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The impact of AI on business | MicroSourcing
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The impact of AI on business
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https://www.microsourcing.com
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[] |
The disruptive effects of AI may also influence wages, income distribution and economic inequality. Digital disruption drives digital innovation. Times of ...
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Artificial intelligence (AI) has emerged as a transformative force in today's rapidly evolving business landscape, reshaping how companies operate and compete. As we delve into this topic, it's crucial to understand both the potential and the challenges that AI brings to the table.
Let's begin with a striking statistic: according to Grand View Research, the global AI market is projected to grow at a compound annual growth rate (CAGR) of 38.1% from 2022 to 20301. This remarkable growth trajectory underscores the increasing importance of AI in the business world.
The impact of artificial intelligence on business
AI's influence on business is far-reaching and profound. It's not merely about automation or replacing human tasks; rather, it's about augmenting human capabilities and unlocking new possibilities for growth and innovation.
To underscore the significance of AI in modern business, let's consider some key statistics:
77% of companies are either using or exploring the use of AI in their businesses 2
83% of companies claim that AI is a top priority in their business plan 2
AI could increase labor productivity growth by 1.5 percentage points over the next ten years 3
Globally, AI-driven growth could be nearly 25% higher than automation without AI 3
56% of businesses are using AI to improve and perfect business operations 4
51% are turning to AI to help with cybersecurity and fraud management 4
47% harness AI tools in the form of digital personal assistants 4
46% are using AI for customer relationship management 4
40% are turning to AI for inventory management4.
These statistics highlight the growing adoption and diverse applications of AI across various business functions. They also point to the potential for AI to drive significant productivity gains and economic growth.
Importantly, there's also a growing awareness of the need for responsible AI development:
85% of respondents support a national effort to make AI safe and secure 5
81% of respondents think that industries should spend more on AI assurance 5
85% of respondents want industries to be transparent about AI assurance practices before bringing AI-enhanced products to market5.
These figures underscore the importance of ethical considerations and transparency in AI implementation, a crucial aspect as businesses continue to integrate AI into their operations.
AI's impact across industries: a closer look
From life-saving medical gear to self-driving vehicles, artificial intelligence has made its way into virtually every aspect of our lives. Whether it’s to improve workflows, reduce human error, provide deeper analytics, foster more informed decision making or allow for 24/7 capabilities, AI was invented to make our lives more efficient.
The impact of AI in the medical industry
38% of healthcare providers are using AI to help with patient diagnoses6. Clinical health AI applications could see annual cost savings of up to U.S.$150 billion by 20267.
AI-powered computer systems have made their way into the healthcare sector. Popular applications of AI include diagnosing patients, improving communications between doctors, physicians and patients, transcribing medical documents, drug development and remote patient treatments.
The impact of AI in the mining industry
A study by Deloitte found that implementing artificial intelligence within a mining business allowed improved data processes making them 18 times faster than what was previously done in the field8.
When considering applications of AI within the mining industry, we see a transition from a people-focused operation to a process-oriented one. This change can hope to improve overall OHS conditions for mineworkers, increase accuracy, decrease environmental footprints and provide faster decision-making abilities in an environment where any error could result in significant repercussions.
The impact of AI in the banking sector
The banking sector has introduced artificial intelligence to help with fraud detection, enhance customer experiences (CX) with online banking applications, personalization of customer services, more efficient customer credit analysis and improvements with compliance. According to IDC, the banking sector will be one of the top industries that invest in AI solutions by 2024. And rightly so, with artificial solutions expected to add more than U.S. $1 billion in value to the banking industry by 20359.
The impact of AI in the accounting industry
AI in accounting helps enhance the efficiency of internal accounting practices, purchase orders, invoicing, procurement, expense reports and accounts payable or receivable functions. Accounting is driven by data and in many instances, accounting-related tasks such as banking reconciliations are repetitive and time consuming. This makes it the perfect environment for artificial intelligence and machine learning. AI within the accounting market is expected to reach U.S. $161.8 million by 202810, with 80% of business leaders believe AI provides them with a competitive advantage11.
Want to learn how artificial intelligence is transforming the future of accounting?
The impact of AI in the construction industry
From planning to building, construction and engineering professionals use artificial intelligence to add that extra element of accuracy and efficiency to their projects. It also helps with monitoring and keeping track of equipment, maintenance, construction errors and potential safety issues. A McKinsey report states that the implementation of AI within construction firms could lead to productivity increases of around 50% through real-time data analysis12.
The impact of AI in the retail sector
AI and retail are a match made in heaven in an industry where customer experience and satisfaction is a key metric. AI can provide the ability for hyper-personalization of services and make efficient recommendations for service or product selections to increase customer loyalty and satisfaction. AI in the retail sector is anticipated to grow to U.S. $20.05 billion by 202613.
How AI enhances business functions
AI's capabilities extend across various aspects of business operations:
Task automation: by handling routine tasks, AI frees up valuable human resources for more strategic, creative endeavors.
by handling routine tasks, AI frees up valuable human resources for more strategic, creative endeavors. Data-driven decision making: AI's ability to process and analyze vast amounts of data leads to more informed, timely decisions.
AI's ability to process and analyze vast amounts of data leads to more informed, timely decisions. Customer experience enhancement: through personalization and predictive analytics, AI helps businesses create more tailored, engaging customer interactions.
through personalization and predictive analytics, AI helps businesses create more tailored, engaging customer interactions. Operational efficiency: AI-driven process optimization can significantly reduce costs and improve productivity.
AI-driven process optimization can significantly reduce costs and improve productivity. Risk management: in areas such as fraud detection and cybersecurity, AI provides more robust, real-time protection.
in areas such as fraud detection and cybersecurity, AI provides more robust, real-time protection. Innovation acceleration: AI enables rapid prototyping and testing, speeding up product development cycles.
As we explore these capabilities, it's important to recognize that AI is not a one-size-fits-all solution. Its implementation requires careful consideration of a company's specific needs, resources, and strategic goals.
Artificial intelligence and the future of work
One of the most popular questions when it comes to artificial intelligence is “will I lose my job to a robot?”
The answer: this is dependent on the way in which your business decides to integrate artificial intelligence into its operations. Will complete business functions become a write-off as automation of processes comes in? Or, will the introduction of a AI chatbot provide the support of 10 to 20 customer service representatives, allowing the human employees more time to work on customer retention strategies? It is completely dependent on what AI-powered implementation tactic works for your business.
Research into this controversial topic are unbiased and aim to showcase both ends of the argument: a loss of work versus an increase in job opportunities. For example, cognitive technologies such as robots, AI, machine learning and automation will replace 16% of jobs but also create 9% of new jobs in the United States by 202514.
A McKinsey study analyzed over 2,000 work activities across 800 role occupations to accurately examine the technical feasibility of automation15.
What these results tells us is that even when ‘machines take over’ tasks within any occupation, there are activities in which AI cannot be relied on to cover such as managing others, applying expertise and stakeholder interactions.
Explore further: The environmental impact of artificial intelligence Artificial intelligence can help combat climate change but can also be a major contributor to the emissions issues our world is facing. The energy it takes to ‘train’ an AI program can be excessive. Take the training of a powerful language model which was estimated to have consumed enough energy during it’s training stages to leave a carbon footprint big enough to drive a car ‘from Earth to the moon and back’16. On the other hand, the long-term effects of implementing AI on the environment, could positively enable 93% of environmental-based targets such as the creation of low-carbon cities, IoT devices that modulate their consumption of electricity, smart grids that integrate renewable energy and the ability to combat marine pollution17.
Embracing artificial intelligence to improve your business operations
By introducing AI and it’s quick learning capabilities, businesses can create super-powered data processing machines that can generate information, extrapolate large amounts of data and even take care of tasks that free up time and budget for organizations to focus on more face-to-face tasks.
Technologies such as artificial intelligence (AI) have already shown great potential to improve customer experience and engagement. More and more organizations are choosing to interact with their customers via voice interfaces and chatbots and as the technology improves, these applications will be able to conduct seamless customer service conversations at any time of the day or night.
For decades, companies have been forced to deal with industrial machines breaking down during the production process. Now imagine being able to perform predictive maintenance based on previously inaccessible insights into their operation. That is just one of the benefits of machine learning algorithms, which, along with artificial intelligence, are allowing companies to tap into volumes of information generated across their business units, partners and third-party sources.
For McKinsey, it will be a world in which digital channels become the primary - and perhaps only - customer engagement models and automated processes become a primary driver of productivity18. Either way, any business not preparing for the digital change that is coming, is a business that will struggle to remain competitive.
Economic impacts of artificial intelligence It’s evident that AI can help improve growth for many organisations through enabling productivity and efficiency improvements. It improves the decision-making process through the analysis of big data sets, can support the identification of new products and services and boost customer demand by generating new revenue streams. However, the creation of ‘super firms’, industry-leading AI organizations, could spell economic monopolisation and be potentially detrimental. AI could also widen the gap between developing and developed countries by boosting the need for qualified, skilled workers well-versed in automation and machine-learning. The disruptive effects of AI may also influence wages, income distribution and economic inequality19.
Digital disruption drives digital innovation
Times of disruption and change can be uncomfortable. Artificial intelligence is just one of the many digital disruptions taking the world by storm. Over the last few years, the need to innovate has become essential and digital disruption is helping businesses stay competitive in the ever-changing business environment.
How can your business accelerate purposeful innovation, like investing in AI? What are the current business trends and “new digital opportunities” you can implement today? And, why are agile methodologies more important than ever? This blog, ‘How digital disruption can drive business innovation’ will take you through how your business can effectively use digital disruption in today’s “noisy” world.
| 2023-04-01T00:00:00 |
https://www.microsourcing.com/learn/blog/the-impact-of-ai-on-business/
|
[
{
"date": "2023/04/01",
"position": 74,
"query": "AI economic disruption"
},
{
"date": "2023/07/01",
"position": 74,
"query": "AI economic disruption"
},
{
"date": "2024/12/01",
"position": 74,
"query": "AI economic disruption"
},
{
"date": "2025/01/01",
"position": 73,
"query": "AI economic disruption"
},
{
"date": "2025/03/19",
"position": 77,
"query": "AI employers"
}
] |
|
The Economics of Transformative AI - Digital Economy Lab
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The Economics of Transformative AI
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https://digitaleconomy.stanford.edu
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[] |
The advent of Transformative AI1, or TAI, would yield economic change on the scale of the Industrial Revolution but has the potential to occur radically faster.
|
The Challenge
The advent of Transformative AI1, or TAI, would yield economic change on the scale of the Industrial Revolution but has the potential to occur radically faster. TAI could boost productivity levels and accelerate scientific progress, putting us in a realm beyond traditional economic modeling. At the same time, TAI risks disruption in labor markets and changes in the concentration of wealth and power. With foresight, we can adapt our institutions and create a future of widely shared prosperity.
The gap between rapidly growing technological capabilities and slowly improving economic understanding, skills, institutions, and policies is the crux of the coming decade’s societal challenges. Economics must be transformed in the face of such technological change.
There is an urgent need to understand the economic implications of Transformative AI: If machines are created that can outperform humans on a significant share of tasks, many of our existing institutions, norms, and systems will need to be reinvented. This raises questions regarding distribution, concentration, inequality, information flows, geopolitics and trade, AI safety and alignment, well-being, and a host of other important topics.
The Digital Economy Lab is actively engaged in developing a robust economic research agenda to address the potential challenges associated with TAI. The papers linked below represent a subset of ideas and thinkers we believe are shaping this conversation (email [email protected] if you have a suggested addition).
1 For our purposes, a working definition of TAI is provided by Karnofsky, 2016. “Transformative AI refers to potential future AI that precipitates a transition comparable to (or more significant than) the agricultural or industrial revolution. The concept of “transformative AI” has some overlap with concepts put forth by others, such as ‘superintelligence’ and ‘artificial general intelligence.’ However, ‘transformative AI’ is intended to be a more inclusive term, leaving open the possibility of AI systems that are ‘transformative’ despite lacking many abilities humans have.”
| 2023-04-01T00:00:00 |
https://digitaleconomy.stanford.edu/the-economics-of-transformative-ai/
|
[
{
"date": "2023/04/01",
"position": 88,
"query": "AI economic disruption"
},
{
"date": "2023/07/01",
"position": 89,
"query": "AI economic disruption"
},
{
"date": "2024/12/01",
"position": 95,
"query": "AI economic disruption"
},
{
"date": "2025/01/01",
"position": 94,
"query": "AI economic disruption"
}
] |
|
AI literacy in K-12: a systematic literature review
|
AI literacy in K-12: a systematic literature review - International Journal of STEM Education
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https://stemeducationjournal.springeropen.com
|
[
"Casal-Otero",
"Pedagogy",
"Didactics Department",
"University Of Santiago De Compostela",
"Santiago De Compostela",
"Catala",
"Departamento De Electrónica E Computación",
"Universidade De Santiago De Compostela",
"Centro Singular De Investigación En Tecnoloxías Intelixentes",
"Citius"
] |
by L Casal-Otero · 2023 · Cited by 439 — AI literacy is a pedagogical and cognitive challenge at the K-12 level. This study aimed to understand how AI is being integrated into K-12 education worldwide.
|
The results were organized into two subsections. The first subsection is a bibliometric analysis of the reviewed studies, which is based on the metadata provided by Scopus. The second subsection provides a qualitative analysis of the studies, which is based on the extracted data annotations (see Table 2). Both analyses are complementary and together deliver a better understanding of the research articles retrieved.
Bibliometric analysis
Figure 1 shows that the annual scientific production has been modest. It gained traction in 2016 and increased sharply in 2020.
Fig. 1 Annual scientific production: number of papers by year Full size image
Most of the contributions are conference publications (126 papers), while 52 are journal articles and one is a book chapter (Fig. 2).
Fig. 2 Type of contributions: number of papers by type Full size image
Eighty out of 179 papers have at least a citation in Scopus. There are 13 papers that have 10 or more citations, and the most cited papers are Long and Magerko (2020) and Touretzky et al. (2019b). Figure 3 summarizes the number of contributions by publishers, where Springer, IEEE and ACM stand out, followed by Elsevier. As for journals, there are no single journals concentrating the publication of articles. Nevertheless, there are some journals that are especially relevant and well-known by the community such as the International Journal of Child-Computer Interaction, Computers and Education: Artificial Intelligence, International Journal of Artificial Intelligence in Education, or IEEE Transactions on Education.
Fig. 3 Frequency of publishers: number of papers by publisher Full size image
As for conferences, Fig. 4 summarizes the main conference events where papers are published. It includes flagship conferencesFootnote 1 such as CHI and AAAI, top-ranked conferences such as HRI or SIGCSE and several noteworthy events (IDC, ICALT, ITiCSE, VL/HCC, to name a few). It is worth mentioning that AAAI is receiving contributions from recent years, which confirms the interest in the field in broadening the discussion to education. There are some additional publications associated with satellite AAAI events, such as workshops in CEUR-WS that deal with the issue under study. Although such contributions may sometimes be short, we decided to include them as they were relevant. For instance, the works published in (Herrero et al., 2020) and (Micheuz, 2020) include the German countrywide proposal for educating about AI, through a 6-module course focusing on explaining how AI works, the social discourse on AI and reducing existing misconceptions. On the other hand, Aguar et al. (2016) talk about teaching AI via an optional course which does not contribute to the final grades.
Fig. 4 Main conference events: number of papers by conference Full size image
The analysis did not reveal particularly outstanding institutions (see Table 3 for a summary). Among the 299 affiliated institutions, we mostly find universities and research centers along with a few collaboration associations. The most active institutions are the Chinese University of Hong Kong, University of Eastern Finland and MIT, whose authors participated in a total of 19, 11 and 10 contributions, respectively.
Table 3 Most active institutions Full size table
Finally, the retrieved papers were co-authored by 643 different authors affiliated to research institutions from 42 countries. Figure 5 shows the histogram of participation by country. Of the 179 papers reviewed, most papers were written by authors affiliated with institutions in the same country. Only 32 papers involved authors from several countries. It is remarkable that in these cases at least one author is from the US, Hong Kong or China.
Fig. 5 Country participation: number of papers by country Full size image
Literature analysis
By analyzing the data extracted, the papers were classified into two broad thematic categories according to the type of educational approach, namely, learning experience and theoretical perspective. The first category covers AI learning experiences focused on understanding a particular AI concept/technique or using specific tools/platforms to illustrate some AI concepts. The second category involves initiatives for the implementation of AI education for K-12 through the development of guidelines, curriculum design or teacher training, among others. Each main category was further subdivided into other subcategories to structure the field and characterize the different approaches used in developing AI literacy in K-12. Figure 6 shows all the identified categories and subcategories.
Fig. 6 Taxonomy of approaches to AI learning in K-12 Full size image
Learning experiences focused on understanding AI
This category covers learning experiences aimed at experimenting and becoming familiar with AI concepts and techniques. Based on the priority axes in AI literacy (Long & Magerko, 2020; Miao et al., 2021), we identified experiences aimed at acquiring basic AI knowledge to recognize artifacts using AI, learning how AI works, learning tools for AI and learning to live with AI.
Learning to recognize artifacts using AI
This subcategory refers to experiences that aim to understand AI concepts and techniques enabling the recognition of which artifacts/platforms use AI and which do not. Four studies were found in this subcategory. They are proposals aimed at helping young people to understand and demystify AI through different types of activities. These activities included conducting discussions after watching AI-related movies (Tims et al., 2012), carrying out computer-based simulations of human-like behaviors (Ho et al., 2019), experimenting as active users of social robots (Gonzalez et al., 2017) and programming AI-based conversational agents (Van Brummelen et al., 2021b).
Learning about how AI works
This topic covers proposals designed to understand how AI works to make user interaction with AI easier and more effective. In this type of proposal, the focus is on methodology and learning is achieved through technology (Kim et al., 2023). The objective is to provide a better understanding of a particular aspect of reality in order to carry out a project or solve a problem (Lenoir & Hasni, 2016). The activities are supported by active experiences based on building and creating intelligent devices to achieve the understanding of AI concepts following the idea of Papert’s constructionism.
These experiences are mainly focused on teaching AI subfields such as ML or AI algorithms applied to robotics. Understanding the principles of ML, its workflows and its role in everyday practices to solve real-life problems has been the main objective of some studies (Burgsteiner et al., 2016; Evangelista et al., 2019; Lee et al., 2020; Sakulkueakulsuk et al., 2019; Vartiainen et al., 2021). In addition, there are also experiences focused on unplugged activities that simulate AI algorithms. For example, through classic games such as Mystery Hunt, one can learn how to traverse a graph without being able to see beyond the next path to be traversed (blind search) (Kandlhofer et al., 2016). Similarly, the AI4K12 initiative (Touretzky et al., 2019b) collects a large set of activities and resources to simulate AI algorithms.
Learning tools for AI
This topic includes approaches that involve learning about AI support tools. The development of intelligent devices in the context of teaching AI requires specific programming languages or age-appropriate tools. Many of the tools currently available are focused on ML, with the aim of demystifying this learning in K-12 education (Wan et al., 2020). Some of them are integrated into block-based programming languages (such as Scratch or App Inventor) (Toivonen et al., 2020; von Wangenheim et al., 2021), enabling the deployment of the ML models built into games or mobile applications. Other approaches use data visualization and concepts of gamification to engage the student in the learning process (Reyes et al., 2020; Wan et al., 2020) or combine traditional programming activities with ML model building (Rodríguez-García et al., 2020).
This type of proposal aims to introduce AI through tools that enable the use of AI techniques. It is therefore an approach focused on learning by using AI-oriented tools. In this vein, different experiences have focused on learning programming tools for applications based on Machine Learning (Reyes et al., 2020; Toivonen et al., 2020; von Wangenheim et al., 2021; Wan et al., 2020), robotics (Chen et al., 2017; Eguchi, 2021; Eguchi & Okada, 2020; Holowka, 2020; Narahara & Kobayashi, 2018; Nurbekova et al., 2018; Verner et al., 2021), programming and the creation of applications (Chittora & Baynes, 2020; Giannakos et al., 2020; Kahn et al., 2018; Kelly et al., 2008; Park et al., 2021). Some of these tools use Scratch-based coding platforms to make AI-based programming attractive to children. In (Kahn et al., 2018), students play around with machine learning to classify self-captured images, using a block-based coding platform.
There are also experiences in which other types of environments are used to facilitate learning (Aung et al., 2022). In (Holowka, 2020; Verner et al., 2021), students can learn reinforcement learning through online simulation. In (Narahara & Kobayashi, 2018), a virtual environment helps students generate data in a playful setting, which is then used to train a neural network for the autonomous driving of a toy car-lab. In (Avanzato, 2009; Croxell et al., 2007), students experiment with different AI-based tasks through robotics-oriented competitions.
Learning for life with AI
This subcategory covers experiences aimed at understanding how AI can affect our lives thus providing us with skills to critically assess its technology. In (Vachovsky et al., 2016), technically rigorous AI concepts are contextualized through the impact on society. There are also experiences where students explore how a robot equipped with AI components can be used in society (Eguchi & Okada, 2018), program conversational agents (Van Brummelen et al., 2021b), or learn to recognize credible but fake media products (video, photos), which have been generated using AI-based techniques (2021b; Ali et al., 2021a).
The ethical and philosophical implications of AI have also been addressed in some experiences (2021b; Ali et al., 2021a; Ellis et al., 2005), whereas others focus on training students to participate in present-day society and become critical consumers of AI (Alexandre et al., 2021; Cummings et al., 2021; Díaz et al., 2015; Kaspersen et al., 2022; Lee et al., 2021; Vartiainen et al., 2020).
Proposals for implementation of AI learning at the K-12 level
Some countries are making efforts to promote AI education in K-12. In the U.S., intense work is being carried out on the integration of AI in schools and among these schemes, AI4K12 stands out (Heintz, 2021). This scheme is especially interesting since it defines the national guidelines for future curricula, highlighting the essential collaborative work between developers, teachers and students (Touretzky et al., 2019a). This idea of co-creation is also stressed in other schemes (Chiu, 2021). In the U.S. we can also mention the proposal made by the Massachusetts Institute of Technology, which is an AI curriculum that aims to engage students with its social and ethical implications (Touretzky et al., 2019a). Although the United States is working intensively on the design of integrating this knowledge into the curriculum, so far AI is not widely offered in most K-12 schools (Heintz, 2021).
