th , 2020. Dialogues were divided into three rounds and all sessions were facilitated by one trained Master’s-level graduate student (TLU). Before taking part, participants reviewed an information package that provided an overview of health AI, key definitions, examples of AI in medicine, possible uses in primary care, and ethical challenges. The study was approved by the research ethics boards of Unity Health Toronto and the University of Toronto. Verbal informed consent was obtained from all participants and documented prior to scheduling their first dialogue session.
This emergent qualitative study used virtual deliberative dialogues. Deliberative dialogue is a participatory method in which people affected by an issue are gathered to provide advice to decisionmakers [ 18 – 20 ]. With facilitator support, participants work together to interpret existing evidence and identify approaches to the issue that reflect their—sometimes conflicting—values and tacit knowledge [ 21 , 22 ]. We selected this method because we believe that the values and wisdom of primary care stakeholders can guide health AI in directions that support equity and advance the Quadruple Aim (optimizing health system performance through improved patient experience, improved clinical experience, better outcomes, and lower costs [ 21 – 23 ]).
This study was conducted virtually within Canada, a high-income country with provincially and territorial-based public health insurance that covers all necessary hospital and physician services [ 15 ]. Both within and across the provinces and territories of Canada, primary healthcare is delivered through different models. Largely, it is funded following a fee-for-service (FFS) model, but has adopted new funding models such as the enhanced fee-for-service model and capitation model. For example, in Ontario, the enhanced FFS model is similar to the traditional FFS model, except that it includes additional targeted fee increases, extended hours, performance-based initiatives, and patient enrollment. In contrast, the capitation model reimburses primary care physicians for each person attributed to them, based on age and sex adjusted factors [ 16 ]. More recently, Ontario has been forming Ontario Health Teams including family physicians, nurse practitioners, social workers, dietitians, and other healthcare professionals to increase interdisciplinary care for a given community [ 17 ].
Building trust, preventing burnout, and preserving patient-provider relationships begins with outlining a clear value proposition for each user and individual affected by the technology. It was highlighted that the benefits, limitations, and uses of the technology must be transparently communicated. Following this, simple AI technology that can complete basic operational tasks should be implemented first, before transitioning into the use of complex AI systems to encourage uptake and trust in the technology.
Strategies to assist with the regulation of AI and big data will require strong leadership commitment by health leaders and government players. Providers felt that this commitment to health AI would act as an example for primary care clinics to follow. Additionally, to increase trust in the technology, national AI rules and regulations for healthcare must be designed and implemented. There was an expressed need for content standards and protocols to further enable interoperability, and a need for regulatory standards to ensure representation and bias mitigation within the algorithms and training data sets.
The second theme, the potential for bias and inequity, may begin to be mitigated by promoting the use of unbiased, representative, good quality data to feed algorithms. It was recommended by one participant that to ensure the AI tool remains free of bias overtime, it should be re-assessed and analyzed routinely to check for bias. There was an expressed desire for ethics training courses for those involved at many stages of the design, implementation, and evaluation life cycle of the technology.
To promote the adoption of health AI in primary care and beyond, participants offered a host of strategies to build patient and provider trust in the technology based on each theme (summarized in Table 1 ). Of these, the most robustly supported was a participatory AI co-development model that meaningfully involves patients, interprofessional primary care providers, clinical administrative staff, HIT specialists, and health policymakers. Participants believed that stakeholder engagement must occur upstream of piloting, ideally at the conception phase, and remain ongoing. They also specified that stakeholders must be diverse in terms of sociodemographic characteristics (e.g., race or ethnicity, gender, income), geographic location, and healthcare needs, as well as professional settings for providers (e.g., telehealth, walk-in, community health centers). Through this model, described by a patient as “designing with, rather than for,” stakeholders could co-construct AI tools that address shared application priorities and promote patient-centered practices. Within the model, participants viewed processes that facilitate iterative stakeholder input (e.g., a participatory piloting program) as particularly useful for ensuring usability and workflow integration, correcting for bias, and improving algorithm performance through feedback. To specifically help overcome the barriers associated with system and data readiness, investments are required to allow for different technological systems to “talk to one another”. This would thereby enable integration between primary care clinics using different EHR technologies.
Secondly, some participants feared that these technologies could harm patient-provider relationships, while others thought it may enhance them. For example, one patient mentioned, “I want my doctor to be present with me in a conversation and not staring at the computer and looking at all the tools or boxes that they need to check off” (PT-4159). Many agreed with this notion, but some did refute and stated that if the AI tools are designed correctly, they may provide more space and time for relationship building and trust between the patient and the provider. Lastly, all participants agreed that the tools must be designed deliberately, to beneficially serve the needs of the users and not contribute to patient, provider, or administrative burnout.
People play a vital role in the success of any given technology; therefore, the barriers that impact the people using and affected by the technology must be properly mitigated. From the dialogue sessions, these barriers included the need for provider training and skill preservation, the fear of changing the provider-patient relationship, and the challenge of designing tools to properly suit the user’s needs. Firstly, many providers were unsure about the training that would be needed to successfully adopt a given AI tool and were worried that it may impede their clinic workflow. As stated by one provider, “oftentimes, you have to adapt your workflow and sometimes your clinical processes to accommodate the tool where I really strongly feel it needs to be the reverse” (PR-4404). After being trained on the tool, some were concerned that the clinical skills and reasoning of primary care providers could be at risk due to dependence on the tool. As one provider said, “we don’t want to lose that ‘art of medicine’ that we have learned over the years, because it’s a very challenging profession where you see people with undifferentiated complaints, so you have to know something about everything. And with AI, if we rely too much on it, we might lose that clinical judgment." (PR-1783).
Participants from all three groups believed that inadequate regulatory oversight of health AI tools places the public at risk of harm to physical health or wellbeing. This was a perceived barrier to health AI adoption because it decreased patient and provider trust in available tools. To feel comfortable using an AI tool in clinical decision-making, providers wanted assurance that it had been approved by a regulatory authority after meeting rigorous standards of performance, in terms of accuracy, risk of bias, and patient safety. Participants from all three groups, but especially patients, were concerned that existing privacy laws do not adequately prevent AI developers from monetizing PHI or allowing PHI to be used in ways that impact patient wellbeing (e.g., insurance or loan decisions). One provider stated that they would like to be aware of “how this data is being used and whether there were any concerns about this data being used by a [private] company” (PR-7905) and a patient mentioned, “I think it’s really important to clearly set out what the data be used for and ensure that the patient understands how the confidential confidentiality and privacy still applies in those situations" (PT-4653). Evidently, all participants stressed the importance of regulatory oversight to ensure the tools are frequently monitored for safety and that there is insight into the ownership, control, and access of health data to ensure it is not being monetized or used inappropriately.
Equity and access to AI technology were discussed from many different perspectives throughout the dialogue sessions. From a patient perspective, there was expressed concern that some populations may not want to or be able to interact with AI tools, which as a result, may further deter them from the health system as a whole and widen the care gap that already exists. From a primary care clinic and provider level, the clinics that are frequently last to procure EHRs and new technology will likely also be the last to adopt a new AI tool; thus, we may further widen the divide between the advantaged and disadvantaged primary care practices and the individuals they treat. Overall, while participants are hopeful for the potential that AI holds, it will likely not serve populations that most frequently “fall through the cracks” (PT-4653), and therefore it is important to consider these populations and their ability to access and trust the tool before it is implemented widely in primary care.
The potential for bias was a concern that came up in every dialogue session. Some participants brought up past studies where AI tools were proven to further perpetuate the biases of their developers and of society. As one patient participant mentioned, “there could be bias in your software. Because these factors are not all equal, they have different weights of importance. And so how that’s applied will affect how good the results are going to be” (PT-5981). There was also concern for the bias that could be present in the guidelines and criteria that feed the AI algorithms. Participants were worried that if pharmaceutical companies or other private enterprises sponsored the tool, they may bias the algorithms to produce outcomes that favor their product. Participants linked this theme to notions of trust. As evidenced by the following quotation, “we want to make sure that the tools we use don’t create new problems… they are an opportunity to address some of the biases that already exist in our system. . .and that these tools are probably only as good as the data we provide them” (PR-7905).
With varying levels of technological readiness and challenges with clinic uptake, the potential for biased algorithms and inequitable access to AI tools becomes increasingly concerning. Several providers worried that lagging digitization in resource-constrained settings (e.g., rural communities), poor interoperability of established HIT systems, or data monopolies held by leading EHR vendors could negatively impact health equity by driving the uneven application of AI tools across settings, or limiting datasets available for developing AI tools to smaller, non-representative populations, thereby increasing the risk of bias and inequity.
Furthermore, when discussing the willingness to adopt AI technology, it was highlighted that gaining buy-in from clinic owners and operators may be challenging since many of them were promised a series of quality improvements during the EHR rollout that were not met. As one participant suggested, “the quality improvements that were expected didn’t happen” (PR-4404). They may be hesitant to invest and place trust in an AI system unless they can clearly and quickly realize the benefit of doing so.
Participants acknowledged the potential for AI to improve health and healthcare. However, a few patients and most providers recognized that the optimal performance of health AI tools depends upon a level of HIT interoperability that does not exist in many jurisdictions. For AI tools to reliably inform clinical decision-making and support integrated, equitable care, providers believed that algorithms must synthesize data from multiple sources, including administrative databases and EHRs across levels of care and care settings. They also believed that certain advanced AI tools (e.g., rare disease diagnosis) would have to synthesize an exponentially increasing body of evidence and ever-changing guidelines to be clinically useful.
Significant concern around adoption and the readiness of Canada’s health system came up in many sessions and was particularly focused on both the readiness of our current technology/data, as well as the willingness for clinics to engage with and adopt new AI technologies. Some primary care clinics still operate using paper and fax machines, and therefore implementing AI may be too advanced. Additionally, primary care clinics operate independently and are fragmented from one another. Each clinic "enters things in all different ways. We have to make sure that the tool is good enough to manage the many different ways that people get information and enter information" (PR-7905). Creating an adoption strategy and designing tools to accommodate each clinic may be challenging.
From these dialogue sessions, a series of barriers and strategies to implement AI in primary care emerged, which were grouped into four major themes: (1) system and data readiness, (2) the potential for bias and inequity, (3) the regulation of AI and big data, and (4) the importance of people as technology enablers. Within each of these 4 themes, participants spoke about trust, either in reference to trust in the technology or in the provider using it, so trust will be discussed within each theme. When asked what they thought about algorithm explainability, all but one of the patient participants agreed that they would trust AI algorithms used in their care by proxy of the trust they placed in their provider. Patients in one session suggested that health AI-enabled harms could damage public trust in health institutions. Patients and providers across sessions identified regulatory oversight, scientific evidence, and participatory co-development processes as builders of trust.
Discussion
The barriers and strategies were derived from dialogue discussions conducted with patients, providers, and healthcare leaders across the country. The four major themes that arose from these discussions were in relation to system and data readiness, the potential for bias and inequity, the regulation of AI and big data, and the importance of people as technology enablers. Participatory co-development and iterative design were the two most important strategies that were heard during the sessions, and span across all four themes. This study also yielded rich insights on priority applications, which have been published elsewhere.
Sittig and Singh’s sociotechnological model for studying health information technology in complex adaptive healthcare systems demonstrates the intricate challenges involved in the design, development, implementation, use, and evaluation of technology in healthcare settings [12]. It was designed to address the both the sociological and technological challenges that are present within healthcare settings that are high-pressured, fast-paced, and fragmented. Their model introduces eight interacting dimensions: hardware and software computing infrastructure, clinical content, human-computer interface, people, workflow and communication, internal organizational features, external rules and regulations, and measurement and monitoring [12]. This model was used as a comparison of our four themes and was chosen over other models such as the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) for its applicability to complex health systems [10, 11]. The TAM and UTAUT aim to understand why users accept or reject a given technology and how acceptance can be improved, but recent reviews show that they have failed to predict acceptance of technologies in healthcare [31]. This may be because of healthcare’s unique cultural, social, and organizational factors that influence technology adoption. Therefore, Sittig and Singh’s model remained the most appropriate choice for our comparison due to its specificity to health information technology.
Interestingly, the four themes captured throughout this dialogue do align with most of the eight dimensions quite remarkably. Most notably, Sittig and Singh’s “people” dimension aligns with our fourth theme, the importance of people as technology enablers, as both recognize the importance of provider training and the user’s perception of the technology.
Additionally, our regulation of AI and big data theme correlates with Sittig and Singh’s “external rules and regulations” dimension since both are focused on the directives that must be designed at a government level to ensure all AI technology is held to a safe and reliable standard.
The system and data readiness theme does not align strongly with any one given dimension but correlates loosely with both the “hardware and software computing infrastructure” and “clinical content” dimensions. The former covers the technological perspective, including equipment and software, which could be extended to cover the contents of our theme including interoperability and digitization of medical records, while the latter addresses personal health information, data quality, and data structure.
The potential for bias and inequity theme is also not explicitly present in Sittig and Singh’s eight dimensions, possibly because AI has the potential to present biases in ways that technology was less capable of before. Furthermore, trust, the overarching foundation to all of our themes is also not present in the eight dimensions but was very important to the participants during the dialogue sessions.
Overall, Sittig and Singh’s sociotechnological framework is advantageous to consider both the social and technical perspectives of technology implementation projects, but based on our deliberative dialogue series, we believe that new dimensions (e.g., trust, bias, and equity) would be required to translate this model to be applicable to AI implementation projects in the primary care space now and in the future.As mentioned in the introduction, the recent review conducted by Lovejoy et al. divides the AI innovation process into three stages (invention, development, and implementation) and outlines key considerations for innovators during each phase [13]. The considerations of the third stage, implementation, include generalizability, regulation, and deployment. Generalizability refers to the need for diversity within the training set, regulation refers to the need for ongoing monitoring of an algorithm or tool throughout its lifetime to ensure continued safety, and deployment refers to the need to minimize disruption to workflow and account for interoperability between healthcare technologies [13]. When comparing the four themes uncovered in our study to the implementation stage of this review, it is evident that our deliberative dialogue results greatly support those of Lovejoy et al. For example, within our ‘potential for bias and inequity’ and ‘regulation of AI and big data’ themes, we heard multiple times in our sessions that diversity within training sets and continuous monitoring of a given tool are both vital to ensure bias and safety are maintained over time. This is very similar to Lovejoy et al.’s generalizability and regulation sections as they state, “The training data must be at least as diverse as the population that the algorithm intends to serve” and “Good performance at the time of deployment does not guarantee that the model will continue to perform well. This introduces the need to regulate throughout the lifetime of an algorithm, and the need to continually demonstrate safe and effective practices” [13]. Therefore, our ‘potential for bias and inequity’ theme is similar to Lovejoy et al.’s concept of generalizability, and our ‘regulation of AI and big data’ compliments the regulation section explained in their review.
The review by Singh et al. outlines AI implementation barriers in relation to the perspective of healthcare organizations, healthcare providers, and patients. These three perspectives are similar to those included in our study, except that instead of healthcare organizations we heard from health system leaders. From the review’s healthcare organization, they discussed the role of “company culture” as important when considering the use of embracing AI in their day-to-day practice–a notion that did not come up explicitly in our deliberative dialogue sessions. The provider perspective outlined concerns for the consideration of racial, ethnic, gender, and other sociodemographic characteristics within the algorithm. This was similar to what was heard in our ‘Potential for bias and inequity’ theme, except that we heard the concern from all three types of stakeholders and not just from the providers alone. The perspective of patients was centered on trust, including the concern for physician-patient confidentiality if AI were to be used in their care [14]. This compliments our finding of the importance of trust, however, in our sessions it was woven into all four themes and from all perspectives (patients, providers and health system leaders).
The strengths of this study include the use of a two-coder analysis approach, its transferability to non-Canadian settings with similar primary care models, and the use of informed interprofessional stakeholder engagement. First, for the analysis of the data, a traditional two-coder approach was employed to ensure trustworthiness and intercoder reliability. Second, based on the services and values that are offered by most primary care clinics [32], our findings are applicable to jurisdictions outside of Canada, confirming interjurisdictional transferability. Last, participants in these sessions were from many different backgrounds (patients, primary care providers, and system leaders), but all learned about AI technology before taking part, nurturing an informed discussion. The interprofessional design of these sessions enabled participants to refute and agree on discussion topics and fostered the ability to explore different perspectives of using AI in primary care.
To further interpret this study, it is essential to consider the limitations. It was completed by engaging participants from eight Canadian provinces but unfortunately did not include the perspectives from Canada’s territories or any self-identifying Indigenous people, both of whom may have unique perspectives regarding barriers to using AI in primary healthcare [33]. To further improve this study and its transferability, future work should focus on the perspectives of those that self-identify as indigenous as well as individuals form Canada’s territories. This would enable more diverse perspectives and would be vital before any AI implementation projects are considered. Additionally, due to the ongoing COVID-19 pandemic, we were only able to include five health system leaders in the study. The health leaders that were able to attend provided rich system-level perspectives that were unique to those heard from patients and providers, thus including more system leaders would have been preferred.
This level of study (48 participants from 8 Canadian provinces including 22 patients, 21 primary care providers, and 5 health system leaders) provides an excellent starting point for the barriers and strategies to use when implementing AI in primary care settings, but a study with larger reach would be recommended before practical AI applications become commonplace in primary care. To increase reach and gain further insight, a deliberative dialogue would not be practical but instead a survey could gain the perspectives of more of the population. This in addition to a focused dialogue with individuals from Canada’s territories and those who self-identify as indigenous would be vital before practical AI solutions are implemented widely in primary care.
| 2023-02-01T00:00:00 |
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0281733
|
[
{
"date": "2023/02/01",
"position": 49,
"query": "universal basic income AI"
}
] |
|
Practical, epistemic and normative implications ...
|
Practical, epistemic and normative implications of algorithmic bias in healthcare artificial intelligence: a qualitative study of multidisciplinary expert perspectives
|
https://jme.bmj.com
|
[
"Yves Saint James Aquino",
"Australian Centre For Health Engagement",
"Evidence",
"Values",
"School Of Health",
"University Of Wollongong",
"Http",
"Stacy M. Carter",
"Nehmat Houssami",
"School Of Public Health"
] |
by YSJ Aquino · 2025 · Cited by 54 — ... universal, a direct contrast to the bias-critical view that claimed AI ... minimum standard that each AI application has to meet to … satisfy this ...
|
Background Artificial intelligence (AI) in healthcare has the potential to improve diagnostic accuracy, personalise therapeutic management and augment skills of healthcare workers. However, AI has also been subject to increasing ethical scrutiny.1–4 One dominant normative concern is the problem of bias: the effect of power asymmetries expressed in data and infrastructure.5–7 In a healthcare context, the presence of algorithmic bias means that without careful intervention, AI systems will disadvantage already under-represented and marginalised groups, systematically worsening existing inequity in healthcare systems. In the context of AI, the term ‘bias’ is challenging as it has numerous connotations across disciplines. For statisticians, bias refers to a systematic error where the results do not reflect the true estimate. This kind of bias occurs for many reasons, including imbalance or unrepresentativeness of data.8 In the social sciences, bias broadly refers to systematic preferences, dispositions or inclinations in human thinking.9 Social bias, in particular, refers to problematic dispositions held by individuals or groups in favour of or against other individuals or groups based on numerous factors such as inappropriate generalisation (stereotyping) and inaccurate information, among others.10 Some theorists identify forms of bias that are of interest to this paper: (A) prejudice as negative attitudes towards individuals or groups and (B) discrimination as practices that unfairly advantage or disadvantage individuals or groups.11 In healthcare, different forms of social biases lead to disparities in access, service provision and treatment outcomes, ultimately leading to health inequities among social groups, making bias in healthcare AI a particularly compelling issue. ‘Algorithmic bias’ consists of both statistical and social meanings as it refers to systematic errors in AI systems that lead to results, interpretations or recommendations that unfairly advantage or disadvantage certain individuals or groups.12 For example, a US-based classification algorithm for skin cancer diagnosis,13 which was trained using mainly images of skin lesions in light-skinned patients, has been found to have approximately half the diagnostic accuracy when used on images of lesions among African-American patients.14 Such inaccuracy could worsen disparities in skin cancer outcomes, given African-Americans have the highest mortality rate for melanoma.14 15 Similar problems of biased results have been demonstrated in AI systems used for radiography-based disease diagnosis15 and health resource allocations.16 The statistical and social causes of algorithmic bias has been discussed elsewhere,17 but what these examples demonstrate is the potential of biased AI systems to not only replicate but amplify existing health inequities.1 The growing evidence of bias in healthcare AI systems has motivated various solutions, as well as critiques of these solutions, to mitigate algorithmic bias. These range from broad calls for ‘open science’ that promotes principles of ‘inclusivity, openness and trust’14 to specific evaluation tools such as the Prediction model Risk Of Bias ASsessment Tool.18 These solutions are yet to be widely implemented. Of particular, challenge is the inability of AI systems to accurately record and incorporate social data, which include cultural beliefs, economic status, linguistic identity and other social determinants of health.19 While efforts to address algorithmic bias are increasingly interdisciplinary, some scholars argue that the dominant approach of relying on data science mechanisms alone fails to address structural and systemic causes of marginalisation.20–22 Feminist ethicopolitical critiques highlight factors including historically entrenched power structures,22 exclusionary social demographics in the AI workforce,20 21 and sociocultural legacies of colonialism23 as some of the key drivers of algorithmic bias that deserve attention. Being biased or prejudiced is an epistemic issue: it is a fault in one’s knowing about others. Several decades of psychological research has demonstrated that this fault is also often implicit. Humans tend to have prejudices against stigmatised groups of which they are unaware: what Fricker calls ‘the attitudinal fall-out from a semitoxic social environment’.24 When algorithmic systems absorb and reproduce this widespread implicit human bias, it can become automated and amplified; the human tendency to agree with machine decisions may then instantiate the epistemic error more firmly. Bias is also a moral issue: being prejudiced or biased may be morally blameworthy, and the existence of bias or prejudice raises questions of who should be responsible for addressing it. We will rely on Fricker’s view of responsibility for implicit prejudice to consider responsibility for bias in healthcare AI systems. On this view, we are rightly held responsible ‘not only for conduct based on things we know but also for conduct based on things we should have known but did not’, and not only for those things we can control, ‘but also those things we ought to be able to control’. Fricker shows that individuals may be non-culpable and yet responsible for the prejudiced thinking they may think themselves immune to, but in fact have absorbed from society. Fricker also shows that this may raise specific epistemic and moral obligations to put things right, which may include taking responsibility, making amends and changing procedures in institutions to prevent the same harms occurring again. These obligations may inhere at both individual and collective levels. This analysis of responsibility for prejudicial errors in knowledge is highly relevant to our discussion. Despite the growing evidence on and proposed frameworks for addressing the problem of algorithmic bias, the moral and political seriousness of the problem remains contested within data science and AI communities. Those who create AI are overwhelmingly male and white,7 and this demographic skewing is worsening rather than improving. Many of those critiquing algorithmic bias are from minorities within the coding community, and often there is strong resistance from the ‘mainstream’ within their profession.7 Given this pattern of resistance, the question remains: Why does the problem of algorithmic bias persist despite growing efforts to counter it? And should stakeholders be held responsible for mitigating bias, even if they are not individually culpable for its cause? Thus, in this paper we aim to: (1) understand whether AI and healthcare professionals see algorithmic bias in healthcare as a problem; (2) understand the range of strategies stakeholders endorse to attempt to mitigate algorithmic bias and (3) consider the ethical question of responsibility for algorithmic bias.
Methodology For purposes of recruitment, interview discussion and analysis, we used the Consolidated Standards of Reporting Trials–Artificial Intelligence's definitions of AI and machine learning (ML): AI broadly as interrelated technologies that can perform tasks normally requiring human intelligence; and ML as a set of approaches to AI that are designed to solve specific tasks by learning patterns from data, rather than by following explicit rules.25 We conducted qualitative, semistructured interviews with a diverse group of professionals involved in developing, selling, regulating or implementing healthcare AI, as further outlined below. Interviews were broad ranging, focusing on the ethical, legal and social implications of implementing AI in healthcare. One key issue discussed extensively by informants was algorithmic bias; the data from these discussions are the focus of this paper. Recruitment and sampling We sought to recruit local and international participants. We aimed to access participants with specialist AI expertise and/or professional or clinical expertise; our inclusion criteria required that informants be involved in some way in the development, deployment and/or regulation of healthcare AI, and were at least knowledgeable enough to be informative about the potential implications of AI in their field. The sampling strategy was designed both to elicit diversity of views, and to allow comparisons between stakeholder groups. Initially, we recruited via an expression of interest to participate on Twitter and in newsletters and mailing lists of AI-related organisations. We also directly approached experts with relevant public profiles and invited them to participate. Over time, our sampling became more theoretically informed,26 and we invited experts who might help us address gaps in our analysis. Data collection YSJA, a clinician and bioethicist trained in social science research methodologies, performed semistructured interviews via Zoom or telephone, taking between 20 and 90 min. The question guide covered views about healthcare AI development in Australia and internationally, imagined futures for healthcare AI, how AI might or might not change things for clinicians and service users, key ethical issues and how they should be addressed in practice, and AI regulation.27–29 Not all participants were asked all questions, either because they had limited time and we had recruited them to answer particular questions, or because they were recruited later in the study and we had already reached theoretical saturation for some categories. Data analysis With participants’ consent, interviews were audiorecorded and professionally transcribed. Analysis was led by YSJA in collaboration with the research team. All participants were assigned a code that included their participant number and a summary of their roles; all transcripts were deidentified using these codes before analysis. Analysis combined approaches from constructivist grounded theory26 and the framework approach.30 31 The analysis steps were as follows: (1) coding interview transcripts; (2) memo-writing on each interview to develop an analytic understanding of how that informant strengthened the data on existing categories or added new categories to the analysis; (3) organising findings into a framework, including both analytical summaries and data excerpts and (4) memo-writing on each of the core concepts in the analysis. Codes and key themes were generated both deductively—that is, based on concepts from the bioethics and AI ethics literature27 29 32—and inductively.28 Data analysis was performed concurrently with data collection and data collection was modified to reflect insights from the developing analysis.
Results Saturation on core themes, including algorithmic bias, was reached after interviews with 72 participants. The majority of participants (n=54) worked in general diagnosis and screening, the rest were involved in breast cancer (n=10) and cardiovascular disease (CVD, n=8) diagnosis and screening, respectively (table 1). While most participants tended to have multiple forms of expertise, they could be classified based on self-identified primary roles, namely clinicians (n=22), regulators (n=17), developers/data scientists (n=10), researchers (n=8), healthcare administrators (n=5) and consumer representatives (n=3). We did not collect data on gender and racial identities of participants. Table 1 Summary of participants’ expertise Our results show participants disagreed on three counts. First, participants disagreed whether or not algorithmic bias exists. Second, there was disagreement about whose responsibility it should be to implement strategies to mitigate bias. Participants who agreed that bias is a problem offered a variety of strategies to mitigate bias, ranging from sociolegal approaches to data science mechanisms. These mechanisms included calls for data governance, equitable research methods and increased diversity in datasets. Finally, for participants who suggested increasing diversity of or representativeness in datasets, there was disagreement on how to handle variables representing complex social information, such as gender and racial identities, that are typically marked with historical or continuing prejudice and marginalisation. Disagreement 1: views about whether algorithmic bias exists The majority of participants took a bias-critical view. These experts were mainly clinicians and regulators, with a small number from other roles. These participants noted potential harms of algorithmic bias, ranging from medico-technical harms (eg, compromising patient safety) to broader social and public health harms (eg, perpetuating health inequity). Participants implied that such harms could undermine the promise of healthcare AI. And …the researchers … discovered that, in this risk assessment, many essentially produced by the algorithm, there was a bias against African Americans. … if you are black American… you are certainly more likely to be mislabelled as low risk if you actually were high risk. Informant 22, regulatory expert Some participants taking the bias-critical view also critiqued misconceptions about AI or computer systems that could lead people to dismiss the problem of algorithmic bias. The misconceptions include misguided faith in AI systems as objective and infallible. For people who are less informed—have this view that kind of computers or machines or programs are sort of objective and they are black and white in their coding and so they are right or they are wrong and they won’t make mistakes, so to speak. I guess, my position on it is that some of the fallibilities that these systems have are based on the same problems that the human brain has in that it can only work with what’s been put into it. Informant 45, consumer representative I think that there’s subjectivity in a lot of [AI] that we act like it’s very objective but it’s not. We’re making all kinds of assumptions in our machine learning, algorithms, in the default or the parameters that we set. Informant 56, researcher in bioinformatics Two other views were expressed by participants. A small group of participants from all roles conveyed a bias-apologist view. On this view, bias was recognised as a problem to some extent, but not seen as an ethical concern—or at least, did not raise any more ethical concerns than the status quo. Some participants argued that bias in AI is unintentional. In contrast to the bias-critical view, the bias-apologist view emphasised the potential benefits of AI, despite the presence of bias. Participants expressing this view argued that bias was an ongoing problem even among clinicians—and AI bias was preferable to human bias. Obviously an algorithm will have biases as a human has biases, but those biases are able to be measured. Informant 1, screening manager First of all I’d say, what, and doctors aren’t biased? I’m sorry. Don’t get me started on doctor bias. We’re used to hurting people the old way, with biased old doctors. Now we’ve got a new set of biases, what are we going to do with them? But it is a valid point. Informant 55, entrepreneur A third small group expressed a bias-denial view. These participants included developers, entrepreneurs and screening programme managers. No regulatory or legal expert subscribed to this view. Participants who expressed this view argued that bias existed in other industries but not medicine, or existed in other countries but not in Australia. I don’t know that bias is one that we’ve seen any evidence that is being realised here [in Australia]. I think it’s more of a concern in health systems like the US. Informant 2, entrepreneur Now imaging, you mentioned imaging? I don’t think there’s any good data or bad data in imaging. An image is just an image that you create from a machine, or like a CT scan or an x-ray. And then you feed that to the machine and it’ll detect and whatever it finds, it finds. Informant 52, developer Some participants expressing the bias-denial view argued that bias was not likely to occur in medical diagnosis because imaging technologies are objective and the human body is universal, a direct contrast to the bias-critical view that claimed AI systems are not always objective or infallible. I can only tell you that in radiology, if we do a C-T scan on your brain and on my brain, it will look exactly the same with a variation of black and white pixels. It doesn’t matter if you’re black, or white, or green, or blue, or whether you’re Australian or Chinese, a rib fracture is a rib fracture and the same is true for a vertebrae fracture or a pneumothorax. There is no bias there. Informant 59, clinician Actually, I’d have no idea with the pathology samples what a person looks like so we don’t even know that. And I don’t know, you’d have to ask the doctor. I think the internal organs from people from different race, backgrounds, are probably pretty similar but I don’t know. Informant 50, developer Disagreement 2: views about acting to mitigate algorithmic injustice Participants who expressed the bias-denial view and some who expressed the bias-apologist view implied that no action was required to address bias because according to their views, bias was not a problem in healthcare AI. Some ‘bias-apologist’ participants, mainly developers and entrepreneurs, agreed that bias was a problem but proposed that it was unintentional (see figure 1). Consequently, these developers and entrepreneurs deemed that they were not responsible for the creation of bias, and therefore, held no obligation to redress it. Some ‘bias apologist’ participants who were all entrepreneurs, researchers or clinicians involved in AI development, argued that they were just working with what was available, implying that research and development should not be hampered by concerns about bias. Figure 1 Views about algorithmic bias as a problem. We’re definitely using biased data sets. So part of my response is that we’re using the data we can get. Informant 23, developer and researcher in bioinformatics Bias-critical and some bias-apologist participants subscribed to the action-required view, which explicitly calls for action to address the problem of bias. There were two subgroups within this view, distinguished by their views on whose responsibility it is to mitigate bias (see figure 1). In one subgroup, participants, mainly developers and data scientists, maintained that mitigating bias was the responsibility of experts outside science and technology, as illustrated by the statement: I think people do understand that there is a problem. But as always the question is how do we—I mean from a science point of view the way to address it is to make sure that we have good representation, that we have a wide enough number of samples from all possible classes, or some groups and so on. But how do we ensure that? And that goes outside technology. That goes outside science really … we need more data, and more of the right data. But how we do it I think is outside science and technology really. Informant 51, developer In contrast, the second subgroup consisting of experts from various roles argued that all professional stakeholders, including data scientists and developers, shared responsibility for and played a role in mitigating AI bias. So we know about racism, so we expect that we need it to be tested. We know about sexism, so we know it needs to—we know ageism, and we know that that needs [to be tested]. Informant 21, entrepreneur You’re only as good as the data. And the data is Caucasian predominantly. So—this is another element why human element needs to be in there to, sort, of take the context into account for doing—for assessing the prediction. I think, here, in Australia there’s a special issue with the Indigenous population, for example, where there’s certain diseases that’s much more prevalent in the Indigenous community and it’s just not understood why. Informant 24, researcher in bioinformatics Both of these two positions—the view that everyone, including data scientists, were responsible, and the view that responsibility lay outside data science—were seen in both bias-critical and bias-apologist informants: there was no observable relationship between these positions. Those participants who expressed a view that action was required offered mechanisms that fell broadly into two categories, sociolegal approaches and data science mechanisms. Participants who suggested data science mechanisms were predominantly clinicians, developers and researchers. The mechanisms included data governance, validation tests and use of synthetic data sets, among others (see figure 2). Figure 2 Sociolegal and data science approaches to mitigate bias described by participants. If it remained impossible to remove bias in AI algorithms, some participants recommended open disclosure of limitations: that is, that when communicating about the predictions or recommendations generated by AI, to always openly acknowledge that these were biased or partial. So hugely important not to have bias, and hugely important to recognise the bias more than anything else … when we publish these trials. We don’t say, this algorithm is great at reading chest x rays, we say this algorithm is great at reading chest x rays in Caucasian males over the age of 70, for example. And for clinicians, we talked a bit before about how much they need to understand about AI. They don’t need to understand anything about AI before they understand everything about statistics. Because how that data is presented, how there are confounders how there is bias, that part is more interesting than the AI. Informant 54, clinician Other participants, predominantly regulatory and legal experts, discussed sociolegal approaches—in addition to data science mechanisms—to mitigate algorithmic bias. These approaches included consumer and patient engagement, representation of marginalised groups, incorporation of equity considerations into sampling methods, and legal recourse. You might essentially violate [human rights] if you create an algorithm that produces biased results, so I think—I don’t think—I don’t know if there have been any cases yet, but I think you could not only end up with ethical problem, but also with a legal problem, infringement of human rights, so from legal perspective. So when people talk about bias as an ethical principle, I think they often forget that it’s also a legal issue, so that’s as far as current law is concerned. Informant 32, regulatory expert In addition to describing different approaches to bias mitigation, participants identified a wide range of stakeholders who may be responsible for addressing AI bias, and these included developers, healthcare workers, manufacturers and vendors, policy makers and regulators, AI researchers and consumers. The numerous responsibilities ascribed, which varied in scope, illustrated the complexity of algorithmic bias and potential ways to address it. See figure 3 for a summary. We emphasise that this is a summary of the views expressed by participants rather than our own normative position. Figure 3 Summary of informants’ perspectives on stakeholder responsibilities regarding bias mitigation. Noting the diversity of possible solutions, as well as the type of stakeholders that should be involved in these solutions, participants identified the need for interdisciplinary collaboration among experts. In particular, an interdisciplinary approach could capture the sociolegal and technical dimensions of the problem and causes of bias, as well as mechanisms to correct causes of and mitigate the impact of bias. That’s where you need clinicians working with the model developers at the very outset, okay, to say if we’re going to use this tool in a certain way this is—these are the peculiarities of our population, so we need to make sure that we sample the population adequately to make sure that we’re not producing biased data. … So we can’t have I think model developers—model developers by themselves, or data scientists just in isolation. You’ve got to have clinicians and others who have a real understanding of the population that we’re dealing with. Informant 41, clinician So when we find technical solutions probably we will get next to standards level, to set standards that are [the] minimum standard that each AI application has to meet to … satisfy this fairness criteria and then when we have standards we can then validate—put them in laws and say you will not be liable, let’s say, for breach of discrimination … if you comply with standards in the industry. Informant 32, regulatory and legal expert Disagreement 3: views on the role of social identities in addressing bias One particularly challenging issue when discussing mechanisms to address bias was the role of information regarding complex social identities (eg, race/ethnicity). Participants had divergent views about the role of social data (eg, information about ethnicity or income). A small number of participants, mostly researchers and developers, took an exclusionary view of social data in AI development. This view meant that in order to minimise algorithmic bias, social identities subject to prejudice should be masked or excluded in datasets. If you are, for example, putting the ethnicity of certain groups as a variable, as a basic variable, and you put much weight of this variable, the whole algorithm will be biased towards or away from these groups. But actually you don’t need to put that because these classifications are human classifications, they are not scientific classifications. Informant 4, developer Some participants took the inclusionary view of social data in AI development, as well as in addressing algorithmic bias. This heterogeneous group included entrepreneurs, clinicians, consumer representatives and regulatory experts. Several reasons to support the inclusionary view were provided. First, social information could make the dataset more robust and representative. If you learn through things like race and ethnicities, if it captures employment status, if we don’t have things even such as housing status, sort of include information around sort of behaviours, substance abuse, alcohol abuse, et cetera, that those things aren’t there and EMR [electronic medical records], as we know, sometimes can omit the sort of information unless you have good structured templates where those things are listed and you have to actually include them, okay but we know that often people don’t necessarily provide all that information when they’re admitting a patient or even subsequently. Then, yeah—then that’s right, you’re not going to have a particularly accurate model or that they have a model that’s biased because it doesn’t include those other features. Informant 41, clinician One of the ones I notice often when I’m looking at surveys or things that ask me about my features, because I’m taking part in a research study, you know, if you look at lists of gender and sexuality, like, asexuality, for example, will be missed from a lot of “pick-an-option” about your sexuality. Right? So, again, people don’t choose it, it doesn’t end up in the system, it doesn’t end up in the coding, it’s more invisible again and it becomes a bit of a cycle, unfortunately. Informant 45, consumer representative Another reason that supported the inclusionary view was to enable healthcare systems to identify which groups were underserved. And we also need to know their sociodemographic, and so there is publicly available data that you can connect up but there are people who don’t turn up and they’re the ones that are missing out often. So we need to get the census data, we need to get everything and go, “Well, okay, who are we missing?” So I think we can figure out who’s not connected with our system. They’re either not connected because there’s a reason or they’re well. Informant 31, regulatory expert Some participants who supported the inclusionary view discussed caveats, such as increased (unjust) surveillance. I get the argument of yes, there is some truth to that argument, that the more data you have, the more—I mean again, to give you an analogy, you could make similar arguments in the biomedical sphere as well. You could say we would get to cures or medical treatments faster if researchers were allowed to do whatever they wanted in the research phase. Just trial this drug that may kill people on people in prison. That would work. But we don’t do it for really good reasons and that is because we don’t accept that level of harm to innocent people. And that’s true also of, I think, research in this area and again, we’ve talked a bit about facial recognition. We know that facial recognition is much less accurate when it’s used on people with darker skin. And in an area I care deeply about, policing, there’s an argument, okay, we’ll make the training data sets a high proportion of those people with darker skin. But if the outcome of that is that those people will be policed more aggressively, that’s really hard to justify. So I think it’s quite a complex area… You’ve got to actually weigh the costs and benefits and specifically consider the uses to which the application may be put. Informant 37, regulatory expert A substantial number of participants, mainly developers and regulatory experts, took neither the exclusionary nor the inclusionary view. This uncertain view about the role of social data in AI development took into account the challenges of collecting sensitive and complex information, and translating this into useful data for developing AI algorithms. Data science relies on categories and classifications that are discrete, a feature that is not always possible for social data. When you say, “What about the culturally and linguistically diverse population?” Right, but for useful purposes you do need information to be much more granular, and that requires people to self-identify and that can be very tricky. I mean, most people don’t simply have one background, they have multiple—I’ve got 10, so what do you use? Informant 10, regulatory expert We don’t capture all the data we need and even if we could capture it all actually making sense of it all and understanding which of these many, many variables actually matter is really unclear. Informant 38, developer Thus, the findings show lack of consensus on how to manage, operationalise or capture complex social identities to mitigate algorithmic bias. The divergent views illustrate the tension between ensuring diversity in datasets and the potential harm of collecting data points marked by historical and continuing prejudice or discrimination.
Discussion Our aims were to understand whether informants saw algorithmic bias in healthcare as a problem, to capture possible strategies to mitigate algorithmic bias, and to consider the ethical question of responsibility for algorithmic bias. Our answers to these questions have practical, epistemic and normative implications. We note that, as authors, we agree that bias is a concern in AI, including healthcare AI, and that bias mitigation is required. Thus, we do not agree with all of the informants, and the Discussion section will reflect this. We found that some stakeholders had the view that bias does not exist in healthcare AI, based on the epistemic belief that these knowledge systems are not biased. People taking this view are incorrect as a matter of fact: algorithmic bias has been demonstrated across domains including healthcare,33 social welfare,34 legal systems35 and finance.36 In healthcare, studies continue to demonstrate the persistence of racial disparities in diagnostic imaging that predates AI applications. A systematic review of evidence shows disparities in non-AI diagnostic radiology, with decreased and inappropriate use of imaging for racial and gender minority groups.37 38 In the USA, members of racial minority groups disproportionately receive care at lower-quality hospitals that use lower-quality imaging technology.39 These lower-quality hospitals tend to have limited human and material resources, relying on generalists who are not experienced in diagnostic imaging interpretation.40 According to a report by the US Institute of Medicine of the National Academies,41 factors including unequal access and unequal quality of care lead increased risk of diagnostic error among racial minority groups.39 Similar patterns of disparities in quality or delivery of diagnostic imaging have been shown across various multi-cultural jurisdictions including Australia42 and the UK.43 The entrenched disparities in diagnostic imaging skew the availability and quality of data on which AI is trained, and thus perpetuating the data imbalance that contributes to algorithmic bias. Moreover, disparities in access to healthcare services, not just in diagnostic radiology, can be embedded in datasets used to develop algorithms. Consider, for example, a commercial algorithm widely used in the USA, designed to direct additional healthcare resources to patients at high risk of exacerbation or complications of their illness. Obermeyer et al 16 showed that this algorithm, which was trained on datasets that did not contain information about race or ethnicity, systematically underenrolled black patients, even if they were sicker than their white counterparts. Obermeyer’s team showed that the algorithm used health costs, which stratify populations along racial lines,44 as proxy for health needs. Since less money is generally spent on black patients due to existing systematic bias, the algorithm predicted that black patients had less need for high-risk management.16 For other participants who denied the existence of algorithmic bias, the denial concerned the relevance of the issue rather than the absence of bias itself, holding that these knowledge systems are biased, but this is normatively acceptable. Either of these views (denial or minimisation) present a barrier to action: these beliefs make responsibility to address bias non-existent and actions to address bias unnecessary. The only position that supported action to mitigate bias was the bias-critical view. It seems likely that those who are bias-critical in professions including data science and medicine will need to convince their colleagues if bias is to be effectively mitigated. As bias produces unjust outcomes, claiming that bias does not exist and does not require mitigation is not normatively defensible. Working to effectively counter these views will require recognition that bias is often implicit.44 This returns us to the issue of responsibility for addressing the complex problem of algorithmic bias.45 In disciplines including law, ethics and political philosophy, the concept of responsibility is used in varied ways.24 46–48 Earlier we introduced Fricker’s distinction between fault and no-fault types of responsibility in the context of prejudice,24 and her argument that we are rightly held responsible ‘not only for conduct based on things we know but also for conduct based on things we should have known but didn’t’. One might expect that experts who are well-informed about AI are similarly well informed about the risk of bias. Our study, which included a small group of experts who denied the existence of bias do so because they have unknowingly absorbed norms and prejudices from society. However, following Fricker, we argue they should have known, and so still have responsibility for algorithmic bias and its effects. Those who acknowledge the existence of bias but are apologists for it may be making a moral judgement rather than an epistemic one. They seem to imply that they are not responsible for action because they are not in control in a relevant sense: however, as Fricker argues, people can be held responsible not just for those things they control, but also those things they ‘ought to be able to control’. This connects to participants’ proposals about bias mitigation. When participants proposed mitigation strategies, ranging from sociological to data science approaches (see figure 3 above), they allocated responsibility to implement those strategies to different parties. For some participants, responsibility for addressing bias lies outside science and technology. One possible interpretation of this view is that experts in science and technology are not at fault for structural and systemic biases that are then reflected in AI bias, and therefore, should not be responsible for addressing the problem. Another interpretation is that experts deem that structural and systemic factors that lead to bias are outside their training and expertise, and they feel ill equipped to address these factors. Both interpretations, but perhaps the first more than the second, require a normative response. Drawing on Fricker’s notion of no-fault responsibility, responsibility to mitigate bias need not be based on fault, attributing blame or contributing to cause. In as much as there are aspects of their own professional domain that they ought to be able to control, they are responsible even if they did not cause the problem. The complex, systemic and multisectoral problem of bias seems likely to require an equally complex, systemic and multisectoral response. The mechanisms required to address bias are located in different disciplines, each offering distinct knowledge and skills. In a practical sense, if all these strategies are required, it follows that an interdisciplinary collaboration among diverse experts will be required to effectively address bias. It seems likely this will require mutual engagement between technical AI experts and those with expertise in the socio-political dimensions of injustice in healthcare, which in turn indicates that that appeals to feeling ill equipped or lacking in expertise will fall normatively short. In relation to specific mitigation strategies, we note participant disagreement about the handling of data concerning complex social identities, such as gender and racial identities. It is difficult to capture information that requires self-identification, that does not adhere to discrete categories, and that is subject to prejudice and marginalisation. In an epistemic sense, participants said they lacked hermeneutic resources to represent complex social identities in data. This lack of hermeneutic resources is one outcome—as pointed out by critics49—of the AI research and development workforce being overwhelming white and male. This means standards of practice are dictated by those in positions of privilege who may not fully understand the reality—including the causes and impact—of algorithmic bias. However, many of the sociolegal strategies advocated by participants were designed to increase the availability of hermeneutic resources, including engagement, representation and equitable research methodologies. Normatively speaking, it is not clear which of these strategies should be prioritised and pursued, given that there is currently no concrete guidance about engaging with or managing data points for complex social identities. Further research is needed to demonstrate the extent to which this lack of guidance on managing data on social identities is contributing to algorithmic bias. These findings echo ongoing calls for ‘open science’,14 which refers to a framework where the entire research process is openly shared to incentivise participation of different stakeholders, as well as encourage members of vulnerable or under-represented groups to become part of the global scientific community. The strength of this study is identifying divergence in the views of a diverse group of experts involved in the development, acquisition, deployment and regulation of healthcare AI regarding the problem of algorithmic bias. Even if it is not clear which strategies to implement and whose responsibility it is to implement those strategies, our findings offer some practical suggestions canvassed from experts. However, our study has limitations. First, the topics covered in each interview were wide-ranging, and may at times have been at the expense of depth of discussion. Second, we did not collect data on gender and racial identities, as our focus was on understanding differences between different professional groups. We were thus unable to investigate any links between participant demographics and critical, apologist or denialist viewpoints. Future research could use stratified sampling to ensure representation of different racial and gender identity groups, and to enable analysis of relationships between demographics and views about bias. Third, while we made all efforts to clarify concepts such as bias and prejudice during the interviews, these concepts were too complex and participants had different interpretations or definitions depending on their expertise.
Conclusion AI applications can improve healthcare services and bring benefits to society. However, there is a growing evidence that benefits may not be equitably distributed due to AI replicating or amplifying existing social biases in healthcare that continue to disadvantage already marginalised and underserved individuals or groups. Despite the growing evidence of AI bias, our qualitative study of expert perspectives shows disagreements about (1) whether bias exists, (2) what actions to mitigate bias are needed and (3) how to handle information about complex identities such as gender and racial identities. We argue that these disagreements provide valuable information about barriers to bias mitigation. In particular, we highlighted the interrelated practical, epistemic and normative implications of participants’ views. We argue that stakeholders are responsible for addressing bias in algorithmic systems, even when they deny its existence, or claim they are not responsible for acting. Actions to start addressing bias include greater and earlier interdisciplinary collaboration from AI development through testing and application, tailored stakeholder engagement activities, empirical studies to understand algorithmic bias, and strategies to modify dominant approaches in AI development such as use of participatory methods, and increased diversity and inclusion in research teams and research participant recruitment and selection.
Data availability statement Data sharing not applicable as no datasets generated and/or analysed for this study.
Ethics statements Patient consent for publication Not applicable. Ethics approval This study was approved by the University of Wollongong and Illawarra and Shoalhaven Health District Social Science Human Research Ethics Committee (ethics number 2020/264). Participants gave informed consent to participate in the study before taking part.
| 2025-06-01T00:00:00 |
2025/06/01
|
https://jme.bmj.com/content/51/6/420
|
[
{
"date": "2023/02/01",
"position": 58,
"query": "universal basic income AI"
}
] |
a new method with an illustration for Lao PDR and urban ...
|
The impact of artificial intelligence on labor markets in developing countries: a new method with an illustration for Lao PDR and urban Viet Nam
|
https://link.springer.com
|
[
"Carbonero",
"University Of Turin",
"Turin",
"Davies",
"East Village Software Consultants",
"London",
"Ernst",
"Ilo Research Department",
"Geneva",
"Fossen"
] |
by F Carbonero · 2023 · Cited by 56 — ... income and least-developed economies. In a nutshell, we ... While the implementation of AI technologies is still rather low in developing countries, basic ...
|
AI is transforming labor markets around the world. Existing research has focused on advanced economies but has neglected developing economies. Different impacts of AI on labor markets in different countries arise not only from heterogeneous occupational structures, but also from the fact that occupations vary across countries in their composition of tasks. We propose a new methodology to translate existing measures of AI impacts that were developed for the US to countries at various levels of economic development. Our method assesses semantic similarities between textual descriptions of work activities in the US and workers’ skills elicited in surveys for other countries. We implement the approach using the measure of suitability of work activities for machine learning provided by Brynjolfsson et al. (Am Econ Assoc Pap Proc 108:43-47, 2018) for the US and the World Bank’s STEP survey for Lao PDR and Viet Nam. Our approach allows characterizing the extent to which workers and occupations in a given country are subject to destructive digitalization, which puts workers at risk of being displaced, in contrast to transformative digitalization, which tends to benefit workers. We find that workers in urban Viet Nam, in comparison to Lao PDR, are more concentrated in occupations affected by AI, which requires them to adapt or puts them at risk of being partially displaced. Our method based on semantic textual similarities using SBERT is advantageous compared to approaches transferring AI impact scores across countries using crosswalks of occupational codes.
| 2023-07-14T00:00:00 |
2023/07/14
|
https://link.springer.com/article/10.1007/s00191-023-00809-7
|
[
{
"date": "2023/02/01",
"position": 59,
"query": "universal basic income AI"
}
] |
Artificial intelligence and robotics on the frontlines ...
|
Artificial intelligence and robotics on the frontlines of the pandemic response: the regulatory models for technology adoption and the development of resilient organisations in smart cities
|
https://pmc.ncbi.nlm.nih.gov
|
[
"Cristiana Lauri",
"European University Institute",
"Fiesole",
"University Of Macerata",
"Macerata",
"Fumio Shimpo",
"Keio University",
"Tokyo",
"Maciej M Sokołowski",
"University Of Warsaw"
] |
by C Lauri · 2023 · Cited by 21 — This is particularly essential given that it is regarded as a universal human right (Blasi Casagran 2017, p 228) and considered basic in many jurisdictions ...
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Abstract Smart cities do not exist without robotics and Artificial Intelligence (AI). As the case of the COVID-19 pandemic shows, they can assist in combating the novel coronavirus and its effects, and preventing its spread. However, their deployment necessitate the most secure, safe, and efficient use. The purpose of this article is to address the regulatory framework for AI and robotics in the context of developing resilient organisations in smart cities during the COVID-19 pandemic. The study provides regulatory insights necessary to re-examine the strategic management of technology creation, dissemination, and application in smart cities, in order to address the issues regarding the strategic management of innovation policies nationally, regionally, and worldwide. To meet these goals, the article analyses government materials, such as strategies, policies, legislation, reports, and literature. It also juxtaposes materials and case studies, with the help of expert knowledge. The authors emphasise the imminent need for coordinated strategies to regulate AI and robots designed for improving digital and smart public health services globally. Keywords: Artificial Intelligence, Robotics, COVID-19 pandemic, Regulation, Smart Cities, Resilience
Introduction Smart cities do not exist without Artificial Intelligence (AI) and robotics. It is not only a matter of definition, because this correlation also has a practical aspect, as AI is becoming an increasingly important component of smart cities. This has been especially important during the COVID-19 pandemic, where AI and robots have been assisting in combating the novel coronavirus with its effects, and preventing its spread. However, as the experience of subsequent lockdowns and distancing measures demonstrates, the deployment of AI and robotics–as a matter of technological advancement and the level of technology available–must be tailored to the situation (pandemic), necessitating the most secure, safe, and efficient use of smart emerging technologies and AI. With this in mind, the primary purpose of this article is to address the regulatory framework for AI and robotics in the context of developing resilient mechanisms in smart cities during the COVID-19 pandemic. The study provides regulatory insights to re-examine the strategic management of technology creation, dissemination, and application in smart cities, in order to address the issues regarding the strategic management of innovation policies nationally, regionally, and worldwide. This is particularly relevant to the deployment of AI solutions and the establishment of resilient organisations in smart cities to handle the regulatory challenges of the pandemic. To meet these goals, the article analyses a range of materials, such as strategies, policies, legislation, reports, and literature and also juxtaposes numerous case studies. This is done with the help of expert knowledge, enhanced by the activity of the Global Pandemic Network which brings together scholars from universities all over the world, in order to conduct research on legal, economic, and social issues related to pandemics (Benjamin et al. 2021). The article seeks to expand the discussion on the governance of AI and robotics during the pandemic and provides an added value to the discussion on recent changes in the institutional environment. For these needs, the following research questions are discussed: what are the contemporary legal issues underlying AI and robotics regulatory choices? how have these issues translated into the use of AI and robotics in the fight against the COVID-19 pandemic? what are the determinants for creating a regulatory model of AI and robotics for the needs of a development of resilient organisations in smart cities? In this light, the structure of this paper is based on three pillars that showcase a discussion of a general regulatory framework for AI and robots, covering such issues as privacy and data protection (Sect. 2), case studies of AI and robots used to combat the COVID-19 pandemic (Sect. 3), and resilience regulatory modes based on public disaster management (Sect. 4). The final section of this study emphasises the main issues and provides some follow-up remarks.
Development of policy approach for AI and robotics Development of AI and robotics in smart cities requires an organised policy approach (see Tsuji 2018; Sokołowski 2022). This paradigm derives from the need to protect fundamental rights and freedoms, including the right to life, liberty and security of a person and privacy, or the right to freedom of opinion and expression (see Ufert 2020). In fact, the development of AI systems and robotics can profoundly undermine the value structure of some legal frameworks, especially in those where democratic constitutions guarantee a high level of protection of the rights and interests of an individual. Observing the pervasive capacity of such emerging technologies, legal analysis has developed along several lines. With reference to the introduction of systems designed to control people, conflicting with their freedom of movement and the protection of their personal sphere, regulators worldwide seek to strengthen the rules on privacy by default and design. Issues related to liability for harms caused by AI tools also require delineating the roles and individual responsibilities of the actors who use these tools, both in public and private activities. Moreover, algorithms can affect individuals’ freedom of choice, self-determination, and awareness, imposing the need for transparency rules to make users aware. The listed concerns are also sparking a debate on the distinction of roles between humans and machines that transcend law and touch the boundaries of ethics and philosophy, innovating the legal approach to be implemented in the future. This compels policymakers to take responsibility for providing the appropriate approach. What measures, however, should be implemented? Depending on the socio-economic structure adopted, the degree of public approach inevitably varies, in case of technology policy as well (see Barge-Gil and Modrego-Rico 2008); however, two extreme points can be differentiated on the axis of this impact: total subordination and complete release, the total opposites (Sokołowski 2018a). These liberal and interventionist approaches, both rooted in the market- and state-based approaches (see Sokołowski 2016) are delimited by an intervening space filled by public law regulation (see Barnett 1986), a regulatory zone representing mixed economy (Sokołowski 2020a; see Harris 1990). The history of the development of such areas as telecommunications, energy, or aviation shows that the regulatory action is becoming the preferred approach (Sokołowski 2020a; Dempsey and Gesell 2013; Kolasa-Sokołowska 2022; Dempsey 1990). This tendency also applies to AI (see Scherer 2015; Clarke 2019), where the race to AI has also spurred a race towards AI regulation (Smuha 2021; see Chawla et al. 2022). In contrast to the regulatory approach, one may find deregulation. It is the process of removing public components from a specific area, sector, or policy (Sokołowski and Heffron 2022). “For some, the problem will always be that the markets were not ‘free’ enough from government interference and a further reduction in regulation is needed” notices Thomas (2006, p 1975). This means that, in the most severe scenario, neither a public agency (regulator) nor the instruments of public regulation, such as control, commands, sanctions, etc., exist (Sokołowski 2018a, p 595). It can lead to a complete release in extreme cases, which, when paired with fraudulent behaviour and market manipulation (see Windolf 2004), can lead to a significant crisis (see Duane 2002; Sokołowski 2020a, pp 174–175). A particularly appealing alternative to business is a policy that enables the avoidance of responsibility, e.g., by applying a soft regulatory approach – a “light touch regulation” (Fisk 2011, p 556) – which takes the risks of non-compliance by relying on the independent achievement of the set goals (Heffron et al. 2018, p 1193). While this approach may work well in the short term, for instance in a new technology development phase, or in the current COVID-19 pandemic (see Bachtiger et al. 2020), it necessitates a high level of trust in regulated entities – as the regulatory arrangement can be used by the regulated entity to relieve a burden of responsibility regarding duties – a regulatory capture (see Galloway 2020, pp 59–60). In the long run, it can result in misconducts, frequently discovered only in the final stages (Sokołowski and Heffron 2022). As a result, leaving the policy implementation in the hands of the regulated businesses (self-regulation) is not the best idea (see Lauri 2020), especially when no enforcement is available under the soft approach or the enforcement is of a very weak nature. Because the regulators’ and regulated parties’ interests differ in various ways (Bella et al. 2021), it is quite likely that this accommodative approach will fail (Sokołowski and Heffron 2022). Nevertheless, there is a risk that during a pandemic, addressed under the state-of-emergency laws or some other extraordinary framework, overregulation characterised by the excessive creation of legislation and legal overweight will occur (Sokołowski 2018a, see 2020b, p 596). Cooperation can aid in preventing overregulation (see Sokołowski 2018a, p 596). In this improvement process, it will be useful not only to build a global data enforcement framework, based on the principles of privacy by design and privacy by default, but also to enforce basic principles for the upcoming regulatory assets. The said principles include transparency, interpretability, accountability, explicability, auditability, traceability, and neutrality or fairness (Bassan 2019; Kritikos 2020). The anticipated solution relates to the application of a global framework for the regulation of AI and robots, especially in order to define the conditions of their use, ensure the fairness of global competition, and conform their use to the protection of health and human rights according to the human centric approach (Lauri 2021b). Smart cities, with their smart infrastructures, are ideal testing grounds for implementing this approach (Cook et al. 2018; Obringer R, Nateghi R 2021). Table 1 summarises the benefits and drawbacks of the discussed policy approaches. Table 1. Advantages and disadvantages of the selected policy approaches Approach Advantages Disadvantages State monopoly Organisational subordination in accordance with state policy Broad state command and control in a rigid, closed to competition centralised structure Regulation Balancing state and private interest to protect fundamental rights and freedoms Quality of regulatory action based on adopted procedures as well as powers and resources attributed to regulator Deregulation Free competition and market openness Risk of fraudulent behaviour and market manipulation Soft regulation Greater market flexibility with some elements of state control Risk of non-compliance, especially in the final phase Self-regulation Rapid regulatory response and adaptation to changing circumstances Risk of mismatching standards with the highest quality of protection Open in a new tab Let us refer this to issues such as privacy and data protection. When numerical statistics are collected and analysed with a help of AI or by AI itself, and then combined with other personal data, they become “personally identifiable information” (Shimpo 2020). This necessitates the development and implementation of appropriate approach, including legal, to protect the right to privacy (Florencio and Ramanathan 2001, p 105). This is particularly essential given that it is regarded as a universal human right (Blasi Casagran 2017, p 228) and considered basic in many jurisdictions around the world (see Parker 2010). Here, in setting the rules to deal with new problems, EU regulatory initiatives have been ahead of the curve in many respects, for instance in terms of tackling climate change (see Perez de las Heras 2013; Sokołowski 2018b). This also concerns the area of data protection (see Pajuste 2019), in which there has been a remarkable degree of legislative upgrading (in the EU), from the Data Protection Directive 95/46/EC (Data Protection Directive 1995) to the EU General Data Protection Regulation (GDPR) 2016/679 (GDPR 2016). The degree of transition and upgrading of the legislation is remarkable. In addition, the EU has been deliberating on an AI Regulation and is looking ahead to lead the debate on AI regulation in the future. Recently, the European Commission (2021a) proposed new rules regarding the promotion of regulation for trustworthy AI. The first-ever legal framework on AI, combined with the new Coordinated Plan of Member States, will ensure people’s and enterprises’ safety and basic rights, while also boosting AI use, investment, and innovation across the EU. Moreover, the proposed new EU rules on machinery (European Commission 2021b) will complement these efforts by adjusting the safety rules to boost users’ confidence in the next generation of products (European Commission 2021c). Regulated regimes can help to mitigate the scope of these issues, but some concerns may still persist (see Dickinson et al. 2021). For instance, if a problem arises because of AI making an autonomous decision, such as taking the wrong action and causing damage, can we hold it liable under traditional product liability? Even if we can claim product liability for a robot equipped with AI, how should we view the fact that in the future AI might make decisions that we cannot predict? Even if we can sue for product liability for AI-enabled robots, how should we compensate for the damage caused by the AI programme itself running out of control or operating in an uncontrollable state? This is especially true when these robots are constantly connected to the network and used in the Internet of Things (IoT) applications daily; as a result, the problem will be present everywhere. Because the IoT aims to influence the use of everyday objects via the Internet, we must consider legal issues when these objects are connected to robots, and the robots themselves are connected to the network and controlled by AI for use in our daily lives (autonomous robots). The issues surrounding autonomous robots equipped with AI, whether general-purpose or specialised, are expected to differ from those surrounding industrial robots. However, the primary legal issues surrounding robots continue to be those pertaining to industrial robots. Furthermore, some believe that existing robot safety standards will be adequate to deal with any new issues that arise. Let us now examine these issues through the lens of the COVID-19 pandemic’s use of robots and AI.