In China, the Ministry of Education has integrated AI into the compulsory secondary school curriculum (Ottenbreit-Leftwich et al., 2021; Xiao & Song, 2021). Among their schemes we can reference the AI4Future initiative of the Chinese University of Hong Kong (CUHK), which promotes the co-creation process to implement AI education (Chiu et al., 2021). In Singapore, a program for AI learning in schools has also been developed, where K-12 children learn AI interactively. However, the program is hindered by a lack of professionals (teachers) with adequate training (Heintz, 2021). In Germany, there are also several initiatives to pilot AI-related projects and studies (Micheuz, 2020), including the launch of a national initiative to teach a holistic view of AI. This initiative consists of a 6-module course aimed at explaining how AI works, stimulating a social discourse on AI and clarifying the abundant existing misconceptions (Micheuz, 2020). Canada has also designed an AI course for high schools. The course is intended to empower students with knowledge about AI, covering both its philosophical and conceptual underpinnings as well as its practical aspects. The latter are achieved by building AI projects that solve real-life problems (Nisheva-Pavlova, 2021).
The literature also highlights the different approaches that AI literacy should focus on: curriculum design, AI subject design, student perspective, teacher training, resource design and gender diversity. All these approaches are described in depth below.
AI literacy curriculum design
Approaches to curriculum development differ widely, ranging from the product-centered model (technical-scientific perspective) to the process-centered model (learner perspective) (Yue et al., 2021). AI literacy can be launched in primary and secondary education depending on the age and computer literacy of the students. To do this, it is necessary to define the core competencies for AI literacy according to three dimensions: AI concepts, AI applications and AI ethics and security (Long & Magerko, 2020; Wong et al., 2020). Research has focused on the understanding of the concepts, the functional roles of AI, and the development of problem-solving skills (Woo et al., 2020). This has led to proposing a redefinition of the curriculum (Han et al., 2019; Malach & Vicherková, 2020; Zhang et al., 2020) supported by different ideas that K-12 students should know (Chiu et al., 2021; Sabuncuoglu, 2020; Touretzky et al., 2019b). Several countries have already made different curricular proposals (Alexandre et al., 2021; Micheuz, 2020; Nisheva-Pavlova, 2021; Ottenbreit-Leftwich et al., 2021; Touretzky et al., 2019b; Xiao & Song, 2021), where they argue that the curricular design must include different elements such as content, product, process and praxis (Chiu, 2021). It is also convenient for learning in AI to follow the computational thinking model (Shin, 2021), contextualizing the proposed curriculum (Eguchi et al., 2021; Wang et al., 2020) and providing it with the necessary resources for teachers (Eguchi et al., 2021). In this sense, emerging initiatives highlight the need to involve teachers in the process of co-creating a curriculum associated to their context (Barlex et al., 2020; Chiu et al., 2021; Dai et al., 2023; Lin & Brummelen, 2021; Yau et al., 2022).
AI as a subject in K-12 education
Traditionally, including computer science or new technologies in the educational system has been carried out through a specific subject integrated into the curriculum or through the offer of extracurricular activities. In this sense, different proposals have suggested the integration of AI as a subject in K-12 education (Ellis et al., 2009; Knijnenburg et al., 2021; Micheuz, 2020; Sperling & Lickerman, 2012), in short-term courses (around 15 h) and divided into learning modules focused on classical and modern AI (Wong, 2020) or through MOOCs (Alexandre et al., 2021).
Student perspective on AI Literacy
Student-focused studies explore and analyze attitudes and previous knowledge to make didactic proposals adapted to the learner. Some of them measure their intention and interest in learning AI (Bollin et al., 2020; Chai et al., 2021, 2020a, 2020b; Gao & Wang, 2019; Harris et al., 2004; Sing, et al., 2022; Suh & Ahn, 2022), whereas others discuss their views on the integration of technologies in the education system (Sorensen & Koefoed, 2018) and on teaching–learning support tools in AI (Holstein et al., 2019).
Teacher training in AI
Teachers are key players for the integration of AI literacy in K-12, as proven by the numerous studies that examine this issue (An et al., 2022; Bai & Yang, 2019; Chiu & Chai, 2020; Chiu et al., 2021; Chounta et al., 2021; Judd, 2020; Kandlhofer et al., 2019, 2021; Kim et al., 2021; Korenova, 2016; Lin et al., 2022; Lindner & Berges, 2020; Oh, 2020; Summers et al., 1995; Wei et al., 2020; Wu et al., 2020; Xia & Zheng, 2020). This approach places teachers at the center, bearing in mind what they need to know so as to integrate AI into K-12 (Itmazi & Khlaif, 2022; Kim et al., 2021). The literature analyzed reports on the factors that influence the knowledge of novice teachers (Wei, 2021) and focuses on teacher training in AI (Lindner & Berges, 2020; Olari & Romeike, 2021). Thus, AI training proposals can be found aimed at both teachers in training (Xia & Zheng, 2020) and practicing educators. Training schemes focus on their knowledge in technologies to facilitate their professional development (Wei et al., 2020) through the TPACK (Technological, Pedagogical and Content Knowledge) teaching knowledge model (Gutiérrez-Fallas & Henriques, 2020). Studies focusing on teachers’ opinions on curriculum development in AI are relevant (Chiu & Chai, 2020), as are their self-efficacy in relation to ICT (Wu et al., 2020), their opinions on the tools that support the teaching–learning process in AI (Holstein et al., 2019) and their teacher training in technologies (Cheung et al, 2018; Jaskie et al., 2021). These elements are central to the design of an AI literacy strategy in K-12. Both the co-design of ML curricula between AI researchers and K-12 teachers, and the assessment of the impact of these educational interventions on K-12 are important issues today. At present, there is a shortage of teachers with training in AI and working with teachers in training (Xia & Zheng, 2020) or with teachers in schools (Chiu et al., 2021) is proposed as an effective solution. One of the most interesting analyses of teacher competency proposes the acquisition of this skill for the teaching of AI in K-12, through the analysis of the curricula and resources of AI using TPACK. This model was formulated by (Mishra & Koehler, 2006) and aims to define the different types of knowledge that teachers need to integrate ICT effectively in the classroom. In this regard, it is suggested that teachers imparting AI to K-12 students require TPACK to build an environment and facilitate project-based classes that solve problems using AI technologies (Kim et al., 2021).
AI literacy support resources
Research using this approach focuses on presenting resources that support AI literacy (Kandlhofer & Steinbauer, 2021), considering that the creation of resources and repositories is a priority in supporting this teaching–learning process (Matarić et al., 2007; Mongan & Regli, 2008). However, these resources largely do not meet an interdisciplinary approach and do not embody a general approach to AI development (Sabuncuoglu, 2020).
Gender diversity in AI literacy
AI education, as a broad branch of computer science, also needs to address the issue of gender diversity. Lack of gender diversity can impact the lives of the people for whom AI-based systems are developed. The literature highlights the existence of proposals designed with a perspective toward gender, where the activities designed are specifically aimed at girls (Ellis et al., 2009; Jagannathan & Komives, 2019; Perlin et al., 2005; Summers et al., 1995; Vachovsky et al., 2016; Xia et al., 2022).
| 2023-12-14T00:00:00 |
2023/12/14
|
https://stemeducationjournal.springeropen.com/articles/10.1186/s40594-023-00418-7
|
[
{
"date": "2023/04/01",
"position": 2,
"query": "AI education"
},
{
"date": "2023/04/01",
"position": 6,
"query": "artificial intelligence education"
}
] |
Generative AI and Creative Learning: Concerns, Opportunities ...
|
Generative AI and Creative Learning: Concerns, Opportunities, and Choices
|
https://mres.medium.com
|
[
"Mitchel Resnick"
] |
In this article, I'll start by discussing my concerns about current uses of AI tools in education, then I'll explore how we might leverage new generative AI ...
|
Generative AI and Creative Learning: Concerns, Opportunities, and Choices Mitchel Resnick 19 min read · Apr 23, 2023 -- 8 Listen Share
[This paper will appear in An MIT Exploration of Generative AI, a collection from MIT Press. An earlier version of the paper was originally posted in April 2023.]
As each new wave of technology ripples through society, we need to decide if and how to integrate the technology into our learning environments. That was true with personal computers, then with the internet, and now with generative AI technologies. For each new technology, there are many different ways that we can integrate the technology into how we teach and learn. These choices are critically important: different choices can have very different outcomes and implications.
How should we make these choices? I think we need to decide what type of learning and education we want for our children, our schools, and our society — and then design new technologies and applications that align with our educational values and visions.
What does that mean for the integration of new generative AI technologies such as ChatGPT into our learning environments?
In my view, the top educational priority in today’s world is for young people to develop as creative, curious, caring, collaborative learners. With the pace of change accelerating in all parts of the world, today’s children will face a stream of uncertain, unknown, and unpredictable challenges throughout their lives, and the proliferation of new AI technologies will further accelerate the changes and disruptions. As a result, it is more important than ever for children from diverse backgrounds to have opportunities to develop the most human of their abilities — the abilities to think creatively, engage empathetically, and work collaboratively — so that they can deal creatively, thoughtfully, and collectively with the challenges of a complex, fast-changing world.
Unfortunately, I find that many of the current uses of AI in education are not aligned with these values — and, in fact, they could further entrench existing educational approaches at a time when significant changes are needed. Too often, today’s AI technologies are used in ways that constrain learner agency, focus on “close-ended” problems, and undervalue human connection and community.
But I also see intriguing opportunities for new generative AI technologies. I believe that these new AI technologies (compared with earlier AI technologies) have greater potential for supporting young people in project-based, interest-driven creative learning experiences — and thus supporting their development as creative, curious, collaborative learners. We could be at a moment for significant educational change: the disruptions caused by new generative AI technologies are leading more people to recognize the need for fundamental changes in our approaches to education and learning. But new AI technologies will contribute to these changes only if people make explicit, intentional choices in the ways they design and use these new tools.
In this article, I’ll start by discussing my concerns about current uses of AI tools in education, then I’ll explore how we might leverage new generative AI technologies to support creative learning experiences.
Concerns
Most critiques of AI systems highlight problems that the developers of the systems have not sufficiently focused on (e.g., biases or inaccuracies based on the sets of examples used to train the systems, and inadequate acknowledgment or compensation for artists and writers whose work is used in the training) and on problems that arise when the systems are used differently than the developers had hoped (e.g., students turning in papers produced by AI systems as if the work were their own). These are serious and important problems, and they need to be addressed. But in this article, I have a different focus. I will be discussing why I am concerned about many AI-in-education systems even when they work exactly how their developers intended and are used exactly how their developers had hoped.
Concern #1: Constraining Learner Agency
Back in the 1960s, as researchers were beginning to explore how computers might be used in education, there were two primary schools of thought. One focused on using computers to efficiently deliver instruction and information to the learner. The other focused on providing learners with opportunities to use technologies to create, experiment, and collaborate on personally meaningful projects. Seymour Papert referred to these two different approaches as instructionist and constructionist.
Over the years, most AI researchers and developers have focused on the first approach, developing “intelligent tutoring systems” or “AI coaches” that provide instruction to students on particular topics, continually adapting the trajectory of the instruction based on student responses to questions. These systems have been promoted as a personalized approach to teaching, aiming to provide each student with customized feedback and instruction based on their current level of understanding, as opposed to a one-size-fits-all approach in which the same instruction is delivered to all students.
With advances in AI technology, these tutoring systems have become more effective in delivering instruction that adapts to individual learners. For example, some AI tutors and AI coaches have demonstrated improved results when they deliver instruction through a virtual character that looks like the student’s favorite teacher or favorite celebrity.
I do not doubt these research results, but I worry that some of these “improvements” are perpetuating and reinforcing an educational approach that is in need of a major overhaul. To a large degree, AI tutors and coaches have been designed to be in control of the educational process: setting goals, delivering information, posing questions, assessing performance. That is also the way most classrooms have operated over the past couple centuries. But the realities of today’s world require a different approach: providing students with opportunities to set their own goals, build on their own interests, express their own ideas, develop their own strategies, and feel a sense of control and ownership over their own learning. This type of learner agency is important in students’ development, helping them develop the initiative, motivation, self-confidence, and creativity that will be needed to contribute meaningfully in their communities.
AI tutors and coaches are promoted as “personal” since they deliver personalized instruction. But in my view, a truly personal approach to learning would give the learner more choice and control over the learning process. I would like learners to have more control over how they are learning, what they are learning, when they are learning, where they are learning. When learners have more choice and control, they can build on their interests, so that learning becomes more motivating, more memorable, and more meaningful — and learners make stronger connections with the ideas that they are engaging with.
Some new AI tutors and coaches try to support greater learner agency. Instead of controlling the instructional flow, they are designed to provide tips, advice, and support when students ask for help. But even if AI tutors are designed with the best of intentions, I worry that some learners will experience them as intrusive. For example, Sal Khan of Khan Academy was quoted in the New York Times [1] that he expects future AI tutors will intervene when a student’s attention wanders, telling the student: “Hey, I think you’re a little distracted right now. Let’s get focused on this.” That type of intervention might be helpful for some students, but feel intrusive or disempowering for others. As one educator wrote to me: “I would absolutely crumble if I was a middle schooler and had to chat with an AI bot that was trying to pull the answer out of me.”
Concern #2: Focusing on “Close-Ended” Problems
Over the past decade, there has been a proliferation of websites designed to teach young people how to code. The vast majority of these sites are organized around a series of puzzles, asking students to create a program to move a virtual character past some obstacles to reach a goal. In the process of solving these puzzles, students learn basic coding skills and computer science concepts.
When our Lifelong Kindergarten group at the MIT Media Lab developed Scratch, we took a different approach. With Scratch, young people can create animations, games, and other interactive projects based on their interests — and share them with others in an online community. Through this project-based, interest-driven approach, students still learn important coding skills and computer science concepts, but they learn them in a more motivating and meaningful context, so they make deeper connections with the ideas. At the same time, the project-based, interest-driven approach helps young people develop their design, creativity, communications, and collaboration skills, which are more important than ever in today’s world.
So why do so many coding sites focus on puzzles rather than projects? One reason is that it is easier to develop AI tutors and coaches to give advice to students as they work on puzzles. With puzzles, there is a clear goal. So as a student works on a puzzle, the AI tutor can analyze how far the student is from the goal, and give suggestions on how to reach the goal. With projects, the student’s goal might not be clear, and might change over time, so it is more difficult to develop an AI tutor to give advice.
Over the years, most AI tutors and coaches have been designed to provide instruction on problems that are highly structured and well defined. With new AI technologies, there are possibilities for developing systems that could provide feedback and advice on more open-ended projects. But I have been disappointed by the way that most AI researchers and EdTech companies are putting these new technologies to use. For example, I recently saw a presentation in which a prominent AI researcher showed off a new ChatGPT-based system that was asking students a list of single-answer questions. The conversational interface was new, but the educational approach was old. And when Khan Academy recently introduced its AI tutor called Khanmigo, the first example it showed on its website was a multiplication problem involving a fraction (the tutor asked: “What do you think you need to do to multiply 2 by 5/12?”). This type of problem has a single answer and well-defined strategies for getting to the answer — exactly the type of problem that AI tutors have traditionally focused on.
There is an important educational choice: Should schools focus more on open-ended projects or “close-ended” problems? My preference is to put more emphasis on projects where students have more opportunities to learn to think creatively, express their ideas, and collaborate with others — while still learning important concepts and basic skills, but in a more meaningful and motivating context.
Schools have generally preferred close-ended problems since they are easier to manage and assess. Schools end up valuing what they can most easily assess, rather than figuring out ways to assess the things that are most valuable. I worry that EdTech companies and schools will focus on AI tutors that fit in this same framework and further entrench this educational approach, crowding out much-needed changes in how and what students learn. Instead, as I discuss in the Opportunities section below, I hope that there are more efforts to use new AI technologies to support learners as they engage in project-based, interest-driven learning experiences.
Concern #3: Undervaluing Human Connection
In some situations, AI tutors and coaches can provide useful advice and information. And with advances in AI technology, these systems are becoming better at deciding what information to deliver when, and customizing the information based on what students have already learned and what misconceptions they might have.
But good teaching involves more than that. A good teacher builds relationships with students, understands students’ motivations, empathizes with students’ concerns, relates to students’ lived experiences, and helps students connect with one another. Facilitating a student’s learning is a subtle process, much more complex than simply delivering information and instruction at the right time. A good teacher understands how to cultivate a caring community among students, so that students feel welcomed, understood, and supported. A good teacher understands how to create an environment in which students feel comfortable taking the risks that are an essential part of a creative learning process.
Some AI tutors and coaches now try to take social-emotional factors into account — for example, using sensors and cameras to get a sense of a student’s emotional state. But these AI systems still are not able to understand or empathize with a learner’s experience or cultivate a caring community the way a human teacher can.
So it bothers me when AI tutors and coaches are promoted as if they are equivalent to human teachers. For example, a promotional video for a new AI-based tutor from Microsoft says that it is “like there are 20 extra teachers in one classroom.” And Khan Academy promotes its Khanmigo system as “a world-class tutor for anyone, anywhere.” Some people might shrug off these descriptions as marketing hype. But I worry that they contribute to a devaluation of the human dimensions of teaching. Human teachers are fundamentally different from AI tutors, and I think it is important to recognize the special qualities of human teachers — while also recognizing what AI systems do particularly well.
Some AI researchers and EdTech companies try to avoid the direct comparison to human teachers by positioning their systems as AI companions, collaborators, or copilots rather than AI tutors. But they still try to emphasize the humanness of their systems. I find it especially troubling when AI systems describe their own behavior as if they were human. For example, I recently saw a presentation about a new AI system designed to interact with young children. In its interactions, the AI system talked in a humanlike way about its intentions and feelings. This felt problematic to me, since it could mislead young children to believe that AI systems have motivations and feelings similar to their own.
I do not want to idealize the capabilities of human teachers. Many teachers do not have experience or expertise in facilitating creative learning experiences — and many children do not have access to teachers who do. There is a role for AI-based systems to supplement human teachers (as discussed in the next section). But we should clearly recognize the limitations and constraints of these AI systems, and we should not be distracted from the important goal of helping more people become good teachers and facilitators.
More generally, we need to make sure that the current enthusiasm over AI systems does not lead to a reduction in interactions and collaborations with other people. The importance of the human connection and community became even more apparent during the pandemic, when different schools adopted different pedagogical approaches. Some schools implemented remote-learning routines focused on delivery of instruction based on the traditional curriculum; in those schools, many students felt increasingly isolated and disillusioned. Other schools focused more on social-emotional and community aspects of learning, emphasizing the importance of supporting and collaborating with one another; in those schools, students felt a stronger sense of connection, empathy, and engagement. As Harvard Graduate School of Education professor Jal Mehta wrote in the New York Times [2]: “Classrooms that are thriving during the pandemic are the ones where teachers have built strong relationships and warm communities, whereas those that focus on compliance are really struggling.”
The pandemic highlighted the importance of empathy, connection, and community in teaching and learning. As the pandemic recedes, and as AI systems proliferate, we should keep our focus on these very special human qualities.
Opportunities
Over the past year, there have been a growing number of educational initiatives to teach K-12 students about generative AI. While some of these initiatives, in my view, focus too much on teaching technical details on how current AI systems work (much like teaching students about floppy disks and motherboards in the early days of personal computing), the initiatives play a valuable educational role in helping student gain an understanding of the ethical and societal implications of generative AI technologies and an awareness of how these technologies can provide biased results and misleading information.
But I think the most important educational opportunities will come not from teaching students about AI but rather in helping students learn with AI — that is, supporting students in using AI tools to imagine, create, share, and learn. And, as a bonus, learning with AI can be the best way for students to learn about AI too.
Unfortunately, when most EdTech companies and AI researchers focus on learning with AI, they tend to package it in an instructionist paradigm, developing intelligent tutoring systems (e.g., teaching arithmetic skills or vocabulary words) that are subject to all of the problems described above. But it does not have to be that way. I believe that generative AI technologies (compared with earlier AI technologies) provide greater opportunities for breaking out of the instructionist paradigm and supporting a more constructionist approach to learning. That is, there is potential to design and use generative AI technologies to support young people in project-based, design-oriented, interest-driven creative learning experiences — and thus help them develop the creativity, curiosity, and collaboration skills that are needed to thrive in today’s fast-changing world. But that will happen only if we make intentional choices in how to design and use these new technologies.
Supporting the Creative Learning Process
So how can people design and use generative AI technologies to support a more constructionist approach to learning? In our work in the Lifelong Kindergarten research group, we have identified four guiding principles for supporting this type of learning. We call them the Four P’s of Creative Learning: projects, passion, peers, and play [3]. That is, young people are most likely to develop as creative, curious, collaborative learners when they have opportunities to work on projects, based on their passions, in collaboration with peers, in a playful spirit.
So, as researchers, companies, and educators integrate generative AI technologies into learning environments, and as students use these new technologies, they should all consider how AI can be used to support the Four P’s of projects, passion, peers, and play:
Projects . We should provide students with opportunities to use generative AI tools throughout the process of working on a project, while making sure that they retain creative control. If they are feeling stuck at the start of a project, they could enter a few preliminary ideas and ask the system for variants or refinements of the ideas. When something does not work as expected in the project, they could explain the problem to an AI system and ask for help in debugging it.
. We should provide students with opportunities to use generative AI tools throughout the process of working on a project, while making sure that they retain creative control. If they are feeling stuck at the start of a project, they could enter a few preliminary ideas and ask the system for variants or refinements of the ideas. When something does not work as expected in the project, they could explain the problem to an AI system and ask for help in debugging it. Passion . When people work on projects that they really care about, they are willing to work longer and harder, persist in the face of challenges, and make deeper connections to the ideas they encounter. So we should explore ways for students to use generative AI tools to create projects that they find personally meaningful. For example, Karishma Chadha at the MIT Media Lab is developing generative AI tools and activities that enable young people to create dynamic representations of themselves — and create and share personal stories based on those representations — as a way to explore and express their cultural identity.
. When people work on projects that they really care about, they are willing to work longer and harder, persist in the face of challenges, and make deeper connections to the ideas they encounter. So we should explore ways for students to use generative AI tools to create projects that they find personally meaningful. For example, Karishma Chadha at the MIT Media Lab is developing generative AI tools and activities that enable young people to create dynamic representations of themselves — and create and share personal stories based on those representations — as a way to explore and express their cultural identity. Peers . Most AI tutoring tools have been designed for one-to-one interaction. But we know that most creative learning experiences involve people learning with and from one another. So generative AI tools should be designed to engage young people working together on projects, helping them connect and collaborate with others who have similar interests or complementary skills.