AI and robots in the fight with COVID-19: governance issues In 2018, the World Health Assembly Resolution on Digital Health recognized the value of digital technologies to reach the Sustainable Development Goals (see WHO 2019; WHO 2021). In 2020, as the COVID-19 pandemic spread worldwide, several forms of technological applications started to be massively implemented. Among them Kritikos (2020) lists: AI, blockchain, open-source technologies, telehealth technologies, three-dimensional printing, gene-editing technologies, nanotechnology, synthetic biology, and, lastly, drones and robots. Without disparaging the initial elements of this list, the final few have a wide range of applications in fighting the novel coronavirus. For instance, according to the Robotics for Infectious Diseases consortium, more than 150 robots are being used to combat COVID-19 (Vargo et al. 2021). Indeed, during the pandemic, digital technologies have been widely introduced in a number of areas of intervention that characterise smart cities (Pacheco Rocha et al. 2019; Lauri 2021b) and their intrinsic purpose of improving the quality of life in densely populated urban contexts (see Lytras et al. 2019b). This can be summarised as follows (Murphy et al. 2020, 2021): public safety (for compulsive quarantine enforcement, disinfection of public spaces, identification of infected individuals, public service announcements, and traffic flow monitoring); clinical care (for point-of-care disinfection, observational telepresence, delivery and inventory, interventional telepresence, patient and family socialization, and patient and visitor admissions); continuity of work and education (for sanitisation at work or school, for telepresence, for private health surveillance and security); laboratory and supply chain automation (for delivery activities, laboratory automation, management of infection materials); quality of life (delivery of food and other purchases, attending social events, interpersonal socialization); and non-hospital care (for delivery to quarantine, socialisation in quarantine, and public health surveillance). Table 2 summarises the technological applications used during the COVID-19 pandemic in relation to the public interest to be fulfilled, considering their type of use and the specific activities performed (Murphy et al. 2020, 2021). Table 2. Technological applications and their use during the COVID-19 pandemic Technological Applications Public interests Type of use Activities AI Blockchain Open-source technologies telehealth technologies Three-dimensional printing Gene-editing technologies Nanotechnology Synthetic biology Drones Robots Health systems resilience Public safety Quarantine enforcement Disinfection of public spaces Identification of infected people Public service announcements Traffic flow monitoring Clinical care Point of care disinfection Observational telepresence Delivery and inventory Interventional telepresence Patient and family socialization Patient and visitors admissions Non-hospital care Delivery to quarantined Quarantine socializing Public health surveillance Off-site testing Resilience of work and social activities Work and education Sanitation of work/school Telepresence Process automation construction and agriculture Private health surveillance Private security Laboratory and supply chain automation Delivery Laboratory automation manifacture Infectious material handling Quality of life Delivery food and purchases Attending public social events Interpersonal socializing Open in a new tab Early detection and infection monitoring by AI and robots, in particular, have been crucial in the fight against COVID-19 (Pazzaglia et al. 2021); however, this activity has aroused concerns about the compatibility with legal compliance. Thus, it is no coincidence that most legal studies have been concerned with studying their utility for public health, considering issues of efficacy, equity, and privacy (Landau 2021). While the collection of real-time public health data has been advantageously implemented in both assisting the policymakers in the planning process and informing the public about the evolution of the pandemic’s spread (Budd et al. 2020), the governments’ access to various data, including a person’s geolocation, has raised numerous privacy issues (see Gerke et al. 2020; Hassandoust et al. 2021; Chan and Saqib 2021). These applications are not limited to a single country (see Chakraborty et al. 2020). For example, since October 2020, the EU ensures interoperability of COVID-19 contact tracing and warning apps in order to facilitate free movement as an integral part of the Single Market (European Commission 2020a). Nevertheless, during the peak months of 2020 and 2021 globally, different models emerged for approaching the tracking activity. One is the Chinese model, while the other is the European model. Several different approaches can be found in the Asian context. Considering China, parallel with the outbreak of the contagion the Chinese authorities started to lay down strong measures to track the movement of people who had visited the Wuhan market. It was done through tools such as mobile phones, mobile payment applications, social real-time data on people’s location (Whitelaw et al. 2020) and facial recognition. This also allowed the authorities to forecast the transmission of the virus and orient border checks and surveillance strategies. For example, China used the AliPay HealthCode app for automatic communication and the enforcement of quarantine measures by limiting transactions permitted for high-risk users (Kupferschmidt and Cohen 2020). While this system has enabled a drastic containment of the pandemic, the need to increase data protection is a topic of growing interest in Chinese law (Greenleaf 2020). The initial response began to emerge in October 2020, when parliamentarians began debating the Personal Information Protection Law (PIPL) to regulate the collection and use of personal data. In August 2021 the law was adopted, and will go into effect at the beginning of November 2021. PIPL is also intended to enhance the exchange of data with countries that have a higher level of protection and do not tolerate collection systems with no users’ consent or through non-transparent processes. In particular, Article 49 of the law stipulates that “[p]ersonal information handlers shall establish mechanisms to accept and handle applications from individuals to exercise their rights. Where they reject individuals’ requests to exercise their rights, they shall explain the reason”. The above mentioned “mechanisms” proceed in the direction of the European-derived legal meaning of privacy by design and privacy by default (Bifulco 2018). More specifically, such mechanisms would seem to recall those automatisms referred to by the GDPR (Article 25) that should allow data controllers (and consequently data processors) to carry out a processing operation by providing, from the outset (by design), the tools and correct settings to protect personal data, so that the framework of principles is respected by default. The PILP, along with the Data Security Law (implemented from since September 2021) mark two major regulations set to govern China’s smart cities in the coming years. These rules will affect the big tech companies who are the main actors in smart cities, as the new law will change the value of data and have a significant impact on their business and relations with institutions. Moreover, countries such as South Korea have integrated AI and robotics into the government-coordinated containment and mitigation processes for early disease detection. These include surveillance, testing, contact tracing, and strict quarantine, even using geolocalisation and video surveillance measures (Zastrow 2020). South Korea, in other terms, has adopted a soft policy of voluntary containment, with widespread dissemination of information to citizens. The system is based on the central government’s existing smart city project, and is being developed in consultation between various ministries and the Centres for Disease Control and Prevention. The Korean system enjoyed a higher level of resilience when compared to others. And indeed, Korean law, amended after the 2015 MERS outbreak, provides a specific legal basis to allow authorities to access camera data, GPS tracking data from phones and cars, credit card transactions and other personal data for infectious disease control purposes. Access to this data by health professionals must still be authorised by law enforcement authorities, but the most recent changes (as of March 2020) allow also direct access by health authorities. The real time data and monitoring can support administration in management of smart services and control for better governance (see Kumar et al. 2020b). A public service enhanced by AI allows for community interaction that is tailored to the end users’ perceptions and abilities, and promotes individuals’ involvement in the community (Lytras et al. 2021). This is especially critical when dealing with the pandemic, which necessitates cohesion, collaboration, and coordination. Thus, the data that flows to the authorities not only supports the government’s (both central and local) activities in combating the spread of the coronavirus, but also keeps the population constantly informed – by the authorities – of these activities, and of the spread of the contagion. The secured data flow represents a tool for cooperation between the authorities and citizens, which can also help to maintain a balance between lockdown rules and normal life in times of high social tension. Based on the experience of South Korea, some European states have begun to design a soft control system, that included: controlling quarantined persons through geolocation; tracking the routes of infected persons to identify those at risk; disseminating information to the public on the movements of infected persons to alert those at risk and invite them to undergo diagnostic tests. Such an approach has made it possible to comply with the requirement of “proportionality” between data protection and the interests of individuals (as highlighted in GDPR). As emphasised in the European Data Protection Supervisor’s guidelines (European Data Protection Supervisor 2020), compliance with the GDPR’s regulatory framework on privacy does not allow for strong pervasiveness of technological tools in the EU. For example, data collection in Norway through the Smittestopp app has been stopped due to its “disproportion to the task” (Budd et al. 2020). Therefore, it should come as no surprise that one of the envisaged solutions has been the implementation of soft apps, such as the Italian one called Immuni. The app is based on technical requirements aimed at balancing privacy and personal rights (De Falco and Maddalena 2020) with the detection action carried out with the support of an algorithm, thanks to the use of Bluetooth technology (European Commission 2020b). The soft approach is based on the following assumptions: first, the freedom of the user to download the app or not (without prejudice to those who evade); second, the transparency towards the subject regarding the use that is made of the users’ data; third, the determinacy and exclusivity of the data as far as statistical or scientific aims are concerned; fourth, data storage on a governmental server, for the duration of the pandemic; fifth, the reciprocity of anonymity, as citizens are limited to receiving a notice only in the event of interaction with an infected person; and finally, the selectivity, the minimisation of data and its pseudonymisation (Article 26 of the GDPR) according to the decentralised PEPPT (Pan-European Privacy Preserving Proximity Tracing initiative, 2020) model (Bonomi 2020). As previously observed, AI and robotics have even been used to prevent rule violations in order to stop the spread of COVID-19, such as general lockdown and quarantine for people exposed to or infected with the virus. Even in legal systems with highly developed privacy-protection regulations, governments have used telephone traffic data obtained from internet providers (data retention) to tackle the pandemic (Oliver et al. 2020), given the need to repress behaviours capable of undermining public health. Only data pseudonymisation and anonymisation could alleviate privacy concerns in this scenario. Prevention and monitoring activities are the prerequisite for stabilising the new situation, but a further step in building a resilient system is bolstering healthcare services. This is especially important in metropolises and urban complexes due to human congestion. As a result, contact tracking applications may aid in the development of smart cities, benefiting public transportation and related industries while also providing valuable insights for city management (see Schmidtke 2020, p 200). The definition of a clear framework in terms of privacy by design and by default constitutes the basis for introducing different AI and robotics systems useful for enhancing smart cities and improving citizens’ well-being. Table 3 summarises the various approaches used by different legal systems to track the activity of infected individuals, considering the technology involved, the main objective pursued by legislators, and highlighting advantages and disadvantages. Table 3. Approaches to track the activity of infected individuals Area Approach Technology Main objective Advantages Disadvantages China Strong Tracking of mobile phones; mobile payment applications; social real-time data; facial recognition Forecasting the transmission of the virus Orienting border checks and surveillance strategies Rapid containment of the pandemic spread Lack of privacy and data protection framework South Korea Soft Contact tracing; geolocalisation; video surveillance measures Supporting public administration through the integration of AI and robotics into the government-coordinated containment and mitigation processes Keeping the population constantly informed Need for enforcement of privacy and data protection framework EU Soft Interoperability of contact tracing and warning apps Facilitating free movement in the EU Voluntary system; high level of privacy protection Risk of lack of effectiveness; need for manual contact tracing Open in a new tab Yet another facet of healthcare and the use of AI in the COVID-19 pandemic is the utilisation of robots for assisting healthcare workers. For instance, disinfection of spaces in public buildings such as schools and hospitals, but also delivering food and medical supplies (see Bogue 2020). Using robots in medical activities has a positive impact on improving the smart city’s health resilience. The rapid use of IoT devices has facilitated the collection of health-related big data (see Lytras et al. 2019a). Many medical facilities have begun to fully digitise electronic health records for clinician testing orders, referrals, and patient scheduling in order to improve the efficacy and efficiency of both medical and administrative healthcare processes (see Flynn et al. 2020). Deep learning has been used to diagnose COVID-19 using X-ray pictures (Wang et al. 2020). AI can be used to track the spread of COVID-19 and predict a patient’s needs. Through computational biology and the use of data analytics, mathematical modelling and computational simulation have helped to study and research the pandemic (Kumar et al. 2020a). Furthermore, many medical facilities have started to introduce robots in therapies. Such is the case of Loccioni, a company which used a robot to prepare a monoclonal to treat COVID-19 patients at the Hospital of Ancona (Italy). Robots can autonomously carry out the most complex operations in order to guarantee the correct composition of the therapy and intercept any possible errors during validation, transcription, preparation and delivery. Based on physicians’ reports, eligible patients are received in special rooms set up at the infectious diseases department, in a protected environment with negative pressure. The therapy requires utmost care and precision during the drug preparation procedure. The personalised preparation of injectable drugs represents a critical aspect for healthcare facilities as it involves numerous risks for the safety of patients and operators, as well as significant costs and possible organisational inefficiencies. The entire drug pathway, from prescription to administration, is controlled through sophisticated automated measurement systems that ensure high accuracy, complete traceability of operations and integrity of information. The prescription is digitised and the preparation phase takes place in a fully automated manner, in a dedicated and constantly monitored work environment. As a result, these therapies are confirmed by quality certificates, offer maximum safety in terms of sterility and accuracy of the injectable drugs prepared, allow safe management of clinical data and the production phase, and reduce clinical risk (Yaniv et al. 2017). Indeed, since the spread of COVID-19, previously harmless tasks may pose serious health risks. In places too dangerous for them, humans are being replaced by robots, which are considered more reliable and cost-effective. However, any advantages in terms of health risk prevention are matched by the risk of job losses for all those whose tasks are going to be taken over by technologies (Ramirez 2021). “Retraining unemployed people was never easy, but it is more challenging now that technological disruption is spreading so rapidly, widely, and unpredictably” highlights Floridi (2017, p 3). This fact is linked to a broader reflection on the loss of humanity in certain activities and relationships, which, along with issues of privacy and security, is part of the debate that many legal systems are facing in preparing a regulatory framework for the use of AI (Bassan 2019). The application of AI and robots, discussed here, is a tool for strengthening the resilience of the public health service on several fronts (Auby 2020). Moreover, it offers at least three methodological considerations helpful for understanding the coordinates on which to develop the regulatory framework. First, the public–private partnership created to develop the robots between the public (in this case, the hospital) and the private company combines the expertise of the national service system and the know-how of the private company. This promotes an increase in the organisational efficiency and ergonomics of the process (Valaguzza and Parisi 2020). It also makes it possible to move away from the dependence of the service on public resources, which are often insufficient, and to be able to rely on the economic investments and resources of the private sector. The adopted method of shared governance also allows the reengineering of processes through the sharing of best practices, in order to bring innovation to the public sector, considered extremely conservative both for the scarcity of resources available and still underdeveloped culture of innovation. Second, AI and robot introduction in ordinary medical activities prevents human errors and accurately controls the appropriateness of the medical prescription. This is a form of “preventive medicine actions” and can be useful to create “personalized services”, adaptable to the patient and highly efficient, as recommended by the European Communication on Digital Health Services (2018). Third, the implementation of robotics simplifies documentation management by making information more usable for the benefit of the patient. There is thus an advantage in terms of transparency of the service provided and of knowability, creating a more collaborative environment and reinforcing trust between treatment facilities and patients.
AI, robots, and smart cities: COVID-19 resilience regulatory model A re-examination of the strategic management of technology creation, dissemination, and application in smart cities is required to build resilient organisations in smart cities during the COVID-19 pandemic with the help of robotics and AI (de Pablos et al. 2022). Disaster management provided by public authorities when dealing with the effects of hurricanes, earthquakes, or tsunamis constitutes a useful benchmark (Sokołowski 2020b). This refers to: the application of AI and robotics to enhance the public’s ability to respond to disasters, policymaking under unusual circumstances (see Schneider 1992), or remedies (ex-post disaster assistance or ex-ante regulation) to limit loss exposure (see Priest 1996, p 219) or alleviate disaster effects (Malawani et al. 2020). A pandemic, if treated as a calamity that may reoccur – like in the case of SARS-COV-3, SARS-COV-4, or any other infectious disease – makes preparedness the key element of regulatory approach (see WHO 2019), and a critical component of true smart cities, which are primarily targeted by the current pandemic’s negative effects (being large clusters of people). As a result, it is necessary to consider the future challenges now, while, at the same time, continuing to implement the measures aimed at combating the current pandemic. In this regard, considering a pandemic as a natural disaster that may reoccur demonstrates the validity of referring to a regime designed to counteract natural disasters (see Sokołowski 2020b; Dixit 2020; Tsuji 2021). Furthermore, many parallels can be found in the current pandemic between activities related to those undertaken by public authorities. For example, following Hurricanes Katrina and Rita in 2005, federal regulatory agencies recognised that, due to extraordinary circumstances, flexibility in the application of rules and simplifying several applications were required (Sokołowski 2020b). This also concerns certain regulatory reliefs offered to professionals vital in disaster response or recovery, e.g. by adjusting licensing requirements, or freezing inspections (Sokołowski 2020b). AI and robotics offer a wide range of possibilities in this area. This is about simplifying procedures, making them more responsive as well as contactless, and conducting them online. Innovative chatbots can offer a straightforward support in administrative procedures, for example, when applying for licences or certificates (see van Noordt and Misuraca 2019), while different AI applications can perform inspections comparable to those carried out by humans (for instance, a drone – an unmanned aerial vehicle equipped with a camera conducts technical monitoring of a power line). However, this requires a regulatory environment that recognises the equivalence of such activities to those carried out in a traditional manner. Smart cities are the perfect environment for introducing such improvements. Furthermore, as in disaster prevention, AI offers enormous modelling possibilities, providing expert forecasts on pandemic development. These models can be utilised on a voluntary basis; however, a legal approach should guide their development. This could be done, for example, by offering specifications for their use (including the scope of the analysed data), as well as listing institutions that should use them (for instance, by making it mandatory for health establishments). This is also a source of concern for city authorities, particularly those in metropolitan areas, as the health-care administration (naturally at the forefront) is not the only one working to combat the pandemic. Other institutions are also striving to ensure compliance with the law and standards, as well as transparency and clarity of rules regarding consumers and competition in extraordinary times (Sokołowski 2020b). Moreover, fighting the pandemic demonstrates the importance of well-functioning coordination systems; coordinating policies can improve the effectiveness of crisis response (OECD 2020, p 2). This is especially true in those circumstances when the central government plays a larger role – worldwide examples of actions performed during the COVID-19 pandemic illustrate that this involvement frequently overshadows activities of other entities, e.g., local authorities (Sokołowski 2020b). In such situations, AI technologies can improve the coordination mechanism of a multi-actor administration system, making it more effective and responsive. The widespread adoption of AI should be a post-pandemic standard, transforming traditional administration into true e-administration (see Wierzbowski et al. 2021). The same applies to the transformation of traditional cities into smart ones (see Bobadilla et al. 2018). This also concerns the structure of administration, both central and local. If – apart from coordination mechanisms – a specialised anti-pandemic authority is established (for example, an agency responsible for combating COVID-19), it can, in addition to all necessary expert knowledge obtained from the health administration, serve as a valuable benchmark of an e-administration scenario. In such conditions, AI applications can not only help with the creation of a structure solely responsible for countering COVID-19 (or future, similar events), but also accelerate the process of transformation to e-administration at different levels (van Noordt and Misuraca 2019). This, of course, also applies on a city level, as no real smart city can exist without e-administration (Lauri 2021a). Finally, AI has a wide range of applications connected to the knowledge-based approach (see Fig. 1), which could result in adopting a system of rules, standards, authorisation, permissions, and guidance dedicated to COVID-19, based on best available practices as much as feasible (see Sokołowski 2020b). These should be accompanied by pandemic-specific monitoring, surveillance, and enforcement that is as safe (non-physical, online, etc.) as possible, free of unnecessary administrative hassle and with deadlines suspended or extended (Sokołowski 2020b). Of course, it must be scaled to the challenge – the recent COVID-19 variants, especially the quickly spreading omicron, make it far more difficult to adjust state logistics to the size of the problem (as is the case, for example, in South Korea). With such an approach, AI can help authorities, also in cities, become smarter and more resilient organisations, guided by pragmatic and responsive regulation. Nevertheless, this process requires some universal standards. The urgent need for coordinated, global, digital and smart public health strategies has been highlighted both by the WHO, in its global strategy on digital health 2020–2025 (WHO 2021) and by the EU, which called for a pan-European approach on the use of data for COVID-19 (European Commission 2020a, b) currently also being implemented through collaborative research projects (Tacconelli et al. 2022). Moreover, coordination serves as an ancillary element to bridge the digital divide by ensuring access to mobile communication and internet services, particularly in low- and middle-income countries, as well as for minorities and people with lower socioeconomic status. Indeed, an unequal access to technology can exacerbate inequalities between countries in terms of their preparedness to fight future pandemics, which can jeopardise the resilience of all the areas of the world. This also concerns cities, as the smart ones are at the forefront, while the “analogue” ones are lagging behind (Thomas et al. 2021). Fig. 1. Open in a new tab Key elements of the COVID-19 resilience regulatory model
Conclusion The rapid advancement of science and technology, the increased use of information technology, and the development of network-related technologies have all resulted in significant improvements to our daily lives, opening several legal issues. This also applies to governance of robotics and AI that have also helped profoundly in the frontline fight against COVID-19 in the urban environments. These actions, however, have frequently caused or exacerbated legal issues related to the employment of AI and robotics. As discussed in this study, looking at issues in an institutional system AI solutions can improve the coordination mechanism of a multi-actor administration system, making it more effective and responsive. For instance, private public partnerships combine the know-how of the private enterprise with the knowledge of the national service system, optimising the possibilities of delivering the best results in terms of preventing human errors, appropriateness of medical actions, and ease of documentation administration. However, as these new and emerging technologies have been introduced into societies and cities and their use has increased, certain problems have arisen – not only from illegal activities or misuse (that should be regulated by law), but also from the lack of rules governing the use of the said technologies. This applies not only to surveillance systems aimed at preventing the spread of a pandemic, which – as can be seen – may follow “soft” or “strong” approaches depending on regulatory frameworks; it refers also to the digitisation of services (as in the use of robotics for health services). Given this scenario, what are the determinants for creating a regulatory model of AI and robotics for the needs of a development of resilient organisations in smart cities? As identified in the paper, among current legal issue there is a need for a responsive regulatory framework of a universal character (at least at a level of principles, as due to the diversity of legal systems, it is challenging to attain complete universality of solutions at the global level), which can simultaneously hold together the protection of privacy and the rights of individuals and the fulfilment of public interests. Indeed, as it turns out, with AI and modern technologies consistent with a regulatory system, it is possible to improve the resilience of health systems and work and social activities, which are essential prerequisites for contextualising smart cities in an institutional system. This calls for smart regulation, driven by knowledge-based approach, with disaster management as a benchmark and preparedness included in regulatory approach, bringing flexibility in the application of rules, simplifying procedures and offering regulatory reliefs, and moving procedures online, with AI-enhanced administrative procedures, inspections, and decisions, and last but not least AI-improved coordination mechanisms.
Acknowledgements This work was supported by JST Moonshot R&D Grant Number JPMJMS2215.
Author contribution MMS proposed the paper’s outline and contributed to the regulatory model put forward by this study. CL contributed to AI and robotics case studies used in the COVID-19 pandemic. FS contributed to the development of privacy and data protection issues. All authors contributed to data collection, analysis, interpretation, and writing.
Footnotes Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
| 2023-03-01T00:00:00 |
2023/03/01
|
https://pmc.ncbi.nlm.nih.gov/articles/PMC9977099/
|
[
{
"date": "2023/02/01",
"position": 64,
"query": "universal basic income AI"
}
] |
Automation: 5 jobs that will never disappear, and 5 ...
|
Automation: 5 jobs that will never disappear, and 5 that will be gone by 2030
|
https://www.linkedin.com
|
[
"Michael Mcqueen",
"Iain Munro",
"M.Sc.",
"Dr. Ahmed S. Elsheikh"
] |
Yet, the use of automation and AI continues to drive everything from fears about the job market to the political debate around policies like universal basic ...
|
For at least the last 30 years – even longer depending on what movies you have seen – we have heard stories about the impending collapse of the human workforce. You know how it goes. Robots are going to come along and take our jobs. Eventually, artificial intelligence (AI) becomes self-sufficient, and we all die anyway, at which point we will not need a job.
The thing is, in much the same way as we were meant to have flying cars by now, much of what we were told would happen, has not actually happened.
Yet, the use of automation and AI continues to drive everything from fears about the job market to the political debate around policies like universal basic income.
Do jobs ever really get replaced?
Back in 2017, a McKinsey report highlighted the following statistics:
50% of work activities are technically automatable by adapting current technologies
More than 30% of activities are technically automatable in around 6 in 10 jobs
The report also talks in detail about workforce displacement, saying as many as 800 million global jobs and 475 million employees could be disrupted by automation before 2030.
It would be easy to get alarmed by those numbers. 6% of the global workforce having to find new jobs because of automation is a scary scenario.
However, all is not as it seems.
Why not?
Because in many cases, jobs will not disappear. Instead, automation and AI will help to evolve job roles and help make human workers more effective. In turn, this will make businesses more successful, helping economies grow and increasing wages, which fuels consumption and further growth.
Even where jobs do disappear, the opportunities brought about by automation, and AI will lead to other job opportunities becoming available. Many of these jobs will be in new industries and sectors created as a direct result of using AI, too!
Which jobs will disappear by 2030, and why? Which ones will hang around and are unlikely ever to disappear?
5 jobs that will disappear by 2030
1. Travel agent
It amazes me that a travel agent is still a job in 2020. As much as I have laughed at "old school" things on the Contractbook blog in the past, going to sit in a shop so someone can book you a holiday on what looks suspiciously like a 1980s IBM system might top everything. Why would anyone do this when you can do it online?
That said, there is an argument that there is still demand for travel agents, so why get rid of them? However, with chatbot platforms becoming ever more refined, and the impact of COVID-19 on the travel industry likely to be long-lasting, I envisage travel companies will decide to cut out the human element sooner rather than later.
Unfortunately, they will probably not get rid of the annoying in resort human reps just yet.
2. Taxi drivers
Before Uber sold its self-driving car division in late 2020, there was a running joke that Uber drivers were working to put themselves out of work, as Uber reinvested millions of dollars in fare revenues back into its driverless car projects.
While problems beset Uber's automated cars, I still struggle to see how taxi drivers do not become obsolete in the next 10 years. If you run a taxi firm, it would be madness to pay someone money to drive a car for you when you have a vehicle that can drive itself!
Demand may leave a market for human-driven taxis, but these will struggle to survive as automation will drive prices down.
3. Store cashiers
A few years ago, I went into a newsagent in a train station and took a magazine and a drink to the cashier. She then proceeded to walk me over to the self-service area to put my transaction through! Now, I have no doubt (having used to manage one of these particular shops myself) that she was told she had to hit a KPI for % of customers who used the self-serve register - but come on!
In bigger stores at least, where you can now scan and pack your shopping as you do it, avoiding the dreaded “Unattended item in packing area” message, store cashier roles are unlikely to exist in a few years.
4. Fast food cooks
Fast food restaurants are not shy about showing us that they are trying to rely less and less on the human workforce. Most of them have already made the change to self-service terminals when placing your order, so the next logical step is to move into the kitchen.
Some companies, particularly in the United States, are already starting to prune their workforce, utilising robot technology to flip burgers and put sandwiches together. In time, your local McDonald’s could end up with just a handful of staff members handing out orders made by the robots, although I would not bet against that process becoming automated, too!
5. Administrative legal jobs
These roles are already under threat, with legal administrative jobs and paralegal roles already increasingly performed by automation and digital tools. Automation and digitalisation will continue to become more refined in the coming decade, and the need for humans to complete these jobs will dwindle further.
While there is a focus on AI in some sectors, this is really unnecessary for things like preparing documents and contract analysis. Legal firms looking to embrace tech can actually get ahead by getting “back to basics” rather than opting for the tool that might sound the most impressive to a client.
5 jobs unlikely to disappear, at least in the near future
1. Lawyer
As much as legal analysis and case preparation will become increasingly automated, we are a long way away from robots representing or cross-examining us in the courtroom! You can automate almost every part of a contract workflow. Still, you need the human element to put arguments, establish social relations in the negotiation phase, and find nuances in the data, rather than relying on data and algorithms outright.
Any move to automate processes in criminal justice systems around the world would be likely to face intense scrutiny, too.
2. HR roles
I recently wrote about the importance of HR tech and how it helps make people better at their jobs, not replacing HR professionals or removing the need for the human touch.
I do not believe we are anywhere close to getting rid of HR functions for recruitment and performance management. Automation will continue to take on more heavy lifting and admin functions, such as payroll or filtering job applicants. However, the human touch will always remain when it comes to HR.
3. Tradespeople
Augmented and virtual reality is already starting to play a role in trade jobs, such as plumbing and construction. In some building projects, you can even find robots laying bricks and performing functions at a far greater rate than a human possibly could.
However, it is difficult to envisage a time when everything of this nature, from plumbing and electrical work to gardening, is taken care of by robots. Things like construction projects certainly might start to look more like a car production line with more robots than people. However, people will still plan and manage processes. In the case of plumbing and electronics, these will probably stay 100% human performed.
4. IT systems analysts
As we’ve already seen in this article - and as you’ll already know if you read this blog with any regularity or even use Contractbook yourself - the ways in which IT systems can be automated are phenomenal.
Whether that’s writing contracts, other SaaS platforms, or analysing things like code and replacing some of the IT QA process, there are automated systems that can do it all.
However, these systems still need analysing and managing to ensure they’re doing the right thing. We’ll never allow automation systems to “mark their own homework,” so to speak. As such, it’s unlikely roles like systems analysts will disappear. Sure, a systems analyst role might become something that bolts on to another person’s job if it doesn’t demand full-time attention, but there’ll still be a space - especially in big companies - for this discipline.
5. Medical professionals
The healthcare sector is one where technology is playing an ever-greater role. If you are anything like me, you will be somewhat taken aback when you visit the doctor and they Google your symptoms. However, that barely scratches the surface of how the profession can use technology.
Automation and AI can work independently and alongside humans to deliver potentially life-saving treatment. However, the human touch will always be needed both for diagnosis and treatment.
Jobs that will disappear and jobs that will stay: what do they have in common?
You may have picked up on some of the common trends throughout the jobs we have looked at here.
Certainly, the jobs that will stay all have a clear reliance on a significant level of human input. They are all also jobs that may not be sustainable were they to be conducted 100% by robots. How many people do you know who would be happy to have a significant operation that robots deal with from the moment the anaesthetist puts you under?!
In contrast, the jobs that will disappear all share a range of characteristics:
Repetitiveness
Low-efficiency
Often unprofitable - as much as it sounds brutal to say people do not make businesses profit, that is the reality of the world
Simple to automate
Prone to fluctuating demand - it is cheaper to pay for a machine or piece of software to work when you need it than to employ someone to stand doing nothing if there is no demand!
Any jobs - or even tasks we perform in our lives - that have these characteristics will increasingly become automated.
Should you be worried?
If you do a job at risk of disappearing in the next decade, it is only natural you might be worried.
However, you are more likely to be redeployed into another function than outright lose your job. You may also have the opportunity to learn and develop new skills as part of that process. To take an out and out positive view of it, why not take the chance to start upskilling now and see the evolution of your current role as the opportunity for a career change?!
| 2023-02-01T00:00:00 |
https://www.linkedin.com/pulse/automation-5-jobs-never-disappear-gone-2030
|
[
{
"date": "2023/02/01",
"position": 74,
"query": "universal basic income AI"
}
] |
|
Challenges and Prospective of AI and 5G-Enabled ...
|
Challenges and Prospective of AI and 5G-Enabled Technologies in Emerging Applications during the Pandemic
|
https://www.intechopen.com
|
[
"Md. Mijanur Rahman",
"Fatema Khatun",
"Written By"
] |
by MM Rahman · 2023 · Cited by 11 — In healthcare applications, AI methods can be divided into two primary ... income that digital markets will generate in the year 2026 [49]. Since the year ...
|
5G is being implemented in the Internet of things (IoT) era. This book chapter focuses on 5G technology and the integration of other digital technologies, such as artificial intelligence (AI) and machine learning, IoT, big data analytics, cloud computing, robotics, and other digital platforms into new healthcare applications. Now, the healthcare industry is implementing 5G-enabled technology to improve health services, medical research, quality of life, and medical professionals’ and patients’ experiences everywhere, at any time. Technology can facilitate faster medical research progress and better clinical and social services management. Furthermore, AI approaches with 5G connectivity may be able to combat the epidemic challenges with minimal resources. This book chapter underlines how 5G technology is growing to address epidemic concerns. The study highlights many technical issues and future developments for creating 5G-powered healthcare solutions. This chapter also addresses the key challenges AI and 5G technology face in emerging healthcare solutions. In addition, this book chapter highlights perspective, policy recommendations, and future research directions of AI and 5G-enabled technologies in confronting future pandemics. More research will be incorporated into future projects, including studies on developing a digital society based on 5G technology in healthcare emergencies.
1. Introduction The healthcare industry benefited from the development of a number of digital technologies in 2020. These technologies are used to address issues in conventional healthcare systems and the pandemic, including the “Internet of things (IoT)” with high-speed wireless networks [1], big data [2], “artificial intelligence (AI)” including machine learning and deep learning [3], and blockchain technology [4]. 2019 was the year that witnessed the broad deployment of the latest wireless mobile phone technology, known as “Fifth Generation (5G).” Even though the 5G network is still in its infancy, some nations have already implemented 5G networks. These nations include China, South Korea, Japan, the United Kingdom, and the United States [5]. 5G home services and some large applications are currently being developed in many cities of the United States [6]. At the “Winter Olympics” in February of 2018, South Korea demonstrated the 5G technology. They have been expanding their 5G networks and anticipate having 5G deployment throughout the nation by 2023. China is extending 5G communication as part of its “Made in China 2025” goal in research and development initiatives. Commercial 5G networks were introduced in China in 2019, and the country is currently expanding 5G communication. In 2020, Japan launched a 5G network for commercial use. Several European countries, like Austria, Spain, and Switzerland, have already launched 5G services and are planning to extend their network capacities. Many other countries have plans to deploy 5G networks by 2025 [7]. By 2025, it is expected that the 5G cloud will support around 1.8 billion connections and cover nearly one-third of the world’s population [8, 9]. Compared to current wireless networks, 5G offers fast data rates, reduced latency, and high-volume device connectivity with excellent energy efficiency, high reliability, and support for mobility [10]. In 2019, 204 billion applications were downloaded over the Internet, and 67% of people worldwide had mobile device subscriptions, of which 65% had smartphones [11]. It was anticipated that there would be 3.8 billion people utilizing social media regularly by January 2020 [12]. Despite the constantly increasing number of digital devices connected to 5G, further research is currently being conducted to determine the level of variety in RF exposure. Meanwhile, the world is facing a public health calamity due to the unique “2019 Coronavirus Disease (COVID-19)” [13]. Many experts researched the genetic code of COVID-19 and attempted to tackle the coronavirus pandemic health emergency when China initially identified the virus in December 2019 [14]. However, the World Health Organization (WHO) identified COVID-19, which was caused by a novel coronavirus named “severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)” in December 2019 in China [15]. On January 30, 2020, the WHO labeled the Chinese COVID-19 outbreak a public health emergency and proclaimed a global pandemic on March 11, 2020, posing a severe threat to public health systems. The COVID-19 pandemic has swept through 228 countries and territories, resulting in almost 6.6 million deaths and 637 million infected cases worldwide, reported by the” Worldometers” on November 4, 2022 (see Figure 1) [16]. As of October 2019, 50 cities in China had commercially provided 5G wireless networks, and several people claimed ownership of the idea of 5G connectivity with the coronavirus. In December 2018, South Korea was the first to market 5G technology using a mobile hotspot successfully. However, South Korea was not the source of just one coronavirus, which has devastated many countries that do not yet have access to 5G networks. These countries include Malaysia, India, Bangladesh, Iran, France, Singapore, and Nigeria. Thus, the 5G-coronavirus theory makes a misleading claim, and the novel coronavirus has nothing to do with 5G, and there is no scientific evidence [17, 18]. Furthermore, according to several studies, 5G-related telecommunications technologies do not affect the human immune system [19]. Nevertheless, the pandemic has negatively influenced economic, medical, and political situations. Initial identification, isolation, quick management, spread prediction, and contact tracing technologies are all approaches to combat the spread of the coronavirus. The key challenges are virus tests, prescription or pharmaceutical delays, and providing services to critical zones. Modern digital technologies, such as artificial intelligence and 5G-based solutions [20], are essential for health, social, and economic outcomes to combat the coronavirus effectively. The worldwide health catastrophe brought on by this pandemic can be mitigated using these technologies, which can give improved digital solutions. With its potential effects in many industries, the use of 5G-enabled technology is overgrowing, offering more real-time services than anticipated. This study intends to highlight the perspective of AI and 5G-based solutions that can address COVID-19 difficulties in various contexts by concentrating on digital technology and existing socioeconomic issues. The chapter also examines numerous technological challenges and policies in implementing AI and 5G-powered emerging applications for handling post-pandemic issues.
Advertisement 2. Related works Individuals and different industries are using multiple types of AI and 5G-powered solutions. The main application categories include diagnosis, patient treatment, administrative tasks, and services. During the global epidemic, numerous studies on AI and 5G-enabled technologies have been conducted, and they suggested many solutions in different sectors. M.M. Rahman et al. [21] aimed to describe the current technical aspects of artificial intelligence and other relevant technologies and their implications for combating COVID-19 and preventing the devastating effects of the pandemic. This study highlighted and categorized AI approaches in tackling the COVID-19 pandemic, including disease detection and diagnosis, data analysis and treatment procedures, research and drug development, social control and services, and predicting outbreaks. An early review paper [22] also discussed the role of AI in the fight against COVID-19 and its current limitations. They identified six critical areas in which AI contributed significantly during the pandemic: (i) early warnings and alerts; (ii) tracking and prediction; (iii) pandemic data dashboards; (iv) diagnosis and prognosis; (v) treatments and medication; and (vi) social control. Fei Jiang et al. [23] looked at how AI is currently being used in healthcare. This survey showed that AI could be used with different kinds of health data (structured and unstructured). Modern AI techniques like “support vector machine” and “artificial neural network” can be used to learn from structured data. In contrast, advanced deep learning models and natural language processing are used to learn from and understand unstructured data. They talked about how AI could be used in three areas: early detection and diagnosis, treatment, predicting the outcome, and figuring out the prognosis. In a survey report [4], the authors looked at how blockchain and AI could be used to stop coronavirus outbreaks. First, they introduced a new conceptual architecture that integrates blockchain and AI for COVID-19 fighting. Then, they talked about how blockchain and AI could help fight the COVID-19 outbreak in fundamental ways. They also looked at the most recent research on how blockchain and AI can be used in different ways to fight COVID-19. Using the geolocation of the patients and massive amounts of data, researchers developed a system capable of detecting and predicting the early spread of an epidemic [24]. A framework [25] enabled by an AI approach was proposed to detect COVID-19 using smartphone sensors. The designed AI-enabled framework can interpret the smartphone sensor’s signal readings to predict pneumonia and the disease’s outcome. Due to the rapid global spread of coronavirus disease, it is desirable to develop an automatic and accurate detection method for COVID-19 using chest CT. Numerous researchers developed a model based on deep learning to identify COVID-19 on a chest CT scan [26]. Using radiology and chest radiography to screen COVID-19-infected patients effectively is a crucial task [27]. The COVID-Net is a deep neural network-based model designed to detect COVID-19 cases in chest X-ray (CXR) images. In a screening approach [28], the authors sought to develop a deep learning-based early screening model to differentiate COVID-19 pneumonia from Influenza-A viral pneumonia and healthy cases using pulmonary CT images. Deep learning-based methods, like the “Deeper-Feature Convolutional Neural Network (DFCNN)” model [29], can effectively find and rank the interactions between proteins and ligands. The DFCNN can screen people quickly through virtual means. It can discover possible drugs for 2019-nCoV protease by screening drugs against four databases of chemical compounds. Other research used three different convolutional neural network (CNN)-based models (like ResNet50, InceptionV3, and Inception-ResNetV2) to look for patients with coronavirus pneumonia in chest X-rays. In addition, models built on AI were created to enhance the critical care provided to COVID-19 patients [30]. Clinical, paraclinical, personalized medicine, and epidemiological data were included in this model. The healthcare system can use an AI-based decision-making system to defeat COVID-19 and assist in better patient management in the ICU. Seven significant applications of AI for the COVID-19 pandemic were identified by R. Vaishya et al. [31]. By gathering and analyzing historical data, this AI-based solution is crucial in determining the cluster of cases and forecasting virus infection in the future. Additionally, it is crucial to comprehend and recommend creating a COVID-19 vaccine. Result-driven technology is employed to screen, analyze, predict, and track current and future patients. This technology has already tracked data from confirmed, recovered, and deceased cases. Furthermore, Industry 4.0 can meet the demand for personalized face masks and gloves and gather data for healthcare systems to effectively manage and treat COVID-19 patients [32]. With the proper surveillance systems, it helps to resolve pandemic-related issues and provide a daily update on an infected patient, area, age, and state-wise. The use of various AI-based automated techniques and tools, including “Brain-Computer Interface (BCI),” “Arterial Spin Labeling—Magnetic Resonance Imaging (ASL-MRI),” biomarkers, iT bra, and different machine learning algorithms, aids in reducing errors and controlling disease progression [33]. AI software, expert systems, decision support systems, and computerized diagnosis can help doctors by minimizing intra- and interobserver variability. Deep learning and machine learning methods like artificial neural network (ANN) models can uncover hidden correlations and patterns in medical data, which can be used to create efficient clinical support systems. The IoT era is ushering in the most recent 5G technology. MM Rahman et al. [20] concentrated on 5G-based solutions that could address COVID-19 problems in various contexts. This study also offered a thorough analysis of 5G technology, incorporating other digital technologies in emerging healthcare applications to address epidemiological challenges. The adoption of 5G-based technologies in healthcare is currently taking place to support better health services, more productive medical research, improved quality of life, and better interactions between medical staff and patients worldwide.
Advertisement 3. AI and 5G-enabled technologies in real world COVID-19 has introduced the capability of digital transformation. Industry 4.0 has the prospects to reshape and restore economic systems in a post-pandemic world via 5G smart infrastructure with IoT and AI, integrated automation, and cloud innovation (see Figure 2). All of the available technologies for Industry 4.0 are linked together with the help of 5G connectivity. Medical stakeholders can talk to each other for many different reasons, such as finding and diagnosing COVID-19, supporting healthcare equipment and logistics, remote health monitoring, improving treatment processes and care, controlling and managing COVID-19 patients, lowering the high risk of death, speeding up drug manufacturing and vaccine production, fighting local and global medical emergencies, etc., with less human physical involvement [34]. Using these technologies correctly would help to improve public health education and communication. While the school is on lockdown, these technologies assist in teaching and learning in remote places [35]. These give digital and many different places to find free educational resources. People are working from home and understanding a new office culture, work hours, virtual offices, virtual meetings, and a lot of written communication thanks to Industry 4.0 technologies. Industry 4.0 uses innovative production methods to make essential disposable items in short supply because of the COVID-19 pandemic. Industry 4.0 technologies can help people find better digital solutions during this crisis. Here are some of the ways that Industry 4.0 can help lessen the effects of the COVID-19 pandemic: Planning activities related to COVID-19; giving patients and healthcare professionals better services;
Making medical items that have to do with the pandemic;
Creating an intelligent healthcare system;
Using robots to treat patients to reduce risks and make the work environment more flexible;
Putting virtual reality and augmented reality to the test for training;
Helping people do the work they need to do to live during the lockdown;
Using advanced digital technologies to come up with many new ideas;
During the pandemic, taking care of local and global public health emergencies;
Helping students and researchers find strange information. Artificial intelligence (AI), including machine learning and deep learning, the Internet of things, big data and e-health, virtual reality (VR) and augmented reality (AR), holography, cloud computing, robots and robotics, 3D scanning and printing, biosensor, blockchain, smart devices/sensors, online digital platforms, are some of Industry 4.0’s powerful technologies that could be useful during this pandemic. Digital technology has significantly altered almost every aspect of human life in the last few years, including how we communicate, work, enjoy, travel, bank, and shop. Nowadays, advanced digital technologies allow for the explosive expansion of the potential of diverse diagnostic and therapeutic instruments and systems [36]. Implementing digital medical technologies can improve the general public’s healthcare accessibility and adaptability. Digital technology is currently a great way to support teaching and learning processes in institutions like schools and colleges. Therefore, rather than being driven by a particular technology, the effective use of digital technology is determined by learning and teaching goals. It enhances interactions between teachers and students. The COVID-19 pandemic clearly illustrates online education’s importance for teaching and learning. Today’s communication is entirely dependent on digital technology. Many digital tools facilitate communication between two or more parties. These include email, phone calls, video conferencing, social media, blogs, news portals, forums, and chat and instant messaging via smart devices. It is the most convenient method of communication, as anyone can have a real-time conversation with people from around the world without leaving their desks. The phenomenon of the digital revolution is gaining increasing attention in tourism management. This industry is undergoing digital transformations, including Tourism 4.0 and Smart Tourism [37]. Consequently, the physical structure is labeled “smart” to describe the integration of the physical and digital worlds, such as smartphones, smartcards, smartTV, and smart cities. Using cutting-edge technologies, media companies can create an efficient end-to-end strategy for developing digital platforms for users. With the development of computer-mediated digital technologies, significant portions of the media and entertainment industries can become a reality. Over the past few years, the entertainment sector has undergone significant digital innovations. Future banking will be transformed by digital technology. The rise in AI, blockchain, and IoT demand has promoted the development of modernizing the banking industry. Banking is undergoing technological disruption due to increased competition from fin-tech startups and growing concern about cybersecurity. The digital revolution is a big chance for the agricultural sector to become more productive and advanced. Farmers can use digital technologies to make their farms more productive and develop long-term solutions to climate change. A smart city is a model for urban development that uses digital technologies to make city operations and services more efficient. It improves life for the people who live there and helps the environment [38]. Almost every part of our daily lives is affected by digital technology. In the last few decades, it has given us new devices like smartwatches, tablets, and voice assistants that have changed our world and daily lives. Also, digital technology improves the safety and security of our homes and lifestyles. 3.1 AI approaches AI can contribute to the coronavirus pandemic in various ways, including early detection, tracing, forecasting, diagnosis, projection, treatments and pharmaceuticals, and social management and services [22]. In healthcare applications, AI methods can be divided into two primary categories: (i) machine learning and deep learning approaches and (ii) natural language processing approaches. AI approaches, particularly machine learning models, have the potential to benefit human civilizations and healthcare systems in the fight against the worldwide pandemic. In healthcare, machine learning techniques provide enormous prospects. These technologies can be used to develop effective strategies and aid scientists and medical professionals in addressing and resolving the difficulties presented by the coronavirus pandemic crisis. Many companies have recently introduced a range of AI skills, including those for outbreak estimation, coronavirus detection, diagnosis, analysis of data and treatment methods, drug development, research, and future outbreak prediction. Moreover, the term “AI” refers to a collection of technologies [39] that have the potential to significantly advance the field of healthcare (see Figure 3). The three terms “artificial intelligence,” “machine learning,” and “deep learning” can occasionally be used interchangeably, which frequently causes misunderstanding among nontechnologists [40]. The phrase “artificial intelligence” refers to a vast, established, and highly developed area of computer science study that addresses issues relating to machine intelligence, such as simulating cognitive functions, detecting the environment, and acting independently. Robotics, vision, natural languages, learning, planning, reasoning, and other areas of study are now being studied. Deep learning, or neural network, is a machine learning model used in clinical data analysis and disease identification [41]. Moreover, data mining and statistics are involved in machine learning, where a decision model is learned rather than explicitly programmed by a person. Traditional machine learning methods can handle issues with hundreds or thousands of features, such as decision trees and support vector machines. Figure 4 illustrates how a machine learning model works in data analysis and prediction. Problems related to computer vision, natural language processing, speech and image recognition, time series analysis, etc. have succeeded when deep learning techniques have been used. With their ability to interpret data effectively, deep learning model can improve their capacity to identify correlations and connections as they analyze additional data, basically learning from prior findings in the healthcare industry [42]. A convolutional neural network (CNN) is one sort of deep learning (see Figure 5) that is particularly well suited to interpreting images, such as MRI data and X-rays. This CNN model can assist medical personnel in detecting health issues in their patients more quickly, accurately, and reliably. Furthermore, deep learning models can assess structured and unstructured data in electronic health records, such as clinical notes, laboratory test results, diagnoses, and prescriptions, at high speeds and with high accuracy. During the global outbreak, deep learning models were used by researchers in a variety of applications, including early COVID-19 detection and prediction, assessing chest X-ray or CT images, managing intensive care, risk analysis for COVID-19, and providing essential services. Figure 6 illustrates the volume of text data (unstructured and structured) produced by healthcare organizations. Some of it is arranged or organized into particular EHR (electronic health record) fields [45]. With the help of this structure, medical professionals and other software programs may easily find, exchange, analyze, and utilize the data they need. However, a sizable portion of clinical data (about 70–80%) is still retained in narrative reports, patient records, observations, and other textual forms. To find the information they need from textual documents, clinicians must manually go through mountains of paperwork. It causes obstacles in administrative processes and emergencies, resulting in hiccups and delays in medical care. Additionally, EHRs receive a lot of unstructured patient data, making it challenging for a system to assist doctors in gathering this crucial information. Another AI model, known as “natural language processing (NLP),” helps computers understand and make sense of what people say and write or what it means. NLP can help us do many things, such as extracting information, turning unstructured data into structured data, putting documents into groups, and summarizing documents [46]. Two main types of algorithms used in NLP: (i) rule-based systems analyze text using pre-established grammatical rules, and (ii) machine learning models employ statistical techniques and acquire knowledge over time by being fed training data. NLP uses free-text medical information to figure out the best ways to treat medical conditions. The use of NLP tools in healthcare offers the ability to accurately give voice to the healthcare industry’s unstructured data, yielding considerable insight into comprehending quality, refining methodologies, and improving patient outcomes. Most modern NLP techniques can understand and analyze data with little or no preprocessing [47]. The following are the critical usage cases: Text Classification: An NLP technique can assist in categorizing vast amounts of unstructured health data, such as organizing patient application forms by urgency or detecting fraudulent claims. Information Extraction: NLP tools can extract useful information from unstructured health data. The technology, for example, can tag data from patient histories, discharge summaries, or call center reports and then organize them in an EHR according to a schema. Improving Clinical Documentation: At the level of care, NLP uses speech-to-text dictation and structured data entry to extract crucial data from EHR. As a result, physicians can concentrate on treating patients with the necessary care while ensuring that clinical data is accurate and up to date. Accelerating Clinical Trial Matching: Using NLP, healthcare providers may search through massive volumes of unstructured clinical and patient data to locate qualified persons for clinical trials. Supporting Clinical Decisions: NLP enables physicians to access health-related information quickly, easily, and efficiently, allowing them to make more informed decisions at the point of treatment. Language Modeling: Using NLP techniques, one may comprehend spoken text and generate natural sounding writing. The software can transcribe medical notes accurately, summarize them, or classify and extract data. 3.2 5G-powered emerging technologies The latest 5G mobile networks have excellent technical characteristics, including faster transfer speeds of up to 20 Gbps, ultrareliable low latency (less than a millisecond), enhanced network security, massive machine-to-machine communications, and improved device energy efficiency. The deployment of 5G networks will expand wireless broadband services far beyond mobile Internet to more sophisticated Internet of things systems. These systems have the low latency and high-reliability level required to handle critical applications in all significant industries. The advent of 5G mobile networks will facilitate the development of novel applications in the medical industry [9]. The provision of a platform for inventive uses that enable segmented degrees of latency will be made possible by enhanced broadband experiences, large-scale Internet of things networks, and mission-critical services. Even while edge computing can be employed in a 4G context, coupling this with 5G networks and AI is likely to open up new possibilities in accelerating the adoption of Industry 4.0. The deployment of 5G networks makes it feasible to construct “smart factories” and reap the benefits of technologies such as automation and robotics, artificial intelligence, computer vision, augmented reality, and the Internet of things in various disciplines and applications. In addition, it is projected that the 5G technology would connect billions of devices while improving their functionality. Applications that are supported by 5G have the potential to deliver transformative impacts in a variety of industries, including healthcare, education, resource management, transportation, agriculture, and other sectors, to address the challenges brought about by the current pandemic [48]. Figure 7 depicts the industries that make the most use of 5G-powered emerging technologies and provides an estimate for the amount of income that digital markets will generate in the year 2026 [49]. Since the year 2020, the entire world has been experiencing a health disaster. The use of 5G in conjunction with other sophisticated digital technologies is an assistance in the fight against the issues posed by the coronavirus in many countries [50]. This cutting-edge 5G technology will revolutionize fast connection, storage in the cloud, billions of intelligent gadgets, and improved medical services in the healthcare field. As a result, 5G will revolutionize the healthcare industry and add more than 1.1 trillion USD to the global economy by 2035 [51]. 5G technology has the potential to assist in medical research, diagnosis, and treatment, and improving healthcare services for both medical professionals and patients remotely [52]. Figure 8 depicts a straightforward 5G-based health platform that can be useful to patients and medical practitioners. Since 5G promises superspeed with large data bandwidth and low latency (around 100 Mbs), AI technologies deployed in 5G networks can enable intelligent and autonomous functionality to control the coronavirus outbreak. According to a report by IHS Market Ltd. [53], 5G would make it possible for the global healthcare industry to sell more than one trillion dollars worth of goods and services by 2020. In addition, it is anticipated that the 5G network will accommodate approximately 212 billion sensors and about 50 billion smart devices [54]. These health gadgets, medical wearables, and remote sensors in 5G networks all efficiently contribute to healthcare to assist the health emergency difficulties that the COVID-19 outbreak has produced. Now, the healthcare industry is implementing digital technologies with 5G connectivity that can provide health services and improve the quality of life and the experiences of medical personnel and patients. It is anticipated that the expansion of this technology will achieve a compound annual growth rate of 16.5% from 2019 until 2023 [55]. 5G connectivity is improving healthcare services in various ways [56], including facilitating home healthcare, digitizing pathological analysis, managing patient information files, robotic surgery and medications, training, and therapeutics, securing staff-patient communication and management, etc. The favorable characteristics of 5G also significantly impact future healthcare research and the advancement of treatment. In today’s world, cutting-edge digital technologies are transforming the healthcare industry. The promising digital technologies powered by the 5G standard have aided the public health schemes to fix the shortcomings in healthcare services and to confront the coronavirus epidemic [57]. Figure 9 illustrates some of the characteristics of the 5G technology that can bring about significant breakthroughs in the medical field [58]. The following paragraphs provide further explanations of these aspects. Telemedicine: It demands a network connection that is dependable as well as speedier, and it must be able to provide high-quality video and real-time conversation. 5G standards make it possible to create a suitable telemedicine environment, enhancing online health consultancy [59]. The market for telemedicine in the healthcare sector is anticipated to expand at a rate of 16.5% each year from 2017 to 2023 [55]. Telesurgery: Telesurgery and other forms of remote medical care are made possible by 5G’s extremely low latency and lightning-fast speeds. A health surgeon in China was the first to undertake a 5G-assisted remote surgery utilizing “da Vinci surgical robots” [60] in January 2019 that was performed on an animal. In China in March of 2019, telesurgery was conducted remotely on a human brain utilizing a 5G mobile network. Internet of Medical Things: The infrastructure of 5G networks can connect one billion digital devices and wearable medical equipment, also known as the “Internet of medical things (IoMT)” objects, which creates a bridge between the digital and physical worlds and enables real-time analytics to improve patient’s health. It can collect important health data, store it in the cloud, and make it available online for users, medical professionals, and researchers [61]. Remote Diagnosis and Treatment: 5G connectivity assists healthcare professionals in continuously monitoring contagion and a remote diagnosis from any location throughout the pandemic [62]. In January 2020, China was the first country to develop a 5G-powered remote diagnosis and treatment system. During the pandemic, this device can assist with the diagnosis and treatment of patients remotely. 5G-Powered Digitized Platforms: Numerous technologically established nations, including the United States, China, Japan, and South Korea, are rapidly launching their own specific 5G wireless networks for digitalized, data-driven, and cloud-based emergency platforms [63]. These digital platforms aid healthcare in various ways, including accelerated reaction times, remote monitoring, data analysis and diagnosis, resource allocation, and many others.
Advertisement 4. Challenges and prospective AI technologies are growing as emerging digital innovations in the healthcare industry. In addition, the 5G network’s real-time superspeed and extremely low latency offer a variety of new prospects to serve developing healthcare applications. In the context of healthcare services during the current pandemic, the following subsections discuss the principal difficulties and opportunities presented by AI and 5G-enabled technologies. 4.1 Key challenges faced by AI Though AI, including machine learning, has fantastic capabilities in healthcare in fighting against the epidemic, this field also has a few limitations or challenges in improving the current healthcare systems. Therefore, this study also addresses some challenges faced by AI in healthcare that are listed below: Require a high volume of relevant data for AI: Finding rich health data is one of the biggest challenges of using AI in healthcare. AI algorithms cannot be fully trusted until they are built and trained on a large amount of relevant data in healthcare applications. Thus, AI depends on various data gathered from millions of people who have suffered from similar conditions. It must require sufficient data on a particular group of such patients in AI databases to make the correct comparison. But enough data on patients is often challenging to find from a specific background. Moreover, medical data has a sensitive nature and ethical constraints that make it challenging to collect. In this case, AI will make an inaccurate diagnosis with insufficient data, and doctors might make a mistake in taking proper treatment. Need a better understanding of applying AI: AI models are becoming increasingly complex to achieve better results. Because of its intricacy, AI sometimes operates in a “black box,” which makes it more challenging to comprehend how the model functions. Therefore, healthcare professionals must frequently understand how and why AI produces specific results to behave appropriately. The absence of explanation raises concerns about reliability for individuals and the healthcare companies they use. Methods of “Explainable Artificial Intelligence (XAI)” [64] can tackle this issue and develop trust between humans and computers by clarifying the processes through which they arrive at particular solutions. Need more testing and verification of AI: Though AI can offer more accurate diagnostics, there is a chance of making mistakes. So, it causes individuals and companies to falter about implementing AI approaches in diagnosis. For example, hundreds of AI systems and tools have been built during the pandemic to diagnose COVID-19 cases. But many of them failed to provide accurate diagnosis results or caused errors [65]. Moreover, if the AI models are not adequately trained or trained on poor-quality data, these do not accurately represent its underlying real-world process to prevent diagnostic errors. Thus, proper testing and verification with the right and representative data must be ensured without underfitting or overfitting against the training data [66]. Invest in data privacy mechanisms for AI: Patient data includes sensitive data, such as medical history, identity, and payment information. So, people who use AI systems need to keep in mind that they are dealing with machines. AI systems have enabled the tracking of a patient’s personal information and health/test reports. A person can promise privacy between a doctor and a patient, but machines cannot do that, and machines can break down. This problem can cause the system to fail or cause data to be lost. It can also hand over control of the system to the wrong people, who can easily use the information against the people involved. It often happens when the AI system is not safe enough from hackers. The healthcare industry needs to use technologies that improve privacy to get the most out of AI while minimizing the risks [67]. Require training or education for AI: Though the rise of AI technology replaces routine tasks and opens up new job opportunities, it causes a slowdown in the industry’s adoption of AI. Even though AI tools can make many technical and nontechnical jobs more efficient, they still require human expertise. Some diagnostic procedures, for example, are hard to understand and require a lot of work. So, healthcare organizations should give their workers the training they need to learn more about machine learning and how it can be used. On the other hand, when people see new technologies and tools, it can make them think twice. Another big problem with using AI in healthcare is that patients do not always want to use it. For example, in the early stages of the pandemic, patients did not feel comfortable with online checkups. So, it needs to teach patients about the benefits of AI in healthcare to help them feel more comfortable with it. 4.2 Key challenges faced by 5G technology As we move toward a 5G world, we’ll have to deal with many problems. Compared to older wireless technologies, 5G needs a new standard to provide customers with high-speed, low latency, reliable, and safe services. Because of this, the design, development, and implementation of 5G networks are full of big problems. Here are some other issues that have been found in the literature: Health Risks: Concerns have been raised regarding possible adverse effects on human health caused by radiofrequency from 5G networks [68]. Rural residents are raising esthetic concerns and anxieties about the superfast network’s effects on their communities. However, a large number of institutions, such as the US “National Institutes of Health” and “Food and Drug Administration,” as well as the “World Health Organization” and the “Federal Communications Commission,” concluded that the concerns were unfounded. 5G’s Range and Coverage: The range of the 5G network is reduced when there are obstructions in the network. Therefore, to obtain a better 5G signal, 5G networks need a more significant number of smaller devices or antennae spaced closely together. It is tough to set up 5G connectivity in rural areas because of this, which are the places with the least developed healthcare systems. Deployment Costs: For 5G-enabled health solutions to work well, there needs to be a good setup for patients, doctors, and clinics. So, the costs of setting up 5G, buying related devices, developing the infrastructure, and paying more for maintenance are big problems in 5G-powered applications. As a result, it makes sense that the patient will have to pay more for their treatment. Training and Adapting New Technology: 5G-powered health solutions are gradually being implemented using intelligent devices and tools. However, healthcare personnel and patients require knowledge and skills to implement new technology and practices. As a result, sufficient training is required for patients and medical personnel to understand how to use these new platforms. Moreover, many developing countries cannot ultimately adopt an innovative healthcare solution based on the 5G standard, particularly in rural locations where building 5G networks is challenging. Security and Privacy Threats: Because 5G is gifted with the quickest data transmission and provides other healthcare services remotely, there is a continuous rise in the variety of potential security and privacy threats. As a result, it is necessary to pay additional attention to the concerns regarding the security of 5G networks, such as the protection of data, devices, and infrastructure; the filtering of data and the management of digital rights; the confidentiality of patient data; national security, network security, cybersecurity, and the protection of cybercrime [69, 70]. 4.3 Prospective of AI and 5G-enabled technologies AI and 5G-enabled technologies are concurrently expanding and enhancing efforts to improve global healthcare. Patients throughout the world benefit from more advanced healthcare systems that include intelligence and 5G standard in their practices. Thus, the fundamental aspects of healthcare could be entirely reimagined by the capabilities of 5G. 5G-powered technologies may prove helpful in many facets of today’s healthcare, such as telehealth, remote surgery, the transfer of substantial medical records, tracking patient activities and real-time monitoring, and providing patients with proper treatment and support. This technology can provide vital services on a massive scale that are precise, efficient, convenient, and cost-effective. The following are many significant prospects that explain why technology enabled by 5G ought to be a component of every healthcare system across the globe. Fastest and Precise Health Services: The fastest 5G network is equipped to provide the speedy and dependable delivery of significant amounts of medical data. The reduced latency feature of 5G technology can allow surgeons to do remote robotic surgery and give patients quicker and more dependable treatment that can be delivered remotely. In addition, AI can forecast potential health issues that a person may have in the future. Real-Time Advancements in Healthcare: The advent of 5G technology has the potential to provide individualized and preventative medical care. Telemedicine enabled by AI and 5G networks make it possible to receive immediate medical advice and treatment for medical emergencies. Therefore, with the 5G network, AI approaches can give surgeons real-time information about patients who are now undergoing treatment. In addition, a completely operational 5G network will improve medical processes and management and deliver a high-quality treatment experience to patients and doctors. Integration of Innovative Technologies: The use of AI models, with the “Internet of medical things,” “augmented reality,” and “virtual reality,” is now permitted in healthcare apps that run on 5G networks. They can improve real-time treatment and diagnosis operations, as well as provide healthcare facilities that are novel and transformative. Besides, robot-assisted or robotic surgery powered by 5G is becoming an emerging thing of the future in the medical field. Meet Service Quality and Cost-Effectiveness: 5G-powered technologies like “mHealth technology,” “telemedicine,” “Internet of medical things,” “wearable devices,” and “digital health platforms” can help patients in both cities and rural areas get medical help from afar. It will save money by preventing costly trips to the hospital without lowering the quality of care. It will also let doctors help with the diagnosis from a distance and meet the service standards needed for a complete medical exam. AI and machine learning can also help doctors diagnose by finding biomarkers [71]. If practitioners use AI to make an accurate diagnosis, it will be less cost-effective, and individuals will not have to undergo expensive lab tests anymore. Advancements in Intervention Management and Administrative Operations: 5G-enabled healthcare systems will bring new insights to the healthcare industry, allowing for uninterrupted data entry and querying. As a result, it is an annoying procedure while documenting medical data. Making available critical healthcare facilities and equipment, like operating rooms and electrocardiogram (ECG) monitors, improves intervention management. These invaluable resources aid in the administration of government operations and guarantee their security and efficacy. Improving Accessibility of Healthcare Worldwide: The World Bank and the WHO have released reports indicating that at least half of the world population does not have access to elementary healthcare services. In addition, people living in rural areas of countries that lack a developed healthcare infrastructure do not have access to healthcare facilities. Many organizations utilize cutting-edge technology powered by 5G to provide medical treatment to underserved communities. These solutions are both cost-free and applicable even in rural areas for serving medical treatment.
Advertisement 5. Policy recommendations to the states Universal accessibility of 5G-enabled technologies depends on the state’s positive measures and various factors (such as socioeconomic, geographic location, and digital ecosystem). Currently, a number of organizations are creating digital frameworks and other ideas to bridge the digital gap. For considering the post-pandemic, we are suggested a few recommendations to the states, listed below. Enhancing digital literacy programs: From a human rights point of view, the states need to speed up the process of making short-term and long-term public policies to improve digital literacy programs. During the pandemic, it will also support digital health-education-works measures that make it possible for everyone to be self-sufficient, independent, and responsible when using AI and 5G-enabled technologies. But digital technologies could limit the right to privacy and other fundamental freedoms. So, states must ensure that laws set up a guideline for the digital environment. Diminishing risks of increased digital devices and activities: During the pandemic, one’s spending time with digital devices (like smartphones, computers, television, or video game console) is increasing. In case of problematic usages of digital technologies, it needs practical recommendations to help reduce the risks of increased use of digital devices and online activities. Professionals and policymakers must convey these recommendations to their clients and the general population. Improving safety and security: The coronavirus pandemic has demonstrated the transformative power of the Internet, and digital technology has saved millions of lives by allowing them to work, study, and interact online in safety. Unfortunately, the epidemic has also exacerbated the digital divide and the negative aspects of technology, such as the rapid dissemination of misinformation, cybercrime, cyberbullying, and digital violence. It requires maintaining a high emphasis on security in government policies and regulations. Authorities and network operators should protect online data flows and maintain Internet users’ and organizations’ trust. Therefore, states and policymakers must guarantee a secure digital environment for the public. Ensuring permanent and sustainable accessibility: Since 2019, the pandemic has highlighted the significance of digital technologies during a crisis. The internet platform has enabled millions of individuals to work and study remotely. We may emerge from this crisis with the knowledge that appropriate digital policies can promote global economic recovery and ensure that no one is left behind. States and officials must ensure that access is permanent and enduring, eliminate obstacles to community-driven connectivity, and make it easy for all groups to access resources.