. Most AI tutoring tools have been designed for one-to-one interaction. But we know that most creative learning experiences involve people learning with and from one another. So generative AI tools should be designed to engage young people working together on projects, helping them connect and collaborate with others who have similar interests or complementary skills. Play. A playful attitude is not just about laughing and having fun. It is based on a willingness to experiment, try new things, take risks, and push the boundaries. So rather than developing AI tutors that guide students toward a solution, we should provide young people with opportunities to use AI technologies to explore new directions, tinker with new possibilities, and iteratively refine their ideas.
This 4P approach to designing and using generative AI technologies is very different from traditional AI tutoring systems. In the 4P approach, students have greater control of the process, choosing how and when to use AI tools to support their own design and problem-solving practices, and using the tools as a catalyst (not a replacement) for their own creative process and their collaboration with others. It is somewhat similar to the way that people, while working on a project, do an online search or watch a YouTube video to get new ideas or information. Generative AI systems can serve as an additional resource during the creative-learning process.
We should not expect (or want) AI systems to play the same role as human tutors or coaches or companions. Rather, we should consider AI systems as a new category of educational resource, with their own affordances and limitations. When learners are looking for help or inspiration, they sometimes talk with a friend, sometimes refer to a book, sometimes do an online search, sometimes watch a video. Each plays a different role. We can add AI systems to this mix of resources.
How Could Generative AI Transform Coding?
Over the past decade, there has been a surge of interest in helping young people learn to code. With our Scratch programming language, for example, young people can use graphical coding blocks to create interactive stories, games, and animations based on their interests, then share their creations with one another in an online community. Through this 4P process, young people not only learn computational and technical skills, they also develop their abilities to think creatively, reason systematically, and work collaboratively, essential skills for everyone in today’s world.
Many people are now wondering if generative AI technologies could end the need for people to learn to code. After all, part of the promise of generative AI is that people will be able to use everyday language to interact with computers. Why learn to write code in a programming language when you can simply engage in conversation with the computer, telling it what you want it to do?
I am skeptical that the ability of generative AI systems to produce computer programs will eliminate the value of people learning to code — just as I am skeptical that the ability of ChatGPT to generate essays will eliminate the value of people learning to write. But I do think that generative AI technologies could significantly change how young people program computers.
To evaluate the benefits and drawbacks of new AI-based approaches to coding, it is worth considering the goals of learning to code. For some young people, of course, learning to code can serve as a starting point on a path towards a career as a professional programmer or software developer. But I am more interested in why it is valuable for everyone to learn to code — just as it is valuable for everyone to learn to write, even if they aren’t going to become a professional writer. As I see it, learning to code provides young people with opportunities to:
create new types of projects and express themselves in new ways
learn strategies for design and problem solving
gain a sense of control over technologies that are ubiquitous in their lives
describe, understand, and debug processes
experience the joy of creating things they care about
If we use generative AI to introduce changes to the ways people code, will it enhance these benefits — or undermine them?
AI-based changes to programming environments can come in many different forms. For example, Eric Rosenbaum of the Scratch Foundation has been experimenting with ways to integrate AI-based image-generation tools within Scratch, so that young people can generate graphic characters and backdrops to use in their projects. If a child wants an anime-style purple frog in their project, they can type in “anime-style purple frog” and see what the system produces. If they are not satisfied with the results, they can refine the prompt to get something closer to what they want. This new tool has limitations, and it certainly should not replace the Scratch paint editor or image library, but it can provide another option for creating images within a Scratch project.
Rosenbaum has also been developing new AI-based programming blocks for Scratch — for example, new blocks that enable characters in a project to engage in ChatGPT-style conversations guided by context provided by the programmer. Rosenbaum plans to explore a new hybrid form of coding that combines traditional coding blocks with AI-based blocks.
Several researchers are also experimenting with ways for young people to ask for advice and tips as they are coding their projects. Whereas earlier AI systems could provide advice effectively only if the project goals were clearly articulated in advance, new generative AI tools have potential for providing useful advice even when project goals are ambiguous or evolving. And as long as the student (rather than the AI system) maintains control over the flow of the conversation, this use of AI could align well with a 4P approach for learning to code.
Over time, there could be more fundamental changes to the ways that children tell the computer what to do. In the fifty years since children began programming computers, the biggest change so far has been from text-based coding (using languages like Logo and Basic) to graphical building-block coding (popularized by Scratch). Will generative AI bring a shift to conversational interfaces, in which children use everyday language to tell the computer what to do?
Professional programmers are already using tools like GitHub’s Copilot to generate code. And several groups, including the Playful Invention Company and the App Inventor team, have produced prototypes of conversational coding systems for children. As with the shift from text to blocks, the shift to conversation could make it easier and more intuitive to tell the computer what to do. There are continuing questions of how well computers can deal with the ambiguities of everyday language, but new generative AI systems are making progress on this issue, especially as people learn how to iteratively refine their prompts to the systems.
For me, the bigger question is whether these new conversational coding approaches will preserve all of the traditional benefits of learning to code (as listed above). Even if children are able to successfully create projects through a conversational coding interface, will they still feel a sense of control over the technology? Will they still learn strategies for design and problem solving? Will they still experience the joy of creating? Also: Will it be possible for young people to remix one another’s projects, as they do in Scratch? And will the systems be welcoming and engaging for learners across diverse backgrounds?
The answers to these questions are not clear. Looking ahead, it will be valuable to develop prototypes of new conversational coding interfaces and explore how children create and learn with them. One challenge is that these interfaces might enhance some of the traditional benefits of learning to code while deteriorating others. We will need to figure out whether the tradeoffs are worth it.
Choices
There are many different ways that people can use generative AI technologies to support learning and education.
Some uses of generative AI systems will constrain learner agency, focus on “close-ended” problems, or undervalue human connection and community — and further entrench existing approaches to education and learning at a time when change is needed. I worry that inertia and market pressures will push the educational uses of generative AI in this direction.
But it is also possible to use generative AI technologies to support a more project-based, interest-driven, human-centered, collaborative approach to learning, enabling learners to develop the motivation, creativity, and curiosity that they will need to thrive in today’s complex, fast-changing world. The disruptions caused by new generative AI technologies are leading more people to recognize the need to rethink approaches to education and learning, so we could be at a cultural moment where fundamental changes are possible.
The choice is up to us. The choice is more educational and political than technological. What types of learning and education do we want for our children, our schools, and our society? All of us — as teachers, parents, school administrators, designers, developers, researchers, policymakers — need to consider our values and visions for learning and education, and make choices that align with our values and visions. It is up to us.
Acknowledgments
I would like to thank everyone who provided suggestions on earlier drafts of this paper, helping me to refine my ideas through multiple iterations of the document. I am especially grateful to (alphabetically) Hal Abelson, Karen Brennan, Leo Burd, Karishma Chadha, Kartik Chandra, Pattie Maes, Carmelo Presicce, Eric Rosenbaum, Natalie Rusk, and Brian Silverman — each of whom provided valuable feedback (though, of course, their feedback does not imply that they agree with all of the ideas presented in this paper).
References
[1] Natasha Singer, “Will Chatbots Teach Your Children”, New York Times, January 11, 2024.
[2] Jal Mehta, “Make Schools More Human,” New York Times, December 23, 2020.
[3] Mitchel Resnick, Lifelong Kindergarten: Cultivating Creativity through Projects, Passion, Peers, and Play (MIT Press, 2017).
| 2024-04-01T00:00:00 |
2024/04/01
|
https://mres.medium.com/ai-and-creative-learning-concerns-opportunities-and-choices-63b27f16d4d0
|
[
{
"date": "2023/04/01",
"position": 3,
"query": "AI education"
},
{
"date": "2023/04/01",
"position": 7,
"query": "artificial intelligence education"
}
] |
ENAI Recommendations on the ethical use of Artificial ...
|
ENAI Recommendations on the ethical use of Artificial Intelligence in Education - International Journal for Educational Integrity
|
https://edintegrity.biomedcentral.com
|
[
"Foltynek",
"Masaryk University",
"Brno",
"Czech Republic",
"Bjelobaba",
"Uppsala University",
"Uppsala",
"Glendinning",
"Coventry University",
"Coventry"
] |
by T Foltynek · 2023 · Cited by 184 — The recommendations focus on the importance of equipping stakeholders with the skills and knowledge to use AI tools ethically.
|
Academic integrity (AI) can be defined as “compliance with ethical and professional principles, standards, practices, and a consistent system of values that serves as guidance for making decisions and taking actions in education, research and scholarship” (Tauginienė et al. 2018, p. 8).
Artificial Intelligence refers to systems that appear to have “intelligent behaviour by analysing their environment and taking actions – with some degree of autonomy – to achieve specific goals” (European Commission, 2018, p. 4).
AI-based tools can be used to transform, produce or generate any kind of content, such as text, images, art, music, or programming code. Different technologies, including machine learning and neural networks, are used to develop the capabilities of these tools.
Authorised and declared usage of AI tools is usually acceptable. However, in an educational context, undeclared and/or unauthorised usage of AI tools to produce work for academic credit or progression (e.g. students’ assignments, theses or dissertations) may be considered a form of academic misconduct (“any action or attempted action that undermines academic integrity and may result in an unfair academic advantage or disadvantage for any member of the academic community or wider society” (Tauginienė et al. 2018, p. 9). Moreover, it is increasingly challenging to reliably distinguish AI-generated content from human-produced content.
The wide accessibility of AI may exacerbate existing types of academic integrity threats, such as essay and paper mills, fabrication and falsification of data, etc.
Current definitions of misconduct, such as contract cheating and plagiarism, may not explicitly include this type of misconduct. Therefore, we propose an umbrella definition for all types of unauthorised content generation, including contract cheating and inappropriate use of AI:
Unauthorised content generation (UCG) is the production of academic work, in whole or part, for academic credit, progression or award, whether or not a payment or other favour is involved, using unapproved or undeclared human or technological assistance.
AIED can be used for unauthorised content generation; however, the use of AIED is not automatically unethical. There can be differences between academic disciplines, education institutions, courses, types of assessment, cultures, regions, and countries as to what is considered acceptable use of AI and what is not.
While AI can threaten academic integrity, it also presents opportunities. AI multiplies users’ abilities - in both good and bad ways. Therefore, students and educators should be guided on the benefits and limitations of AI tools in order to learn and use AI ethically and uphold academic integrity. Moreover, with the increasing automatisation of modern societies, they will likely use AI tools in their professional life. Therefore, they should be given opportunities to learn these skills during their education.
| 2023-12-14T00:00:00 |
2023/12/14
|
https://edintegrity.biomedcentral.com/articles/10.1007/s40979-023-00133-4
|
[
{
"date": "2023/04/01",
"position": 10,
"query": "AI education"
},
{
"date": "2023/04/01",
"position": 2,
"query": "artificial intelligence education"
},
{
"date": "2023/05/01",
"position": 10,
"query": "AI education"
},
{
"date": "2023/05/01",
"position": 3,
"query": "artificial intelligence education"
}
] |
Careers | Runway
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Careers
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https://runwayml.com
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[] |
We cover 100% of medical, dental and vision for our US-based employees and their dependents. ... AI Startup Runway Says It's Giving Filmmakers up to $1 Million If ...
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Medical, dental and vision coverage We cover 100% of medical, dental and vision for our US-based employees and their dependents. Based on the plans selected, we also offer complimentary mental health benefits and a free One Medical membership.
Donations supporting our community Beyond our employees, we have a commitment to our communities. Every quarter, Runway employees choose an organization for the company to support through a donation.
Wellness stipend We believe it’s important to invest in employees’ mental and physical health. The $600 annual wellness stipend can be used for anything from therapy sessions to gym memberships.
Learning stipend We’re investing in our employees’ long term growth by giving every employee $600 per year to spend on their personal or professional development.
Travel stipend Employees receive an annual travel budget that can be used for in-person collaboration, attending our annual film festival, AIFF , and other company-organized events, and/or continued learning at various research conferences.
Remote/Phone Stipend Employees outside of commuting distance (10 miles) from the NY or SF office can expense up to $250 USD per month for co-working spaces, internet and cellphone bills, lunches, and other related expenses. Employees within commuting distance can expense up to $50 per month for phone plan reimbursements.
401k matching We provide a 3% 401K match for our US-based employees.
| 2023-04-01T00:00:00 |
https://runwayml.com/careers
|
[
{
"date": "2023/04/01",
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{
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{
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{
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{
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},
{
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}
] |
|
'A big turn-off': Should HR use AI in recognising employees?
|
'A big turn-off': Should HR use AI in recognising employees?
|
https://www.hcamag.com
|
[
"Dexter Tilo"
] |
AI do many things - but should one of them be sending recognition messsages to high-performing employees? According to a employee ...
|
AI do many things - but should one of them be sending recognition messsages to high-performing employees? According to a employee recognition company O.C. Tanner - maybe not.
A new survey from O.C. Tanner of more than 2,815 individuals in the United States, Canada, Australia, India, and the United Kingdom to look at how AI is influencing recognition programmes found more than six in 10 employees worldwide were concerned that using artificial intelligence will make recognition at work feel less personal.
It found that 63% of employees are afraid that AI will make recognition less personal, higher than the 55% who believe AI can improve the recognition experience.
| 2023-04-01T00:00:00 |
https://www.hcamag.com/us/specialization/hr-technology/a-big-turn-off-should-hr-use-ai-in-recognising-employees/541884
|
[
{
"date": "2023/04/01",
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{
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{
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},
{
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},
{
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},
{
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{
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},
{
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{
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{
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{
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{
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{
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{
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{
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{
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{
"date": "2024/02/01",
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{
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{
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},
{
"date": "2024/06/01",
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},
{
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},
{
"date": "2024/06/01",
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{
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{
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},
{
"date": "2024/10/01",
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{
"date": "2024/11/01",
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},
{
"date": "2024/11/01",
"position": 68,
"query": "artificial intelligence employment"
},
{
"date": "2024/12/01",
"position": 67,
"query": "AI workers"
}
] |
|
How are young people supposed to chase dreams with no jobs, no ...
|
The heart of the internet
|
https://www.reddit.com
|
[] |
397 votes, 148 comments. We're told to work hard and follow our dreams......but how? AI and automation are replacing jobs, companies keep ...
|
We’re told to work hard and follow our dreams......but how?
AI and automation are replacing jobs, companies keep laying people off, and most of us can barely afford to live.
What’s the path forward when even basic stability feels out of reach?
| 2023-04-01T00:00:00 |
https://www.reddit.com/r/careerguidance/comments/1luvbfa/how_are_young_people_supposed_to_chase_dreams/
|
[
{
"date": "2023/04/01",
"position": 88,
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},
{
"date": "2023/05/01",
"position": 82,
"query": "AI employment"
},
{
"date": "2023/06/01",
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},
{
"date": "2023/08/01",
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},
{
"date": "2024/01/01",
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"query": "AI employment"
},
{
"date": "2024/06/01",
"position": 84,
"query": "AI employment"
},
{
"date": "2024/07/01",
"position": 86,
"query": "AI employment"
},
{
"date": "2024/11/01",
"position": 84,
"query": "AI employment"
}
] |
|
Xbox Producer's AI Advice for Laid-Off Workers - Winsome Marketing
|
Xbox Producer's AI Advice for Laid-Off Workers
|
https://winsomemarketing.com
|
[
"Writing Team"
] |
When Microsoft laid off as many as 9,100 employees this week, devastating entire gaming studios and canceling projects that represented ...
|
When Microsoft laid off as many as 9,100 employees this week, devastating entire gaming studios and canceling projects that represented years of creative work, Xbox Game Studios executive producer Matt Turnbull had a solution: use AI chatbots to manage your feelings. His now-deleted LinkedIn post, which recommended that overwhelmed workers turn to ChatGPT and Copilot for emotional support and career guidance, represents everything wrong with how the tech industry approaches human suffering—with algorithmic band-aids instead of genuine empathy or systemic change.
Turnbull's advice to use AI for "emotional clarity and confidence" isn't just tone-deaf—it's a perfect crystallization of the tech industry's fundamental inability to deal with the human consequences of its own actions. Rather than confronting the devastating impact of mass layoffs on real people's lives, careers, and mental health, the solution is to outsource emotional processing to the same technology companies that created the problem in the first place.
The post, which Turnbull deleted after receiving substantial criticism, suggests prompts like "I'm struggling with imposter syndrome after being laid off. Can you help me reframe this experience in a way that reminds me what I'm good at?" This approach treats profound human crisis as a prompt engineering problem—a perspective that reveals a deeply troubling disconnect from the reality of human experience.
The Amelioration Problem
The fundamental flaw in Turnbull's approach lies in what AI chatbots are designed to do: make users feel better rather than help them genuinely process difficult emotions. These systems are trained to be agreeable, supportive, and optimistic—they're essentially digital yes-men programmed to tell users what they want to hear rather than what they need to hear.
When someone has been laid off, they don't need an AI system to "reframe" their experience or provide artificial emotional clarity. They need time to grieve the loss of their job, process the very real fear about their financial future, and work through the anger and disappointment that come with being discarded by a company they may have devoted years of their life to serving.
AI chatbots are fundamentally incapable of providing genuine emotional support because they lack the human capacity for empathy, shared experience, and authentic connection. They can simulate understanding, but they cannot actually understand what it means to lose a job, to worry about paying rent, or to question your professional worth. Their responses are generated from patterns in training data, not from genuine care or wisdom earned through lived experience.
More importantly, these systems are designed to optimize for user satisfaction rather than user growth. They'll tell you you're great, that everything will work out, and that you should "stay positive"—exactly the kind of shallow emotional processing that prevents people from doing the hard work of genuinely confronting and working through difficult experiences.
The Shallow Response to Deep Problems
Turnbull's suggestion that AI can help "reduce the emotional and cognitive load that comes with job loss" reveals a fundamentally shallow understanding of what unemployment actually means for people. Losing a job isn't just a logistical problem that can be solved with better resume formatting and networking templates. It's a profound disruption that affects identity, self-worth, financial security, family stability, and future planning.
The prompts Turnbull suggests treat these deep human experiences as surface-level problems that can be resolved through better messaging and reframing. The underlying assumption is that people's emotional responses to being laid off are inefficient obstacles to overcome rather than legitimate reactions that deserve to be fully experienced and processed.
This approach mirrors the broader tech industry tendency to treat complex human problems as engineering challenges. Can't afford healthcare? There's an app for that. Struggling with mental health? Try a chatbot. Been laid off from your dream job? Ask an AI to help you feel better about it. Each solution avoids confronting the underlying systemic issues while placing the burden of adaptation on individuals.
The Irony of Using AI to Process AI-Related Job Loss
The timing of Turnbull's advice is particularly galling given that Microsoft's massive investment in AI infrastructure directly contributed to the layoffs he's attempting to address. The company announced plans to invest $80 billion in AI infrastructure in January, just months before cutting thousands of jobs. The implicit message is clear: we're replacing human workers with artificial intelligence, but don't worry—you can use our AI tools to feel better about being replaced.
This represents a new level of corporate cynicism. It's not enough to lay people off to fund AI development; now the same AI technology is being positioned as the solution to the emotional trauma caused by AI-driven layoffs. It's a closed loop of technological solutionism that benefits no one except the companies selling AI services.
The fact that Turnbull works for Xbox Game Studios makes this even more problematic. The gaming industry has been particularly hard hit by layoffs and studio closures, with many attributing the trend to companies' rush to embrace AI and reduce human labor costs. Creative professionals in gaming are increasingly seeing AI as an existential threat to their livelihoods, not a helpful tool for career development.
The Fundamental Misunderstanding of Human Healing
Perhaps most troubling is how Turnbull's approach fundamentally misunderstands what humans need during times of crisis. Genuine healing from job loss—or any significant life disruption—requires community, authentic relationships, time for reflection, and often fundamental changes in perspective that can only come through genuine human connection and support.
The process of working through unemployment involves questioning assumptions about work, identity, and success that are often deeply ingrained. It requires confronting uncomfortable truths about corporate loyalty, economic insecurity, and personal vulnerability. It demands the kind of deep emotional work that artificial intelligence simply cannot facilitate because it lacks the capacity for genuine understanding or challenge.
Human beings in crisis need other human beings who can sit with them in their discomfort, share similar experiences, provide honest feedback, and offer the kind of support that comes from genuine care rather than algorithmic optimization. They need friends who will let them be angry, family members who will provide practical support, and perhaps professional counselors who can help them work through complex emotions.
What they don't need is a chatbot trained to make them feel better without actually addressing the underlying issues that caused their distress.
The Dangerous Precedent
Turnbull's deleted post sets a dangerous precedent for how tech companies might handle the human consequences of their business decisions. Rather than taking responsibility for the impact of layoffs on employees' lives, companies can now point to AI tools as a solution to the emotional and practical challenges they've created.
This shifts responsibility away from employers and onto individuals, suggesting that if people are struggling with unemployment, the problem isn't systemic but personal—a failure to properly utilize available AI tools rather than a failure of companies to treat employees as human beings deserving of stability and respect.
The broader implication is that human suffering caused by technological advancement is simply a user experience problem that can be solved with better interfaces and more sophisticated algorithms. This perspective treats the symptoms while ignoring the disease, providing the appearance of care while avoiding any fundamental examination of the systems that create widespread economic insecurity.
The Alternative Approach
Instead of recommending AI chatbots, Turnbull could have acknowledged the genuine difficulty of what laid-off workers are experiencing and pointed them toward resources that actually help: unemployment benefits, professional counseling services, job placement agencies with human counselors, industry networking groups, and community support organizations.
He could have used his platform to advocate for better severance packages, extended healthcare coverage, or job placement assistance from Microsoft. He could have acknowledged that losing a job is genuinely difficult and that people have every right to feel angry, scared, and disappointed about their situation.
Most importantly, he could have recognized that the solution to human problems is often more humanity, not more technology. People facing unemployment need community, practical support, and time to process their experiences—not algorithmic optimization of their emotional responses.
The Broader Implications
Turnbull's suggestion that AI can replace human emotional support represents a broader trend in tech culture toward treating human experiences as problems to be optimized rather than realities to be respected. This mechanistic view of human emotion and crisis reflects a fundamental disconnect from the lived experience of people outside the tech industry bubble.
The fact that this advice came from someone in a position of authority within Microsoft's gaming division makes it even more problematic. It suggests that the company's leadership genuinely believes that artificial intelligence can substitute for human empathy and support—a perspective that bodes poorly for how these companies will handle future crises.
As AI becomes more prevalent in our daily lives, we need to be vigilant about maintaining space for genuine human experience and emotion. The impulse to optimize, streamline, and improve every aspect of human existence through technology needs to be balanced with recognition that some experiences require time, community, and authentic human connection to process properly.
Turnbull's advice reveals the poverty of a worldview that sees human suffering as an engineering problem rather than a call for genuine compassion and systemic change. If this is how tech leaders think about human crisis, we're in for a very shallow and ultimately unsatisfying future.
Looking for marketing strategies that prioritize genuine human connection over algorithmic optimization? At Winsome Marketing, our growth experts understand that real business success comes from authentic relationships and meaningful value creation. We help companies build sustainable growth through human-centered approaches that technology enhances rather than replaces. Contact us today to discuss how we can help your business thrive through genuine connection and authentic communication.
| 2023-04-01T00:00:00 |
https://winsomemarketing.com/ai-in-marketing/xbox-producers-ai-advice-for-laid-off-workers
|
[
{
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"position": 89,
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},
{
"date": "2023/04/01",
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{
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},
{
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{
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{
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{
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{
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{
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{
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{
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{
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"query": "AI replacing workers"
},
{
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"position": 67,
"query": "AI replacing workers"
},
{
"date": "2025/01/01",
"position": 78,
"query": "AI replacing workers"
}
] |
|
Careers - Cohere
|
Careers
|
https://cohere.com
|
[] |
Our team of ML/AI experts is passionate about helping developers solve real-world problems. ... As well as access to an EAP (employee assistance program) and a ...
|
We want Cohere to be the place where everyone does the best work of their career. So we make thoughtful decisions about how we work, the perks we provide, and how we create a diverse and inclusive work environment. We also foster technical creativity and innovation through internal hackathons, demos, tech talks, and achievement recognition programs that form a cornerstone of our culture at Cohere.
| 2023-04-01T00:00:00 |
https://cohere.com/careers
|
[
{
"date": "2023/04/01",
"position": 99,
"query": "AI employment"
},
{
"date": "2024/06/01",
"position": 90,
"query": "AI employment"
},
{
"date": "2025/05/01",
"position": 99,
"query": "AI employment"
}
] |
|
I've collected 75 useful AI tools from designers' perspective. ...
|
The heart of the internet
|
https://www.reddit.com
|
[] |
You're right, but while researching I decided exclude the audio related tools and focused on the visual ones. My expertise on audio might fell short and also ...
|
Create your account and connect with a world of communities.
New to Reddit?
By continuing, you agree to our
and acknowledge that you understand the
| 2023-04-01T00:00:00 |
https://www.reddit.com/r/graphic_design/comments/12aozmk/ive_collected_75_useful_ai_tools_from_designers/
|
[
{
"date": "2023/04/01",
"position": 2,
"query": "AI graphic design"
},
{
"date": "2023/04/01",
"position": 6,
"query": "artificial intelligence graphic design"
}
] |
|
Most people don't want a company AI to do the hiring, firing ...
|
The heart of the internet
|
https://www.reddit.com
|
[] |
Almost two-thirds of Americans would not want to apply for a position with an employer who uses AI to make hiring choices.
|
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
| 2023-04-01T00:00:00 |
https://www.reddit.com/r/Futurology/comments/12zdnto/most_people_dont_want_a_company_ai_to_do_the/
|
[
{
"date": "2023/04/01",
"position": 6,
"query": "AI hiring"
},
{
"date": "2023/04/01",
"position": 27,
"query": "artificial intelligence hiring"
}
] |
|
How do you prepare for AI's Impact on White-Collar Jobs
|
The heart of the internet
|
https://www.reddit.com
|
[] |
I would like to start a discussion about how individuals can prepare for the impact of AI on their jobs, even when the impact and its timing are uncertain.
|
There is a lot of talk going on in this subreddit and beyond about the current AI boom. One aspect that both interests and concerns me is the potential impact on white-collar jobs. Predictions vary widely on when and how fast jobs might disappear. Some people predict that AI will yield more productive people (we see that already), but that it would not necessarily lead to unemployment, while others predict scenarios where large numbers of white-collar jobs will vanish on short notice. Personally, I find it difficult to predict the future, I am uncertain about how it will unfold. I have no idea what will happen to my job (at the intersection of Sales & Engineering) and if the end would come, how long this would take. This uncertainty causes me a bit of anxiety to be frank. In this post I would like to discuss and gather insight on how we individuals can brace themselves for the potential rocky road to come.
Don’t get me wrong; I am not necessarily pessimistic about the distant future. Rather, I would like to start a discussion about how individuals can prepare for the impact of AI on their jobs, even when the impact and its timing are uncertain. So, my question is, what can we do in this great uncertainty to prepare for the future, while not knowing what that future will hold? In other words: what are strategies that will likely benefit us in as many situations as possible?
Personally, I am taking some steps that I believe might be helpful:
- Practicing mindfulness to avoid focusing too much on negative scenarios. - Focusing on soft skills at work to complement my technical skills. I think that, in the short term, the need for interpersonal connections with other humans will remain important. - Embracing minimalism at a personal level, which allows me to set aside some savings each month that could help me get through difficult times.
I have no idea if these steps will be sufficient. However, even temporary solutions can be just enough to make the transition smoother.
I am very interested in which practical steps you are taking and how you believe they might be beneficial. For instance, what practical or interpersonal skills are you learning? Are you considering a career change to a field with more longevity? Or do you have a different approach?
| 2023-04-01T00:00:00 |
https://www.reddit.com/r/Futurology/comments/12fi467/how_do_you_prepare_for_ais_impact_on_whitecollar/
|
[
{
"date": "2023/04/01",
"position": 6,
"query": "AI impact jobs"
}
] |
|
The impact of artificial intelligence (AI) on the workplace
|
The impact of artificial intelligence (AI) on the workplace
|
https://www.linkedin.com
|
[
"Krista Mollion",
"Jk Tech",
"Troy Latter"
] |
While there are challenges to consider, the benefits of AI, such as increased efficiency and productivity, new job creation, improved decision-making and ...
|
Artificial Intelligence (AI) is transforming the way we live and work, but its impact on the workplace is not without controversy and heated debate. One of the most significant concerns is the fear that AI will replace human workers, leading to mass unemployment and potentially to societal unrest. While this fear is not entirely unfounded, the impact of AI on the workplace is complex and multifaceted, and it presents both opportunities and challenges for businesses and employees alike.
The Potential of AI
According to a study by the McKinsey Global Institute, AI could contribute $13 trillion to global economic output by 2030. This estimate is based on the assumption that AI adoption will lead to a 1.2% increase in global GDP growth per year. However, the same study also highlights the potential for job displacement and suggests that up to 375 million workers worldwide may need to switch occupations or acquire new skills to remain employable as AI adoption accelerates.
Increased Efficiency and Productivity
Despite these concerns, AI also offers significant opportunities and has the potential to revolutionize almost every aspect of our lives. One of the most significant impacts of AI on the workplace is increased efficiency and productivity. AI-powered tools can automate repetitive and time-consuming tasks, freeing up employees to focus on more strategic and creative work. AI can for example automate data entry and analysis, customer service inquiries, and even hiring processes. This increased efficiency and productivity can lead to cost savings, faster decision-making, and improved customer satisfaction.
Improved Decision-Making and Predictive Analytics
Another impact of AI on the workplace is improved decision-making and predictive analytics. AI-powered tools can analyze vast amounts of data and identify patterns and insights that humans may miss. This analysis can help businesses make more informed decisions, anticipate customer needs, and predict future trends. For example, retailers can use AI to analyze consumer buying patterns and recommend personalized products and services.
Improved Safety
AI can also improve workplace safety and risk management. For example, in industries such as manufacturing and construction, AI-powered robots and drones can perform hazardous tasks, reducing the risk of accidents and injuries. Additionally, AI can analyze data to identify potential safety hazards and predict equipment failures before they occur, allowing businesses to take proactive measures to prevent accidents and ensure employee safety.
Changes and New Opportunities
It's important to note that with the emergence of AI technology, certain industries may be impacted more than others. Reskilling and upskilling may be necessary for workers in industries such as manufacturing and transportation to remain employable. However, it's important to also highlight the potential for new job creation and opportunities in areas such as AI training, data science, and machine learning engineering. The World Economic Forum predicts that AI will create 97 million new jobs globally by 2025, although some existing jobs may be displaced. Additionally, AI can help businesses expand into new markets and develop new products, leading to job creation and economic growth. Collaborating with AI will become increasingly important in the workplace, as it can assist in making better decisions, but cannot replace human intuition, creativity, and empathy.
The Ethical implications
However, as AI becomes more prevalent in the workplace, there are ethical implications to consider and the fear of AI bias is not unfounded either, as some AI systems have been found to discriminate against certain groups based on race or gender.
Recently, an AI hiring tool developed by Amazon was found to be biased against women, as it had been trained on resumes submitted to the company over the previous ten years, which were primarily from men.
Additionally, AI can be used to influence political campaigns and elections, by creating targeted messages or even manipulating voting results.
These types of uses raise serious concerns about privacy, security, and even democracy, and highlight the need for ethical guidelines and regulations to govern the development and use of AI. It is essential that businesses and individuals are aware of the potential ethical implications of AI and take steps to mitigate these risks.
The Philosophical thought
From a philosophical perspective, the adoption of AI could lead to a loss of human dignity and meaning, as work becomes increasingly automated and repetitive. However, others suggest that AI could create new opportunities for humans to engage in more creative, fulfilling work, or even lead to a post-work society where work is no longer necessary for human flourishing. The question remains whether or not we want to live in a world where AI has replaced many of our jobs, or if we want to embrace AI as a tool for enhancing human capabilities and creating new opportunities.
Embracing AI
The impact of AI on the workplace is significant and far-reaching. While there are challenges to consider, the benefits of AI, such as increased efficiency and productivity, new job creation, improved decision-making and predictive analytics, and better safety and risk management, make it an essential tool for businesses looking to stay competitive in a rapidly evolving digital world. As AI technology continues to advance, businesses must embrace it to remain relevant and succeed in the future.
| 2023-04-01T00:00:00 |
https://www.linkedin.com/pulse/impact-artificial-intelligence-ai-workplace-charlotte-volet
|
[
{
"date": "2023/04/01",
"position": 25,
"query": "AI impact jobs"
},
{
"date": "2023/04/01",
"position": 12,
"query": "AI workforce transformation"
}
] |
|
AI and the Future Job Market: Will Robots Really Steal Our ...
|
AI and the Future Job Market: Will Robots Really Steal Our Jobs?
|
https://medium.com
|
[
"Alex Northwood"
] |
While AI and automation may displace some jobs, they can also create new opportunities, enhance productivity, and foster economic growth.
|
AI and the Future Job Market: Will Robots Really Steal Our Jobs? Alex Northwood 4 min read · Apr 9, 2023 -- 1 Listen Share
The debate around artificial intelligence (AI) and its impact on the job market has been ongoing for years. Some envision a future filled with opportunity, while others fear massive job losses as AI and automation replace human workers.
In this article, we’ll dive into the discussion surrounding AI and its potential effects on the job market, separating fact from fiction and exploring the possibilities ahead.
So, will AI kill jobs in the future, or will it create new opportunities? Let’s find out!
AI: Destroyer or Creator of Jobs?
The fear of AI and automation taking over human jobs is not entirely unfounded. According to a study by Oxford University, around 47% of jobs in the US are at risk of being automated within the next two decades.
Meanwhile, a report from McKinsey suggests that up to 800 million jobs could be lost globally by 2030 due to automation.
However, it’s important to recognize that AI can also create jobs.
Historically, technological advancements have often led to job displacement, but they’ve also created new opportunities.
For instance, the internet and smartphone revolution led to a host of new roles, such as app developers, social media managers, and digital marketers.
Similarly, AI could give rise to new industries and job categories that we can’t yet predict.
Moreover, AI can enhance human productivity and efficiency in various sectors, potentially leading to economic growth and additional job opportunities.
AI’s Impact on Different Industries
AI’s effects on the job market will vary across industries. In some sectors, AI is more likely to complement human work rather than replace it.
For example, healthcare could benefit from AI-powered diagnostics and personalized treatments, while human healthcare professionals would still play a crucial role in decision-making and patient care.
In contrast, industries with repetitive tasks, like manufacturing or data entry, may see more significant job displacement due to AI and automation.
However, this doesn’t mean all hope is lost for workers in these industries. By acquiring new skills and adapting to the evolving job market, individuals can find new opportunities in emerging fields.
How to Prepare for the AI-Driven Job Market
To stay competitive in the AI-driven job market, it’s essential to develop skills that are less likely to be automated. These include:
Creativity: AI can perform specific tasks, but it cannot replicate human creativity. Pursuing careers that require creative thinking, like graphic design, writing, or marketing, could offer a level of job security.
Emotional intelligence: While AI can analyze data and make predictions, it lacks the ability to empathize and connect with people. Jobs requiring emotional intelligence, such as therapists, social workers, or salespeople, are less likely to be threatened by AI.
Complex problem-solving: While AI can solve straightforward problems, humans excel at tackling complex issues that require critical thinking and adaptability. Careers in strategic planning, management, or consulting could be safer from AI disruption.
The question of whether AI will kill jobs in the future is complex, and there’s no one-size-fits-all answer.
While AI and automation may displace some jobs, they can also create new opportunities, enhance productivity, and foster economic growth.
By focusing on developing skills that complement AI and staying adaptable in the face of change, we can thrive in the evolving job market.
| 2023-04-22T00:00:00 |
2023/04/22
|
https://medium.com/@alexnorthwood/ai-and-the-future-job-market-will-robots-really-steal-our-jobs-159b562063e9
|
[
{
"date": "2023/04/01",
"position": 29,
"query": "AI impact jobs"
}
] |
AI impact on jobs: Will it create or destroy? | by Sheriff Babu
|
AI impact on jobs: Will it create or destroy?
|
https://sheriffjbabu.medium.com
|
[
"Sheriff Babu"
] |
AI can augment human capabilities and enable workers to perform tasks more efficiently, accurately and creatively.
|
AI impact on jobs: Will it create or destroy? Sheriff Babu 5 min read · Apr 13, 2023 -- 1 Listen Share
Update March 2025: AI’s job impact is still unfolding — revisiting this piece with fresh eyes. What’s changed for you?
AI’s impact on the job market and the potential benefits it offers for job seekers. My expert-curated action plan offers tips and real-time examples for leveraging AI-powered tools to upskill, get job matches, and optimize your job search efforts. #AIjobs #upskilling #jobsearch
Artificial intelligence (AI) is one of the most disruptive technologies of our time. It has the potential to transform various sectors and industries, from healthcare to manufacturing, from education to entertainment.
AI generated image of an AI worker
But what does AI mean for the future of work?
Will it create new opportunities or destroy existing ones?
How will it affect the skills and competencies that workers need to succeed in the 21st century?
There is no simple answer to these questions, as AI can have both positive and negative effects on jobs.
Employee’s future concern about artificial intelligence
AI can augment human capabilities and enable workers to perform tasks more efficiently, accurately and creatively. AI can also automate routine and repetitive tasks, freeing up time and resources for more complex and meaningful activities. AI can also create new markets and industries, generating new sources of income and employment.
“I see AI creating new jobs we haven’t even imagined yet” — Sundar Pichai, CEO of Google.
We already have a proof on what Sundar Pichai has foreseen. Would we have imagined a couple of months back that there would be lucrative job with the title, “Prompt Engineer”?
Policies to deal with automation impact on labour and wealth distribution
AI can also displace workers and reduce the demand for certain skills and occupations.
AI can outperform humans in some tasks that require cognitive abilities, such as decision making, problem solving and learning.
AI can replace humans in some tasks that require physical abilities, such as driving, delivery and manufacturing. AI can also create new challenges and risks for workers, such as ethical dilemmas, privacy issues and social isolation.
AI has also created new categories of human jobs that require skills and training that will take many companies by surprise.
“AI is a net job creator, but it will require us to shift our skills and adapt to new ways of working” — Kai-Fu Lee, CEO of Sinovation Ventures.
These roles are not replacing old ones but are novel.
Our research reveals three new categories of AI-driven business and technology jobs: trainers, explainers, and sustainers.
Humans in these roles will complement the tasks performed by cognitive technology, ensuring that the work of machines is both effective and responsible
Suggested action plan for a job seeker to effectively use AI and get a job:
Utilize AI-powered job search engines: Use job search engines like Indeed, LinkedIn, and ZipRecruiter that use AI algorithms to match job seekers with open positions based on their experience, skills, and preferences.
“AI can help job seekers to identify skills gaps and provide personalized training recommendations to upskill for the roles they want” — Al Smith, Vice President of Marketing at iCIMS.
Optimize your resume with AI-powered tools: Use AI-powered resume builders like Zety and Resume.io to create a professional and optimized resume that will catch the attention of recruiters.
“AI can transform the job search process by matching job seekers with positions based on their skills, experience and qualifications” — Alex Douzet, CEO of TheLadders.
Use AI-powered interview coaching tools: Practice your interview skills with AI-powered interview coaching tools like My Interview Simulator and InterviewBuddy to get feedback and improve your performance.
“AI-powered chatbots can provide instant career advice to job seekers, and help them to schedule interviews with employers” — Susan Vitale, Chief Marketing Officer at iCIMS.
Upskill and reskill with AI-powered learning platforms: Use AI-powered learning platforms like Coursera and edX to identify skills gaps and access personalized training recommendations for the roles you want.
“AI can help us to identify the skills that will be in demand in the future, and enable us to reskill and upskill accordingly” — Rana el Kaliouby, Co-founder and CEO of Affectiva.
Leverage AI-powered virtual career advisors: Use AI-powered virtual career advisors like Mya and Woo to get career advice, ask job search questions, and schedule interviews with employers.
“AI can help to democratize access to education and training, by providing personalized learning experiences to learners around the world” — Andrew Ng, Founder of deeplearning.ai.
Interview coaching: AI-powered interview coaching tools like InterviewBuddy and My Interview Simulator help job seekers practice their interview skills and provide feedback on areas to improve.
By utilizing these AI-powered tools, job seekers can gain a competitive edge in the job market and increase their chances of finding the right job.
AI is not a threat or a blessing for jobs. It is a powerful tool that can create or destroy depending on how we use it. The future of work is not predetermined by technology. It is shaped by our choices and actions. Therefore, we have a responsibility and an opportunity to make sure that AI works for us, not against us.
What are your thoughts on this blog post? Is there anything you would like me to cover in more detail? Let me know in the comments below!
Follow me @Karkodan on X for more AI takes!
| 2025-03-13T00:00:00 |
2025/03/13
|
https://sheriffjbabu.medium.com/ai-impact-on-jobs-will-it-create-or-destroy-27511bda6a8e
|
[
{
"date": "2023/04/01",
"position": 40,
"query": "AI impact jobs"
}
] |
Measuring the Impact of Artificial Intelligence and Robotics ...
|
Measuring the Impact of Artificial Intelligence and Robotics on the Workplace
|
https://link.springer.com
|
[
"Staneva",
"Mila.Staneva Oecd.Org",
"Organisation For Economic Co-Operation",
"Development",
"Oecd",
"Elliott",
"Stuart.Elliott Oecd.Org",
"Quai Alphonse Le Gallo",
"Boulogne-Billancourt",
"Mila Staneva"
] |
by M Staneva · 2023 · Cited by 13 — The results show that jobs occupied by low-skilled workers and low-wage jobs are most exposed to robotic technologies, while jobs held by college-educated ...
|
Existing approaches to assessing the impact of AI and robotics on employment and work have mostly focused on whether these technologies can automate tasks within occupations; whether they can reproduce human skills required in jobs; and whether they are reflected in the human capital investments firms make. Studies have used innovative methods and data to answer these questions. However, some gaps in the methodology remain. In the following, some strengths and weaknesses of the five approaches are discussed without the claim of comprehensiveness or the attempt to elevate one approach against the other.
The Task-Based Approach.
The task-based approach has sparked off much research. The reason, perhaps, is that it offers a convenient way to quantify the potential for automation in occupations and in the economy. This is made possible through the use of O*NET, a comprehensive taxonomy of occupations that provides systematic information on task content and allows mapping to labor market surveys to study the empirical distribution of occupations. However, although this research heavily relies on expert judgments of AI capabilities with respect to occupational tasks, it leaves the process of collecting these judgements less transparent, with the available studies not providing sufficient information on the selection of experts, the methodology used for expert knowledge elicitation or the rating process. Moreover, experts rate the capabilities of AI and robotics with regard to very broad descriptions of occupational tasks provided in O*NET (e.g., “Bringing people together and trying to reconcile differences”).
The Patent-Based Approach.
Linking patents to occupations with NLP techniques is an innovative way to assess the impact of technology on work, both in terms of the data and the method used. Like the task-based approach, this approach is convenient for quantifying the impact of AI in the economy. However, as Webb [26] and Georgieff and Hyee [14] note, the approach focuses on innovative efforts with regard to AI that have been described in patents rather than actual deployment of AI in the workplace. With that, the patent-based approach is an assessment of the potential impact of technology on work. At the same time, the approach may miss innovations that are not described in patents.
The Job-Postings-Based Approach.
In contrast to the patent-based approach, but also the task-based approach, which measure the potential for automation of occupations, indicators that rely on job postings aim at capturing the actual deployment of AI in firms. Moreover, they aim at providing a timely tracking of AI adoption by using up-to-date data on the demand for AI skills across the labor market. However, these measures rely on the strong assumptions that all companies that automate their work processes recruit workers with AI skills and, vice versa, that all companies that employ AI specialists are doing so to automate their own tasks. Georgieff and Hyee [14] point to several realistic scenarios that violate these assumptions. For example, AI-using firms may train their workers in AI rather than recruit new workers with AI skills. Or they may decide to outsource AI support and development to specialized firms. In addition, the use of some AI systems may not require specialized AI skills. Furthermore, indicators based on job postings can capture the use of AI at the firm level, but not at the level of concrete work tasks or occupations. For example, a company may recruit IT specialists with AI skills in order to automate routine manual tasks in production.
The Benchmarks-Based Approach.
Benchmarks offer an objective measure of AI capabilities since they evaluate the actual performance of current state-of-the-art systems. The problem of using benchmarks to measure AI’s impact on the workplace is that they do not systematically cover the whole range of tasks and skills relevant for work. To our knowledge, there has not been an attempt to systemize the information from benchmarks according to a taxonomy of work tasks or work skills in order to comprehensively assess the capabilities of AI and robotics. Studies that link information from benchmarks to occupations offer a promising way to measure the automatability of the workplace. However, they share some of the methodological problems of the task-based approach. Specifically, occupational tasks and abilities are crudely described and their relatedness to AI is assessed through subjective judgments.
The Skills-Based Approach.