Advertisement 6. Conclusion In healthcare, using 5G networks to integrate other digital technologies (such as AI and machine learning, IoT, big data analytics, and cloud computing) is now a reality. The results of this study are summed up, and a deep connection with 5G-enabled technologies, especially artificial intelligence and machine learning. This study aims to find out the existing technological facets of AI strategies that can be used in healthcare to deal with the pandemic. This book chapter addresses several challenges faced by implementing AI and 5G-enabled technologies in medical services and highlights the prospects of emerging technologies. AI has played a significant role in combating the coronavirus pandemic and assisting researchers in developing systems to limit human interaction in afflicted areas, provide services, and manage health emergencies. In addition, they can help with the legal and ethical difficulties associated with producing medications in response to public health emergencies. Future pandemic concerns and public health issues will necessitate the most effective and convincing AI methods, AI-based searching strategies, probabilistic models, and supervised learning. Thus, professionals must thoroughly understand the system they are utilizing and be aware of its security measures. Even if artificial intelligence and 5G-enabled technologies have many benefits for healthcare, AI will not replace doctors or other professionals; instead, it will improve their performance. Additionally, 5G-enabled digital technologies have been utilized to control the COVID-19 outbreak and enhance public health plans in 2020. Some advanced technology leaders are studying 5G-related applications to tackle the health hazards associated with undesired diseases. The 5G network will give a comprehensive road to a smart society with numerous potentially beneficial applications in the field of healthcare when combined with the latest technology advancements. When deploying the 5G network in healthcare, some issues need to be considered since it is a new field of research. These issues include the development of infrastructure, the establishment of technical standards, the implementation of efficient regulations and policies, the safeguarding of personal information, and the accessibility of research data. More studies need to be done on how to expand a digital society based on 5G while addressing some challenges such as safety, security, privacy, availability, accessibility, and integrity, and improving resilience to future health crises, which lead to the following research directions in fighting against future pandemics: To develop the specialized AI and 5G-based architectures, along with the Internet of things and big data that will solve issues related to epidemics and build a comprehensive system to respond to crises similar to the COVID-19 pandemic.
To modernize the medical industry that will be aided by applying AI and 5G-enabled technologies to support decision-making, drug development and therapy, administrative automation, and storing patient information in private clouds.
To digitize the patients-doctors communication by implementing natural language processing, speech recognition, and text recognition that could be used to assist patients and physicians in communicating with one another and analyzing clinical records during remote treatment.
To build centralized and comprehensive databases that will be helpful for the investigation of technical issues and for constructing intelligent systems for predicting, diagnosing, forecasting, transmissibility, pathogenicity, and treating the disease.
Advertisement Acknowledgments The authors wish to acknowledge the research and extension cell of Jatiya Kabi Kazi Nazrul Islam University, Bangladesh, for their support and cooperation in conducting the research.
| 2023-02-15T00:00:00 |
2023/02/15
|
https://www.intechopen.com/chapters/85738
|
[
{
"date": "2023/02/01",
"position": 75,
"query": "universal basic income AI"
}
] |
Tech disruptions can inform the economic impact of AI | EY - Global
|
Tech disruptions can inform the economic impact of AI
|
https://www.ey.com
|
[
"Lydia Boussour",
"Authorsalutation",
"Authorfirstname Lydia Authorlastname Boussour Authorjobtitle Ey-Parthenon Strategy",
"Transactions Senior Economist Authorurl Https",
"Www.Ey.Com En_Gl People Lydia-Boussour",
"Content Dam Content-Fragments Ey-Unified-Site Ey-Com People Global En L Lydia-Boussour",
"Ey-Parthenon Strategy",
"Transactions Senior Economist",
"Gregory Daco",
"Dan Diasio"
] |
Nuanced job reshuffling: AI technologies are poised to cause significant labor market disruptions by automating some tasks and displacing workers, but it will ...
|
In recent years, no technology has created more excitement than generative AI (GenAI), but that excitement has been tempered by uncertainty and concerns among executives, policymakers and other stakeholders.
GenAI systems are so complex and developing so rapidly that it is difficult to predict how they will impact organizations, economies and societies. In this first article of the series, we use history as a guide to shed light on the potential future impact of GenAI and the economic opportunities and challenges that it may bring.
Technology has unrelentingly and fundamentally transformed economies throughout history by changing the nature and organization of work, increasing business efficiency and productivity, and bringing along new forms of work.
New technological innovations have also caused significant disruptions by displacing workers and have often been accompanied by adoption hesitancy, slow economic progress and rising inequality in their early adoption phase.
Three key lessons from past episodes of rapid technological change can help inform how AI may affect the economy:
Significant productivity boost: GenAI will likely lead to a significant acceleration in productivity growth and raise living standards like prior general-purpose technologies. By examining the 1990s IT-driven acceleration in productivity growth, we estimate that GenAI has the potential to lift productivity growth by 50% to 100% in the coming decade. However, it will likely fall short of the doubling or tripling of productivity growth resulting from the Industrial Revolution or adoption of electricity. Potentially delayed impact: the productivity boost from GenAI will likely occur with a lag, but the faster speed of technological diffusion and adoption could mean that the boost to economic activity is felt in the next three to five years versus multiple decades for the steam engine and 10 years for the computer age. Nuanced job reshuffling: AI technologies are poised to cause significant labor market disruptions by automating some tasks and displacing workers, but it will also create new types of jobs and functions within roles across many sectors of the economy that will help offset AI-related job losses.
These observations suggest that it will likely take time for the economy and society to reap the benefits of GenAI, but historical evidence indicates that an AI-powered productivity acceleration probably lies ahead. The ability of workers to adapt by learning new skills and relocating across sectors and occupations will be a key determinant of how successful the transition to a GenAI future will prove to be.
| 2024-03-02T00:00:00 |
2024/03/02
|
https://www.ey.com/en_gl/insights/ai/tech-disruptions-can-inform-the-economic-impact-of-ai
|
[
{
"date": "2023/02/01",
"position": 4,
"query": "AI economic disruption"
},
{
"date": "2023/04/01",
"position": 5,
"query": "AI economic disruption"
},
{
"date": "2023/07/01",
"position": 5,
"query": "AI economic disruption"
},
{
"date": "2023/09/01",
"position": 4,
"query": "AI economic disruption"
},
{
"date": "2023/10/01",
"position": 3,
"query": "AI economic disruption"
},
{
"date": "2023/11/01",
"position": 5,
"query": "AI economic disruption"
},
{
"date": "2024/01/01",
"position": 4,
"query": "AI economic disruption"
},
{
"date": "2024/11/01",
"position": 4,
"query": "AI economic disruption"
},
{
"date": "2024/12/01",
"position": 5,
"query": "AI economic disruption"
},
{
"date": "2025/01/01",
"position": 5,
"query": "AI economic disruption"
},
{
"date": "2025/03/01",
"position": 4,
"query": "AI economic disruption"
},
{
"date": "2025/04/01",
"position": 5,
"query": "AI economic disruption"
},
{
"date": "2025/06/01",
"position": 5,
"query": "AI economic disruption"
}
] |
Panel - Part 2: AI and the Economy: Transformation and Disruption
|
Panel - Part 2: AI and the Economy: Transformation and Disruption
|
https://milkeninstitute.org
|
[] |
Artificial intelligence is rapidly reshaping economies, industries, and society, challenging us to rethink how we work, live, and innovate.
|
Artificial intelligence is rapidly reshaping economies, industries, and society, challenging us to rethink how we work, live, and innovate. Optimists envision AI as a transformative force for economic growth and human progress—potentially contributing up to $19.9 trillion to the global economy by 2030. At the same time, skeptics warn of AI’s risks, including workforce disruptions, ethical dilemmas, and unpredictable social impact. Leaders from technology, academia, and policy will explore how we must responsibly develop AI and guide its economic impact.
| 2023-02-01T00:00:00 |
https://milkeninstitute.org/content-hub/event-panels/part-2-ai-and-economy-transformation-and-disruption
|
[
{
"date": "2023/02/01",
"position": 14,
"query": "AI economic disruption"
},
{
"date": "2023/04/01",
"position": 12,
"query": "AI economic disruption"
},
{
"date": "2023/07/01",
"position": 12,
"query": "AI economic disruption"
},
{
"date": "2023/09/01",
"position": 12,
"query": "AI economic disruption"
},
{
"date": "2023/10/01",
"position": 12,
"query": "AI economic disruption"
},
{
"date": "2023/11/01",
"position": 12,
"query": "AI economic disruption"
},
{
"date": "2024/01/01",
"position": 12,
"query": "AI economic disruption"
},
{
"date": "2024/12/01",
"position": 11,
"query": "AI economic disruption"
},
{
"date": "2025/01/01",
"position": 11,
"query": "AI economic disruption"
},
{
"date": "2025/03/01",
"position": 11,
"query": "AI economic disruption"
},
{
"date": "2025/06/01",
"position": 14,
"query": "AI economic disruption"
}
] |
|
Economic disruption and runaway AI: what can governments do?
|
Economic disruption and runaway AI: what can governments do?
|
https://oxfordinsights.com
|
[
"André Petheram"
] |
How to manage the transition in the economy? AI is forecast to cause significant economic disruption, especially through changes to the labour market.
|
Short-term action
Governments around the world are grappling with the same questions on AI: What is it? What can it do? What does it mean for our country? There are three pressing questions that governments need to answer in the short-term:
How to manage the transition in the economy? AI is forecast to cause significant economic disruption, especially through changes to the labour market. Governments should have a comprehensive strategy in place to manage these economic changes, including potential job losses.
AI is forecast to cause significant economic disruption, especially through changes to the labour market. Governments should have a comprehensive strategy in place to manage these economic changes, including potential job losses. How to ensure the use of AI reflects your country’s values? AI development should be unique to each country’s circumstances and values. The danger of AI development is that it can involve relying on Silicon Valley, and in particular a small cluster of companies in Silicon Valley, who control the majority of the world’s data and most of its AI talent. How can Governments ensure that decision-making in their country continues to reflect the values of their citizens, and not just the preferences, approaches and commercial strategies of Silicon Valley?
AI development should be unique to each country’s circumstances and values. The danger of AI development is that it can involve relying on Silicon Valley, and in particular a small cluster of companies in Silicon Valley, who control the majority of the world’s data and most of its AI talent. How can Governments ensure that decision-making in their country continues to reflect the values of their citizens, and not just the preferences, approaches and commercial strategies of Silicon Valley? How to use the power of AI to deliver better services to citizens? Governments are now waking up to the transformative potential of AI in the field of public services. How can AI be used most effectively – and most ethically – to help deliver better public services, to more people, more cheaply?
Governments’ responses to these three questions will help determine whether they become world leaders in the AI revolution. Comprehensive, wide-reaching AI strategies enable countries to set out their policy aims for addressing these key issues. As we discussed in a previous blog post on the current state of global AI policy, some countries have made much more progress developing AI strategies than others.
Regulating artificial general intelligence
Alongside the excitement surrounding AI’s potentially enormous economic and social benefits, there was plenty of hand wringing and existential angst. These concerns focused on the rise of artificial general intelligence (AGI). AGI is considered by many researchers to be the ‘holy grail’ of AI; a general-purpose system that can successfully perform any task a human can.
In theory, the value of artificial general intelligence is huge. There is, however, a risk that it could turn into a less benign super intelligence, and present an existential risk to the human race. The type of risk is unclear: would we end up working for it? Would it commandeer all our resources? In his talk, Nick Bostrom gave a useful example to demonstrate a potential unintended consequence of developing an AGI. An artificial general intelligence sees you are hungry – but you don’t have any food in the house. So, it kills your cat – to feed you, because you are hungry.
| 2018-02-15T00:00:00 |
2018/02/15
|
https://oxfordinsights.com/insights/economic-disruption-and-runaway-ai-what-can-governments-do/
|
[
{
"date": "2023/02/01",
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},
{
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"query": "AI economic disruption"
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{
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"query": "AI economic disruption"
},
{
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},
{
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},
{
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"query": "AI economic disruption"
},
{
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},
{
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"position": 39,
"query": "AI economic disruption"
},
{
"date": "2025/03/01",
"position": 33,
"query": "AI economic disruption"
}
] |
Artificial Intelligence and International Economic Law: Disruption ...
|
Amazon.com
|
https://www.amazon.com
|
[] |
Artificial intelligence (AI) technologies are transforming economies, societies, and geopolitics. Enabled by the exponential increase of data that is collected, ...
|
Click the button below to continue shopping
| 2023-02-01T00:00:00 |
https://www.amazon.com/Artificial-Intelligence-International-Economic-Reconfiguration/dp/1108844936
|
[
{
"date": "2023/02/01",
"position": 38,
"query": "AI economic disruption"
},
{
"date": "2023/04/01",
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"query": "AI economic disruption"
},
{
"date": "2023/07/01",
"position": 41,
"query": "AI economic disruption"
},
{
"date": "2023/09/01",
"position": 36,
"query": "AI economic disruption"
},
{
"date": "2023/11/01",
"position": 37,
"query": "AI economic disruption"
},
{
"date": "2024/01/01",
"position": 36,
"query": "AI economic disruption"
},
{
"date": "2024/12/01",
"position": 42,
"query": "AI economic disruption"
},
{
"date": "2025/01/01",
"position": 38,
"query": "AI economic disruption"
},
{
"date": "2025/03/01",
"position": 35,
"query": "AI economic disruption"
}
] |
|
Artificial Intelligence and International Economic Law
|
Artificial Intelligence and International Economic Law
|
https://www.cambridge.org
|
[
"Shin-Yi Peng",
"Ching-Fu Lin",
"Thomas Streinz",
"Gregory Shaffer",
"Rolf H. Weber",
"Dan Ciuriak",
"Vlada Rodionova",
"Aik Hoe Lim",
"Bryan Mercurio",
"Ronald Yu"
] |
Disruption, Regulation, and Reconfiguration. Search within full text.
|
‘This book is a must-read for the AI policy community, which has been slow to reckon with the global political economy of AI. The focus on international economic law challenges the dominant conception of what counts as ‘AI regulation’ to expand beyond concerns of privacy and discrimination, even as it meditates on the potential limits of these regulatory approaches. As the economic drivers of AI and data regulation become increasingly explicit, this collection could not be more timely.’
Amba Kak - Director of Global Policy & Programs, New York University AI Now Institute
‘As the transformative force of artificial intelligence starts to define the future of our economies and societies, it gives rise to numerous complex legal questions at the international level. This book consolidates contributions that provide an eminently readable treatment of complex issues with a vision into the future of international trade. It offers an excellent point of reference for policymakers, practitioners, and scholars on such a vital subject for our future.’
Hamid Mamdouh - Senior Counsel at King & Spalding LLP, former Director of WTO Trade in Services and Investment Division
‘The set of technologies included in AI present existential and more ordinary threats, in addition to utopian opportunities. These technologies, and their threats, are global, and will therefore require regulatory coordination among states through international law, and will also challenge settled rules of international economic law. This volume, with exciting and trenchant chapters written by a dream team of authors, illuminates our path to the future.’
Joel P. Trachtman - Professor of International Law, The Fletcher School of Law and Diplomacy, Tufts University
| 2021-10-14T00:00:00 |
2021/10/14
|
https://www.cambridge.org/core/books/artificial-intelligence-and-international-economic-law/856ECE3C70C6713D7B86A37CE8BC5B59
|
[
{
"date": "2023/02/01",
"position": 44,
"query": "AI economic disruption"
},
{
"date": "2025/04/01",
"position": 38,
"query": "AI economic disruption"
},
{
"date": "2025/06/01",
"position": 40,
"query": "AI economic disruption"
}
] |
Tech disruptions can inform the economic impact of AI | EY Malaysia
|
Tech disruptions can inform the economic impact of AI
|
https://www.ey.com
|
[
"Lydia Boussour",
"Authorsalutation",
"Authorfirstname Lydia Authorlastname Boussour Authorjobtitle Ey-Parthenon Strategy",
"Transactions Senior Economist Authorurl Https",
"Www.Ey.Com En_My People Lydia-Boussour",
"Content Dam Content-Fragments Ey-Unified-Site Ey-Com People Global En L Lydia-Boussour",
"Ey-Parthenon Strategy",
"Transactions Senior Economist",
"Gregory Daco",
"Dan Diasio"
] |
To what extent can GenAI power productivity and economic growth? · Why the productivity boost from GenAI could lag · Are fears about AI-driven mass unemployment ...
|
In recent years, no technology has created more excitement than generative AI (GenAI), but that excitement has been tempered by uncertainty and concerns among executives, policymakers and other stakeholders.
GenAI systems are so complex and developing so rapidly that it is difficult to predict how they will impact organizations, economies and societies. In this first article of the series, we use history as a guide to shed light on the potential future impact of GenAI and the economic opportunities and challenges that it may bring.
Technology has unrelentingly and fundamentally transformed economies throughout history by changing the nature and organization of work, increasing business efficiency and productivity, and bringing along new forms of work.
New technological innovations have also caused significant disruptions by displacing workers and have often been accompanied by adoption hesitancy, slow economic progress and rising inequality in their early adoption phase.
Three key lessons from past episodes of rapid technological change can help inform how AI may affect the economy:
Significant productivity boost: GenAI will likely lead to a significant acceleration in productivity growth and raise living standards like prior general-purpose technologies. By examining the 1990s IT-driven acceleration in productivity growth, we estimate that GenAI has the potential to lift productivity growth by 50% to 100% in the coming decade. However, it will likely fall short of the doubling or tripling of productivity growth resulting from the Industrial Revolution or adoption of electricity. Potentially delayed impact: the productivity boost from GenAI will likely occur with a lag, but the faster speed of technological diffusion and adoption could mean that the boost to economic activity is felt in the next three to five years versus multiple decades for the steam engine and 10 years for the computer age. Nuanced job reshuffling: AI technologies are poised to cause significant labor market disruptions by automating some tasks and displacing workers, but it will also create new types of jobs and functions within roles across many sectors of the economy that will help offset AI-related job losses.
These observations suggest that it will likely take time for the economy and society to reap the benefits of GenAI, but historical evidence indicates that an AI-powered productivity acceleration probably lies ahead. The ability of workers to adapt by learning new skills and relocating across sectors and occupations will be a key determinant of how successful the transition to a GenAI future will prove to be.
| 2024-03-02T00:00:00 |
2024/03/02
|
https://www.ey.com/en_my/insights/ai/tech-disruptions-can-inform-the-economic-impact-of-ai
|
[
{
"date": "2023/02/01",
"position": 49,
"query": "AI economic disruption"
},
{
"date": "2023/04/01",
"position": 58,
"query": "AI economic disruption"
},
{
"date": "2023/07/01",
"position": 61,
"query": "AI economic disruption"
},
{
"date": "2023/09/01",
"position": 53,
"query": "AI economic disruption"
},
{
"date": "2023/10/01",
"position": 52,
"query": "AI economic disruption"
},
{
"date": "2023/11/01",
"position": 65,
"query": "AI economic disruption"
},
{
"date": "2024/01/01",
"position": 53,
"query": "AI economic disruption"
},
{
"date": "2024/12/01",
"position": 57,
"query": "AI economic disruption"
},
{
"date": "2025/01/01",
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"query": "AI economic disruption"
},
{
"date": "2025/04/01",
"position": 53,
"query": "AI economic disruption"
},
{
"date": "2025/06/01",
"position": 53,
"query": "AI economic disruption"
}
] |
Generative AI's Disruption: Is this Time Really Different? - AEI
|
Generative AI’s Disruption: Is this Time Really Different?
|
https://www.aei.org
|
[
"Michael R. Strain"
] |
Will generative AI both disrupt and benefit economies? Michael Strain joins EconoFact Chats to make a case for AI optimism, highlighting how America's ...
|
Economies have been subjected to profound disruptions from technological change in the past — from the adoption of weaving machines in the 19th century, to the mechanization of agriculture, and the use of robotics in manufacturing. Yet, these disruptions very often led to a broad increase in societal wealth, and the creation of entirely new occupations. Will generative AI both disrupt and benefit economies? Michael Strain joins EconoFact Chats to make a case for AI optimism, highlighting how America’s experiences with technology-driven disruption have proved a net benefit historically.
| 2023-02-01T00:00:00 |
https://www.aei.org/multimedia/generative-ais-disruption-is-this-time-really-different/
|
[
{
"date": "2023/02/01",
"position": 53,
"query": "AI economic disruption"
},
{
"date": "2023/04/01",
"position": 61,
"query": "AI economic disruption"
},
{
"date": "2023/07/01",
"position": 59,
"query": "AI economic disruption"
},
{
"date": "2023/09/01",
"position": 52,
"query": "AI economic disruption"
},
{
"date": "2023/10/01",
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"query": "AI economic disruption"
},
{
"date": "2023/11/01",
"position": 53,
"query": "AI economic disruption"
},
{
"date": "2024/01/01",
"position": 52,
"query": "AI economic disruption"
},
{
"date": "2024/12/01",
"position": 59,
"query": "AI economic disruption"
},
{
"date": "2025/01/01",
"position": 58,
"query": "AI economic disruption"
},
{
"date": "2025/03/01",
"position": 49,
"query": "AI economic disruption"
}
] |
|
Power & Prediction: The Economics of AI Disruption - Umbrex
|
Power and Prediction: The Disruptive Economics of Artificial Intelligence
|
https://umbrex.com
|
[] |
They argue that AI's ability to make cheaper, better, and faster predictions will revolutionize strategic business decisions, leading to significant economic ...
|
“Power and Prediction: The Disruptive Economics of Artificial Intelligence” is a seminal work by Ajay Agrawal, Joshua Gans, and Avi Goldfarb that delves into the transformative impact of artificial intelligence (AI) on decision-making and economic systems. Following their earlier book “Prediction Machines,” this new work provides a comprehensive analysis of AI’s role as a prediction technology and its potential to disrupt industries and economies.
AI as a Prediction Technology
The authors position AI as a powerful tool for prediction, akin to historical innovations like electricity and the internet. They argue that AI’s ability to make cheaper, better, and faster predictions will revolutionize strategic business decisions, leading to significant economic disruptions across various sectors, including banking, finance, pharmaceuticals, automotive, medical technology, and retail.
Economic Disruption and Transformation
Agrawal, Gans, and Goldfarb emphasize that AI is currently in a phase they term “In the Between Times,” where its potential is recognized but not yet fully realized. This period is marked by both opportunities and threats, as AI begins to upend traditional business models and power structures. The book highlights the importance of understanding these dynamics to leverage AI effectively and avoid being left behind.
Point Solutions vs. System Solutions
The authors differentiate between “point solutions” and “system solutions” in AI adoption. Point solutions involve specific AI applications that enhance existing processes without major system changes, offering immediate benefits like cost savings and efficiency improvements. In contrast, system solutions require a more profound transformation of organizational processes to fully leverage AI’s capabilities, leading to more significant and sustainable innovations.
Practical Advice for Businesses
“Power and Prediction” offers practical advice for businesses navigating AI’s economic impacts. The authors suggest that companies should focus on creating value rather than just reducing costs, emphasizing the need for continuous learning from data to enhance predictions and gain a competitive advantage. They also caution against resistance to change and highlight the importance of balancing reliability with flexibility in AI-driven processes.
Historical Context and Future Implications
The book draws historical parallels with other transformative technologies, illustrating how AI’s impact will be similarly profound. Agrawal, Gans, and Goldfarb discuss how AI can address pressing issues like public health, inequality, and climate change, envisioning a future where new technologies benefit everyone, not just a select few.
Reception and Impact
“Power and Prediction” has received positive reviews for its insightful analysis and practical guidance. Readers appreciate its accessible explanations, real-world examples, and thought-provoking ideas about AI’s future role in decision-making and system-level changes. While some critics note that the book may lack technical depth and could be more updated regarding recent AI advancements like generative AI, it remains a valuable resource for understanding AI’s economic potential.
| 2023-02-01T00:00:00 |
https://umbrex.com/resources/recommended-reading/best-books-on-artificial-intelligence/power-and-prediction-the-disruptive-economics-of-artificial-intelligence/
|
[
{
"date": "2023/02/01",
"position": 54,
"query": "AI economic disruption"
},
{
"date": "2023/04/01",
"position": 65,
"query": "AI economic disruption"
},
{
"date": "2023/07/01",
"position": 64,
"query": "AI economic disruption"
},
{
"date": "2023/09/01",
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"query": "AI economic disruption"
},
{
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"query": "AI economic disruption"
},
{
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"query": "AI economic disruption"
},
{
"date": "2024/01/01",
"position": 54,
"query": "AI economic disruption"
},
{
"date": "2024/12/01",
"position": 64,
"query": "AI economic disruption"
},
{
"date": "2025/01/01",
"position": 63,
"query": "AI economic disruption"
},
{
"date": "2025/03/01",
"position": 43,
"query": "AI economic disruption"
}
] |
|
AI x Economy: The Data Behind the Disruption - Luma
|
AI x Economy: The Data Behind the Disruption · Luma
|
https://lu.ma
|
[] |
How AI is being adopted across industries. What tasks are being automated (and which aren't). Where new economic opportunities are emerging. ...
|
How is AI actually changing the economy? Let’s find out.
Join us for a deep-dive discussion on the Anthropic Economic Index — a groundbreaking, data-rich look at how AI is transforming real-world work and the global economy.
We'll explore:
How AI is being adopted across industries
What tasks are being automated (and which aren’t)
Where new economic opportunities are emerging
📖 Pre-read material (very readable + startup-idea friendly!):
https://www.anthropic.com/news/anthropic-economic-index-insights-from-claude-sonnet-3-7
https://www.anthropic.com/economic-index-
https://www.anthropic.com/research/clio
Whether you're into research, policy, startups, or just love a good economic trend, this is for you. Bring your questions, ideas, and let’s have a sharp, data-backed conversation about the future of work.
| 2023-02-01T00:00:00 |
https://lu.ma/vwxcp8tm
|
[
{
"date": "2023/02/01",
"position": 58,
"query": "AI economic disruption"
},
{
"date": "2023/04/01",
"position": 71,
"query": "AI economic disruption"
},
{
"date": "2023/07/01",
"position": 70,
"query": "AI economic disruption"
},
{
"date": "2023/10/01",
"position": 58,
"query": "AI economic disruption"
},
{
"date": "2024/12/01",
"position": 71,
"query": "AI economic disruption"
},
{
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"position": 69,
"query": "AI economic disruption"
},
{
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"position": 66,
"query": "AI economic disruption"
}
] |
|
Artificial Intelligence and International Economic Law: Disruption ...
|
Artificial Intelligence and International Economic Law: Disruption, Regulation, and Reconfiguration
|
https://www.iilj.org
|
[
"Shin-Yi Peng",
"Shin-Yi Peng Is Distinguished Professor Of Law At National Tsing Hua University",
"Nthu",
"Ching-Fu Lin",
"Assistant Professor Of Law",
"National Tsing Hua University",
"Thomas Streinz",
"Adjunct Professor Of Law At Nyu School Of Law",
"Iilj Fellow",
"Executive Director Of Guarini Global Law"
] |
This volume examines the dynamic interplay between AI and IEL by addressing an array of critical new questions.
|
Artificial intelligence (AI) technologies are transforming economies, societies, and geopolitics. Enabled by the exponential increase of data that is collected, transmitted, and processed transnationally, these changes have important implications for international economic law (IEL). This volume examines the dynamic interplay between AI and IEL by addressing an array of critical new questions, including: How to conceptualize, categorize, and analyze AI for purposes of IEL? How is AI affecting established concepts and rubrics of IEL? Is there a need to reconfigure IEL, and if so, how? Contributors also respond to other cross-cutting issues, including digital inequality, data protection, algorithms and ethics, the regulation of AI-use cases (autonomous vehicles), and systemic shifts in e-commerce (digital trade) and industrial production (fourth industrial revolution). The book is available open access on Cambridge Core.
Read PDF
| 2023-02-01T00:00:00 |
https://www.iilj.org/publications/artificial-intelligence-and-international-economic-law-disruption-regulation-and-reconfiguration/
|
[
{
"date": "2023/02/01",
"position": 62,
"query": "AI economic disruption"
},
{
"date": "2023/04/01",
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"query": "AI economic disruption"
},
{
"date": "2023/09/01",
"position": 64,
"query": "AI economic disruption"
},
{
"date": "2023/10/01",
"position": 62,
"query": "AI economic disruption"
},
{
"date": "2024/01/01",
"position": 64,
"query": "AI economic disruption"
},
{
"date": "2024/12/01",
"position": 76,
"query": "AI economic disruption"
},
{
"date": "2025/01/01",
"position": 75,
"query": "AI economic disruption"
},
{
"date": "2025/04/01",
"position": 57,
"query": "AI economic disruption"
},
{
"date": "2025/06/01",
"position": 56,
"query": "AI economic disruption"
}
] |
|
The Economics of Transformative AI | NBER
|
The Economics of Transformative AI
|
https://www.nber.org
|
[
"Karen Glenn",
"Stephen Goss"
] |
Transformative AI may exacerbate these challenges by concentrating economic power in a few increasingly advanced economies. The capital- and knowledge-intensive ...
|
The rapid advancement of artificial intelligence (AI) may usher in the most significant economic transformation since the Industrial Revolution. For nearly a decade, as I witnessed the continuous progress in deep learning, I have been studying the economics of transformative AI — how our economy may be transformed as AI systems advance toward mastering all forms of cognitive work that can be performed by humans, including new tasks that don’t even exist yet. The prospect of understanding the strange new world we will inhabit when transformative AI is developed has felt both intellectually urgent and personally meaningful to me as a father of two young children.
Today, AI systems are approaching and exceeding human-level performance in many domains, and it looks increasingly like our world will be transformed before my children have grown up. In this research summary, I outline my analysis of how transformative AI could reshape our economy, discuss frameworks for preparing for this transition, and explore how AI tools are already transforming economic research itself.
The pace of advancement in AI has been nothing short of extraordinary. Over the past 15 years, the computational resources employed to train cutting-edge AI systems have grown by a factor of four every year, as illustrated in Figure 1. The costs of such training are currently in the realm of hundreds of millions of dollars, as described in a market structure analysis with Jai Vipra.1 This exponential growth in compute has been accompanied by significant improvements in algorithmic efficiency, which Epoch AI estimates to be occurring at a rate of two and a half times per year. Taken together, these advances imply increases in the effective compute of frontier AI systems of 10 times per year. So-called scaling laws describe how the rapid growth in inputs translates into AI’s performance gains, providing AI labs and their investors with some predictability for the returns on their investments and facilitating their bets on the next billion-dollar training runs. While returns to additional computing power may eventually diminish in some domains due to data scarcity, there are compelling reasons to expect that scaling will continue to yield significant capability gains in the coming years.
Figure 1
A growing number of leading AI researchers and industry figures now predict transformative AI could arrive within years, not decades. Geoffrey Hinton, the 2024 Nobel laureate in physics, considers it a possibility before the decade’s end. Sam Altman of OpenAI anticipates superintelligence “within a few thousand days,” while Anthropic’s CEO Dario Amodei expects transformative AI by 2027, if not sooner. While these experts — and critics who view recent AI advances as overhyped — acknowledge the profound uncertainty in such predictions, the potential consequences of transformative AI are so significant that I consider it crucial for economists to analyze them.
Given the rapid pace of advancement of AI, my research agenda focuses on two critical areas: (1) analyzing transformative AI’s economic implications, and (2) leveraging AI to enhance economic research and increase our research productivity.
In the context of (1), I have recently laid out a research agenda for the economics of transformative AI together with Ajay Agrawal and Erik Brynjolfsson.2 The agenda poses what we view as key economic questions to help us better prepare for the age of transformative AI. They cover economic growth, innovation, income distribution, decision-making power, geopolitics, information flows, AI safety, and human wellbeing under AI.
A New Economic Paradigm
To analyze the economic implications of transformative AI, it is instructive to examine how past technological revolutions have reshaped the structure of our economy. The transition from the Malthusian to the Industrial Age is particularly relevant for the changes and challenges that may lie ahead, as I explain in a recent paper.3
In the Malthusian Age, land was the critical bottleneck factor, while human labor could be considered reproducible on the relevant time scales. As technology was largely stagnant, the available supply of land limited the size of the human population it could support. In this era, land was the most valuable economic resource. Human labor, in contrast, was not particularly valuable.
The Industrial Age — the world we still inhabit — transformed this reality. Rapid technological progress became a key driver of growth, accompanied by reproducible capital in the form of machines and factories, as captured by the standard neoclassical production function. With technology advancing and capital accumulating, labor suddenly became the bottleneck. This scarcity of labor led to large increases in wages, giving rise to today’s living standards, which have grown about twentyfold in advanced economies since the Industrial Revolution.
Transformative AI could usher in another paradigm shift by making human-level intelligence reproducible. AI systems and robots could eventually substitute for both cognitive and physical human labor. In this new age, both traditional capital and intelligent machines would be reproducible resources, with the distinction between them increasingly blurred. These factors could be accumulated without bounds and generate ever more economic capacity.4
The implications are profound. Growth would accelerate as capital accumulates and artificial brainpower drives innovation. However, labor would lose its special status, and therefore the main bottleneck of the Industrial Age would be surmounted.
Understanding these changes requires careful analysis of how relative prices will evolve. While many noneconomists predict that transformative AI will dramatically reduce all prices, relative prices are what matter economically. For instance, the relative prices of computers, robots, and human labor may decline while those of energy, food, and housing may rise. Systematic analysis must distinguish between reproducible factors — like compute and robots — and irreproducible ones that may become relatively more valuable, such as land, raw materials, and perhaps energy. This may fundamentally challenge our present system of income distribution.