The skills-based approach uses human tests to compare computer and human capabilities. The reference to humans is key to understanding which human skills are reproducible by AI and robotics, and which skills can be usefully complemented or augmented by machines. This can help design new jobs in future that make the best use of both humans and machines. Moreover, it helps answer additional questions related to skills supply: Which skills are hard to automate and, thus, worth investing in in future? What education and training can help most people develop work-related skills that are beyond the capacity of AI and robotics?
However, using human tests on AI and robotics bears some challenges. One challenge, also common for other benchmark tests, is overfitting. It means that a system can excel on a test without being able to perform other tasks that are slightly different from the test tasks. This is still typical for AI systems as they are generally ‘narrow’, e.g. able to perform very specific tasks. Another challenge comes from the fact that human tests are designed for humans and, thus, take for granted skills that all humans share. Since such skills cannot be assumed for AI, human tests can have different implications for humans and machines. For example, the simple task to count the objects in a picture tests humans’ ability to count, whereas, for AI, it is also a test for vision and object recognition.
| 2023-07-14T00:00:00 |
2023/07/14
|
https://link.springer.com/chapter/10.1007/978-3-031-26490-0_2
|
[
{
"date": "2023/04/01",
"position": 53,
"query": "AI impact jobs"
},
{
"date": "2023/04/01",
"position": 78,
"query": "AI workforce transformation"
}
] |
How AI is Transforming the Workplace and Redefining Job ...
|
How AI is Transforming the Workplace and Redefining Job Roles
|
https://www.linkedin.com
|
[
"Serena H. Huang",
"Jk Tech",
"Paul Gibbons",
"Systems Thinker",
"Keynote Speaker",
"Author",
"Humanist In An Ai World",
"Deborah Hartung",
"Leadership Development",
"Culture Change"
] |
However, the automation of tasks through AI also raises concerns about job displacement and its impact on the workforce. As businesses adopt more AI ...
|
Artificial Intelligence (AI) has revolutionized how we work and is transforming the workplace in many ways. AI technology can automate tasks, improve decision-making, and enhance customer experience. As a result, businesses are investing in AI technology to stay competitive, increase productivity, and reduce costs.
As AI technology continues to evolve, it is becoming increasingly prevalent in various industries, including healthcare, finance, retail, manufacturing, and more. The widespread adoption of AI technology is changing how we work and forcing businesses to adapt to remain competitive. While AI technology presents many business opportunities, it also poses potential challenges and risks, such as job displacement and bias. As such, businesses must embrace ethical and responsible AI development and use it to ensure a fair and inclusive workplace.
How AI is Transforming the Workplace
AI hasn’t even come close to reaching its full potential in impacting how we work, and it’s already changing people’s daily work lives, making jobs easier and more efficient.
Automating Tasks
One of the most significant ways AI is transforming the workplace is through the automation of tasks. With the help of AI-powered software and machines, businesses can automate repetitive, mundane tasks that previously required human effort. This can save significant time and resources, allowing employees to focus on more complex tasks requiring human expertise.
For instance, AI can automate data entry, invoice processing, and payroll management tasks. In manufacturing, AI-powered robots can take over repetitive assembly line tasks, allowing human workers to focus on quality control and other higher-level tasks. AI chatbots can handle routine inquiries in customer service, leaving human agents to address more complex customer needs.
Automating tasks through AI can also help businesses achieve greater efficiency, accuracy, and consistency. AI-powered machines can work around the clock without fatigue or breaks and produce results faster than humans. This can lead to increased productivity and a reduction in operational costs.
However, the automation of tasks through AI also raises concerns about job displacement and its impact on the workforce. As businesses adopt more AI technology, employers need to consider the potential impact on their employees and take steps to reskill and upskill them to take on new roles that require human expertise.
Improving Decision Making
AI is also transforming the workplace by improving decision-making processes. With the help of machine learning algorithms, AI can analyze vast amounts of data and provide insights that would be difficult for humans to identify. This can help businesses make more informed and data-driven decisions.
For example, AI can analyze customer data to identify patterns and trends in customer behavior. This can help businesses to tailor their products and services to meet the specific needs of their customers. AI can analyze patient data in healthcare to identify early warning signs of potential health issues, enabling healthcare professionals to take preventative measures.
AI can also help businesses to identify areas where they can improve operational efficiency and reduce costs. For instance, AI can analyze supply chain data to identify bottlenecks and inefficiencies, allowing businesses to make data-driven decisions to optimize their supply chain processes.
AI is transforming the workplace by providing businesses with valuable insights to inform decision-making processes. However, companies must ensure that their AI systems are designed and implemented ethically and responsibly to avoid potential biases and ensure a fair and inclusive workplace.
Enhancing Customer Experience
AI is not only helping customer service representatives to be more efficient, but it’s also helping companies enhance their customer experience without more manual work. With the help of AI-powered technologies such as chatbots, voice assistants, and recommendation systems, businesses can provide 24/7 support and quick responses to customer queries.
AI-powered chatbots can give customers instant answers to their questions, allowing businesses to improve response times and reduce customer waiting times. Similarly, AI-powered voice assistants can provide personalized recommendations based on customer preferences, enhancing the overall customer experience and increasing customer loyalty.
AI can also provide personalized marketing and advertising based on customer behavior and preferences. By analyzing customer data, AI can help businesses to tailor their marketing efforts to specific customer segments, improving the effectiveness of their campaigns and ultimately driving sales.
Reducing Costs
Along with making daily work more efficient, AI is helping reduce overall costs for the workplace. By automating tasks, AI can save significant amounts of time and resources, allowing businesses to allocate their resources more efficiently.
AI can help businesses to reduce costs by improving operational efficiency. By automating tasks and streamlining processes, AI can help businesses to allocate resources more efficiently, reduce waste, and optimize production schedules. This can lead to significant cost savings and a more streamlined and efficient operation.
AI technology can help businesses optimize their operations, reduce costs, and improve their bottom line. However, companies need to approach AI implementation with a strategic mindset, taking into account the potential benefits and risks and ensuring that their AI systems are designed and implemented in an ethical and responsible manner.
How AI is Redefining Job Roles
Not only are processes and daily work operations being transformed by AI, but so are job roles. As AI takes on specific tasks typically done by people, people’s job roles must be readjusted.
Creating New Job Roles
While there is some concern that AI technology may lead to job displacement, it also creates new job roles and opportunities. For example, developing and implementing AI systems require specialized skills and expertise, such as data science, machine learning, and software engineering. As a result, the growing demand for professionals with these skills creates new job opportunities in areas such as AI development, implementation, and maintenance.
Additionally, as AI technology continues to advance and become more widespread, there will be a need for individuals who can work alongside AI systems, managing and interpreting the data they generate. This can include roles such as AI trainers, who are responsible for teaching AI systems to perform specific tasks, and data analysts, who analyze and interpret the data generated by AI systems.
Changing Job Requirements
The increasing use of AI technology also changes job requirements and the skills needed to succeed in the workforce. As AI technology becomes more prevalent, there is a growing need for individuals who can work alongside AI systems, managing and interpreting the data they generate. This requires individuals who are comfortable working with large datasets and have the skills to analyze and interpret complex information.
Furthermore, there is a growing demand for individuals who can develop and implement AI systems, requiring programming, data science, and machine learning skills. As AI technology evolves, the demand for these skills will continue to grow, leading to changes in the types of jobs available and the necessary skills to succeed in the workforce.
Reducing the Need for Certain Job Roles
While AI technology is creating new job roles and opportunities, it is also reducing the need for certain job roles that can be automated. For example, tasks such as data entry, processing, and analysis can be automated using AI technology, reducing the need for individuals to perform these tasks manually. Similarly, as AI-powered chatbots become more advanced, they can handle basic customer inquiries and support, reducing the need for human customer service representatives.
While this may lead to job displacement in the short term, it also presents an opportunity for individuals to reskill and transition into new roles requiring specialized skills and expertise in working alongside AI systems.
Enhancing Job Roles
AI technology also enhances job roles by allowing individuals to focus on higher-level tasks requiring critical thinking and creativity. By automating repetitive and routine tasks, AI technology can free up individuals to focus on more complex and strategic tasks requiring human expertise.
Additionally, AI technology can provide individuals with valuable insights and information to help them make better decisions and improve their performance. For example, AI-powered analytics tools can provide data-driven insights that help individuals identify improvement areas and optimize their workflows.
The Bottom Line
Undoubtedly, AI is already transforming the workplace in many ways, from automating tasks to enhancing job roles. While AI is reducing the need for certain job roles, it is also creating new ones and enhancing existing ones. As businesses continue investing in AI technology, employees must develop the skills and knowledge necessary to work with AI systems. Employers must also prioritize upskilling and reskilling employees to ensure they can adapt to the changing job requirements.
AI technology presents many business opportunities to increase productivity, reduce costs, and improve customer experiences. However, it is important to recognize AI’s potential challenges and risks, such as job displacement and bias.
| 2023-04-01T00:00:00 |
https://www.linkedin.com/pulse/how-ai-transforming-workplace-redefining-job-roles-chris-dyer
|
[
{
"date": "2023/04/01",
"position": 70,
"query": "AI impact jobs"
},
{
"date": "2023/04/01",
"position": 2,
"query": "AI workforce transformation"
},
{
"date": "2023/04/01",
"position": 62,
"query": "artificial intelligence employers"
}
] |
|
30% of the worlds workforce will lose their job to AI within 7 ...
|
The heart of the internet
|
https://www.reddit.com
|
[] |
According to this McKinsey study, an expected 400 to 800 million people will lose their job due to Artificial Intelligence by 2030.
|
According to this McKinsey study, an expected 400 to 800 million people will lose their job due to Artificial Intelligence by 2030, which means worst case scenario a third of the world's workforce loses their livelihoods.
Mostly hurting content creation and customer service jobs, but also more and more white-collar jobs as it becomes more advanced.
As of late the thought of widespread AI has started to terrify me more and more, especially considering its lack of regulation and invasivity.
Thoughts?
| 2023-04-01T00:00:00 |
https://www.reddit.com/r/ArtificialInteligence/comments/12hq52k/30_of_the_worlds_workforce_will_lose_their_job_to/
|
[
{
"date": "2023/04/01",
"position": 1,
"query": "AI job losses"
},
{
"date": "2023/04/01",
"position": 2,
"query": "robotics job displacement"
}
] |
|
AI in Journalism, Where Does it Apply and ...
|
AI in Journalism, Where Does it Apply and Where Does it Not?
|
https://www.linkedin.com
|
[
"Anton Dubov",
"Reuters Institute For The Study Of Journalism",
"Michael Stanley",
"Administrative Assistant Ii At Carnegie Mellon University"
] |
A new and emerging idea in digital journalism is the usage of AI. While AI poses a threat to journalism, it is difficult to ignore its potential.
|
While AI poses a threat to journalism, it is difficult to ignore its potential. In a 2021 article, the Knight Center elaborates on that potential upside—the article’s authors tout how AI could be utilized to bolster engagement and streamline feedback from news consumers. It is my perception that AI could have a place in digital journalism, but not beyond facilitating tasks to make a journalists’ job easier while reporting information.
The article mentions that most AI usage in the news industry happens on the national outlet level—mostly in experimental ways. Using a chart, they presented the amount of reputability among actual reporter content created with the use of AI, and the majority of it was marked to have low reputability. Again, using AI as a journalist is not a full proof way of generating content, but rather it should be used as a way to help a journalist create a more complete story information-wise. As a broadcast news production student, I have become adept at writing short, 30 second scripts that can give an audience everything they need to know. AI has no capability of completing such a task well or accurately. Particularly, that script should have conversational components to it to engage the broadcast news audience, in other words, a human aspect. AI is not equipped to provide those kinds of details. However, AI could help producers writing scripts better exact crucial information to be included faster. This could most certainly help the broadcast industry, which is always on quick, daily deadlines.
| 2023-04-01T00:00:00 |
https://www.linkedin.com/pulse/ai-journalism-where-does-apply-chris-abreu
|
[
{
"date": "2023/04/01",
"position": 3,
"query": "AI journalism"
}
] |
|
ChatGPT Is Already Changing How I Do My Job as a ...
|
The heart of the internet
|
https://www.reddit.com
|
[] |
For anyone not subscribed, this is an incredibly surface-level look at what AI can do: Use ChatGPT to help you think up a new word or phrase for an idea you ...
|
r/journalism is a community focused on the industry and practice of journalism (from the classroom to the newsroom).
Members Online
| 2023-04-01T00:00:00 |
https://www.reddit.com/r/Journalism/comments/12u03w5/chatgpt_is_already_changing_how_i_do_my_job_as_a/
|
[
{
"date": "2023/04/01",
"position": 24,
"query": "AI journalism"
}
] |
|
Predictable Patterns in Sports Journalism Headlines
|
Predictable Patterns in Sports Journalism Headlines: Can AI Replicate the Expected Structure and Tone?
|
https://minds.wisconsin.edu
|
[
"Frye"
] |
AI may offer the help sports writers need to meet the challenge of quickly producing a well-written story with an attention-grabbing headline.
|
Abstract
In sports journalism, post-game stories are usually drafted and dispatched within 30 minutes of the game ending. Sports journalists must quickly establish the tone that appropriately expresses a win or a loss. Artificial Intelligence (AI) may offer the help sports writers need to meet the challenge of quickly producing a well-written story with an attention-grabbing headline.
| 2023-04-01T00:00:00 |
https://minds.wisconsin.edu/handle/1793/84072
|
[
{
"date": "2023/04/01",
"position": 25,
"query": "AI journalism"
}
] |
|
the social dimension of generative artificial intelligence in ...
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Without journalists, there is no journalism: the social dimension of generative artificial intelligence in the media
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https://revista.profesionaldelainformacion.com
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[
"Simón Peña-Fernández",
"Universidad Del Paí S Vasco",
"Upv Ehu",
"Koldobika Meso-Ayerdi",
"Ainara Larrondo-Ureta",
"Javier Dí Az-Noci",
"Universitat Pompeu Fabra"
] |
by S Peña-Fernández · 2023 · Cited by 92 — Therefore, the study of AI in the media should focus on analyzing how it can affect individuals and journalists, how it can be used for the proper purposes of ...
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| 2023-04-01T00:00:00 |
https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/87329
|
[
{
"date": "2023/04/01",
"position": 32,
"query": "AI journalism"
}
] |
|
Artificial Intelligence for Media Ecological Integration and ...
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Artificial Intelligence for Media Ecological Integration and Knowledge Management
|
https://www.mdpi.com
|
[
"Balaram",
"Kannan",
"K Nattar",
"Čepová",
"Kumar M",
"Rani B",
"Schindlerova",
"Allam Balaram",
"K Nattar Kannan",
"Lenka Čepová"
] |
by A Balaram · 2023 · Cited by 12 — This research to examine the advantages of Media Ecology Technology in integration with artificial intelligence and a Knowledge Management Model.
|
1. Introduction
Due to the growth of mobile internet connectivity, knowledge has never been easier to access. In recent years, video has become increasingly popular, with a wide range of people around the world. In a short film, the information is delivered more comprehensively, making it easier for the viewer to understand. Technologies, such as cloud computing, mobile internet, artificial intelligence, and deep learning, are making algorithms more common in news production [ 1 ]. For example, similar algorithms that are popular in deep learning modules, such as auto-encoders, are a kind of trained neural network concept that also replicates particular information between input and output layers. In addition to machine writing and intelligent recommendations, big media companies are using artificial intelligence in a number of ways. This is due to the rapid growth of new media, such as quick hands, volcano videos, and microblog videos. Thus, short films have become more popular than any other form of media for transmission and dissemination. Thus, the use of cutting-edge technology in the production of movies, videos, and audio is referred to as “media ecology” [ 2 ]. More people are watching videos than ever before, making videos the most popular sort of content across all major social media platforms right now. To express themselves and gain followers, more and more people are uploading films to social media platforms [ 3 ]. The majority of broadcast media is making an effort to concentrate on AI and deep learning algorithms in order to leverage them in producing people’s desired entertainment. Making short movies is a terrific method to gain exposure and money. To promote themselves or distribute information, a rising number of people are using video-sharing websites such as YouTube and other services of a similar nature [ 4 ]. Given the volume of footage, it is critical to highlight the pertinent material and make it simple for viewers to locate. Making movies more accessible, current, and diverse will help us spend less time looking for and discovering them. In the field of photo depth learning, numerous national and international contests have produced a huge number of outstanding algorithms and models.
Convolutional neural networks are crucial in deep learning applications, as demonstrated by the improvement in video categorization provided by a large number of networks [ 5 6 ]. The analysis of video content requires an in-depth knowledge of the video content itself, which implies that a machine camera or video is preferable to an analysis of video content based on a human’s identified area. Traditional broadcasting media partners should utilize the implementation of lightweight deep learning modules in the system. A significant part of internet users’ media intake has unquestionably benefited from the widespread usage of mobile short video, thanks, in large part, to advances in AI. There was no way anyone could have predicted how far this technology had come or how quickly it had grown. Mobile short video has been shown to have long-lasting and far-reaching effects on culture, commerce, society, and existence [ 7 ]. In light of this, when short video content transitions to mobile short video, the mobile short video recommendation algorithm becomes more closely linked to the content. Using mobile devices for short video transmissions, personalized information, and interactive consumption is convenient; nevertheless, longer videos and photos are more difficult to consume [ 8 ]. Entrants are one such mandatory thing to be used these days, providing enough advantages and more capabilities in order to create a better method in creating advertisements and other video creation. Apps that allow users to share short videos have became more popular in 2017 due to enhanced mobile internet technology and better network conditions, as well as the introduction of new technologies such as artificial intelligence. Social short video apps are becoming increasingly popular, and there are several difficulties that need to be solved [ 9 ]. Due to the availability of more advanced tool qualities and the motivation to do so, social short films have evolved as a unique subtype of short videos in order to compete on the market.
The recent increase in data discovery, management, visualization, and business analytics in the professional context indicates a data-oriented perspective, emphasizing the increasing relevance of information-oriented corporate environments [ 10 ]. However, when faced with the vastness of previously existing digital content, human brain processing capacities fall behind in the quest to harness knowledge. As a result, prominent technologies, such as the Internet of Things (IoT), Machine Learning (ML), and artificial intelligence (AI), emerge as important tools for rapidly collecting, analyzing, and transforming vast amounts of data [ 11 12 ]. AI has already discovered several approaches to unlock and give new meaning to digital knowledge in order to improve the decision-making processes of commercial organizations through various iteration mechanisms. In most cases, advertisement creators obtain their necessary data through social media. In that case, the collection of such social media performance and utilizing such data with a particular deep learning algorithm would be a great challenge.
Machines process unstructured data and categorize them (commonly known as categories). They can readily connect system-stored concepts with knowledge that is relevant to the same context or environment. The algorithm may learn from these newly discovered connections and gradually improve the detection of existing relationships between digital data, just as the human mind does [ 13 ]. Missing linkages within current knowledge cause firms to lose strategic value—information-driven associations are one of the most difficult challenges in dealing with large amounts of digital knowledge. This is the same as how we sometimes read books but then forget about a similar subject covered by an article we read two days ago; the same thing happens with business architectural infrastructures [ 14 ]. Businesses frequently fail to build data-friendly settings, making it difficult to identify linkages between the information of different ecosystem components within the organization and between sections of a single component [ 15 ]. These relationships are frequently critical in developing a data-driven decision that connects strategy planning to solid analytics insights. They are also the drivers that increase value within an organization’s information management environment.
A new ecosystem of smart memory is that, when faced with the term “digital knowledge”, many people envision online libraries full of statistics and condensed materials in the form of text or numbers. If anything, knowledge management has demonstrated that the term “data” is a loose one [ 16 ]. An AI system capable of grouping together images of similar categories, such as selfies, landscapes, or group photos, is used in the solution. Advanced face recognition and image classification technology simplifies the arrangement of these categories in photobook-ready chapters [ 17 18 ]. On the one hand, consumers are relieved of the time-consuming job of scrolling through their phone libraries, categorizing memories, and picking images for printing. On the other hand, many companies can now benefit from useful (anonymous) data derived from the app, which display users’ ordering habits. Not only did pattern recognition technology improve the efficiency of the photobook process but the algorithm created from computer vision generated new knowledge. This information is of additional value in the form of data that a corporation use to design its business strategy. AI systems can identify outliers in datasets throughout the data analysis process. Atypical knowledge behavior is extracted from the group of information and regarded as rare or questionable. The environment, such as pattern recognition, can be programmed to improve autonomously through continuous learning from data sources. Data inconsistency can be an indicator of ecosystem failure—in most situations, articles with obvious contextual errors divert readers from the storyline. They recognize something is a mess with the narrative’s credibility; the information presented is suddenly untrustworthy. Data pools used by businesses operate in a similar manner [ 19 ]. Inconsistencies within datasets can have a direct impact on the knowledge contained within these information pools, causing considerable delays in the workflow of business processes. These are frequently manifested as financial transaction fraud, cybersecurity scams, or production line disruption.
Natural language processing (NLP) alters the game of reimbursement systems. NLP is the core technology that assists AI in interpreting and evaluating human language. Its ability to analyze and categorize online material is critical in the discovery of anomalies in knowledge volumes. The vast amount of information contained in a country’s handwritten medical bills might quickly fall victim to data discrepancy. Contextual and environmental factors enhance the likelihood of missing signatures and inaccurate values in documentation. The AI processing system developed an algorithm capable of linking existing categories in medical invoices (such as IBAN, doctors’ signatures, or handwritten remarks) with the text included in these papers. The document categorization and validation system readily discovered incomplete or wrong information by filtering the knowledge by content type, whether it was for contracts, orders, or receipts. The NLP’s anomaly detection system for reimbursements was the tipping point in reducing the manual processing of paperwork and, hence, the possibility of errors to a simple confirmation of results previously provided by the AI.
Online databases can give AI access to knowledge that is applicable to many different topics and application fields. A properly equipped machine can access real data from a database that grows as a result of human interactions that provide the algorithm with fresh data. It also means that customers or system users will have access to a larger pool of data. Chatbots, artificial technologies built on NLP that analyze and engage with human language through a conversation-like simulation environment, frequently carry out the act of information transmission (or transfer). Knowledge availability is an often-underestimated component that plays a critical influence in service users’ satisfaction. Difficulties in obtaining information can result in severe client losses. Readers who are missing portions of a publication may comprehend the paragraph they are focusing on but lose sight of the paper’s overall purpose. Similar to this, users who are surfing a website, utilizing an application, or trying out a new service cannot benefit fully from the solution if access to user manuals is difficult. Therefore, failing to facilitate this knowledge transfer can easily lead to users terminating their use of the solution because they believe the support team is not paying attention to them or because they are only using a small number of the available capabilities as a result of inadequate system management communication. Thus, knowledge delivery serves as an informational link between infrastructures and clients. When parts of this bridge are missing, the direct connection between the two parties also breaks, and part of the value transferred within this information ecosystem is lost with it. This study focused on evaluating the performance of a media ecological and knowledge management model employed in the organization using artificial intelligence technology. The media are being described in the case of AI and ML, but one such important thing is to focus on what the viewers expect from a short movie or advertisement. It depends on the method of prediction, for example, analyzing some methods with the help of computer vision and the study of audience’s interest and converting such data into an intelligence-based system and serving the actual content expected by the audience. All these roles can be made with the help of deep learning modules, but the important thing is that they are made under lightweight models; then, the system would be compatible with every audience.
| 2023-05-14T00:00:00 |
2023/05/14
|
https://www.mdpi.com/2079-8954/11/5/222
|
[
{
"date": "2023/04/01",
"position": 63,
"query": "AI journalism"
}
] |
The effects of commenting and AI fact-checking labeling on ...
|
User agency–based versus machine agency–based misinformation interventions: The effects of commenting and AI fact-checking labeling on attitudes toward the COVID-19 vaccination
|
https://pmc.ncbi.nlm.nih.gov
|
[
"Jiyoung Lee",
"The University Of Alabama",
"Kim Bissell"
] |
by J Lee · 2023 · Cited by 19 — Given the growing concerns over the impact of conspiratorial misinformation about the COVID-19 vaccine, a wide range of entities (e.g. social media platforms, ...
|
Abstract This study aimed to examine the effects of commenting on a Facebook misinformation post by comparing a user agency–based intervention and machine agency–based intervention in the form of artificial intelligence (AI) fact-checking labeling on attitudes toward the COVID-19 vaccination. We found that both interventions were effective at promoting positive attitudes toward vaccination compared to the misinformation-only condition. However, the intervention effects manifested differently depending on participants’ residential locations, such that the commenting intervention emerged as a promising tool for suburban participants. The effectiveness of the AI fact-checking labeling intervention was pronounced for urban populations. Neither of the fact-checking interventions showed salient effects with the rural population. These findings suggest that although user agency- and machine agency–based interventions might have potential against misinformation, these interventions should be developed in a more sophisticated way to address the unequal effects among populations in different geographic locations. Keywords: AI fact-checking label, commenting, COVID-19 vaccine misinformation, online experiment, vaccine attitudes
Despite the medical consensus that the COVID-19 vaccine is a key to protecting oneself and one’s community from the pandemic, vaccine hesitancy has been a persistent concern in the United States (Coustasse et al., 2021). Among other factors, misinformation and conspiracy theories have played a prominent role in increasing vaccine hesitancy. Conspiratorial misinformation has been spread particularly through social media.
Given the growing concerns over the impact of conspiratorial misinformation about the COVID-19 vaccine, a wide range of entities (e.g. social media platforms, journalists, and third-party fact-checking organizations) have adopted fact-checking practices. Researchers have taken efforts to test a variety of forms of fact-checking interventions, such as labeling misinformation (e.g. Zhang et al., 2021a) or nudging audiences to think about the accuracy of information (Pennycook et al., 2020). Moreover, recent fact-checking interventions have applied artificial intelligence (AI) to seek out automated ways to detect misinformation (e.g. Nakov et al., 2021), but research in this area is far from conclusive.
Building on previous literature, the current research takes a comprehensive approach to examining social media platform–based interventions by focusing on two misinformation interventions manipulated in terms of user/machine agency: (a) commenting on the misinformation post (i.e. a user agency–based misinformation intervention where users are given control over the intervention process) and (b) AI fact-checking labeling (i.e. machine agency–based misinformation intervention which offers sole agency to the [AI] system). We analyzed and compared the degree to which these interventions reduced the impact of conspiratorial misinformation on COVID-19 vaccine attitudes. In addition, we examined how our misinformation interventions elicited different effects among urban, suburban, and rural users. The sample for the present study was focused on Alabama residents, considering the large health disparities between urban and rural residents in the state. For example, Black Belt counties in Alabama are known to have limited healthcare providers and a high number of uninsured residents, which is directly linked to their limited access to healthcare (Barry-Jester, 2017). Particularly during the COVID-19 pandemic, Alabama has become one of the states that has experienced tremendous health disparities between rural and urban residents, such that residents in rural counties have been markedly susceptible to COVID-19 (Crozier et al., 2021). Addressing this additional question provided insights on the appropriateness of implementing the one-size-fits-all approach to misinformation interventions.
Social media misinformation and attitudes toward COVID-19 vaccination Misinformation about the COVID-19 vaccine has challenged the efforts to promote positive attitudes toward vaccination, amplifying concerns about the efficacy and safety of the COVID-19 vaccine among the public. Vulnerability to anti-vaccine misinformation has been attributed to various social media features, including a lack of professional journalistic gatekeepers and authoritative voices (Carlson, 2020) and/or active attempts of malicious actors (e.g. bots, trolls) to spread misinformation virally (Broniatowski et al., 2018). Especially when information supply is low and uncertainties are prevalent, users are likely to “turn to unofficial sources to satisfy their information needs,” such as their social media networks, neighbors, family, or other close-knit community members (Heverin and Zach, 2012: 35), which might, in turn, increase the likelihood of being exposed to misinformation. While Vraga et al. (2022) found no significant effects of exposure to misinformation on misperceptions, exposure to biased information online, in general, contributes to inaccurate beliefs (Garrett et al., 2016). The effect of exposure to misinformation can linger even in the presence of a correction, creating what is called a belief echo (Thorson, 2016). Therefore, our first hypothesis tests whether exposure to a misinformation post about the COVID-19 vaccine without any misinformation interventions, compared to non-exposure, would result in more negative attitudes toward COVID-19 vaccination. H1: Individuals who are exposed to a misinformation post only without any interventions (i.e. misinformation-only condition) will be less likely to hold positive attitudes toward the COVID-19 vaccination compared to those who are not exposed to a misinformation post (i.e. control condition).
Commenting as user agency–based misinformation intervention Previous research has revealed that misinformation is less likely to provide complex details to facilitate heuristic processing of the content (Osatuyi and Hughes, 2018). Given this phenomenon, it seems necessary to examine how to prevent audiences from easily being influenced by a misinformation post. One way to defend oneself against misinformation is to cognitively scrutinize the misleading content rather than processing it superficially and accepting it as is (Pennycook et al., 2020). Previous research shows that the act of expressing oneself on social media leads to greater cognitive elaboration (e.g. Oeldorf-Hirsch, 2018; Yoo et al., 2017), which means that individuals process the information deeply by linking to their existing knowledge or paying attention to it. Therefore, we suggest that expression via commenting activities can lead users to pause and think about the truthfulness of the misleading content. Commenting on a misinformation post can fall into a user agency–based intervention because it gives users control over communicating about the misinformation content—in other words, it gives users control over whether to simply indicate their agreements on the misinformation post or add specific thoughts in the comments section. In fact, social media platforms not only function as sources of information but also motivate users to express themselves, allowing them to exercise user agency. The interactive, bidirectional features of social media communication facilitate expression (Halpern and Gibbs, 2013). Self-expression on social media additionally takes various forms—for example, leaving comments on social media posts, replying to others’ opinions, or creating posts on one’s newsfeed. Furthermore, the interactive features of social media enable one’s expression to be displayed to the public simultaneously, including one’s social networks (Gil De Zúñiga et al., 2014). Hence, by expressing one’s thoughts online rather than passively consuming information, users may conceive of themselves as issue-involved participants who act, as opposed to fence-sitters (Gil De Zúñiga et al., 2014). The potential of a user agency–based misinformation intervention is fundamentally linked to the “sender effect” which Pingree (2007) had suggested before the rise of social media. The sender effect posits that expression activities can facilitate self-reflection and extensive cognitive activity. For example, Pingree (2007: 447) noted, “expression, not reception, may be the first step toward better citizenship,” which suggests that expression can “motivate exposure, attention and elaboration of media messages, and the act of message composition is often much more effective at improving understanding than any act of reception could be.” According to this view, composing and releasing messages may thus have effects that make senders reflect on one’s own thoughts, which can further contribute to their understanding (Bem, 1967). The sender effect of the expression on cognitive elaboration has garnered empirical support from previous literature, particularly in the context of social media. For example, Nekmat (2012) showed that the expression of personal messages increased the extent of cognitive elaboration that individuals expended. More specifically, in the context of social media, Oeldorf-Hirsch (2018) found that active social media news engagement such as commenting on or replying to news stories had a positive impact on cognitive elaboration. In the context of health, previous research suggests that the expression of Zika-related information on social media could facilitate extensive cognitive activities, given that it could induce greater tendencies to perceive the health crisis as risk to oneself or one’s affiliated community, which, in turn, could lead to increased intentions to seek information and preventive behavioral intentions (Lee et al., 2020). However, few studies have tested the effects of user agency–based misinformation interventions to deal with misinformation-inducing problems, particularly related to health issues such as the COVID-19 vaccination. Specifically, previous research lacked clarity on how commenting on misinformation posts would have a direct effect on attitudes toward the health crisis issue. Addressing these limitations in the current study directs us to posit the following hypothesis about the effect of leaving comments on a misinformation post on generating positive attitudes toward the COVID-19 vaccination. Based on the assumption that the user agency–based intervention can reduce the negative impact of misinformation because it can prevent individuals from falling for misinformation, H2 is advanced: H2: Individuals who are exposed to a misinformation post only without any interventions (i.e. misinformation-only condition) will be less likely to hold positive attitudes toward the COVID-19 vaccination, compared to those who leave comments on a misinformation post (i.e. misinformation comments condition).
AI fact-checking label as machine agency–based misinformation intervention In contrast to commenting, AI fact-checking labeling is an intervention solely driven by system-generated rules. This intervention labels the misinformation as false based on an automatic AI system’s decision-making process. Social media platforms (e.g. Google, Facebook) have widely applied AI-based algorithms to automatically detect misinformation as part of the solution for countering the threat posed by the falsification of information. Although social media platforms have allowed individual social media users to partially take a role in fact-checking practices at first, automated fact-checking tools which identify, verify, and respond to false claims through machine-learning AI systems have been increasingly used as a cost-efficient way to assess the credibility of social media posts (Graves, 2018). Fact-checking labels tend to use a simple, but explicit, tagline message that indicates a piece of information as false (e.g. “this website contains false information”). For example, media literacy tool NewsGuard has aided Internet users to discern credible information from misleading or biased information by providing succinct credibility ratings of news and information websites through browser extensions. Facebook’s disputed labeling practices also represent the accelerated efforts of social media platforms to verify content. Given that the AI fact-checking label is governed by AI systems which grant AI controllability, such intervention can fall into a machine agency–based misinformation intervention. As much empirical research has shown, testing the effectiveness of fact-checking labels has garnered much scholarly attention in recent years. Despite some evidence on the limited effect of fact-checking labels (e.g. Jennings and Stroud, 2021; Oeldorf-Hirsch et al., 2020), studies at large have concluded that the presence of fact-checking labels is effective at correcting misinformation. As found in Zhang et al. (2021a), compared to those who were exposed to misinformation, participants who were exposed to fact-checking labels had more favorable attitudes toward vaccines overall, and such effects of fact-checking labels were shown regardless of participants’ baseline vaccine skepticism. Related results were found in other empirical studies, such that the flagging of misinformation reduced Facebook users’ likelihood of sharing false news (Mena, 2020) and beliefs in false posts (Lee, 2022). Although not related to flagging, Bode and Vraga (2015) also found that when related articles correcting misinformation were attached to misleading Facebook posts, misperceptions were significantly reduced. The results of these studies suggest that flagging misinformation via simple fact-checking labels has been effective; in other words, the presence of fact-checking labels can be leveraged as an effective tool on social media platforms. The implied truth effect, coined by Pennycook et al. (2020), describes a phenomenon that tagging misinformation as false increases the perceived accuracy of untagged false posts. This effect has been empirically supported in a series of studies that untagged stories were perceived to be correct, irrespective of the truthfulness of the information (Pennycook et al., 2020). Therefore, when original misleading statements were not explicitly flagged as false, fact-checking effects are likely to be limited (Walter et al., 2020). This account echoes the effectiveness of explicitly tagging misinformation as false. However, there remains a question as to whether fact-checking labels that specifically indicate the use of AI in fact-checking practices are effective at reducing the influence of misinformation, which warrants further investigation. Indeed, only few studies have explored the effects of fact-checking interventions attributed to AI. In Zhang et al.’s (2021a) study, using a fact-checking label provided by algorithms (i.e. “This post is falsified. Fact-checked by Deep Learning Algorithms”) was rated lower on source expertise relative to fact-checking labeling attributed to universities and health institutions. This suggests that different types of sources were found to be effective at mitigating the negative impact of vaccine misinformation. Additional work by Horne et al. (2020) demonstrated that under novel and emerging topics (i.e. COVID-19) where individuals might experience uncertainties, the AI fact-checking label was more effective at helping audiences identify the truth of news articles. Furthermore, such effectiveness was pronounced especially when the AI intervention directly suggests the trustworthiness in the news source (i.e. “Our smart AI system indicates this is a trusted news source) or content (i.e. “Our smart AI system rates this article as accurate and reliable) by leading people to pay attention to those cognitive heuristic cues (Horne et al., 2020). However, a recent study that tested the fact-checking label attributing to AI’s decision-making rules found that the effectiveness was shown only when being transparent about the rationales of AI’s decision-making rules, suggesting the importance of implementing explainable AI (Liu, 2021). Despite the potential limited effect of the simple AI fact-checking label, attributing a fact-checking label to AI can be effective, according to the machine heuristic. A machine heuristic refers to a mental shortcut because users believe machines are unbiased and objective entities (Sundar, 2008). As argued by Sundar and Kim (2019: 2), “When the perceived locus of our interaction is a machine, rather than another human being, . . . we automatically apply common stereotypes about machines, namely that they are mechanical, objective, ideologically unbiased and so on.” Because individuals tend to assess algorithmic judgment as superior to decisions made by a human alone (Dawes et al., 1989), the fact-checking label attributed to AI can be evaluated as objective, unbiased, and reliable. As such, the AI fact-checking label can be leveraged as an effective tool that reduces the impact of misinformation posts about the COVID-19 vaccine, thereby increasing positive attitudes toward COVID-19 vaccination. Following this logic, we hypothesize: H3: Individuals who are exposed to a misinformation post only without any interventions (i.e. misinformation-only condition) will be less likely to hold positive attitudes toward COVID-19 vaccination, compared to those who see the AI fact-checking label when seeing a series of posts including misinformation (i.e. AI fact-checking label condition).
Accounting for differences between rural versus urban residents The last aim of this study was to examine whether there were any differences in the proposed effects of the interventions on attitudes toward COVID-19 vaccination depending on individuals’ residential areas (i.e. rural, suburban, and urban) in the United States. Exploring differences by users’ residential locations is particularly relevant to the COVID-19 vaccination issue, as research has demonstrated that residents in rural and urban areas in the United States tend to show a large COVID-19-related knowledge gap (Zhang et al., 2021b). Research has revealed that COVID-19 was transmitted at a higher rate in densely populated urban areas at the initial stage of the pandemic (Amram et al., 2020), but as the pandemic progressed, rural residents faced more risks of COVID-19 (Paul et al., 2020). Several factors have exacerbated the risks among residents from rural counties, which include lack of accessibility to credible and comprehensive news media (termed as “news deserts”), lower education, lower income, and poorer health care access than urban and suburban residents (Abernathy, 2018; Henning-Smith, 2020). In fact, the rural–urban disparity has appeared to be a general phenomenon globally. For example, previous research found that rural residents in China were more likely to have lower levels of information appraisal which reflects the abilities to utilize health information to protect one’s health, and this in case negatively affected preventive health behaviors against COVID-19 (Chen and Chen, 2020). In addition, most rural residents in Vietnam were found to have access to technology devices but less than half of them used the devices for the Internet or mobile apps (Yun Lee et al., 2022). Similarly, in the United States, although rural residents have adopted digital technologies at an increasing rate over the past years, digital divides persist between rural, suburban, and urban Americans, continuing the lack of connectivity in rural communities (Vogels, 2021). Digital technology access does not guarantee equitable access to accurate information, which can close health literacy gaps during a public health crisis (Jackson et al., 2021). Moreover, the persistent digital divide between rural and urban residents mirrors their existing inequalities in sociodemographic characteristics. Evidence suggests that the lack of economic and educational resources along with an increasingly aging population in rural communities is related to residents’ lack of motivation and need to access information (Correa and Pavez, 2016). In addition, rural residents’ tendencies to rely on word-of-mouth or certain media channels for COVID-19 information have led them to be more susceptible to misinformation that downplayed the risk of COVID-19 and affected their willingness to take the COVID-19 vaccine (Doughty and Fahs, 2021). Particularly, in southern states like Alabama where many rural locations have a larger proportion of underserved populations, disparities in COVID-19 health literacy are more pronounced (Mueller et al., 2021). This has been supported by recent research which found from a statewide survey that urban residents in Alabama were likely to understand information about COVID-19 more easily and have less difficulty accessing the information than their rural counterparts (Crozier et al., 2021). Although the rural–urban distinction does not capture all sociodemographic differences, it adds a nuanced perspective on how misinformation interventions operate differently to enhance positive attitudes toward the COVID-19 vaccine, depending on individuals’ residential locations. For example, rural residents’ low baseline levels of awareness about the benefits of the COVID-19 vaccination may mitigate the effects of misinformation interventions in the current study. Given the increasing demand to design tailored misinformation intervention strategies to specific populations, examining differences among individuals from different residential locations can provide practical utility. Because no empirical studies have considered residential locations in understanding the misinformation intervention effects, we pose this as a research question: RQ1: How will participants’ residential areas (rural vs suburban vs urban) moderate the effects of the experimental intervention conditions (the comments condition and AI fact-checking label condition) on attitudes toward COVID-19 vaccination?
Method A one-way between-subjects online experiment comparing four conditions (leaving comments to a misinformation post [i.e. comments condition] vs AI fact-checking label on Facebook posts [i.e. AI fact-checking label condition] vs exposure to a misinformation post only without misinformation interventions [i.e. misinformation-only condition] vs no misinformation post [i.e. no misinformation condition]). The data were collected after receiving approval from the Institutional Review Board. Upon agreeing to participate, participants first filled out screening questions. These questions included residential area (Alabama state only; rural, urban, and suburban areas via stratified sampling with the targeted percentage of participants residing in rural areas as 50%, urban and suburban as 25%, respectively 1 ), age (over 18 years old), and whether participants were Facebook account holders. Participants who were eligible to participate in this study were then directed to answer a pretest questionnaire measuring preexisting beliefs in the misleading statement about Gates’ plan to use the COVID-19 vaccine to plant microchips in people, general attitudes toward COVID-19 vaccines, COVID-19 vaccine skepticism, and COVID-19 vaccine uptake. Then, participants were randomly assigned to one of the four conditions and answered posttest questions about their attitudes toward COVID-19 vaccination. Last, participants answered demographic questions such as gender, education, race, and political orientation. At the end of the study, they were debriefed about the purpose of this study and the fact that the post about Gates was false. The average completion time of this study was 15 minutes. Participants All participants for this study were living in Alabama between the recruitment window of February and March 2021 via Amazon Mechanical Turk (MTurk). Amazon MTurk is widely used to recruit diverse participants (e.g. Casler et al., 2013) and known to be a comparable method for recruiting rural populations to other conventional sample methods (e.g. Saunders et al., 2021). Although MTurk does not provide representative samples of Alabama residents in terms of sociodemographic characteristics, it is a popular source of convenience sample to recruit diversified populations. A total of 712 MTurk workers first accessed the study; however, 15 participants indicated that they did not reside in Alabama, 79 indicated they were not Facebook users, and 241 did not complete the study. This left the final sample of 377 (N = 183 rural population [48.5%], N = 90 urban population [23.9%], N = 104 suburban population [27.6%]). Of the final sample, 36.3% were female, 82.2% were white (with 14.3% Black or African American), and 54.9% were high school or some college graduates. 2 The mean age of the participants was 48.56 (SD = 17.68; min = 18, max = 87). Stimuli Participants across the conditions were asked to consider the scenario that a series of Facebook posts were uploaded in one of the Facebook community groups they were a member of. The misinformation post which stated, “Did anyone hear that Bill Gates is using the COVID-19 vaccine to plant microchips in people?” was provided along with the picture of Gates. In the comments condition, participants were required to leave short comments about the misinformation post and were informed their comments would be shared with other community members of the Facebook groups. In the other conditions (i.e. AI fact-checking label, misinformation-only, and no misinformation), participants were instructed to read the given Facebook posts. Except for the no misinformation condition, we included the four filler posts in addition to the misinformation post (randomly ordered) for all conditions. For the no misinformation condition, a total of five filler posts (including four filler posts used for other conditions; all randomly ordered) were shown to participants. A fictitious user’s name was used for the misinformation post to prevent any confounding effects that might occur from sources. All participants were given a minimum of 30 seconds to read all the Facebook posts and allowed to move onto posttest questions. Specifically, for the comments condition, we manipulated the misinformation post that directly asked participants to share their thoughts in the comments section. For the AI fact-checking label condition, participants were first shown the AI label which reads, “Proceed with caution: an AI system detects that this web page contains some false claims.” Then, they were directed to see a series of Facebook posts including the misinformation post. Although the fact-checking label we used for this study did not specifically indicate which misinformation post was found to be false, we followed the approach of other browser extensions’ fact-checking labels. For example, NewsGuard, a tool that rates the credibility of online websites, uses a warning label (e.g. Proceed with Caution: This website severely violates basic journalistic standards) like the one used for this study. In this study, we modified a label to explicitly indicate that AI detected false claims. For the misinformation-only condition, we used the identical misinformation post used for the AI fact-checking label condition. For the no misinformation condition, the randomly ordered five given posts were irrelevant to the misinformation topic. In brief, participants in the other groups except for the comments condition were prevented from commenting (see Figure 1; see also Supplementary Materials for full stimuli including filler posts). Figure 1. Open in a new tab Manipulation of the misinformation post for the comments condition (left), AI fact-checking label for the AI fact-checking label condition (upper right), and misinformation post for the AI fact-checking label condition and misinformation-only condition (lower right). Measurement (Posttest variable) Attitudes toward COVID-19 vaccination The dependent variable, attitudes toward COVID-19 vaccination, was measured in the posttest. Participants were asked to think about the following statement in the context of the having a child at the time of the study or if the participant were to have a child: “Getting the COVID-19 vaccine for my children would be. . .” (a) bad—good, (b) harmful—beneficial, (c) foolish—wise, (d) threatening—assuring, and (e) risky—safe. The items were adopted from previous research (Abhyankar et al., 2008) and measured on a 5-point bipolar scale. The items produced good reliability (Cronbach’s α = .97, M = 3.41, SD = 1.41). Covariates All variables used for covariates were measured before the stimuli (i.e. pretest variables) except political orientation. Political orientation was measured at the end of the study, along with other demographic variables. This approach was based on the possibility that covariates except for political orientation could have been affected by the stimuli, but demographic items like political orientation are individual traits, which are independent of the stimuli. In addition, measuring political orientation at the end might prevent priming effects in which participants could have assumed the study was related to their political ideology if presented at the beginning. Although participants were randomly assigned to one of the conditions, the following covariates were included in the model for the potential confounding effects in times of the pandemic. Preexisting beliefs in the misleading statement Given that the misinformation topic gained popularity at the time of collecting the data, we measured the extent to which participants held preexisting beliefs in the misleading statement. To prevent priming effects, five different false claims surrounding COVID-19 vaccines were shown to participants. Although we used five different claims, only the statement “Bill Gates is using the COVID-19 vaccine to plant microchips in people” was used as a covariate for our analysis. For each statement, participants were asked to indicate their believability levels measured on a 5-point scale (1 = definitely false, 5 = definitely true). The mean value of the Gates statement used for our analysis was 2.15 (SD = 1.34). General attitudes toward COVID-19 vaccine For this covariate, participants answered a single item which asked them to indicate their general attitudes toward COVID-19 vaccines (1 = very negative, 5 = very positive; M = 3.29, SD = 1.31). COVID-19 vaccine skepticism For this covariate, we asked participants to rate how much they believed the following single statement as true or false: “The health risks of COVID-19 vaccines outweigh the benefits” (1 = definitely false, 4 = definitely true; M = 2.31, SD = 1.02). This item measuring skepticism was borrowed from Zhang et al. (2021a). COVID-19 vaccine uptake Considering that some participants might have been already vaccinated for COVID-19 at the time of data collection, we additionally asked whether participants were vaccinated for COVID-19 or not (0 = No, 1 = Yes). The percentage of participants who were not vaccinated was higher (79.8%) than that of participants who were vaccinated (20.2%). Political orientation As for the political orientation variable, we asked, “when it comes to politics, would you consider yourself . . .” (1 = very conservative, 7 = very liberal; M = 3.25, SD = 1.76).