Given the profound uncertainty about the trajectory and timeline of AI progression, I have developed a systematic scenario planning approach. In recent work, Donghyun Suh and I compared a “business as usual” scenario where AI automates tasks gradually as in past decades with two scenarios where transformative AI emerges in either 5 or 20 years.5 For each scenario, we model how automation and capital accumulation interact to determine economic outcomes. The various scenarios produce starkly different trajectories — from steady growth with rising wages in the business-as-usual case to more than tenfold output expansion but collapsing wages in the transformative AI scenarios, as illustrated in Figure 2.
Figure 2
This scenario-based framework provides a structured way for policymakers and business leaders to stress test existing institutions and develop contingency plans for possible futures. Rather than betting everything on a single prediction, it allows us to identify robust strategies that work reasonably well across scenarios while maintaining the flexibility to adapt as the future unfolds. The stark differences between scenarios also highlight the importance of considering the labor market challenges that may emerge.6
Labor Market Challenges
Labor serves three vital functions in our modern economy: it acts as the key bottleneck factor in production, provides the main source of income for most people, and constitutes the primary use of time for working-age individuals. If transformative AI and advanced robots can substitute for human cognitive and physical capabilities, it threatens to fundamentally disrupt all three functions.7
As a first approximation, if AI becomes a substitute for human labor, it will eliminate labor’s role as a bottleneck factor in production. Just as the Industrial Revolution ended the Malthusian era, transformative AI could end the Industrial Era by making labor reproducible. This would likely lead to significant devaluation of human labor as machines become progressively cheaper and more capable. As I observe in a paper with Joseph Stiglitz, it also calls for discussion of potential systems of income distribution that are independent of labor market earnings.8 Moreover, these economic challenges could pose significant risks to democratic stability, as rising inequality may trigger a vicious cycle of eroding democracy and further increasing inequality.9
However, as we transition to such a state, there are also opportunities to actively steer the direction of technological progress. In other joint work with Stiglitz, we develop a theoretical framework for identifying and promoting innovations that increase labor demand and create better-paying jobs as a second-best measure to obtain a desirable distribution of income.10 Examples of such innovations include intelligent assistants that enhance worker productivity rather than replace workers entirely. The framework analyzes how different innovations affect labor demand and factor shares through their technological complementarity to workers and their impacts on relative incomes. Building on this, my work with Katya Klinova proposes practical guidelines for AI developers to evaluate the labor market impacts of their innovations, providing them with a framework to advance shared prosperity rather than exacerbate inequality.11
The decline of work as society’s primary use of time raises important questions about whether humans need work beyond its economic value. Economic analysis suggests that if the meaning people derive from work is purely a private good, like most other work amenities, there is no inherent reason for policy intervention — those who gain sufficient personal value can continue working even at low or zero wages. However, if work generates positive externalities, for example, through social connections and political stability, or if individuals systematically undervalue work’s benefits due to internalities, there may be a role for policy to encourage work participation. That said, as autonomous machines become more capable, it may be more efficient to develop alternative institutions that provide these social benefits without requiring humans to work.
The AI transformation also challenges the traditional value proposition of education and human capital development, which has historically served as society’s primary mechanism for economic advancement. A fundamental reevaluation of education’s role and purpose will be necessary in a world where cognitive skills are increasingly automatable.12
The challenges of managing transformative AI’s distributive effects become even more complex in our globalized economy. As I detailed in a third paper with Stiglitz, while domestic policy measures can potentially compensate losers within countries, there are no effective mechanisms for cross-border compensation if technological progress deteriorates the terms of trade of entire nations.13 Transformative AI may exacerbate these challenges by concentrating economic power in a few increasingly advanced economies. The capital- and knowledge-intensive nature of AI development may make it harder for developing countries to keep up. Without international action to ensure an equitable distribution of AI’s benefits, there is a risk of reversing decades of progress in global development.
Advancing Economic Research with AI
Having outlined these critical challenges facing our economy, let me return to the second prong of my research agenda: exploring how we can leverage AI to enhance economic research. The ongoing advances in generative AI are creating opportunities to revolutionize how we conduct economic research, making economists more productive and better equipped to address the complex challenges discussed above. My work explores both the practical applications of these technologies and their broader implications for the economics profession.
In a recent paper and on the dedicated website genaiforecon.org, I demonstrate with tangible examples how large language models (LLMs) can serve as powerful research assistants across the entire research workflow.14 LLMs can help with ideation and brainstorming, providing fresh perspectives and counterarguments to evaluate and strengthen analyses. They excel at writing tasks, from drafting and editing to generating engaging summaries for different audiences. In background research, they can process and synthesize vast amounts of information, making literature reviews more comprehensive and efficient. For data analysis, they can extract information from text, classify content, and even simulate human subjects. They are particularly capable at coding tasks and can increasingly assist with mathematical derivations.
In a November 2024 paper, I describe the latest advances in generative AI that are useful for researchers.15 These include improved math and reasoning capabilities, real-time search, and more sophisticated collaboration tools that are changing how we can interact with these systems. New LLM-powered workspaces allow for dynamic, iterative collaboration between researchers and AI assistants. Moreover, the introduction of real-time voice interfaces and autonomous computer-use capabilities is making these interactions more natural and powerful.
These developments suggest a future where the role of economists will evolve significantly. In the short to medium term, we may focus more on our comparative advantages, such as posing questions, suggesting research directions, discriminating between useful and irrelevant content, and coordinating complex research projects. The basic and mundane aspects of research may be increasingly automated, allowing economists to focus on higher-level thinking and creative problem-solving.
A research agenda emerges from these observations. We need to develop frameworks for evaluating AI-augmented research output as the bottleneck shifts from generation to assessment. We should investigate how to best integrate AI tools into our research workflows while maintaining rigorous standards and avoiding potential pitfalls like the homogenization of research approaches. We must also consider how to optimally time research projects given the rapid pace of AI advancement; some inquiries might be optimally postponed until more powerful tools are available.
Looking further ahead, when transformative AI is reached, it will surpass human capabilities in generating and articulating economic insights. As with all human labor, this possibility also raises profound questions about the future of the economics profession.
It may well be that we have only a handful of research projects left before our ability to write insightful economics papers is outpaced by machines that surpass our intellectual capabilities. This increases the stakes and makes it important to carefully choose the most impactful work to pursue. I believe that one of the highest-value research priorities is to help ensure that increasingly powerful AI systems are developed and deployed in our economies in ways that are aligned with human values. As economists, we are uniquely positioned to translate concepts from the social sciences into analytic frameworks that can guide the development of aligned AI systems, making this an urgent and worthy focus.16
| 2023-02-01T00:00:00 |
https://www.nber.org/reporter/2024number4/economics-transformative-ai
|
[
{
"date": "2023/02/01",
"position": 63,
"query": "AI economic disruption"
},
{
"date": "2023/04/01",
"position": 85,
"query": "AI economic disruption"
},
{
"date": "2023/07/01",
"position": 94,
"query": "AI economic disruption"
},
{
"date": "2024/12/01",
"position": 92,
"query": "AI economic disruption"
},
{
"date": "2025/01/28",
"position": 60,
"query": "artificial intelligence wages"
}
] |
|
Digital disruption: artificial intelligence and international trade policy
|
Digital disruption: artificial intelligence and international trade policy
|
https://ideas.repec.org
|
[
"Emily Jones",
"Author",
"Listed"
] |
In a digitalized global economy, trade rules have implications for AI innovation, uptake, and governance, yet existing trade rules have significant shortcomings ...
|
Digitalization of the global economy is occurring apace and has spurred a new wave of trade negotiations, as governments and technology firms vie to establish international rules and standards for the digital era. This article examines the ways that trade policy-makers are responding to artificial intelligence (AI), arguably the most disruptive of the new digital technologies. In a digitalized global economy, trade rules have implications for AI innovation, uptake, and governance, yet existing trade rules have significant shortcomings and need updating in order to assist with effective AI governance. Updating is happening but, so far, the changes focus on promoting AI and disproportionately reflect the interests of large technology firms, the major innovators and owners of AI. New digital trade rules include stringent intellectual property protections for source code and algorithms, and strong commitments to enable the free flow of data across borders. However, much less progress has been made in addressing cross-border risks and harms associated with AI, in areas such as competition policy; ethical, transparent, and accountable use of AI; personal data protection; and protections against the exploitative use of algorithms in consumer and labour markets.
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| 2023-07-14T00:00:00 |
2023/07/14
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https://ideas.repec.org/a/oup/oxford/v39y2023i1p70-84..html
|
[
{
"date": "2023/02/01",
"position": 67,
"query": "AI economic disruption"
},
{
"date": "2025/04/01",
"position": 35,
"query": "AI economic disruption"
},
{
"date": "2025/06/01",
"position": 38,
"query": "AI economic disruption"
}
] |
AI and the future of work: Three ways to navigate disruption
|
AI and the future of work: Three ways to navigate disruption
|
https://www.mastercard.com
|
[] |
The enormous potential of artificial intelligence could radically transform the way we work. In fact, the World Economic Forum's just-released Future of ...
|
AI and the future of work: Three ways to navigate disruption
The enormous potential of artificial intelligence could radically transform the way we work. In fact, the World Economic Forum’s just-released Future of Jobs Report 2020 says that within five years, the amount of time spent on tasks at work by humans and machines will be equal. The possibilities for disruption are real, but Mastercard leaders say we can avoid unintended consequences of innovation and ensure that AI can augment and empower humans workers, ensuring a smarter future for all.
| 2023-02-01T00:00:00 |
https://www.mastercard.com/news/perspectives/2020/ai-and-the-future-of-work-three-ways-to-navigate-disruption/
|
[
{
"date": "2023/02/01",
"position": 75,
"query": "AI economic disruption"
},
{
"date": "2023/04/01",
"position": 89,
"query": "AI economic disruption"
},
{
"date": "2023/07/01",
"position": 90,
"query": "AI economic disruption"
},
{
"date": "2023/09/01",
"position": 75,
"query": "AI economic disruption"
},
{
"date": "2023/10/01",
"position": 72,
"query": "AI economic disruption"
},
{
"date": "2023/11/01",
"position": 75,
"query": "AI economic disruption"
},
{
"date": "2024/01/01",
"position": 75,
"query": "AI economic disruption"
},
{
"date": "2024/12/01",
"position": 96,
"query": "AI economic disruption"
},
{
"date": "2025/01/01",
"position": 95,
"query": "AI economic disruption"
},
{
"date": "2025/03/01",
"position": 73,
"query": "AI economic disruption"
}
] |
|
5 Powerful Ways HR Can Prepare for Economic Disruption - SHRM
|
5 Powerful Ways HR Can Prepare for Economic Disruption
|
https://www.shrm.org
|
[
"Roy Maurer",
"Molly Cohen"
] |
“As HR leaders and business executives see AI adoption growing, having the right talent becomes even more important,” Field said. “The role 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.
| 2023-02-01T00:00:00 |
https://www.shrm.org/topics-tools/news/5-powerful-ways-hr-can-prepare-economic-disruption
|
[
{
"date": "2023/02/01",
"position": 79,
"query": "AI economic disruption"
}
] |
|
Artificial Intelligence Market Disruption - IDEAS/RePEc
|
Artificial Intelligence Market Disruption
|
https://ideas.repec.org
|
[
"Julia M. Puaschunder",
"The New School",
"Department Of Economics",
"Author",
"Listed"
] |
The emerging autonomy of AI holds unique potentials of eternal life of robots, AI and algorithms alongside unprecedented economic superiority.
|
The introduction of Artificial Intelligence in our contemporary society imposes historically unique challenges for humankind. The emerging autonomy of AI holds unique potentials of eternal life of robots, AI and algorithms alongside unprecedented economic superiority, data storage and computational advantages. Yet to this day, it remains unclear what impact AI taking over the workforce will have on economic growth.
Citations are extracted by the CitEc Project , subscribe to its RSS feed for this item.
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All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:smo:dpaper:01jp. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
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For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Eduard David (email available below). General contact details of provider: http://rais.education/ .
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| 2019-07-14T00:00:00 |
2019/07/14
|
https://ideas.repec.org/p/smo/dpaper/01jp.html
|
[
{
"date": "2023/02/01",
"position": 83,
"query": "AI economic disruption"
},
{
"date": "2023/04/01",
"position": 68,
"query": "AI economic disruption"
},
{
"date": "2023/07/01",
"position": 68,
"query": "AI economic disruption"
},
{
"date": "2023/09/01",
"position": 59,
"query": "AI economic disruption"
},
{
"date": "2023/10/01",
"position": 59,
"query": "AI economic disruption"
},
{
"date": "2023/11/01",
"position": 58,
"query": "AI economic disruption"
},
{
"date": "2024/01/01",
"position": 59,
"query": "AI economic disruption"
},
{
"date": "2024/12/01",
"position": 69,
"query": "AI economic disruption"
},
{
"date": "2025/01/01",
"position": 67,
"query": "AI economic disruption"
},
{
"date": "2025/03/01",
"position": 57,
"query": "AI economic disruption"
}
] |
Focus on artificial intelligence and technological disruption
|
Focus on artificial intelligence and technological disruption
|
https://fsc-ccf.ca
|
[] |
Automation is indeed a significant risk for Canadian workers; about 40 per cent of people are working in jobs at medium risk of automation, while 20 per cent ...
|
Learning Bulletin Ensuring that technology-driven change benefits workers as well as employers will require new approaches and new thinking. Our September 2021 Learning Bulletin takes a deeper look at the different implications of AI for the future of work and explores a set of FSC projects that address both the risks and opportunities presented by AI. Download the AI learning bulletin
Artificial intelligence is seen as bringing both positive and negative implications to our work environment.
Perhaps the most common fear is that AI will replace humans in performing cognitive tasks and displace people from their jobs. Automation is indeed a significant risk for Canadian workers; about 40 per cent of people are working in jobs at medium risk of automation, while 20 per cent are in jobs at high risk. At the same time, AI presents a number of opportunities to drive new economic efficiencies within and across sectors, complement human intelligence, and even create new jobs.
AI’s potential should not be confused with its impact; the former can be directed, and the latter is far from inevitable. What is sure is that AI is now – and will continue – to disrupt the world of work. With this prediction comes a realization that there will likely be a need for more hybrid skill sets that combine AI skills with other types of abilities. AI platforms can also become a tool for continuous learning by revealing correlations between production needs and worker training opportunities. Ensuring that technology-driven change benefits workers as well as employers will require new approaches and new thinking.
The Future Skills Centre is partnering with industries, governments, universities and workers across Canada to test new ways to address and harness AI in skills delivery. These partnerships explore the specific requirements and opportunities for AI in sectors as diverse as health care, insurance and transportation.
| 2023-02-01T00:00:00 |
https://fsc-ccf.ca/engage/artificial-intelligence/
|
[
{
"date": "2023/02/01",
"position": 85,
"query": "AI economic disruption"
},
{
"date": "2025/03/01",
"position": 89,
"query": "AI economic disruption"
}
] |
|
Beyond Disruption: AI, Expertise, and the Future of Middle-Class ...
|
Beyond Disruption: AI, Expertise, and the Future of Middle-Class Workers
|
https://www.aei.org
|
[
"James Pethokoukis"
] |
Advances in artificial intelligence — including the promise of generative AI — are having an impact on jobs, at least in the tech sector where companies are ...
|
Quote of the Issue
“The capitalist achievement does not typically consist in providing more silk stockings for queens but in bringing them within the reach of factory girls in return for steadily decreasing amounts of effort.” – Joseph Schumpeter, Capitalism, Socialism and Democracy
The Essay
🤖👷♂️ Beyond disruption: AI, expertise, and the future of middle-class workers
Advances in artificial intelligence — including the promise of generative AI — are having an impact on jobs, at least in the tech sector where companies are reorganizing their workforces and investment spending in a pivot toward the technology. This from the New York Times:
Tech’s biggest companies are rushing to hire engineers to build A.I. systems. Last year, there were 180,000 job postings in the United States related to A.I., including roles in software development, semiconductor engineering and cloud computing, according to CompTIA. The number of A.I. job openings has expanded this year. Those employees are helping Microsoft, Google, Amazon and Meta improve chatbots and build other A.I. systems. Apple is hiring for A.I. engineers, as the company develops its own A.I. offering to release later this year. … The companies are spending billions of dollars on the expensive chips and supercomputers necessary to train and build A.I. systems. By the end of the year, Meta expects to have purchased 350,000 of specialized chips from the chip maker Nvidia, which cost an estimated $30,000 each.The push into generative A.I. has coincided with cuts elsewhere. Google’s layoffs reduced the number of people working on augmented reality technology. Meta, which laid off nearly 20,000 people last year, has been cutting some of its program managers, who oversee different projects and are responsible for keeping teams on schedule.
Short version: Tech progress is generating creative destruction. Some jobs are cut, others are created, the latter both at the companies building AI systems and those selling the equipment to build those systems. And if machine learning broadly and GenAI specifically are so important as to be considered powerful general-purpose technologies, this will be the story across the American economy. Dynamism is action.
The big question, of course: How will all these changes net out? Are we on a path toward widespread and persistent technological unemployment? Should we expect history to repeat itself — jobs are lost, jobs are gained even as America remains a high-employment economy — or is this time different? Fire up those plans for a universal basic income?
Some good news on the jobs front, courtesy of a thoughtful analysis by MIT economist David Autor in the new working paper (an essay, really), “Applying AI to Rebuild Middle Class Jobs.” (Autor is probably best known for his work on the “China shock,” the impact of the sudden and large increase in Chinese manufacturing to the United States in the decades before and after the year 2000.) It’s a highly readable analysis, and I urge my readers to have a look if they’re interested in the topic. But here are my take-aways (all the quotes from the paper, by the way):
Labor shortages, not joblessness. Regular Faster, Please! readers know I’ve written quite a bit recently about global population decline, and how it’s happening way faster than many probably realize. Autor incorporates this demographic reality in his analysis. Despite ad nauseam predictions out there that AI and robotic automation will lead to widespread joblessness, Autor notes that advanced economies currently face labor shortages rather than tech unemployment. Going forward, plummeting birth rates and aging workforces mean many countries will lack a sufficient number of working-age adults. “The industrialized world is awash in jobs, and it’s going to stay that way. … This is not a prediction, it’s a demographic fact. … Barring a massive change in immigration policy, the U.S. and other rich countries will run out of workers before we run out of jobs.”
Regular readers know I’ve written quite a bit recently about global population decline, and how it’s happening way faster than many probably realize. Autor incorporates this demographic reality in his analysis. Despite ad nauseam predictions out there that AI and robotic automation will lead to widespread joblessness, Autor notes that advanced economies currently face labor shortages rather than tech unemployment. Going forward, plummeting birth rates and aging workforces mean many countries will lack a sufficient number of working-age adults. “The industrialized world is awash in jobs, and it’s going to stay that way. … This is not a prediction, it’s a demographic fact. … Barring a massive change in immigration policy, the U.S. and other rich countries will run out of workers before we run out of jobs.” The Digital Revolution’s surprise impact. As Autor explains, many techies have argued that the Digital Revolution — especially those internet devices in every pocket — would “flatten economic hierarchies” by democratizing information. But just the opposite has happened, as he sees it. Information is now merely a commoditized input to decision-making that’s predominantly done by the minority of US adults with higher education. “By making information and calculation cheap and abundant, computerization catalyzed an unprecedented concentration of decision-making power, and accompanying resources, among elite experts.” This phenomeon has undermined a large segment of middle-skill jobs in administration, clerical work, and some blue-collar sectors. Those without a bachelor’s degree, some 60 percent of adults, have been pushed into low-paid, non-expert service roles.
As Autor explains, many techies have argued that the Digital Revolution — especially those internet devices in every pocket — would “flatten economic hierarchies” by democratizing information. But just the opposite has happened, as he sees it. Information is now merely a commoditized input to decision-making that’s predominantly done by the minority of US adults with higher education. “By making information and calculation cheap and abundant, computerization catalyzed an unprecedented concentration of decision-making power, and accompanying resources, among elite experts.” This phenomeon has undermined a large segment of middle-skill jobs in administration, clerical work, and some blue-collar sectors. Those without a bachelor’s degree, some 60 percent of adults, have been pushed into low-paid, non-expert service roles. How AI augments expertise. Rather than purely replacing human jobs, AI has the capacity to enhance expertise. “The unique opportunity that AI offers humanity is to push back against the process started by computerization—to extend the relevance, reach and value of human expertise for a larger set of workers.” For example: An AI medical assistant could analyze patient symptoms, identify potential diagnoses, and then present its findings to a nurse practitioner to validate and incorporate into their assessment.
Because of AI’s capacity to weave information and rules with acquired experience to support decision-making, it can be applied to enable a larger set of workers possessing complementary knowledge to perform some of the higher-stakes decision-making tasks that are currently arrogated to elite experts, e.g., medical care to doctors, document production to lawyers, software coding to computer engineers, and undergraduate education to professors.
(Autors also points to several studies, ones familiar to Faster, Please! readers, that show how GenAI can effectively upskill workers.) Those with training could then leverage AI and take on higher-level work. AI tools could enable more non-elite workers to take on decision-making roles currently reserved for the most trained experts. This could help restore middle-class, middle-skill jobs eroded by technology, while moderating inequality and — this superimportant, I think — lowering costs of key services like healthcare and education that have been productivity resistant.
A key point:
If AI unleashes a surge of productivity in radiology, customer service, software coding, copywriting and many other domains, won’t that mean that we’ll be left with fewer workers doing the jobs previously done by many? In some arenas, the opposite may well be true. Demand for healthcare, education and computer code appears almost limitless—and will rise further if as expected AI brings down the cost of these services. But in other domains, yes, rapid productivity growth will erode employment. In 1900, about 35% of U.S. employment was in agriculture. After a century of sustained productivity growth, that share in 2022 was around 1%—and not because we’re eating less. But what’s true about employment in a specific product or service has never been true of the economy writ large. When nearly 40% of U.S. workers were on farms, the fields of health and medical care, finance and insurance, and software and computing had barely germinated. The majority of contemporary jobs are not remnants of historical occupations that have so far escaped automation. Instead, they are new job specialties that are inextricably linked to specific technological innovations [“air traffic controllers, electricians, or gene editors”]. They demand novel expertise that was unavailable or unimagined in earlier eras.
Expertise and creative destruction. Yes, AI will automate some forms of human expertise, but it will also create new forms of expert work. Past innovations like radar and GPS enhanced air traffic controllers’ expertise, Autor points out, rather than replacing it. While machines that automate tasks encroach on existing skills, technology more often generates new specialized skills and jobs. AI will reshape and eliminate some occupations, but it will also create new goods, services, expertise demands, and that are hard to predict now. Entrepreneurs still have to invent them! “If innovations were used exclusively for automation, we would have run out of work long ago. Instead, the industrialized world appears poised to run out of workers before it runs out of jobs. The likely reason is that the most important innovations have never been about automation. Automation did not, for example, give rise to airplanes, indoor plumbing, penicillin, CRISPR or television. Rather than automating existing tasks, these innovations opened fundamentally new vistas of human possibility.” Viewing innovation mainly as automation that replaces humans is misguided and ahistorical.
Finally, Autor offers a pointed rebuttal to those who think even a huge AI leap will create mass joblessness:
AI poses a real risk to labor markets, but not that of a technologically jobless future. The risk is the devaluation of expertise. A future where humans supply only generic, undifferentiated labor is one where no one is an expert because everyone is an expert. In this world, labor is disposable and most wealth would accrue to owners of Artificial Intelligence patents. The political contours of such a world would be a hellscape: “WALL-E” meets “Mad Max.” Remarkably, it is also the economic future that many AI visionaries seem to have in mind. For example, the charter of OpenAI, developer of ChatGPT and DALL-E, defines Artificial General Intelligence (AGI) as “highly autonomous systems that outperform humans at most economically valuable work.” … The most charitable thing I can say about these ominous statements is that they are likely wrong — a flattening of the complexity of innovation into a single dimension of automation. Do these technological visionaries believe that Black and Decker tools make contractors’ skills less valuable and that airplanes outperform their passengers? The latter question is of course nonsensical. Airplanes are not our competitors; we simply couldn’t fly without them. Replicating our existing capabilities, simply at greater speed and lower cost, is a minor achievement. The most valuable tools complement human capabilities and open new frontiers of possibility.
This isn’t a contest between good tech and bad. An economy needs tech progress that automates some tasks, reshapes others, and creates brand-new ones that generate new goods and services. The role of public policy is to make sure government plays its important role in supplying the basic science for technologies and entrepreneurs to draw up and make sure there are no unnecessary regulatory barriers to building new businesses using those innovations. Beyond that, as I have written before, I’m skeptical of the notion of somehow steering tech progress so that it produces more pro-worker results. I just fundamentally question our ability to identify and promote certain technologies because we understand exactly their long-term impacts.
| 2023-02-01T00:00:00 |
https://www.aei.org/articles/beyond-disruption-ai-expertise-and-the-future-of-middle-class-workers/
|
[
{
"date": "2023/02/01",
"position": 86,
"query": "AI economic disruption"
},
{
"date": "2024/02/13",
"position": 2,
"query": "AI economic disruption"
},
{
"date": "2024/02/13",
"position": 52,
"query": "future of work AI"
},
{
"date": "2025/03/01",
"position": 95,
"query": "AI economic disruption"
},
{
"date": "2025/04/01",
"position": 58,
"query": "AI economic disruption"
},
{
"date": "2025/06/01",
"position": 58,
"query": "AI economic disruption"
}
] |
|
Artificial intelligence and international economic law
|
Artificial intelligence and international economic law : disruption, regulation, and reconfiguration
|
https://www.loc.gov
|
[] |
Title. Artificial intelligence and international economic law : disruption, regulation, and reconfiguration. Summary. "This book was finalized while ...
|
The books in this collection are licensed under open access licenses allowing for the reuse and distribution of each book following the terms described in each license. Researchers should consult the Rights Advisory statement for each title and the accompanying license details for information about rights and permissions associated with each of the licenses.
More about Copyright and other Restrictions.
| 2023-02-01T00:00:00 |
https://www.loc.gov/item/2021024541/
|
[
{
"date": "2023/02/01",
"position": 89,
"query": "AI economic disruption"
},
{
"date": "2023/10/01",
"position": 89,
"query": "AI economic disruption"
},
{
"date": "2023/11/01",
"position": 98,
"query": "AI economic disruption"
}
] |
|
Adapting to the AI Disruption: Reshaping the IT Landscape ... - arXiv
|
Adapting to the AI Disruption: Reshaping the IT Landscape and Educational Paradigms
|
https://arxiv.org
|
[] |
Artificial intelligence (AI) signals the beginning of a revolutionary period where technological advancement and social change interact to completely reshape ...
|
Murat Ozer1, Yasin Kose2, Goksel Kucukkaya1, Assel Mukasheva3, Kazim Ciris1
1 School of Information Technology, University of Cincinnati, Cincinnati, Ohio, USA
2 Cybercrime and Forensic Computing, Friedrich-Alexander-Universität, Erlangen, Germany
3 Information Systems, Kazakh-British Technical University, Almaty, Kazakhstan
[email protected], [email protected], [email protected], [email protected], [email protected]
Artificial intelligence (AI) signals the beginning of a revolutionary period where technological advancement and social change interact to completely reshape economies, work paradigms, and industries worldwide. This essay addresses the opportunities and problems brought about by the AI-driven economy as it examines the effects of AI disruption on the IT sector and information technology education. By comparing the current AI revolution to previous industrial revolutions, we investigate the significant effects of AI technologies on workforce dynamics, employment, and organizational procedures. Human-centered design principles and ethical considerations become crucial requirements for the responsible development and implementation of AI systems in the face of the field’s rapid advancements. IT education programs must change to meet the changing demands of the AI era and give students the skills and competencies they need to succeed in a digital world that is changing quickly. In light of AI-driven automation, we also examine the possible advantages and difficulties of moving to a shorter workweek, emphasizing chances to improve worker productivity, well-being, and work-life balance. We can build a more incslusive and sustainable future for the IT industry and beyond, enhancing human capabilities, advancing collective well-being, and fostering a society where AI serves as a force for good by embracing the opportunities presented by AI while proactively addressing its challenges.
Index Terms: Artificial Intelligence, IT Industry, Information Technology Education, Future of Work, Digital Transformation, Ethical AI, Talent Development, Lifelong Learning, Socio-economic Impact.
I Introduction
There are turning points in human history that permanently change the course of civilization and the way we work, live, and engage with the outside world. Every technological advance, from the development of the abacus to the onset of the computer era, has opened up new avenues for creativity and increased the limits of human understanding. A new narrative, however, is beginning to emerge amid the many tales of technological wonders and human ingenuity; this narrative speaks to the dawn of a truly transformative era: the era of artificial intelligence (AI).
We have all heard the myths and stories, handed down through the ages, about how the invention of the calculator revolutionized mathematics by relieving people of the mental strain of doing calculations by hand and igniting a wave of scientific inquiry. On the other hand, the development of the computer promised unparalleled computational power, allowing individuals and organizations to tackle challenging problems at a pace and efficiency never seen before.
But this is also the point at which the story takes an unexpected turn that fundamentally and significantly distinguishes artificial intelligence from its predecessors. You see, computers and calculators still require human operators to enter data, execute commands, and interpret results. In contrast, artificial intelligence operates at an entirely different level of autonomy. Unlike its predecessors, AI transcends rather than just augments human capabilities [1] . It performs each of these functions without the use of labor or the tangible infrastructure associated with conventional technology. It can make decisions, identify trends, and analyze big datasets.
The invention of computers, for instance, did not lead to widespread unemployment as initially feared. Instead, it opened up new vistas of employment opportunities, from software development to IT support. However, the advent of artificial intelligence represents a paradigm shift of a different magnitude altogether. While computers extended human capabilities, AI has the potential to replace them. It’s not just about automating manual tasks anymore; it’s about machines being able to learn, adapt, and even innovate independently. This level of autonomy has far-reaching implications for industries, economies, and societies at large. As AI continues to advance, it’s not just blue-collar jobs that are at risk; white-collar professions like accounting, legal services, and even medicine are facing the prospect of automation.
Essentially, the development of artificial intelligence signals the beginning of a new chapter in human history, one in which the definition of productivity and labor itself are being altered. It is about radically changing how we view and engage with technology, not just about streamlining procedures or improving current workflows. It is crucial that we foresee the consequences of this brave new world of AI-driven automation and proactively shape laws and procedures to guarantee a time when people and machines can live in harmony. In essence, artificial intelligence (AI) represents a paradigm shift—a venture into the unknown where machines are endowed with sentience and agency. It makes it possible for algorithms to navigate challenging situations, resolve challenging issues, and even develop and adapt over time [2] . The thought of this technological wonder’s ability to completely change every aspect of our lives—from healthcare and transportation to finance and entertainment—is both amazing and unsettling. The question of what this means for the future of work and the millions of people whose livelihoods depend on jobs that could soon be automated away lingers, though, amidst the excitement and anticipation surrounding the rise of AI. It’s a question that requires careful thought, strategic planning, and a dedication to making sure the advantages of AI are distributed fairly throughout society [3] .
| 2023-02-01T00:00:00 |
https://arxiv.org/html/2409.10541v1
|
[
{
"date": "2023/02/01",
"position": 96,
"query": "AI economic disruption"
},
{
"date": "2023/10/01",
"position": 94,
"query": "AI economic disruption"
}
] |
|
1.2 Billion Hours at Stake: Can the US Government Catch Up With AI?
|
1.2 Billion Hours at Stake: Can the US Government Catch Up With AI?
|
https://www.vktr.com
|
[
"Catherine Brinkman",
"About The Author"
] |
When done right, AI won't replace government workers. AI will make their jobs more impactful. It will help overworked teams focus on judgment, ...
|
While the private sector races ahead with AI, automating customer service, optimizing supply chains and rewriting the rules of work, public systems are caught in a high-stakes balancing act. On one hand, there’s mounting pressure to modernize, to keep pace with both expectations and cyber threats. On the other, there's an outdated infrastructure, a workforce built for a different era and a trust deficit that can't be patched with a chatbot.
The question isn’t if AI will transform government — it already is. The real question is: Are we ready?
The Infrastructure Gap: Modern Tech, Ancient Pipes
Let’s start with the pipes. Much of our public infrastructure, including digital, was built for a pre-cloud, pre-AI world. Legacy code, siloed databases, inconsistent security protocols and spotty broadband access in rural areas mean that simply “plugging in” AI tools isn’t realistic for many agencies.
Federal departments with advanced resources are scrambling to inventory their data, standardize formats and build secure environments where AI can be deployed responsibly. Local and state governments? Many are still trying to digitize basic records. You can’t automate what you can’t access.