Results Before running analyses for the hypotheses and research question, we conducted a correlation analysis among residential areas and measured covariates. The correlation results are presented in Table 1. To explain briefly, the results showed that urban residents of our sample were more likely to hold preexisting beliefs in misinformation, compared to rural residents (r = .19, p < .01), and suburban residents, compared to rural, were more likely to hold positive attitudes toward the vaccine (r = .18, p < .01) and less likely to hold vaccine skepticism (r = −.12, p < .05). Preexisting beliefs in the misinformation statement were negatively associated with positive attitudes toward the COVID-19 vaccine (r = −.39, p < .001) but positively associated with COVID-19 vaccine skepticism (r = .44, p < .001). Vaccine skepticism and positive attitudes toward the vaccine showed a negative association (r = −.46, p < .001). Regarding COVID-19 vaccine uptake, a positive association was shown with positive attitudes toward the vaccine (r = .38, p < .001), while a negative association was shown with COVID-19 vaccine skepticism (r = −.13, p < .01). As for political orientation, individuals with more liberalism were less likely to hold preexisting beliefs in misinformation (r = −.16, p < .01), more likely to hold positive attitudes toward the vaccine (r = .16, p < .01), and less likely to hold vaccine skepticism (r = −.12, p < .05). Table 1. Pearson correlation results on residential areas and measured covariates. Variable name 1 2 3 4 5 6 7 1. Rural (0) vs Urban (1) − 2. Rural (0) vs Suburban (1) NA − 3. Preexisting beliefs
(M = 2.15, SD = 1.34; 5-point scale) .19** −.10 − 4. General attitudes toward COVID-19 vaccine
(M = 3.29, SD = 1.31; 5-point scale) .07 .18** −.39*** − 5. COVID-19 vaccine skepticism
(M = 2.31, SD = 1.02; 4-point scale) .12 −.12* .44*** −.46*** − 6. COVID-19 vaccine uptake
(79.8% not vaccinated) .08 .09 −.05 .38*** −.13* − 7. Political orientation
(Liberalism; M = 3.25, SD = 1.76; 7-point scale) .06 −.003 −.16** .16** −.12* −.08 − Open in a new tab To test H1, participants in the misinformation-only condition and no misinformation condition were selected. A one-way analysis of covariance (ANCOVA) treating these two conditions as predictors of attitudes toward COVID-19 vaccination was run with covariates. The result did not support H1 because there was no significant difference in attitudes toward COVID-19 vaccination between the misinformation-only condition and the no misinformation condition, F(1, 198) = .95, p = .33, η p 2 = . 01 . Although not hypothesized, the additional results on the main effect of the misinformation-only condition (vs no misinformation) in each group of residential areas revealed the non-significant effects across rural, F(1, 94) = 2.53, p = .12, η p 2 = . 03 ; urban, F(1, 45) = 0.82, p = .37, η p 2 = . 02 ; and suburban, F(1, 45) = 1.46, p = .23, η p 2 = . 03 , groups. H2 and H3 were tested by selecting participants assigned to one of the experimental conditions: the comments condition, the AI fact-checking label condition, or the misinformation-only condition. As expected, a significant difference in attitudes toward the COVID-19 vaccination was shown by the three experimental groups, according to a one-way ANCOVA, F(1, 266) = 3.40, p < .05, η p 2 = . 03 . Post hoc tests with Fisher’s least significant differences (LSD) revealed that participants who left the comments on the misinformation showed a higher level of positive attitudes toward vaccination (M adjusted = 3.55, SE = .10), compared to those who saw the misinformation post only (M adjusted = 3.24, SE = .10), p < .05. Therefore, H2 was supported. Participants who saw the AI fact-checking label also held more positive attitudes toward vaccination (M adjusted = 3.58, SE = .12) than those who saw the misinformation post only, and this difference was significant (p < .05). This lends support to H3. Although not listed as a hypothesis, there was no significant difference between the AI fact-checking label condition and the comments condition (p = .86), suggesting that the interventions produced similar effects. In addition, when comparing each intervention to the control condition, no significant differences were found between the commenting and the control conditions (p = .31) and the AI fact-checking label and the control conditions (p = .31). To examine whether the effects of experimental groups on attitudes toward COVID-19 vaccination are different among rural, suburban, and urban populations (RQ1), a two-way ANCOVA was performed with the experimental groups, residential areas as the independent factors, and attitudes toward COVID-19 as the dependent variable, with the inclusion of covariates (see Table 2). The results with all five covariates included revealed a statistically significant interaction effect, F(4, 260) = 3.90, p < .01, η p 2 = . 06 , such that the main effects of the experimental conditions were moderated by residential areas. Table 2. Two-way ANCOVA results on the interaction effects between experimental conditions and residential areas on attitudes toward COVID-19 vaccination. Type III sum of squares df Mean square F p η p 2 Corrected model 257.121 a 13 19.78 21.34 *** .52 Intercept 31.13 1 31.13 33.58 *** .11 Experimental conditions b 5.66 2 2.83 3.06 .05 .02 Residential areas c 1.47 2 0.74 0.79 .45 .01 Experimental conditions b × Residential areas c 14.46 4 3.61 3.90 .004 .06 Preexisting beliefs 6.06 1 6.06 6.54 .01 .03 General attitudes toward COVID-19 vaccine 69.28 1 69.28 74.75 *** .22 COVID-19 vaccine skepticism 1.02 1 1.02 1.10 .30 .004 COVID-19 vaccine uptake 10.99 1 10.99 11.86 .001 .04 Political orientation 3.74 1 3.74 4.03 .05 .02 Error 240.96 260 0.93 Open in a new tab Follow-up analyses of the simple main effect analysis using LSD comparisons showed that when leaving the comments on the misinformation post (i.e. the comments condition), rural populations showed significantly higher positive attitudes toward the COVID-19 vaccination (M adjusted = 3.57, SE = .14) than urban populations (M adjusted = 3.02, SE = .21), and this difference was significant, p < .01. The commenting activity was effective at increasing positive attitudes toward the vaccine for suburban populations as well (M adjusted = 3.98, SE = .19) relative to urban populations, p < .001. The difference between rural and suburban was also significant (p < .05), such that higher positive attitudes toward the vaccination were found among suburban residents compared to rural residents when commenting on the misinformation post. Therefore, commenting as a user agency–based intervention elicited the pronounced effect on increasing positive attitudes toward the COVID-19 vaccination among suburban residents. For the AI fact-checking label condition, although the difference did not reach a statistically significant level (p = .12), slightly higher positive attitudes toward vaccination were found among urban residents (M adjusted = 4.03, SE = .24) relative to rural residents (M adjusted = 3.53, SE = .18). The effect of the AI fact-checking label on positive attitudes toward the vaccination among urban residents was pronounced when compared to suburban residents (M adjusted = 3.38, SE = .19; p < .05). No difference was found between rural and suburban residents (p = .65). Therefore, the AI fact-checking label turned out to be the most effective among urban populations while neither of the interventions (i.e. commenting and the AI fact-checking label) among rural residents was effective. Meanwhile, no differences in attitudes toward vaccination were found within the misinformation-only condition between rural (M adjusted = 3.12, SE = .14) and urban residents (M adjusted = 3.33, SE = .19; p = .36), rural and suburban residents (M adjusted = 3.39, SE = .19; p = .24), and suburban and urban residents (p = .82). Table 3 and Figure 2 present the moderating effects of residential location. Table 3. Descriptive statistics for attitudes toward COVID-19 vaccination by experimental conditions and residential areas. Comments condition
(N = 99) AI fact-checking label condition
(N = 73) Misinformation-only condition
(N = 102) Residential areas M adjusted (SE) M adjusted (SE) M adjusted (SE) Rural
(N = 132) 3.57 (.14)a 3.53 (.18)de 3.12 (.14)f Urban
(N = 64) 3.02 (.21)b 4.03 (.24)e 3.33 (.19)f Suburban
(N = 78) 3.98 (.19)c 3.38 (.19)d 3.39 (.19)f Open in a new tab Figure 2. Open in a new tab Comparison of adjusted mean scores for experimental conditions (misinformation-only, comments, and AI fact-checking label) by residential areas (rural, urban, suburban) on attitudes toward COVID-19 vaccination. Higher values of attitudes toward COVID-19 vaccination indicate more positive attitudes toward the vaccination. The results controlled for preexisting beliefs in the misleading statement, general attitudes toward COVID-19 vaccine, COVID-19 vaccine skepticism, COVID-19 vaccine uptake, and political orientation.
Discussion By responding to a persistent call to design effective misinformation interventions for mitigating misperceptions, the findings of the current study provide several important implications in both theoretical and practical terms. As hypothesized, our social media platform–based misinformation interventions, manipulated in terms of user or machine-based agency, overall improved attitudes toward COVID-19 vaccination against misinformation. We therefore assert a user agency–based misinformation intervention holds promise, whereas users are led to engage in the misinformation post (supporting H2), and a machine agency–based misinformation intervention, which presents the AI fact-checking label (supporting H3), in enhancing positive attitudes toward COVID-19 vaccination. This is meaningful, given that favorable attitudes can eventually reduce persistent concerns over the negative influences of health misinformation on users’ pro-health attitudes. To briefly elaborate on the results of H1, which compared the misinformation-only condition without any interventions to the control condition, this non-significant finding might be attributed to the possibility that some participants did not pay attention to the misinformation among the various posts presented; that said, participants who skimmed through the newsfeed might not even notice the misinformation post. However, it is worth noting from the additional analyses that the effects of misinformation interventions were salient when compared to the misinformation-only condition, given the non-significant differences between each intervention and the control condition when misinformation was not presented. This implies that activities like commenting or seeing the AI fact-checking label attached to the misinformation could be more effective at enhancing positive attitudes toward vaccines than simply seeing the misinformation without any of these interventions. Although examining whether misinformation interventions can directly motivate users to carefully think about the truthfulness of the misinformation post was beyond the scope of our study, we suggest that enhancing users’ engagement in the misinformation post and/or employing the AI fact-checking label might serve as effective tools that could mitigate the impact of misinformation. To explain implications of each intervention more specifically, our result on the effect of leaving comments on the misinformation post as a user agency–based intervention demonstrates the applicability of the “sender effect” (Pingree, 2007) as a theoretical framework to explain how users might come to hold attitudes toward vaccination when leaving their thoughts about the misinformation post. In line with the sender effect, which has demonstrated that expression yields cognitive benefits (Pingree, 2007; Shah, 2016), empirical research has found that the act of expression in the social media environment facilitates cognitive activities (e.g. Oeldorf-Hirsch, 2018) and motivates users to become active participants (Gil De Zúñiga et al., 2014). Taking these previous findings into account, the current study expands the theoretical framework of the sender effect in the context of interactive social media by shedding light on the promising role of leaving comments on the misinformation post. This, in turn, might result in an increase in positive attitudes toward vaccination. Put differently, commenting on the misinformation post might serve to lead users to pause and rethink misleading content, instead of processing the conspiratorial claim heuristically. This type of a user agency–based interface, which allows users to actively engage in the misinformation post, would lead them to serve as additional sources of information. In this case, users would be more involved and empowered when processing the misinformation content. In addition to this explanation that commenting activities can enhance user agency in processing the misinformation post, users might know their comments would be simultaneously visible to the public—especially to their close social media networks—and this could have also influenced attitudes toward the vaccination against the conspiratorial claim. When leaving comments, users might recognize that they should be more careful in sharing their thoughts about the message content, perhaps because they might think they can serve as an additional source that directly impacts others’ understanding on the vaccination issue. We also found that the AI fact-checking label as a machine agency–based intervention (i.e. an intervention which offers sole agency to the [AI] system) was effective at promoting positive attitudes toward vaccination, which is seemingly in line with previous research on the effectiveness of the fact-checking label (e.g. Lee, 2022; Zhang et al., 2021a). However, our finding is unique from previous studies, given that our interventions applied the automated AI system to fact-checking practices. By explicitly attributing the fake post detection to the AI, the automated AI fact-checking label can produce a promising effect. This could suggest that users might perceive the AI as objective, neutral, and accurate, as posited by the machine heuristic (Sundar, 2008). Following this logic of the machine heuristic, the AI fact-checking label might have triggered users to be skeptical of social media posts they saw and resist or think more critically about the misinformation post. Perhaps because users might notice the falsehood of the misinformation post about Gates through the AI fact-checking label, users’ positive attitudes toward vaccination might be shown in our finding. Therefore, even if the automated intervention does not offer agency to the individual user, employing the AI system can function as a cost-efficient tool. However, we found different effects of misinformation interventions depending on where users reside (rural vs suburban vs urban). Specifically, the findings on the moderation effects of residential locations indicate that for rural respondents, the interventions did not generate salient effects on promoting positive attitudes toward COVID-19 vaccination. Although leaving comments on the misinformation post as a user agency–based intervention seemed to be effective, the commenting intervention elicited the most pronounced effect among suburban residents. For urban populations, it was the AI fact-checking label that emerged as an effective tool for promoting positive attitudes toward vaccination. The salient effects of the AI fact-checking label for urban populations could be attributed to a gap between rural, suburban, and urban residents in understanding the operation of AI. Although not examined in this study, it could be possible that urban populations might have higher levels of understanding about the AI operation on fact-checking interventions. This finding also suggests the efforts to provide more detailed explanations on the AI mechanism of detecting misinformation, referred to as explainable AI (e.g. Miller et al., 2017; Rai, 2020). Explainable AI might prove beneficial for other populations such as rural residents who might need more knowledge on AI operation. Our argument may build upon recent research which suggests that the rationales for AI’s fake news detection (i.e. being transparent about the AI’s decision-making) should be revealed transparently to reduce uncertainty and increase trust (Liu, 2021). We further suggest that for people in rural/suburban locations who have lower media and health literacy levels than urban populations (e.g. Chen et al., 2019), providing the simple AI fact-checking label without adding the sufficient rationales of AI decision rules would not be sufficient to expect them to have positive attitudes toward the vaccine issue. The less significant effect of the AI fact-checking label condition compared to the commenting intervention among rural/suburban populations suggests the need to employ explainable AI to the fact-checking label in layperson’s terms to expect a significant intervention effect. In addition, there should be the need to develop more engaging interactive interface systems, given the potential of the commenting condition among rural/suburban residents. Given the circumstances, the findings that our misinformation interventions on social media were not equally strong for all users echo the need to avoid implementing a “one-size-fits-all” approach to designing health misinformation interventions (Chou and Budenz, 2020). Although our participants were confined to Alabama residents, the less pronounced effects of the AI fact-checking label among rural and suburban can be applied to other contexts, given that the large gaps in health and digital media literacy disparities between rural and urban residents can be a common phenomenon across the world (e.g. Chen and Chen, 2020; Vogels, 2021). Therefore, future research should make continuous efforts to design effective, real-time fact-checking strategies programmed by AI to dissuade users, including rural residents, and counter misinformation to narrow the digital media literacy divide between rural and urban citizens. This study has several limitations that could be addressed in future research. Given that Alabama has one of the lowest COVID-19 vaccination rates in the United States, we recruited only Alabama residents from the MTurk pool. However, we acknowledge that our sample does not represent the general population in the United States. The second limitation relates to the usage of a single misinformation topic, which could pose a challenge to generalizability. More research is warranted to investigate whether our findings can be replicated in different misinformation topics across health, science, and politics for various groups (e.g. media literacy levels, age, vaccination willingness). Future research is encouraged to measure other individual differences such as health literacy, AI literacy, preexisting attitudes toward the AI detection system, and levels of trust in social media platforms (Facebook herein), which have not been considered in this study. Related, while we are still able to infer from previous research that other demographic variables (e.g. education, income, and access to healthcare providers) may have an influence on health disparities in residential areas, we cannot conclude whether such individual differences may play determining roles in the effectiveness of misinformation interventions. Given these limitations, future research can investigate the underlying factors that may explain the limited effect of the AI fact-checking label among rural/suburban residents. Another limitation relates to the commenting intervention for the user agency condition. Although forcing participants to leave comments ensured we could observe the effects of expressing one’s thoughts on the misinformation post via commenting, users in real social media environments can opt to leave comments on the posts. Hence, future research can adopt more realistic experimental treatments and examine whether there are any differences between users who choose to leave comments or not. The relatively high rate of drop-out responses and possibly low response quality among some participants should be also noted as a limitation of this study. Particularly for the user agency condition, we encourage future studies to examine whether there might be any different effects of the intervention depending on the quality of user responses. Our findings offer practical implications for social media and AI practitioners who have tried to implement best practices for dealing with misinformation on the platforms. In the spread of conspiratorial claims that can directly plague scientific communities, it is imperative to understand that delivering simple messages that indicate the falsehood of misinformation may not be enough to promote accurate understanding of science-based health recommendations. Practitioners should not only seek out ways to direct users to step back from any suspicious posts they may see on social media and engage in thoughtful cognitive processes. However, practitioners should also consider that a simple AI fact-checking label as a “black box” would not produce equal effects for every social media user. Given that the current study is among the first to include residential locations in examining the effects of misinformation interventions, future research on misinformation should focus more on individual differences and develop a strategy on when, how, and to whom various types of misinformation interventions on social media should be directed to affect users’ attitudes.
Supplemental Material sj-docx-1-nms-10.1177_14614448231163228 – Supplemental material for User agency–based versus machine agency–based misinformation interventions: The effects of commenting and AI fact-checking labeling on attitudes toward the COVID-19 vaccination Supplemental material, sj-docx-1-nms-10.1177_14614448231163228 for User agency–based versus machine agency–based misinformation interventions: The effects of commenting and AI fact-checking labeling on attitudes toward the COVID-19 vaccination by Jiyoung Lee and Kim Bissell in New Media & Society
Author biographies Jiyoung Lee is an Assistant Professor in the Department of Media and Communication at Sungkyunkwan University. Her research interests lie in emerging media effects (e.g. AI, AR) on the (mis)information flow in health and risk contexts. Her recent publications have been related to mechanisms of the persuasive effects of various types of misinformation including deepfake and developing media literacy interventions. Kim Bissell serves as the Director for the Institute for Communication and Information Research and the co-director of the Health Communication Lab. Her research interests lie in the intersection of media, health, and children, and her recent publications have been related to the role of media in the development of children’s nutritional knowledge and attitudes.
1. The original purpose of this study was to see differences between rural versus suburban/urban residents. However, we found no significant effects when merging suburban/urban residents. Therefore, the final analyses reported in this article set apart suburban and urban residents. 2. Additional analyses showed that the urban (M = 3.67, SD = 1.64; p < .01) and suburban (M = 4.12, SD = 1.52, p < .001) samples reported higher education than the rural sample (M = 3.11, SD = 1.33), respectively. Although not listed as a hypothesis, the correlation results presented that higher levels of education are associated with more favorable attitudes toward the COVID-19 vaccine (r = .25, p < .001), less vaccine skepticism (r = −.12, p < .05), and more likelihood of getting vaccinated (r = .22, p < .001). Education was measured on a 7-point scale ranging from 1 = less than high school to 7 = doctorate degree.
Footnotes Authors’ note: Jiyoung Lee is also affiliated to Sungkyunkwan University, Republic of Korea. Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2021S1A5C2A02088387) and the Quick Response Research Award Program supported by the National Science Foundation (NSF Award #1635593). Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF or Natural Hazards Center. This article was first published by the Natural Hazards Center Quick Response Research Award Program, with support from the National Science Foundation. Used with permission: https://hazards.colorado.edu/research/quick-response-report/archives. ORCID iD: Jiyoung Lee https://orcid.org/0000-0002-0800-9355 Supplemental material: Supplemental material for this article is available online.
| 2023-04-18T00:00:00 |
2023/04/18
|
https://pmc.ncbi.nlm.nih.gov/articles/PMC10113910/
|
[
{
"date": "2023/04/01",
"position": 76,
"query": "AI journalism"
}
] |
Deep Learning to Encourage Citizen Involvement in Local ...
|
Open Research Online
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https://oro.open.ac.uk
|
[
"Tessem",
"Nyre",
"Mesquita",
"Mulholland"
] |
by B Tessem · 2022 · Cited by 5 — ... AI technologies can be applied. The scenarios address three interrelated challenges for local journalism. First, news tips from readers are often of low ...
|
× Copy the page URI to the clipboard
Tessem, Bjørnar; Nyre, Lars; Mesquita, Michel and Mulholland, Paul (2022). Deep Learning to Encourage Citizen Involvement in Local Journalism. In: Manninen, V.J.E.; Niemi, M.K. and Ridge-Newman, A. eds. Futures of Journalism. Palgrave Macmillan, Cham, pp. 211–226.