A Workforce That Wants In — But Can’t Gain Entry
Contrary to what people assume, public-sector employees aren’t resisting AI. A growing body of research shows most government workers want to use tech to improve their impact. They don’t have the tools or training to do it, yet.
Simpler Media Group
In fact, McKinsey reports a clear misalignment between leadership perceptions and employee readiness: US workers report high confidence in adopting AI, but many say management is slow to act, overly cautious or lacks a clear strategy. Meanwhile, surveys show that 1 in 5 public workers fear being replaced by AI, and less than half feel their organization is preparing them for what’s ahead.
This isn’t a resistance problem. It’s a resource and communication problem. When governments fail to demystify AI, when they skip training, transparency, and inclusion, they foster fear instead of curiosity.
Related Article: C-Suite Warnings Mount While Workers Brace for AI Shake-Up
The Education & Training Dilemma
The answer isn't just more data scientists in government buildings. It’s building AI fluency across all roles, from frontline case managers to procurement teams to municipal planners.
Programs like Intel’s AI for Workforce and federal initiatives like the National AI Talent Surge are great starts, but they reach a fraction of the people who need access. Most public-sector employees don’t need to build models from scratch. They need to understand what AI can and can’t do, how to interpret its outputs and when to override it.
The future-proof government workforce isn’t just more technical, it’s more translational.
Tactical Wins That Actually Work
While the big transformation is slow-moving, smart public agencies are already finding wins in small-scale deployments:
Fraud detection in benefits programs, using AI to flag anomalies and reduce waste.
AI-powered chatbots reducing call volume at state DMVs by 40–50%, while freeing staff for complex cases.
Predictive maintenance for city infrastructure, identifying water main failures and road damage before they happen.
Cybersecurity automation, catching intrusion attempts faster than any human SOC team could.
These aren’t moonshots. They’re high-leverage applications that offer measurable ROI without overhauling an entire agency.
Risk Without Guardrails: Bias, Black Boxes and Blowback
Let’s be blunt: the risks are real. AI deployed irresponsibly in government settings can amplify bias, reinforce systemic inequality and erode trust. Think about predictive policing gone wrong. Or housing algorithms that filter out low-income applicants. Or AI denial decisions for benefits with no human recourse.
Government has a different social contract than business. The stakes are higher. Transparency, accountability and explainability aren’t optional, they’re foundational. If citizens don’t understand how a decision was made, or worse, if that decision was wrong, they lose trust in the system.
That’s why AI governance must be baked in from the start:
Audit trails
Human-in-the-loop safeguards
Bias detection and mitigation
Citizen-facing explainability
Anything less isn’t innovation, it’s abdication.
Related Article: Cracking the AI Black Box: Can We Ever Truly Understand AI's Decisions?
What AI-Ready Public Systems Look Like
What does “AI readiness” mean for the public sector?
It means:
Data infrastructure that’s accessible, secure and ethically governed
Leadership alignment that matches vision with budget, strategy and urgency
Workforce fluency — not just data scientists, but analysts, clerks, managers and auditors who can use AI tools responsibly
Clear governance frameworks, from local councils to federal agencies
Public trust built on transparency, inclusion and accountability
This isn’t a one-and-done checklist. A mindset. A commitment to iterate forward.
The Opportunity: Tech-Driven Public Good
Simpler Media Group
Here’s the upside: Deloitte estimates that automation could save US public agencies up to 1.2 billion hours annually, unlocking as much as $41 billion in value. That’s not just budget savings — it is time redirected to strategic priorities, improved service and a better citizen experience.
When done right, AI won’t replace government workers. AI will make their jobs more impactful. It will help overworked teams focus on judgment, empathy and complexity and modernize how the government earns trust in the digital age.
But none of that happens by accident. It takes intention. It takes investment. Because AI isn’t just another IT tool. AI is a shift in how government sees the world, and how the world sees government.
Learn how you can join our contributor community.
| 2023-02-01T00:00:00 |
https://www.vktr.com/digital-workplace/12-billion-hours-at-stake-can-the-us-government-catch-up-with-ai/
|
[
{
"date": "2023/02/01",
"position": 3,
"query": "government AI workforce policy"
},
{
"date": "2023/04/01",
"position": 3,
"query": "government AI workforce policy"
},
{
"date": "2023/08/01",
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},
{
"date": "2023/12/01",
"position": 3,
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},
{
"date": "2024/01/01",
"position": 4,
"query": "government AI workforce policy"
},
{
"date": "2024/03/01",
"position": 3,
"query": "government AI workforce policy"
},
{
"date": "2024/07/01",
"position": 3,
"query": "government AI workforce policy"
},
{
"date": "2024/08/01",
"position": 3,
"query": "government AI workforce policy"
},
{
"date": "2025/07/03",
"position": 12,
"query": "government AI workforce policy"
},
{
"date": "2025/07/03",
"position": 6,
"query": "government AI workforce policy"
},
{
"date": "2025/07/03",
"position": 10,
"query": "government AI workforce policy"
}
] |
|
U.S. AI Workforce: Policy Recommendations
|
Center for Security and Emerging Technology
|
https://cset.georgetown.edu
|
[] |
Our research implies that U.S. AI education and workforce policy should have three goals: (1) increase the supply of domestic AI doctorates, (2) sustain and ...
|
Executive Summary
The U.S. artificial intelligence workforce, which stood at 14 million people in 2019, or 9 percent of total U.S. employment, has grown rapidly in recent years. This trend is likely to continue, as AI occupational employment over the next decade is projected to grow twice as fast as employment in all occupations.
Such an important and increasing component of the U.S. workforce demands dedicated education and workforce policy. Yet one does not exist. To date, U.S. policy has been a piecemeal approach based on inconsistent definitions of the AI workforce. For some, current policy is focused on top-tier doctorates and immigration reform. For others, the conversation quickly reverts to STEM education.
This report addresses the need for a clearly defined AI education and workforce policy by providing recommendations designed to grow, sustain, and diversify the domestic AI workforce. We use a comprehensive definition of the AI workforce—technical and nontechnical occupations—and provide data-driven policy goals.
Our policy goals and recommendations build off of previous CSET research along with new research findings presented here. Previous research in this series defined the AI workforce, described and characterized these workers, and assessed the relevant labor market dynamics. For example, we found that the demand for computer and information research scientists appears to be higher than the current supply, while for software developers and data scientists, evidence of a supply-demand gap is mixed.
To understand the current state of U.S. AI education for this report, we manually compiled an “AI Education Catalog” of curriculum offerings, summer camps, after-school programs, contests and challenges, scholarships, and related federal initiatives. To assess the current landscape of employer demand and hiring experiences, we also interviewed select companies engaged in AI activities.
Our research implies that U.S. AI education and workforce policy should have three goals: (1) increase the supply of domestic AI doctorates, (2) sustain and diversify technical talent pipelines, and (3) facilitate general AI literacy through K-12 AI education.
To achieve these goals, we propose a set of recommendations designed to leverage federal resources within the realities of the U.S. education and training system. Our first recommendation sets the foundation for facilitating these goals by creating a federal coordination function. We believe such a function is critical given ongoing fragmented AI education initiatives, and would harness the potential of the newly established National Artificial Intelligence Initiative Office for Education and Training within the White House Office of Science and Technology Policy. We recommend this office coordinate federal and state initiatives, convene key stakeholders to share lessons learned and best practices of state-level AI education initiatives, and compile and publish information on AI education and careers on a publicly available “AI dashboard.”
The remaining recommendations advocate for a multipronged approach to implement policies across goals, including:
Creating and Disseminating AI Educational and Career Information
Establishing AI Education and Training Tax Credits
Investing in Postsecondary AI Education and Scholarships
Facilitating Alternative Pathways into AI Jobs
Investing in PreK-12 AI Education and Experiences
Integrating K-12 AI Curriculum and Course Design
Cultivating and Supporting K-12 AI Educators
Funding AI Education and Careers Research
Importantly, our recommendations prioritize creating multiple viable pathways into AI jobs to diversify the AI workforce and leverage all U.S. talent. Our research shows the dominant pathway to enter the AI workforce remains having a four-year college degree. However, this may be restricting the amount of talent entering the AI workforce, unnecessarily limiting opportunity for those who are otherwise qualified and able.
Our recommendations therefore prioritize harnessing the potential of community and technical colleges, minority-serving institutions, and historically Black colleges and universities in training tomorrow’s U.S. AI workforce. In addition, to promote alternative pathways into AI jobs, we propose that the National Institute of Standards and Technology work with industry to establish industry-accepted standards for AI and AI-related certifications to enhance their legitimacy. And as a top employer of technical talent, the federal government could modify its hiring criteria to lead by example.
We hope that this report and recommendations advance the discourse on AI education and workforce policy. Now is a critical time to invest in training and equipping a globally competitive AI workforce for tomorrow. With concerted and targeted efforts, it is possible to lead the world in AI talent. Ultimately, an AI workforce policy inclusive of all of our report’s elements is more likely to be the most effective. However, we also present our recommendations as a road map to guide U.S. policymakers in crafting an AI education and workforce agenda.
| 2023-02-01T00:00:00 |
https://cset.georgetown.edu/publication/u-s-ai-workforce-policy-recommendations/
|
[
{
"date": "2023/02/01",
"position": 12,
"query": "government AI workforce policy"
},
{
"date": "2023/08/01",
"position": 15,
"query": "government AI workforce policy"
},
{
"date": "2023/12/01",
"position": 11,
"query": "government AI workforce policy"
},
{
"date": "2024/01/01",
"position": 15,
"query": "government AI workforce policy"
},
{
"date": "2024/07/01",
"position": 13,
"query": "government AI workforce policy"
},
{
"date": "2024/08/01",
"position": 12,
"query": "government AI workforce policy"
},
{
"date": "2024/12/01",
"position": 11,
"query": "government AI workforce policy"
},
{
"date": "2025/03/01",
"position": 10,
"query": "government AI workforce policy"
},
{
"date": "2025/04/01",
"position": 5,
"query": "government AI workforce policy"
},
{
"date": "2025/06/01",
"position": 9,
"query": "government AI workforce policy"
}
] |
|
Strengthening the U.S. AI Workforce | Center for Security and ... - CSET
|
Center for Security and Emerging Technology
|
https://cset.georgetown.edu
|
[] |
Our long-term recommendation is to launch education and R&D programs to lay the foundation for domestic AI workforce growth (Section 2, “The Policy Agenda”).
|
Artificial intelligence is increasingly important to national security and economic growth, and human capital is a key determinant of nations’ strength in AI. To help strengthen the U.S. AI workforce, this report lays out what is currently known about domestic and global AI talent, identifies priorities for U.S. policymakers, and describes policy-relevant knowledge gaps that researchers should fill.
Our research highlights the existence of global AI talent shortages; the United States’ reliance on foreign talent to sustain its AI workforce; and recent increases in international competition for students, workers, and entrepreneurs (Section 1, “Background on the U.S. AI Workforce”). We recommend that policymakers adopt immigration reforms to ensure continued U.S. competitiveness in attracting and retaining foreign AI talent in the short term. Our long-term recommendation is to launch education and R&D programs to lay the foundation for domestic AI workforce growth (Section 2, “The Policy Agenda”). Researchers can support these efforts to strengthen the U.S. AI workforce by, among other things, measuring the global stock and flow of AI talent, identifying the key immigration problems encountered by foreign AI talent in the United States, and assessing the potential effectiveness of different domestic education programs (Section 3, “The Research Agenda”).
A billboard in San Francisco advertising Canada’s start-up visa to U.S.-based international tech workers on H-1B visas.
(Peter Dasilva/The New York Times/Redux)
Key Findings
There is a significant talent shortage in AI, both domestically and globally. One consequence of U.S. talent shortages is that U.S. companies are moving AI R&D abroad.
The United States heavily relies on foreign-born talent. For example, more than 50 percent of computer scientists with graduate degrees employed in the country today were born abroad, as were nearly 70 percent of enrolled computer science graduate students.
The vast majority of foreign-born talent wants to stay in the United States. Among U.S.-trained PhD graduates in AI-related fields, around 80 percent have remained in the country.
The United States’ established strength in top talent recruitment and retention is at risk due to adverse trends in U.S. immigration policy and efforts by other countries to open up new immigration pathways and launch talent attraction programs.
Key Policy Priorities
Adopting immigration policies that eliminate existing barriers to recruiting and retaining foreign-born AI talent and halting the implementation of ongoing immigration reforms that reduce U.S. competitiveness.
Formulating targeted policies that counter the harmful transfer of AI technologies and know-how. In so doing, ensuring against overly-broad restrictions that could make the United States inhospitable to foreign researchers and workers, which would worsen talent shortages.
Launching education and R&D initiatives that simultaneously address domestic workforce shortages and fund neglected but important research areas.
Developing strategies for government AI workforce development based on agency led investigations of AI talent demand and potential supply.
| 2023-02-01T00:00:00 |
https://cset.georgetown.edu/publication/strengthening-the-u-s-ai-workforce/
|
[
{
"date": "2023/02/01",
"position": 28,
"query": "government AI workforce policy"
},
{
"date": "2023/08/01",
"position": 30,
"query": "government AI workforce policy"
},
{
"date": "2023/12/01",
"position": 27,
"query": "government AI workforce policy"
},
{
"date": "2024/01/01",
"position": 31,
"query": "government AI workforce policy"
},
{
"date": "2024/07/01",
"position": 28,
"query": "government AI workforce policy"
},
{
"date": "2024/08/01",
"position": 30,
"query": "government AI workforce policy"
},
{
"date": "2025/03/01",
"position": 24,
"query": "government AI workforce policy"
},
{
"date": "2025/04/01",
"position": 34,
"query": "government AI workforce policy"
},
{
"date": "2025/06/01",
"position": 29,
"query": "government AI workforce policy"
}
] |
|
Artificial Intelligence for the American People
|
Artificial Intelligence for the American People
|
https://trumpwhitehouse.archives.gov
|
[] |
This EO defines principles for the use of AI in Government, establishes a common policy for implementing the principles, directs agencies to catalogue their AI ...
|
Overview
The age of artificial intelligence (AI) has arrived, and is transforming everything from healthcare to transportation to manufacturing.
America has long been the global leader in this new era of AI, and is poised to maintain this leadership going forward because of our strong innovation ecosystem. Realizing the full potential of AI for the Nation requires the combined efforts of industry, academia, and government. The Administration has been active in developing policies and implementing strategies that accelerate AI innovation in the U.S. for the benefit of the American people. These activities align with several areas of emphasis: AI for American Innovation, AI for American Industry, AI for the American Worker, and AI with American Values. This AI.gov website provides a portal for exploring these activities in more depth, and serves as a resource for those who want to learn more about how to take full advantage of the opportunities of AI.
| 2023-02-01T00:00:00 |
https://trumpwhitehouse.archives.gov/ai/
|
[
{
"date": "2023/02/01",
"position": 29,
"query": "government AI workforce policy"
},
{
"date": "2023/08/01",
"position": 34,
"query": "government AI workforce policy"
},
{
"date": "2023/12/01",
"position": 36,
"query": "government AI workforce policy"
},
{
"date": "2024/01/01",
"position": 36,
"query": "government AI workforce policy"
},
{
"date": "2024/07/01",
"position": 35,
"query": "government AI workforce policy"
},
{
"date": "2024/08/01",
"position": 37,
"query": "government AI workforce policy"
},
{
"date": "2024/12/01",
"position": 37,
"query": "government AI workforce policy"
},
{
"date": "2025/03/01",
"position": 23,
"query": "government AI workforce policy"
},
{
"date": "2025/04/01",
"position": 42,
"query": "government AI workforce policy"
},
{
"date": "2025/06/01",
"position": 33,
"query": "government AI workforce policy"
}
] |
|
AI Training Series for Government Employees | GSA
|
GSA - IT Modernization Centers of Excellence
|
https://coe.gsa.gov
|
[] |
This session guides government leaders on how to audit AI systems to ensure they adhere to performance, safety, and ethical standards. The session emphasizes ...
|
Welcome to the AI Training Series for Government Employees
Enhance your skills and knowledge of the rapidly evolving world of artificial intelligence (AI).
Our comprehensive training series, which meets the training requirements of the Artificial Intelligence Executive Order, is designed to inform and educate government employees at all levels, offering specialized tracks to meet the diverse needs of the government workforce. The TTS Centers of Excellence AI Community of Practice (AI CoP) conducted this AI Training in partnership with Stanford HAI, GWU Law School, Princeton CITP, Wilson Center, GSA OGP, and OMB.
Access the 2024 Training Series
The 2024 AI Training Series held live sessions in September and October 2024. The recorded sessions have been transformed into e-learning modules and are now available for government employees via USA Learning.
Technical Track
In partnership with Stanford Human-Centered AI, this track breaks down complex AI concepts into plain language, covering human-centered AI development, privacy and security concerns, and risk mitigation techniques.
Navigating the AI Landscape This course provides a comprehensive overview of AI, including the definition, theories of AI and machine learning, neural networks, narrow vs. general AI, gradient descent, use cases, and more. Privacy & Security This course covers how different social values around privacy, data ownership, and data creation will impact what AI technologies are possible today and what the future paths of innovation in AI will look like. AI Safety & Robustness The course looks at the considerations that AI developers must evaluate when designing AI systems for safety such as how to address biased inputs, navigate constantly evolving conditions, and address explainability issues. The course will look at how we can navigate all these risks and design the right parameters for safety. Generative AI Fairness This course provides an in-depth understanding of how biases embedded in data can lead generative AI models to make certain predictions that are systematically different across groups and how to assess algorithmic fairness for human-facing applications of generative AI. HELM and Benchmarking Foundation Models This course covers the importance of benchmarking AI models as a way to understand the capabilities of systems and promote transparency amongst model developers. The course will also discuss Stanford’s Holistic Evaluation of Language Models (HELM) benchmarking approach which serves as a model to evaluate language models. Building and Training Foundation Models This course provides more depth into what goes into building a foundation model. It discusses the challenges associated with training and the various stages of model development. Training Cost-Effective Large Language Models The cost of training large language models (LLM) is currently quite high with top of the line models costing billions of dollars to train. However, are costs expected to always remain that high? This course looks into the resources required to train LLMs and what could be done to make training them more cost effective. Multimodal Foundation Models Multimodal foundation models are capable of processing and generating both visual and textual information. This course will discuss how the development of these models could further expand generative AI capabilities and their potential downstream uses.
Acquisition Track
In partnership with George Washington University Law School, these sessions cover the fundamentals of AI procurement to understanding risk management and ethics in AI acquisition.
Buying AI: Government Contracts 101 A foundational overview of basic federal procurement policies and requirements as they relate to AI, so that attendees have a better understanding of the goals and constraints of U.S. federal acquisition. How Does AI Benefit the Federal Government? Customer needs and satisfaction are a foundational underpinning of the U.S. procurement system. This session will discuss the ways in which AI may benefit “the business” of the U.S. federal government. Risk Management & Ethics An overview of the ethical considerations and risk mitigation measures essential to responsible AI acquisition. Developing a Long-Term AI Acquisition Strategy This session will focus on special considerations for a long-term AI acquisition strategy, such as ensuring explainability, how AI could harm the federal government and U.S. citizens, as well as statutory and policy compliance, including meeting U.S. government technical best practices. National Security AI Acquisitions AI acquisitions to advance national security agency missions and programs or those that enhance tech stacks require special considerations of existing and new requirements. This session will survey the landscape and outline key considerations to make sound decisions. Data Privacy Considerations An overview of general and AI procurement-specific data privacy considerations that are crucial to an effective and compliant procurement strategy, including mitigating the risks of harms associated with AI. Compliance with AI-Related Regulations The acquisition of AI is governed by a myriad of regulatory requirements. This session will help attendees identify and understand the key regulations that govern the purchase of this technology.
Leadership and Policy Track
In partnership with the Center for Information Technology Policy (CITP) at Princeton University, these sessions explore AI policy development, ethical leadership, and strategic planning, ensuring leaders are well-prepared to handle the societal impacts of AI technologies.
AI Foundations for Decision Makers This session provides a comprehensive introduction to the science behind artificial intelligence, focusing on how AI systems work and their key technological features. It also equips decision-makers with the skills to identify misleading AI claims and distinguish them from genuine technological advances. AI Strategies and Insights This session looks at how the government can leverage AI for enhanced decision-making and operational efficiency. The session also introduces frameworks for the ethical adoption and implementation of AI tools in public sector initiatives. Risk & Mitigation This course examines the potential risks AI poses, such as bias and privacy concerns. The session offers practical strategies for mitigating these risks to ensure responsible and secure AI implementation. AI Auditing This session guides government leaders on how to audit AI systems to ensure they adhere to performance, safety, and ethical standards. The session emphasizes the critical role of rigorous assessments in maintaining accountability and trust in AI technologies within the public sector. Future Trends in AI This course examines emerging trends in artificial intelligence and provides insights on how to anticipate and prepare for future developments in the field. AI & Security This course explores the vulnerabilities of AI systems to potential attacks and methods for protecting against these threats. The session also highlights efforts to develop and identify AI that is reliable, safe, and trustworthy.
Access the 2023 Trainings Series
The recordings of the 2023 and 2024 sessions are available to government employees on the AI CoP USDA Connect page.
2024 AI Training Series Frequently Asked Questions
| 2023-02-01T00:00:00 |
https://coe.gsa.gov/communities/AITraining.html
|
[
{
"date": "2023/02/01",
"position": 37,
"query": "government AI workforce policy"
},
{
"date": "2023/12/01",
"position": 42,
"query": "government AI workforce policy"
},
{
"date": "2024/08/01",
"position": 39,
"query": "government AI workforce policy"
},
{
"date": "2024/12/01",
"position": 39,
"query": "government AI workforce policy"
},
{
"date": "2025/04/01",
"position": 85,
"query": "government AI workforce policy"
}
] |
|
Governor's Task Force on Workforce and Artificial Intelligence
|
Governor’s Task Force on Workforce and Artificial Intelligence
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https://dwd.wisconsin.gov
|
[] |
Artificial Intelligence (AI) technologies promise to profoundly shape the nature of work, altering the skills workers need for success, changing the ...
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Overview
Artificial Intelligence (AI) technologies are profoundly shaping the nature of work, altering the skills workers need for success, changing the competitive landscape for employers, and forcing educational and workforce development systems to overhaul their offerings in order to sustain a thriving Wisconsin economy. Already, Wisconsin employers and educational institutions are implementing AI applications in fields ranging from manufacturing and health care to transportation, agriculture, and the sciences.
At the same time, the rise of generative AI offers the potential to advance equity and economic opportunity for the people of Wisconsin. To help harness these technologies and strengthen Wisconsin's workforce for the 21st century and beyond, Gov. Tony Evers signed Executive Order #211, creating the Governor’s Task Force on Workforce and Artificial Intelligence.
The task force is expected to serve as a crucial mechanism to adapt and equip a workforce capable of capitalizing on this transformation. The task force is chaired by the secretary of the Department of Workforce Development or a designee with additional leadership from the secretary of the Department of Administration or a designee and the secretary of the Wisconsin Economic Development Corp. or a designee.
Task Force Membership
Other task force members to be appointed by the governor include:
A representative from the University of Wisconsin System,
A representative from the Wisconsin Technical College System, and
Other individuals including members from state and local government and individuals representing the business community, the education community, organized labor, the technology industry, and other leaders from impacted workforce sectors and industries.
Task Force Members
Task Force Charge
The Governor’s Task Force on Workforce and Artificial Intelligence is charged with gathering and analyzing information to produce an advisory action plan for the governor. The action plan is expected to:
Identify the current state of generative AI's impact on Wisconsin’s labor market,
Develop informed predictions regarding its opportunities and impact for the near term and into the future,
Identify how these workforce opportunities and impacts may touch Wisconsin's key industries, occupations, and foundational skillsets,
Explore initiatives to advance equity and economic opportunity in the face of these changes, and
Based on these findings, recommend policy directions and investments related to workforce development and educational systems to capitalize on the AI transformation.
Action Plan and Public Input
While the action plan will focus on potential policies and investments for the coming three to five years, the strategic directions it establishes may have far-reaching implications for workers, employers, job seekers, and economic development. To assure that Wisconsin communities continue to thrive – with an economy that works for everyone – the task force will provide opportunities for public engagement and input as its work progresses.
Task Force Objectives
| 2023-02-01T00:00:00 |
https://dwd.wisconsin.gov/ai-taskforce/
|
[
{
"date": "2023/02/01",
"position": 42,
"query": "government AI workforce policy"
},
{
"date": "2023/08/01",
"position": 41,
"query": "government AI workforce policy"
},
{
"date": "2023/10/25",
"position": 59,
"query": "AI employment"
},
{
"date": "2023/10/25",
"position": 41,
"query": "artificial intelligence workers"
},
{
"date": "2023/10/25",
"position": 3,
"query": "government AI workforce policy"
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{
"date": "2023/12/01",
"position": 29,
"query": "government AI workforce policy"
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{
"date": "2024/01/01",
"position": 42,
"query": "government AI workforce policy"
},
{
"date": "2024/07/01",
"position": 39,
"query": "government AI workforce policy"
},
{
"date": "2024/08/01",
"position": 43,
"query": "government AI workforce policy"
},
{
"date": "2024/12/01",
"position": 48,
"query": "government AI workforce policy"
},
{
"date": "2025/03/01",
"position": 31,
"query": "government AI workforce policy"
},
{
"date": "2025/04/01",
"position": 48,
"query": "government AI workforce policy"
},
{
"date": "2025/06/01",
"position": 39,
"query": "government AI workforce policy"
}
] |
|
Sustaining the County Government Workforce with Artificial ...
|
Doing More with Less: Sustaining the County Government Workforce with Artificial Intelligence
|
https://www.naco.org
|
[] |
Understand the impact of AI on workforce sustainability: Participants will gain insights into how AI and modern technologies are transforming local government ...
|
Today’s county government landscape is at a pivotal juncture, with artificial intelligence (AI), machine learning, and big data technologies spearheading a revolutionary shift. A transformation is essential in an era where service delivery, technological advancements, and evolving workforce dynamics intersect. County governments face significant challenges, including a dwindling workforce due to retirements, competition for employees with the private sector, and a new generation seeking technologically savvy work environments. Amidst these challenges lies a critical question: How can county governments efficiently serve their communities with fewer resources while meeting the increasing expectations of today’s public?
In this session, attendees will learn how AI and modern technologies are not merely tools for keeping pace with change but are strategic imperatives for sustaining and empowering the county government workforce. By highlighting adoptions of AI, from accelerating document processing to reducing field inspection times, we will show the power of these technologies to enhance productivity and service delivery.
Learning Objectives:
| 2023-02-01T00:00:00 |
https://www.naco.org/event/doing-more-less-sustaining-county-government-workforce-artificial-intelligence
|
[
{
"date": "2023/02/01",
"position": 44,
"query": "government AI workforce policy"
},
{
"date": "2023/12/01",
"position": 47,
"query": "government AI workforce policy"
},
{
"date": "2025/03/01",
"position": 50,
"query": "government AI workforce policy"
},
{
"date": "2025/06/01",
"position": 49,
"query": "government AI workforce policy"
}
] |
|
4 ambitious government initiatives preparing the workforce for a ...
|
4 ambitious government initiatives preparing the workforce for a future of AI
|
https://rossdawson.com
|
[] |
Singapore's SkillsFuture Program · UK's National Retraining Scheme · France's Compte Personnel de Formation (CPF) · American Workforce Policy Advisory Board.
|
Futurist > Implications of AI > Government workforce initiatives
4 ambitious government initiatives preparing the workforce for a future of AI
Governments all around the world have implemented initiatives and programs to prepare their populations for a world driven by AI. Many of them rely on their own unique approaches, from subsidized retraining courses in Singapore to personal training accounts in France. These initiatives can serve as an inspiration for other governments around the world, providing different approaches to dealing with the results of AI.
Here are four current government initiatives for preparing the workforce for a future of AI.
Singapore’s SkillsFuture Program
One nation with an effective approach to AI technology in the workplace is Singapore. In 2016, the country launched its SkillsFuture program, a national re-education model created to address concerns about the changing economy.
Since its launch, SkillsFuture has seen the participation of over 285,000 people, which is more than 10% of adult residents in Singapore. A study found that 43% of CIOs in the country believe the program helps address the skills shortage, and 56% stated that they are confident about the workforce’s ability to adapt to market changes. Like many countries, it is only a start, with 67% stating that there is still a need for more external training initiatives.
Singapore’s government, working through the program, reimburses its citizens up to SG$500 per year for retraining courses. The courses must be approved, and the government has a partnership with universities and online educational platforms. Through this partnership, citizens can sign up with their reimbursement to take classes in technology-related fields.
The program is even better for individuals over the age of 40, who can receive subsidies of up to 90% on training costs. Since July 2019, small and medium-sized businesses have been able to apply for a SkillsFuture grant to cover most of the training costs for employees.
According to the program, “Skills mastery is more than having the right paper qualifications and being good at what you do currently – it is a mindset of continually striving towards greater excellence through knowledge, application and experience.”
UK’s National Retraining Scheme
In 2018, the UK government announced the National Retraining Scheme (NRS). The path forward for the program is currently being debated, but the original funding amount was £100 million. The scheme consists of multiple initiatives that are aimed at preparing individuals for the future of work.
The first part of the scheme is called Get Help to Retrain and was tested in 6 areas across the nation, with plans for it to be expanded in 2020. This initiative is aimed at adults who are 24 years old and above, do not hold a degree and are low or medium wage. Through the use of online and in-person training, workers can improve their technical skills.
The scheme also provides qualified national careers service advisors who can offer guidance around new jobs and opportunities. The advisers are overseen by the National Retraining Partnership, which has representatives from the government, CBI, and Trades Union Congress.
The UK is set to be one of the harder hit nations with the changing economy. According to the Organisation of Economic Cooperation and Development (OECD), 13.7% of workers will require moderate training of up to a year to transition to more secure jobs, compared to the world average of 10.9%.
France’s Compte Personnel de Formation (CPF)
France is a nation that does not receive as much attention as others when it comes to artificial intelligence in the workplace. However, they do have an ambitious program that is currently taking place called Compte Personnel de Formation (CPF).
Employees in France that are over the age of 16 can gain new skills through the use of personal training accounts. Individuals use the accounts to collect hours that can then be traded for training courses. While employees complete the training courses, they are guaranteed paid leave from work.
In 2016, the government approved around 500,000 filed requests to use the collected hours, 65% coming from job-seekers and 35% from employed workers. That was a 139% increase from the previous year, a very promising trend. Some of the most popular courses were preparatory courses for exams to acquire language and IT certificates.
American Workforce Policy Advisory Board
In 2019, the United States established the American Workforce Policy Advisory Board due to the need to fill 7.3 million job openings, which were largely caused by the skills gap from the evolving economy. The Advisory Board is tasked with creating more than 6.5 million education, training, and skill-building opportunities over five years.
The Advisory Board includes 25 prominent leaders in the country, including the CEOs of companies like Apple, Lockheed Martin, Siemens USA, and IBM. The initiative brings together the private sector, educational institutions, and state and local governments. By working together, the goal is to create an effective approach to addressing workforce issues in communities and businesses throughout the country.
The Advisory Board assists in developing a national campaign to promote education and training pathways, recommending a course of action for improving labor market data, identifying strategies to improve private sector investments in American students, establishing a culture of lifelong learning, and increasing transparency and outcomes of education and job training programs. This initiative is a good example of how nations should be bringing together all the different sectors of a society to address workforce issues regarding automation, AI technology, and the skills gap.
Government’s Role
Governments all around the globe will play a key role in preparing the workforce for a world driven by AI, holding a unique position to create incentives and establish policies that protect workers. They are responsible for pushing these initiatives out to the sector of the population that is most at-risk, those who might not have the means to begin their own re-education process.
The approach taken for employed and unemployed individuals will need to be different. Governments will be most responsible for those without jobs, but it is contingent upon the private sector taking responsibility for their own workers. Many individuals will lose their jobs because their skills are in many cases completely incompatible with the new economy, as opposed to employed individuals who will be forced to build on their current skills.
In order to get those unemployed people back into the workforce, governments will need to lead big re-education programs, since the private sector has few reasons to help those outside their own companies. For this, initiatives like Singapore’s SkillsFuture program can serve as inspiration.
Governments can put money into the hands of these individuals, through reimbursements or other means like UBI, which will provide the opportunity to gain skills in the fields relevant to AI.
In the case of UBI, which is likely to become a reality in many nations, perhaps a portion of it can be sectioned off for education. Governments will save money in the long-term by avoiding serious economic hardships due to the skills gap, AI, and automation.
By establishing advisory boards similar to the American Workforce Policy Advisory Board, industry leaders can advise governments on what skills are in demand. Governments can then partner with educational institutions to establish approved courses relevant to those skills.
While the private sector will hold most of the responsibility for employed individuals, governments should still have a presence. One major step they can take is to give grants and create tax incentives to encourage employer-led programs. Another option is to establish personal training accounts, as in the case of France. This can address the skills gap problem early on, allowing training hours to be collected as soon as an individual reaches the legal working age. These training hours, as well as the courses that they can be exchanged for, act as a security blanket for employed individuals. Governments should be looking at new ways like this to provide job security, during a time when it is decreasing rapidly.
In order for any of this to help avert massive unemployment due to AI technology, it needs to start immediately. In places where initiatives are already taking place, there needs to be a drastic expansion. AI technology is exponentially increasing, meaning the workforce is seeing its implementation faster as time goes on. There will not be enough time for governments to implement these massive programs when the technology reaches its peak.
Governments are up against the greatest technological revolution humans have ever experienced, which can either improve the quality of life for populations all around the globe or drastically widen the inequalities that already exist. The outcome greatly depends on the actions governments take now.
Author: Alex McFarland is a journalist who covers developments in AI
| 2023-02-01T00:00:00 |
https://rossdawson.com/futurist/implications-of-ai/government-initiatives-preparing-workforce-future-ai-artificial-intelligence/
|
[
{
"date": "2023/02/01",
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"query": "government AI workforce policy"
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"date": "2023/12/01",
"position": 51,
"query": "government AI workforce policy"
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"date": "2024/01/01",
"position": 50,
"query": "government AI workforce policy"
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"position": 49,
"query": "government AI workforce policy"
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"query": "government AI workforce policy"
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{
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"query": "government AI workforce policy"
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"position": 15,
"query": "government AI workforce policy"
},
{
"date": "2025/06/01",
"position": 52,
"query": "government AI workforce policy"
}
] |
|
How the public sector can prepare for AI in the workforce | EY - US
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How the public sector can prepare for AI in the workforce
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https://www.ey.com
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Equipped with an understanding of how roles and functions are likely to shift with the increased adoption of AI, government agencies should then take steps to ...
|
Behind the AI “buzz,” thousands of organizations in both the public and private sector are moving quickly to understand these technologies, their potential uses and how to integrate them into their operations. According to IBM, 77% of companies are either currently using AI in their operations or exploring its use for future implementation.
And while AI promises to enhance customer and constituent experience, streamline processes, and increase speed to service, among other benefits, it also poses its share of risks. Chief among them is the threat of displacing potentially hundreds of millions of jobs as its capabilities advance to be able to perform key tasks faster and often with higher quality than humans. In the US alone, experts predict approximately 46% of the current workforce to be affected by AI-related disruptions by 2030, according to Forbes.
The future of AI in the workforce isn’t grim, however. AI’s entrance in the workforce, particularly in the public sector, presents a tremendous opportunity for organizations to employ talent in ways they never have before – by tapping into the uniquely human capabilities of their civil servants and utilizing employees in high-value ways. For organizations looking to implement AI into their operations, striking the balance between leveraging AI’s tremendous benefits, managing its risks and capitalizing on the opportunities it provides will be critical to successfully navigating the AI era.
To navigate the age of AI, government agencies around the world are acting now to understand AI and its impact on the public workforce. By understanding and actively preparing for AI in the workforce in the coming years, government agencies can choose to champion their civil servants by taking action such as building new career pathways for at-risk employees, implementing tailored upskilling programs aimed at building AI-resistant skill sets, and changing the recruitment criteria for new candidates.
| 2024-03-14T00:00:00 |
2024/03/14
|
https://www.ey.com/en_us/insights/government-public-sector/how-the-public-sector-can-prepare-for-ai-in-the-workforce
|
[
{
"date": "2023/02/01",
"position": 53,
"query": "government AI workforce policy"
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{
"date": "2023/10/05",
"position": 83,
"query": "AI employment"
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{
"date": "2023/10/05",
"position": 6,
"query": "government AI workforce policy"
},
{
"date": "2023/10/05",
"position": 85,
"query": "workplace AI adoption"
},
{
"date": "2023/12/01",
"position": 52,
"query": "government AI workforce policy"
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{
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"position": 26,
"query": "government AI workforce policy"
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{
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"position": 24,
"query": "government AI workforce policy"
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"query": "government AI workforce policy"
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{
"date": "2024/12/01",
"position": 20,
"query": "government AI workforce policy"
},
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"date": "2025/03/01",
"position": 54,
"query": "government AI workforce policy"
},
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"date": "2025/04/01",
"position": 56,
"query": "government AI workforce policy"
},
{
"date": "2025/06/01",
"position": 51,
"query": "government AI workforce policy"
}
] |
Empowering the Government Workforce with the AI PC
|
Empowering the Government Workforce with the AI PC
|
https://ironbow.com
|
[
"Darren Pulsipher",
"Darren Is The Chief Solution Architect For Public Sector At Intel. He Works Directly With Governments",
"Federal",
"State",
"Local",
"Enterprise Organizations Such As Ibm",
"Ge",
"Toyota To Help Them Modernize Their It Organizations. Through Several Executive",
"Management Positions",
"Cio"
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Integrating Artificial Intelligence (AI) into personal computers is revolutionizing the way we work, especially how work gets done in the public sector.
|
Integrating Artificial Intelligence (AI) into personal computers is revolutionizing the way we work, especially how work gets done in the public sector. Gone are the days when AI capabilities were confined to specialized servers or cloud-based solutions. Thanks to solutions like Intel’s AI PC offerings, the government workforce now has access to AI tools right at their desktops, opening the door to all types of applications be it personal assistants, document and policy analysis, language interpretation, and real-time translation.
With AI acceleration seamlessly integrated into every Intel® Core™ Ultra processor, the possibilities are boundless, heralding a new era where artificial intelligence is truly for everyone.
Unlocking Individual Efficiencies
AI's integration into laptops and workstations brings a new dawn of efficiency for government personnel. Mundane tasks that once consumed valuable time and energy can now be automated or streamlined with AI algorithms. From digesting lengthy reports to parsing complex datasets, AI empowers employees to focus on strategic initiatives.
Furthermore, AI-driven automation tackles routine administrative tasks, liberating employees to concentrate on high-value work. This shift not only boosts individual productivity but also enhances overall efficiency by optimizing resource allocation.
A Shift in Work Dynamics
As AI becomes ingrained in everyday workflows, government agencies are witnessing a transformative shift in work dynamics. Seamless collaboration is facilitated as AI-driven insights inform decision-making processes. Armed with real-time data analytics and predictive modeling, government teams make informed choices, driving better outcomes for the public.
Moreover, AI fosters a culture of innovation within agencies, encouraging teams to explore novel ways of leveraging technology to address complex challenges. This innovative spirit is paramount for driving progress and ensuring government services remain effective and relevant.
Real-World Example: The California DMV
The California Department of Motor Vehicles (DMV) exemplifies AI's transformative impact on government service delivery. Amidst the COVID-19 lockdown, the DMV leveraged AI-driven solutions to digitize processes and enhance the customer experience.
Through AI-powered self-service applications and virtual assistants, the DMV streamlined service delivery, reduced wait times, and provided superior customer support, even during a global pandemic. This successful integration of AI underscores the tangible benefits of technological innovation in improving public service delivery.
Training for the Future
To fully harness the potential of AI PC technology, government agencies must invest in training and upskilling initiatives for their personnel. Equipping employees with the necessary skills and knowledge to leverage AI effectively is vital for maximizing its benefits.
Training programs should encompass a range of topics, including AI fundamentals, data analytics, machine learning, and cybersecurity. By prioritizing continuous learning and development, government agencies ensure their workforce remains adaptable and resilient in the face of technological advancement.
Ultimately, the integration of AI acceleration into processors represents a paradigm shift for government agencies. By empowering individual employees with AI tools and fostering a culture of innovation and learning, agencies across the Federal Government and at the state and local level can drive efficiency, improve service delivery, and ultimately better serve the public.
Stay tuned for the next installment in our series, where we delve into the significance of context-aware models in government workflows and their role in shaping the future of public service.
Ready to see how AI PCs can empower your workforce? Reach out to see how our team of experts at Iron Bow Technologies and partners like Intel can help.
| 2023-02-01T00:00:00 |
https://ironbow.com/techsource/empowering-the-government-workforce-with-the-ai-pc
|
[
{
"date": "2023/02/01",
"position": 57,
"query": "government AI workforce policy"
},
{
"date": "2023/08/01",
"position": 55,
"query": "government AI workforce policy"
},
{
"date": "2023/12/01",
"position": 58,
"query": "government AI workforce policy"
},
{
"date": "2024/01/01",
"position": 53,
"query": "government AI workforce policy"
},
{
"date": "2024/07/01",
"position": 53,
"query": "government AI workforce policy"
},
{
"date": "2024/08/01",
"position": 53,
"query": "government AI workforce policy"
}
] |
|
US federal AI governance: Laws, policies and strategies - IAPP
|
US federal AI governance: Laws, policies and strategies
|
https://iapp.org
|
[
"Müge Fazlioglu",
"Cipp E",
"Cipp Us"
] |
when setting regulatory policy for AI-enabled products" and fostering a federal workforce with diverse perspectives on AI technology. The report built on three ...
|
Resource Center / Resource Articles / US federal AI governance: Laws, policies and strategies
US federal AI governance: Laws, policies and strategies
This article provides a breakdown of artificial intelligence governance at the federal level, including the White House, Congress and federal agencies.
Contributor:
Navigate by Topic
Halfway into 2023, generative artificial intelligence tools such as OpenAI's ChatGPT have achieved growing and sustained popularity. In May, chat.openai.com received about 1.8 billion visits over the previous month, with an average visit duration of eight and a half minutes.
Yet, as AI is adopted around the world, it raises as many questions as it provides answers. Chief among these questions is: How should AI be governed?
AI governance around the world
With AI making inroads into every sphere of life, lawmakers and regulators are working to regulate the technology in ways that appreciate its full range of potential effects — both the benefits and the harms. Unsurprisingly, countries have taken differing approaches to AI, each reflective of their respective legal systems, cultures and traditions.
On 11 May, European Parliament voted in favor of adopting the Artificial Intelligence Act, which, in its current form, bans or limits specific high-risk applications of AI. The law is now set for plenary adoption in June, which will trigger trilogue negotiations between Parliament, the European Commission and the Council of the European Union.
In the U.K., Secretary of State for Science, Innovation and Technology Michelle Donelan recently released a white paper, aiming to establish the U.K. as an "AI superpower." The strategy provides a framework for identifying and addressing risks presented by AI while taking a "proportionate" and "pro-innovation" approach.
In Canada, the proposed Artificial Intelligence and Data Act is part of a broader update to the country's information privacy laws, and is one of three pieces of legislation that comprise Bill C-27, which passed its second reading in the House of Commons in April.
Singapore's National AI Strategy, meanwhile, consists of the 2019 launch of its Model AI Governance Framework, its companion Implementation and Self-Assessment Guide for Organizations and Compendium of Use Cases, which highlights practical examples of organizational-level AI governance.
And, on 11 April, the Cyberspace Administration of China released its draft Administrative Measures for Generative Artificial Intelligence Services, which aim to ensure content created by generative AI is consistent with "social order and societal morals," avoids discrimination, is accurate and respects intellectual property.
AI governance policy at the White House
Within the context of these global developments in AI law and policymaking, a federal AI governance policy has also taken shape in the U.S. The White House, Congress, and a range of federal agencies, including the Federal Trade Commission, the Consumer Financial Protection Bureau and the National Institute of Standards and Technology, have put forth a series of AI-related initiatives, laws and policies. While numerous city and state AI laws also came into effect over the years, federal laws and policies around AI are of heightened importance in understanding the country's unique national AI strategy. Indeed, the foundation of the federal government's AI strategy has already been established and provides insight into how the legal and policy questions brought about by this new technology will be approached in the months and years ahead.
AI governance policy in Congress
The deliberative branch of government, Congress, has approached AI law and policymaking in its characteristically incremental fashion. Until 2019, most of lawmakers' attention around AI was absorbed by autonomous or self-driving vehicles and concerns about AI applications within the national security arena.
For example, in the 115th Congress in 2017-2019, Section 238 of the John S. McCain National Defense Authorization Act for Fiscal Year 2019 directed the Department of Defense to undertake various AI-related activities, including the appointment of a coordinator to oversee activities in the realm. This Act also codified (at 10 U.S.C. § 2358) a definition of AI, which is:
"Any artificial system that performs tasks under varying and unpredictable circumstances without significant human oversight, or that can learn from experience and improve performance when exposed to data sets.
An artificial system developed in computer software, physical hardware, or another context that solves tasks requiring human-like perception, cognition, planning, learning, communication, or physical action.
An artificial system designed to think or act like a human, including cognitive architectures and neural networks.
A set of techniques, including machine learning, that is designed to approximate a cognitive task.
An artificial system designed to act rationally, including an intelligent software agent or embodied robot that achieves goals using perception, planning, reasoning, learning, communicating, decision-making, and acting."
Another key AI-related legislative development occurred when the National AI Initiative Act of 2020 became law on 1 Jan. 2021. Included as part of the William M. (Mac) Thornberry National Defense Authorization Act for Fiscal Year 2021, this legislation focused on expanding AI research and development and further coordinating AI R&D activities between the defense/intelligence communities and civilian federal agencies. The Act also legislated the creation of the National Artificial Intelligence Initiative Office, which sits within the White House OSTP and is tasked with "overseeing and implementing the U.S. national AI strategy."
Congress has also amended existing laws and policies to account for the increasing use of AI in various arenas. For example, in passing the FAA Reauthorization Act of 2018, Congress added language to advise the Federal Aviation Administration to periodically review the state of AI in aviation and for it to take necessary steps to address new developments. The Advancing American AI Act and the AI Training Act were among other AI-related pieces of legislation introduced or passed by the 117th Congress.
expand_more Recently proposed legislation related to AI Within the current 118th Congress other bills have also been proposed to amend existing laws and better equip them for the AI era. Proposed in May 2023, HR 3044 would amend the Federal Election Campaign Act of 1971 to provide transparency and accountability around the use of generative AI in political advertisements. Also, in January, House resolution 66 was introduced, expressing support for Congress to focus more on AI. The stated goal of the resolution was to "ensure that the development and deployment of AI is done in a way that is safe, ethical, and respects the rights and privacy of all Americans, and that the benefits of AI are widely distributed, and the risks are minimized." Other federal privacy bills have also sought to regulate various uses of AI. The Stop Spying Bosses Act would prohibit employers from engaging in workplace surveillance using automated decision systems, including ML and AI techniques, to predict the behavior of their workers. Many recently proposed federal privacy bills are also already cognizant of AI. The definition of a "covered algorithm" within the American Data Privacy and Protection Act, for example, includes computational processes that use ML, natural language processing or AI techniques. Among other proposed rules, the most recent version of the ADPPA requires impact assessments of such systems if certain entities use them "in a manner that poses a consequential risk of harm to an individual or group of individuals." Separately, it would require the documentation of an "algorithm design evaluation" process to mitigate risks whenever a covered entity develops a covered algorithm "solely or in part, to collect, process, or transfer covered data in furtherance of a consequential decision." Similarly, the Filter Bubble Transparency Act would apply to platforms that use "algorithmic ranking systems," which includes computational processes "derived from" AI. In addition, the SAFE DATA Act includes both above-mentioned definitions. Lastly, the Consumer Online Privacy Rights Act would also regulate "algorithmic decision-making" defined similarly to include computational processes derived from AI. Moving forward, comprehensive federal privacy bills may also become more explicit in their treatment of AI. Moreover, bills drafted in previous sessions may be reintroduced and further amended to account for the risks/opportunities presented by AI. Several congressional hearings on AI have also recently been held. Both the House Armed Services' Subcommittee on Cyber, Information Technologies, and Innovation, and the Senate Armed Services Subcommittee on Cybersecurity met in March and April, respectively, to discuss AI and ML applications to improve DOD operations. On 16 May, the Senate Judiciary Subcommittee on Privacy, Technology and the Law held a hearing titled "Oversight of A.I.: Rules for Artificial Intelligence," while the Senate Committee on Homeland Security and Governmental Affairs held a full committee meeting, "Artificial Intelligence in Government," the same day.
AI governance policy within federal agencies
Virtually every federal agency has played an active role in advancing the AI governance strategy within the federal government and, to a lesser extent, around commercial activities. One of the first to do so was the NIST, which published "U.S. LEADERSHIP IN AI: A Plan for Federal Engagement in Developing Technical Standards and Related Tools" in August 2019 in response to EO 13859. The report identified areas of focus for AI standards and laid out a series of recommendations for advancing national AI standards development in the U.S. NIST's AI Risk Management Framework, released in January, also serves as an important pillar of federal AI governance and is an oft-cited model for private sector activities.
By mid-2020, the FTC entered the picture to provide the contours of its approach to AI governance, regulation and enforcement. Its guidance has emphasized the agency's focus on companies' use of generative AI tools. Questions about whether firms are using generative AI in a way that, "deliberately or not, steers people unfairly or deceptively into harmful decisions in areas such as finances, health, education, housing, and employment," fall within the FTC's jurisdiction.
In late April, the FTC, along with the CFPB, the Justice Department's Civil Rights Division and the Equal Employment Opportunity Commission, issued a joint statement, clarifying that their enforcement authorities apply to automated systems, which they define as "software and algorithmic processes, including AI, that are used to automate workflows and help people complete tasks or make decisions." In line with these promises, the EEOC also released a bulletin around its interpretation of existing antidiscrimination rules in employment, specifically, Title VII of the Civil Rights Act of 1964, as they apply to the use of AI-powered systems.
Meanwhile, the National Telecommunications and Information Administration has issued an "AI Accountability Policy Request for Comment," seeking public feedback on policies to "support the development of AI audits, assessments, certifications and other mechanisms to create earned trust in AI systems," with written responses due 12 June. The NTIA will likely use the information it receives to advise the White House on AI governance policy issues.
Numerous other U.S. agencies have led their own AI initiatives and created AI-focused offices within their departments. For example, the Department of Energy's AI Intelligence and Technology Office developed an AI Risk Management Playbook in consultation with NIST and established an AI Advancement Council in April 2022. Within the Department of Commerce, the U.S. Patent and Trademark Office created an AI/emerging technologies partnership to examine and better understand use of these technologies in patent and trademark examination and their effect on intellectual property.
More recently, the U.S. Department of Education Office of Educational Technology released a report on the risks and opportunities AI presents within and educational settings.
AI governance policy and existing laws
A key point emphasized by U.S. regulators across multiple federal agencies is that current laws do apply to AI technology. Indeed, at least in the short term, AI regulation in the U.S. will consist more of figuring out how existing laws apply to AI technologies, rather than passing and applying new, AI-specific laws. In their joint statement, the FTC, EEOC, CFPB and Department of Justice noted how "existing legal authorities apply to the use of automated systems and innovative new technologies just as they apply to other practices." Expressing concern about "potentially harmful uses of automated systems," the agencies emphasized that they would work "to ensure that these rapidly evolving automated systems are developed and used in a manner consistent with federal laws."
On numerous occasions, the FTC stated the prohibition of unfair or deceptive practices in Section 5 of the FTC Act applies to the use of AI and ML systems. In its business guidance on using AI and algorithms, the FTC explained the Fair Credit Reporting Act of 1970 and the Equal Credit Opportunity Act of 1974 "both address automated decision-making, and financial services companies have been applying these laws to machine-based credit underwriting models for decades."
Separately, the CFPB issued a circular clarifying the adverse action notice requirement of the Equal Credit Opportunity Act and its implementing Regulation B, requiring creditors to explain the specific reasons why an adverse credit decision was taken against an individual, still applies even if the credit decision is based on a so-called "uninterpretable or 'black-box' model." Such complex algorithm models may make it difficult — or even impossible — to accurately identify the specific reason for denial of credit. Yet, as the CFPB further noted, creditors cannot rely on post-hoc explanation methods and they must be able to "validate the accuracy" of any approximate explanations they provide. Thus, this guidance interprets the ECOA and Regulation B as not permitting creditors to use complex algorithms to make credit decisions "when doing so means they cannot provide the specific and accurate reasons for adverse actions." In his keynote address at the IAPP Global Privacy Summit 2023, FTC Commissioner Alvaro Bedoya echoed this point, explaining the FTC "has historically not responded well to the idea that a company is not responsible for their product because that product is a black box that was unintelligible or difficult to test."
Conclusion
Around the world, and particularly in the U.S., the most pressing questions around AI governance concern the applicability of existing laws to the new technology. Answering these questions will be a difficult task involving significant legal and technological complexities. Indeed, as the Business Law Section of the American Bar Association explained in its inaugural Chapter on Artificial Intelligence, "Companies, counsel, and the courts will, at times, struggle to grasp technical concepts and apply existing law in a uniform way to resolve business disputes."
Pro-social applications of AI abound, from achieving greater accuracy than human radiologists in breast cancer detection to mitigating climate change. Yet, anti-social applications of AI are no less numerous, from aiding child predators in avoiding detection to facilitating financial scams.
AI can be neither responsible nor irresponsible in and of itself. Rather, it can be used or deployed — by people and organizations — in responsible and irresponsible ways. It is up to lawmakers to determine what those uses are, how to support the responsible ones and how to prohibit the irresponsible ones, while professionals who create and use AI work to implement these governance principles into their daily practices.
Additional resources
| 2023-02-01T00:00:00 |
https://iapp.org/resources/article/us-federal-ai-governance/
|
[
{
"date": "2023/02/01",
"position": 59,
"query": "government AI workforce policy"
},
{
"date": "2023/06/13",
"position": 33,
"query": "AI labor union"
},
{
"date": "2023/06/13",
"position": 7,
"query": "AI regulation employment"
},
{
"date": "2023/06/13",
"position": 1,
"query": "government AI workforce policy"
},
{
"date": "2023/08/01",
"position": 57,
"query": "government AI workforce policy"
},
{
"date": "2023/12/01",
"position": 60,
"query": "government AI workforce policy"
},
{
"date": "2024/01/01",
"position": 60,
"query": "government AI workforce policy"
},
{
"date": "2024/07/01",
"position": 57,
"query": "government AI workforce policy"
},
{
"date": "2024/08/01",
"position": 58,
"query": "government AI workforce policy"
},
{
"date": "2024/12/01",
"position": 55,
"query": "government AI workforce policy"
},
{
"date": "2025/03/01",
"position": 65,
"query": "government AI workforce policy"
},
{
"date": "2025/04/01",
"position": 79,
"query": "government AI workforce policy"
},
{
"date": "2025/06/01",
"position": 62,
"query": "government AI workforce policy"
}
] |
|
Artificial Intelligence - FY 2024 Human Capital Reviews - OPM
|
FY 2024 Human Capital Reviews
|
https://www.opm.gov
|
[] |
Sixteen of the 24 agencies either have workforce planning strategies in place to meet AI needs or are in progress of developing them. Having these strategies in ...
|
Trends
Agencies are at different levels of maturity when it comes to AI. In terms of AI-specific workforce planning activities, many are in the early stages. Most agencies are working to identify the AI and AI-enabling talent already on board while exploring opportunities to upskill the existing workforce. Sixteen of the 24 agencies either have workforce planning strategies in place to meet AI needs or are in progress of developing them. Having these strategies in place to retain and upskill potential AI talent is a step agencies can take to be well-equipped to meet AI requirements.
Within many agencies, internal communities of practice and working groups have been formed; some formally and others ad hoc. These groups are serving as collaboration hubs, and they are commonly being used to support training initiatives and information sharing. Some agencies are using their formal working group and governance bodies to identify and prioritize skill and position needs.
Recruitment and hiring efforts for most are centered around the AI Executive Order (EO) requirement to establish the Chief AI Officer position, along with other core advisory and technical roles (e.g., governance, risk management, infrastructure). In each of the agency meetings, OPM shared available AI-related resources including Pay Flexibility, Incentive Pay, and Leave and Workforce Flexibility Programs for AI, AI-enabling and Other Key Technical Employees; the AI Classification Policy and Talent Acquisition Guidance; and Guidance and Policy on Skills-based, Federal Government-wide Hiring of AI, Data, and Technology Talent. A number of agencies indicated they were leveraging Direct Hire Authorities, Intergovernmental Personnel Act (IPAs), and fellowships (e.g., Presidential Management Fellows and U.S. Digital Corps) to address AI hiring needs.
Some agencies described progress with hiring and gap analysis and use of AI skills assessments, including the Social Security Administration (SSA) and Department of Defense (DoD). The Department of Homeland Security (DHS) described establishing an internal AI Corps that would serve as a centralized talent pool to be deployed based on need. Multiple agencies referenced a desire to weave AI skills into the broader workforce, and that they were incorporating AI language into position descriptions and job postings.
There is agreement across agencies that implementing AI will impact how work roles are designed and that keeping a people-first mindset will be critical as agencies integrate AI into the workforce. The need for training to enhance AI capacity was consistently referenced, specifically foundational and leadership training. Many agencies highlighted their ability to build upon data literacy efforts, to include AI, through related course tracks. NASA, for example, described Digital Academy content for data literacy, sandbox trainings, AI, and digital engineering topics available on its training platform. NASA also described its “Summer of AI” activities, designed to spread awareness and connect the workforce to AI-related resources and experts.
Across the board, agencies highlighted the importance of partnering throughout their components to reduce duplication of effort and to share resources. Additionally, many agencies included representatives from their data, CIO, and learning teams in the HCR discussion on AI.
People First Enhancing AI capacity through training and other initiatives is underway in the following agencies: Department of Labor
Small Business Administration
Department of Transportation
Department of State
Department of Veterans Affairs
Department of Treasury
AI Use Cases
In terms of HR use cases, most agencies are in the brainstorming stage, although many shared that AI had been used in other parts of their agencies for some time. Ideas on the use of AI in HR include training curation and search features, onboarding, processing functions (e.g., awards, personnel actions), classification, and HR assistant capabilities. Agency use cases include:
NASA – developed NASA’s OCHCO Virtual Assistant (NOVA), a software program designed to simulate human-like conversations in response to user requests and queries
State – developed the Civil Service Career Pathing tool, a career development resource for Civil Service employees to learn about career mobility options and development opportunities based on self-identified skills and interests
DOI – working on machine learning prototypes for competency matching with the “My DOI Career” tool
SBA – introduced an AI-simulated coach through the agency Learning Management System that provides a safe space to practice important business conversations (e.g., providing performance feedback)
NRC – exploring knowledge management and search capabilities
DoD – exploring a tool that allows the extraction of skills, experiences, and characteristics from resumes and applicant information
DOL – has a number of use cases for form recognition, including benefits and claims
Challenges
The challenges at the forefront of agencies’ minds are:
budget
data and IT infrastructure
risk management
To best leverage available tools, agencies want to establish a foundational understanding amongst the workforce before implementing AI broadly. This is both to develop AI awareness and to mitigate fears and uncertainty around the impact of emerging technologies. Agencies including NASA, State, and SBA referenced multi-pronged approaches to providing AI resources to the workforce, including training offerings, communities of interest, working groups, office hours, brown bags, and communication campaigns.
Recruitment and retention surfaced as concerns, specifically how to be competitive with the private sector and how to keep highly sought after talent long term. Some approaches to assist recruitment and retention are leveraging pay and leave flexibilities, bringing on early career talent, improved storytelling around mission and public service, and taking advantage of resources such as shared certificates and hiring events. DoD and NSF referenced their use of partnerships to supplement their technical workforce, DoD through an industry consortium that helps them meet talent needs, and NSF through its Visiting Scientist, Engineer, and Educator (VSEE) authority.
People First
Agencies that establish a foundational understanding of AI develop awareness and mitigate fears and uncertainty of the impact of the emerging technologies on the workforce and roles.
Solutions
In support of agency efforts to hire the right people with the right skills to leverage AI, OPM will continue to build on its work to support federal agencies to bring on AI talent. In the first quarter of FY25, OPM hosted its first ever Federal Human Capital AI Summit. In consideration of CHCO feedback and input from key stakeholders across OPM, the one-day event, held in person at OPM’s Theodore Roosevelt Building, brought together over 200 attendees representing over 40 agencies and components. Human capital and transformation leaders, data experts, IT professionals, privacy officers, and learning officers from across Government heard from both federal and private industry leaders grappling with how to leverage AI in the human capital space while mitigating risks. OPM’s goal is to continue building and cultivating a federal AI community positioned to drive innovation in the human capital space by providing:
Opportunities to share thought leadership in the use of AI in federal human capital.
Rich discussions around AI foundational knowledge, guidance, strategy, tools, commonality, and support available for federal agencies to advance human capital goals and priorities.
Space for federal agency HR leaders and staff to connect with one another on AI topics and activities.
In support of Executive Order 14110 on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence and Public Law 116-260, the AI in Government Act of 2020 (the Act), OPM issued skills-based hiring guidance and a competency model for AI, data, and technology talent on April 29, 2024. The guidance assists agencies in identifying key skills and competencies needed for AI professionals and increases access to these technical roles for individuals with nontraditional academic backgrounds. Additionally, OPM plans to publish an AI and Tech Talent playbook. This playbook consolidates Federal resources from OPM, the Office of Management and Budget, and others to allow agencies to recruit and effectively utilize AI and tech talent. OPM has also hosted Tech to Gov Virtual Hiring Forms, including an AI focused event, to help attract prospective federal employees, attracting over 10,000 prospective federal employees. Lastly, OPM developed and delivered an AI Fundamentals learning series to nearly 18,000 employees, supervisors, and senior executives across more than 100 agencies, providing a foundational understanding of AI and preparing them to engage in informed discussions and effectively implement approved AI tools.
| 2023-02-01T00:00:00 |
https://www.opm.gov/policy-data-oversight/fy-2024-human-capital-reviews/artificial-intelligence/
|
[
{
"date": "2023/02/01",
"position": 62,
"query": "government AI workforce policy"
},
{
"date": "2023/08/01",
"position": 62,
"query": "government AI workforce policy"
},
{
"date": "2023/12/01",
"position": 56,
"query": "government AI workforce policy"
},
{
"date": "2024/01/01",
"position": 17,
"query": "government AI workforce policy"
},
{
"date": "2024/07/01",
"position": 20,
"query": "government AI workforce policy"
},
{
"date": "2024/08/01",
"position": 21,
"query": "government AI workforce policy"
},
{
"date": "2024/12/01",
"position": 19,
"query": "government AI workforce policy"
},
{
"date": "2025/03/01",
"position": 16,
"query": "government AI workforce policy"
},
{
"date": "2025/04/01",
"position": 11,
"query": "government AI workforce policy"
},
{
"date": "2025/06/01",
"position": 15,
"query": "government AI workforce policy"
}
] |
|
AI Government Leadership Program - Partnership for Public Service
|
AI Government Leadership Program
|
https://ourpublicservice.org
|
[
"Partnership For Public Service"
] |
Artificial intelligence has the potential to improve how government works—more so than any other recent technological innovation.
|
FOR FEDERAL GOVERNMENT EMPLOYEES
AI Government Leadership Program
Artificial intelligence has the potential to improve how government works—more so than any other recent technological innovation. From increasing efficiency to finding data insights that enhance the customer experience, AI is an invaluable tool for government leaders to serve the public and transform their agencies. But to capitalize on these benefits, leaders must understand AI fundamentals and how to use AI effectively.
With support from Microsoft and Google.org, we are creating a cohort of senior leaders across government who are prepared to guide their agencies’ AI strategy. This program is designed to:
Educate agency decision-makers on the opportunities around AI.
Highlight best practices for how to make the case for and develop AI solutions.
Prepare leaders to incorporate AI technology into their strategies and equip their workforce.
The Partnership has extensive experience delivering leadership development programs that support government employees at all levels. Building on our success running the program for federal audiences, we are also offering this program to executives at the state and local government levels. This program is offered at no cost to:
GS-15 federal employees
Senior Executive Service members and SES equivalents
Apply by August 15 at 5:00 pm EST!
Program Details PDF
| 2023-02-01T00:00:00 |
https://ourpublicservice.org/course/ai-government-leadership-program/
|
[
{
"date": "2023/02/01",
"position": 64,
"query": "government AI workforce policy"
},
{
"date": "2023/08/01",
"position": 60,
"query": "government AI workforce policy"
},
{
"date": "2023/12/01",
"position": 63,
"query": "government AI workforce policy"
},
{
"date": "2024/01/01",
"position": 62,
"query": "government AI workforce policy"
},
{
"date": "2024/07/01",
"position": 62,
"query": "government AI workforce policy"
},
{
"date": "2024/08/01",
"position": 61,
"query": "government AI workforce policy"
},
{
"date": "2024/12/01",
"position": 56,
"query": "government AI workforce policy"
}
] |
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