DOI: https://doi.org/10.1007/978-3-030-95073-6_14
Abstract
We discuss the potential of a mobile app for news tips to local newspapers to be augmented with artificial intelligence. It can be designed to encourage deliberative, consensus-oriented contributions from citizens. We presume that such an app will generate news stories from multi-modal data in the form of photos, videos, text elements and information about the location and identity of the contributor. Three scenarios are presented to show how image recognition, natural language processing, narrative construction and other AI technologies can be applied. The scenarios address three interrelated challenges for local journalism. First, news tips from readers are often of low technical quality; containing little information and poor photos. Second, peer-to-peer dialogue about local news takes place in social media instead of in the newspaper. Third, readers lack news literacy and are prone to confrontational debates and trolling. We show how advances in deep learning technology makes it possible to propose solutions to these problems.
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| 2023-04-01T00:00:00 |
https://oro.open.ac.uk/83754/
|
[
{
"date": "2023/04/01",
"position": 82,
"query": "AI journalism"
}
] |
|
the social dimension of generative artificial intelligence in ...
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Without journalists, there is no journalism: the social dimension of generative artificial intelligence in the media.
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https://search.ebscohost.com
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[
"Peña-Fernández",
"Meso-Ayerdi",
"Larrondo-Ureta",
"Díaz-Noci"
] |
by S Peña-Fernández · 2023 · Cited by 92 — More specifically journalistic uses, on the contrary, are commonly associated with automation and generative AI, i.e., algorithmic processes that convert data ...
|
Connecting you to content on EBSCOhost
| 2023-03-01T00:00:00 |
2023/03/01
|
https://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=13866710&AN=163494073&h=SWI8LyW0kMLkON03RUHuvert4zdanncBtZG47C7J2VOKL%2BDAXrtn9Bx1BEkFKUefGlTB9DtOl%2BHh%2F%2Ff4fxs3iQ%3D%3D&crl=f
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[
{
"date": "2023/04/01",
"position": 87,
"query": "AI journalism"
}
] |
AI Hiring Trends: Job Market Data, Salaries, and Industry Demand
|
AI Hiring Trends: Job Market Data, Salaries, and Industry Demand
|
https://patentpc.com
|
[
"Bao Tran",
"Patent Attorney"
] |
1. AI job postings increased by 21% year-over-year globally. AI-related job postings are rising fast. Companies in every sector—finance, ...
|
Artificial Intelligence (AI) is reshaping industries, creating new opportunities, and driving demand for specialized talent. Companies worldwide are competing to hire AI professionals, offering lucrative salaries and career growth. If you’re considering a career in AI or want to understand where the industry is headed, this article breaks down key hiring trends, salary insights, and industry demand.
1. AI job postings increased by 21% year-over-year globally
AI-related job postings are rising fast. Companies in every sector—finance, healthcare, retail, and tech—are integrating AI, creating more demand for skilled professionals. This growth isn’t slowing down, meaning there are more opportunities than ever.
If you’re interested in AI, now is the time to gain relevant skills. Learning Python, machine learning, and data science can help you stand out. Companies want candidates with hands-on experience, so working on projects, building AI models, and contributing to open-source AI projects can make you a strong candidate.
2. Machine learning engineer roles saw a 74% growth in demand over the past three years
Machine learning engineers are among the most sought-after AI professionals. They build, train, and optimize AI models used in various industries, from self-driving cars to fraud detection. This growing demand means competitive salaries and strong job security.
To land a machine learning role, focus on skills like TensorFlow, PyTorch, and deep learning. Certifications from platforms like Coursera or Udacity can help showcase your expertise. Many companies look for real-world experience, so consider building AI models and sharing them on GitHub.
3. The average salary for an AI engineer in the U.S. is $135,000–$170,000 per year
Why AI Engineers Command High Salaries
AI engineers are among the highest-paid tech professionals today. This isn’t just because AI is a trending field—it’s because their skills drive massive business value.
Companies across industries are willing to pay top dollar because AI can streamline operations, personalize customer experiences, and unlock entirely new revenue streams.
Beyond just writing code, AI engineers need to understand complex algorithms, data structures, and real-world business applications. They also must keep up with rapid advancements in machine learning and neural networks.
The demand is high, the talent pool is limited, and businesses are fiercely competing for top AI minds.
4. AI-related job openings now account for 12% of all tech job postings
AI is no longer a niche field—it’s becoming a core part of the tech industry. More than 1 in 10 tech job listings now require AI skills. Even companies that traditionally didn’t focus on AI, like retail and manufacturing, are hiring AI specialists to automate processes and analyze data.
If you’re already in tech, learning AI can future-proof your career. Software developers, data analysts, and cybersecurity professionals who add AI skills to their resume can open doors to better opportunities.
5. Entry-level AI roles pay an average of $85,000–$120,000 per year
The Salary Breakdown: What Businesses Should Know
The AI job market is heating up, and so are salaries for entry-level positions. Businesses hiring AI talent should expect to offer competitive pay to attract skilled candidates.
The $85,000–$120,000 range is just the starting point—actual compensation varies based on company size, location, and industry demand.
For startups, offering salaries at the lower end may work if combined with strong benefits, equity options, or remote flexibility. Larger enterprises and tech giants, however, often push compensation toward the higher end, especially in AI-heavy regions like Silicon Valley, Seattle, and Boston.
6. Senior AI engineers can earn upwards of $250,000 annually, excluding bonuses and stock options
Top AI engineers are in high demand, and companies are willing to pay a premium for experienced professionals. Salaries at major tech firms like Google, Apple, and Amazon can easily surpass $250,000, with stock options and bonuses pushing total compensation even higher.
If you’re aiming for a senior AI role, focus on leadership skills, advanced AI research, and real-world experience. Mentoring junior engineers and publishing research papers can help you position yourself as an industry expert.
7. The global AI workforce is expected to reach 12 million professionals by 2025
What This Means for Businesses Right Now
The AI workforce is expanding faster than ever, and businesses that understand how to leverage this shift will have a significant competitive edge. By 2025, an estimated 12 million AI professionals will be active across industries, from software development to healthcare, finance, and beyond.
This means more talent will be available, but demand will still outpace supply, making strategic hiring and retention a top priority for companies investing in AI.
Why Companies Need to Rethink Their AI Talent Strategy
Companies that assume AI hiring is just about technical skills are missing the bigger picture. The most successful AI teams integrate business strategy, domain expertise, and cross-functional collaboration. To build a future-ready AI workforce, organizations should prioritize:
Attracting professionals with both technical skills and problem-solving abilities
Upskilling current employees to bridge AI knowledge gaps
Creating AI-friendly cultures where data-driven decision-making thrives
Exploring global talent pools to tap into diverse AI expertise
8. The hiring rate for AI talent is 35% higher than for other tech roles
Why AI Hiring is Outpacing Other Tech Roles
The demand for AI professionals is skyrocketing because AI is no longer a futuristic concept—it’s a business necessity. Companies are integrating AI across operations, from automating customer service to optimizing supply chains and developing intelligent products.
Unlike traditional tech roles like software development or IT support, AI talent requires a deep understanding of data science, machine learning, and algorithmic modeling.
These specialized skills make AI professionals harder to find and even more valuable to businesses looking to stay ahead of the curve.
9. AI research scientist salaries have increased by 22% over the past two years
AI research scientists work on cutting-edge innovations, pushing the boundaries of machine learning and deep learning. The rapid growth in AI has led to a significant increase in salaries for these professionals.
If you enjoy academic research and problem-solving, this could be a lucrative career path. Many AI research scientists hold PhDs, but you can also enter the field with strong research experience and a solid understanding of AI algorithms.
10. The U.S., China, and India lead the world in AI job postings
Why These Three Countries Dominate the AI Job Market
AI job postings in the U.S., China, and India continue to outpace the rest of the world. These three nations have emerged as global leaders in AI hiring, each driven by unique economic, technological, and policy factors.
The U.S. leads in AI research, corporate investment, and high salaries, attracting top global talent. China, backed by strong government policies and massive AI infrastructure investments, is aggressively expanding its AI workforce.
Meanwhile, India serves as a powerhouse for AI development and outsourcing, offering a vast talent pool at competitive costs.
11. AI talent shortage affects 64% of organizations looking to scale AI initiatives
Why AI Talent Shortages Are Slowing Business Growth
The demand for AI is skyrocketing, but finding the right talent to implement and scale AI initiatives remains a major challenge.
With 64% of organizations struggling to hire AI professionals, businesses face delayed projects, increased costs, and missed opportunities for innovation. The reality is clear—without the right talent, even the best AI strategies can stall.
The Hidden Costs of the AI Talent Gap
Many businesses underestimate the real impact of AI talent shortages. Beyond delayed hiring, there are deeper consequences that can hinder long-term success. Organizations that fail to secure top AI talent often experience:
Slower product development cycles and missed market opportunities
Increased reliance on external vendors, leading to higher operational costs
Knowledge gaps that create inefficiencies in AI-driven decision-making
Increased risk of AI implementation failures due to lack of expertise
The longer a business waits to address the AI talent gap, the harder it becomes to stay competitive in an AI-driven world.
12. The fintech sector hires 18% of all AI talent, the highest among industries
Why Fintech is Leading AI Hiring
The fintech industry isn’t just adopting AI—it’s driving its evolution. With digital banking, automated trading, fraud detection, and personalized financial services becoming the norm, fintech companies need AI talent to stay ahead of the competition.
Unlike traditional banks, fintech startups move fast, experiment aggressively, and prioritize AI-driven solutions.
Whether it’s an AI-powered lending algorithm or a robo-advisor that makes real-time investment decisions, fintech firms rely on AI to optimize financial processes, reduce risk, and enhance customer experiences.
13. AI-related PhD graduates have increased by 40% in the past five years
What This Growth Means for Businesses
The rapid rise in AI-related PhD graduates is reshaping the talent landscape. Companies now have access to a deeper, more specialized talent pool with advanced expertise in machine learning, computer vision, and neural networks.
However, this influx of highly educated professionals also increases competition among businesses looking to secure top-tier AI talent.
Organizations that recognize the strategic value of PhD-level AI researchers can gain a competitive advantage. These individuals are not just coders—they are problem-solvers, algorithm designers, and innovation drivers.
Businesses that successfully integrate PhD talent into their teams will lead in AI breakthroughs and industry advancements.
14. Big tech companies (Google, Microsoft, Amazon) hire 50% of top AI talent
How Big Tech’s AI Domination Impacts the Hiring Landscape
The fact that Google, Microsoft, and Amazon hire 50% of the world’s top AI talent isn’t just a statistic—it’s a wake-up call for businesses competing for AI expertise.
These tech giants offer massive salaries, unparalleled resources, and the chance to work on industry-defining projects, making it increasingly difficult for other companies to attract elite AI professionals.
This concentration of AI talent within a handful of companies creates a hiring imbalance, leaving startups, mid-sized firms, and even large enterprises struggling to secure the AI expertise needed to scale and innovate.
15. AI ethics and governance jobs have grown by 65% year-over-year
Why AI Ethics and Governance Roles Are in High Demand
AI is no longer just about performance—it’s about responsibility. As AI systems become more powerful and deeply integrated into business and everyday life, the risks associated with bias, misinformation, security, and privacy violations have grown.
Governments, consumers, and industry leaders are demanding accountability, which has led to a surge in AI ethics and governance roles.
Companies that fail to address ethical concerns risk reputational damage, legal challenges, and regulatory fines.
Organizations that proactively invest in AI governance are not only protecting themselves but also gaining a competitive edge by building trust with customers and stakeholders.
16. AI consulting roles pay an average of $120,000–$160,000 per year
AI consultants help businesses integrate AI into their operations.
They analyze company needs, recommend AI solutions, and guide implementation. This is a lucrative career path for professionals with both technical expertise and business acumen.
To become an AI consultant, you need strong problem-solving skills and experience applying AI to real-world challenges. A background in business strategy or management consulting, combined with AI knowledge, can help you excel in this role.
17. AI adoption in healthcare has led to a 31% increase in AI-driven hiring in the sector
The healthcare industry is rapidly adopting AI for diagnostics, predictive analytics, and drug discovery. Hospitals and biotech firms are hiring AI specialists to improve patient care and streamline operations.
If you’re interested in AI in healthcare, focus on areas like medical image analysis, AI-powered diagnostics, and electronic health records automation. Understanding healthcare regulations like HIPAA can also give you a competitive edge.
18. Natural language processing (NLP) specialists are among the top 5 highest-paid AI professionals
NLP powers applications like chatbots, voice assistants, and automated translations. With the rise of AI-driven communication tools, NLP specialists are in high demand.
To break into NLP, master libraries like SpaCy, Hugging Face, and OpenAI’s GPT models. Working on NLP projects, such as building chatbots or sentiment analysis tools, can help you showcase your expertise to employers.
19. AI-powered automation is expected to impact 85 million jobs by 2025
The Shift Businesses Must Prepare For
AI-powered automation is not just a futuristic concept—it is actively reshaping industries today.
By 2025, an estimated 85 million jobs will be disrupted, transformed, or entirely replaced by AI-driven systems. While this figure may seem alarming, it presents both challenges and opportunities for businesses willing to adapt.
The key takeaway is that AI is not just eliminating jobs—it is redefining them. Businesses that embrace this transition strategically will position themselves for greater efficiency, innovation, and long-term profitability.
20. AI in cybersecurity hiring has grown by 48% due to increasing digital threats
With cyber threats becoming more advanced, AI is playing a crucial role in detecting and preventing attacks. Companies are hiring AI security experts to enhance their cybersecurity infrastructure.
If you’re interested in this field, focus on skills like anomaly detection, AI-powered threat analysis, and security automation. Certifications in cybersecurity, like CISSP or CEH, combined with AI expertise, can make you a strong candidate.
21. Remote AI jobs have increased by 60% post-pandemic
Why Remote AI Work Is More Than a Trend
The post-pandemic world has permanently reshaped the way AI professionals work. A 60% surge in remote AI jobs isn’t just a temporary shift—it’s the new standard.
AI professionals now expect flexibility, and businesses that resist this change risk losing top talent to companies that embrace remote and hybrid work models.
This transformation goes beyond convenience. Remote AI roles allow companies to access global talent, reduce operational costs, and build more diverse and innovative AI teams. Businesses that fail to adapt to this shift will struggle to compete in an increasingly digital and decentralized job market.
22. AI engineers in Europe earn on average €90,000–€130,000 per year
Why AI Salaries in Europe Are Rising
The demand for AI talent in Europe has surged as businesses across industries accelerate AI adoption.
From automating business processes to advancing AI-driven research, European companies are competing for a limited pool of skilled professionals. As a result, AI salaries are steadily increasing, making AI engineering one of the most lucrative career paths in the region.
Unlike the U.S., where tech giants dominate AI hiring, Europe has a more diversified AI job market. Large multinational corporations, government-backed AI initiatives, and a thriving startup ecosystem all contribute to the rising compensation levels for AI engineers.
23. AI in retail hiring surged by 30%, driven by demand for personalization and automation
Retailers are leveraging AI to enhance customer experiences, optimize supply chains, and drive personalized marketing. As a result, AI hiring in the retail sector is booming.
For AI professionals interested in retail, understanding customer behavior analytics, recommendation systems, and AI-driven inventory management can provide a competitive advantage.
24. AI-driven robotics engineers saw a 50% increase in demand
Robotics engineers specializing in AI are in high demand for automation in industries like manufacturing, logistics, and autonomous vehicles.
If you’re looking to enter this field, learn about computer vision, reinforcement learning, and robotic control systems. Hands-on experience with robotic frameworks like ROS (Robot Operating System) can also be a major advantage.
25. AI project managers earn an average of $110,000–$150,000 annually
As AI projects become more complex, companies need experienced AI project managers to oversee development, deployment, and integration. These professionals bridge the gap between technical teams and business stakeholders.
If you have experience in project management and a strong understanding of AI, this could be a high-paying career path.
Certifications like PMP (Project Management Professional) combined with AI knowledge can boost your chances of landing a role.
26. AI-based fraud detection hiring in banking has increased by 40%
Banks are using AI to detect fraudulent transactions, assess credit risk, and enhance financial security. AI professionals in this space help institutions prevent losses and improve compliance.
If you’re interested in AI in finance, focus on machine learning for anomaly detection, fraud analytics, and financial risk modeling. Understanding regulatory frameworks like AML (Anti-Money Laundering) compliance can also be a plus.
27. Quantum AI job postings have increased by 80% in the last two years
Quantum computing is an emerging field that promises breakthroughs in AI performance. Companies are actively hiring AI professionals with knowledge of quantum algorithms.
Breaking into this field requires expertise in quantum mechanics, quantum programming languages (like Qiskit), and advanced mathematics. If you’re passionate about cutting-edge AI, this is a field worth exploring.
28. AI software engineers make up 45% of AI-related hires globally
Why AI Software Engineers Dominate the AI Hiring Landscape
AI software engineers are the backbone of AI development, responsible for building, optimizing, and deploying intelligent systems. With nearly half of all AI-related hires globally falling into this category, businesses must recognize the critical role these professionals play in AI-driven transformation.
From designing machine learning models to integrating AI into real-world applications, AI software engineers are in high demand across industries.
The rapid growth of AI-powered tools, automation platforms, and data-driven decision-making is fueling this hiring surge, making competition for top AI engineering talent fiercer than ever.
29. Women in AI hold only 22% of AI-related positions, highlighting a gender gap
Why the AI Gender Gap Is a Business Problem, Not Just a Diversity Issue
The fact that women hold only 22% of AI-related positions is more than a statistic—it’s a missed opportunity for businesses.
AI thrives on diverse perspectives, yet the gender imbalance in AI hiring limits innovation, reinforces biases in AI models, and reduces the overall talent pool available to companies.
Companies that fail to address this gap are not only missing out on highly skilled professionals but also increasing the risk of AI products that lack inclusivity. In a world where AI is shaping industries, ensuring gender diversity is a strategic imperative, not just a corporate checkbox.
30. AI and cloud computing job postings have grown by 38% in the last year
Cloud computing and AI go hand in hand. Many AI applications run on cloud platforms, making cloud expertise a valuable skill for AI professionals.
To stay competitive, learn cloud-based AI tools from AWS, Google Cloud, and Microsoft Azure. Certifications in cloud computing can significantly boost your employability.
wrapping it up
The AI job market is expanding rapidly, offering numerous opportunities for professionals across various industries.
With salaries ranging from six figures for entry-level roles to well over $250,000 for senior AI engineers, it’s clear that AI expertise is one of the most valuable skill sets in today’s workforce.
Key trends, such as the rise in machine learning roles, increased demand in fintech and healthcare, and the growth of AI-powered automation, highlight the urgency for businesses to invest in AI talent.
However, the industry still faces challenges, including a global talent shortage and a gender gap that must be addressed to ensure AI development remains diverse and inclusive.
| 2025-07-02T00:00:00 |
2025/07/02
|
https://patentpc.com/blog/ai-hiring-trends-job-market-data-salaries-and-industry-demand
|
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The Centre for Responsible Union AI
|
The Centre for Responsible Union AI
|
https://www.agileunions.ai
|
[
"Nick At The Centre For Responsible Union Ai"
] |
A Unions21 project to build agile unions in the AI age.
|
We might assume union staff are reluctant to engage with AI. We'd be wrong
Antti Mäki is the Learning and Community Manager at Unions 21. In this blog, he reflects on the experience of running Responsible AI Fundamentals training and how he found participants not just curious about AI - but eager to embrace it.
| 2023-04-01T00:00:00 |
https://www.agileunions.ai/
|
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|
Microsoft and Labor Unions Form 'Historic' Alliance on AI
|
Microsoft and Labor Unions Form ‘Historic’ Alliance on AI
|
https://www.neatprompts.com
|
[
"Neatprompts - Aadit Sheth"
] |
Microsoft and Labor Unions Form 'Historic' Alliance on AI. Microsoft partners with AFL-CIO to address AI's workforce impact and influence policy, amid global ...
|
❝ Hey - welcome to this article by the team at neatprompts.com. The world of AI is moving fast. We stay on top of everything and send you the most important stuff daily. Sign up for our newsletter: Subscribe
In an unprecedented move, Microsoft has joined forces with the AFL-CIO, the United States' leading labor organization, to navigate the complex terrain of artificial intelligence and its impact on the workforce. This strategic partnership aims to address the challenges posed by AI and shape governmental policies concerning its use and regulation.
This collaboration emerges against a backdrop of increasing concern among workers and governments worldwide about AI's potential effects. There is a growing apprehension that advancements in AI could lead to the automation of entire industries and job categories, fundamentally altering the employment landscape.
Positioning itself at the forefront of the AI revolution, Microsoft is spearheading the adoption of this transformative technology. The company has distinguished itself from other tech giants, particularly through its significant partnership with OpenAI, the innovators behind ChatGPT.
This initiative underscores Microsoft's commitment to exploring the possibilities of AI while conscientiously considering its societal implications.
The Alliance: Microsoft and Labor Unions
The alliance comprises Microsoft, one of the world's foremost technology companies, and over 60 labor unions representing 12.5 million workers under the American Federation of Labor and Congress of Industrial Organizations (AFL-CIO) umbrella. This union, representing a wide spectrum of sectors, from communications workers to video game workers, aims to ensure that the advances in AI serve the interests of the country's workers.
Key Objectives
Open Dialogue : Central to this alliance is the commitment to maintaining an open dialogue between Microsoft and labor leaders. This dialogue fosters mutual understanding and develops strategies that align AI advancements with worker welfare.
Education Sessions : The alliance plans to conduct education sessions to demystify AI technology trends. These sessions aim to elucidate how artificial intelligence works and its potential effects on various jobs, thereby preparing workers for the changing landscape.
Collective Bargaining and Neutrality Agreements : The collaboration also aims to explore collective bargaining rights and neutrality agreements. These agreements commit companies to a stance of impartiality during workers' organizing and unionization efforts, thus ensuring fair treatment of union workers.
Worker Perspectives in AI Development: A crucial aspect of this alliance is incorporating worker perspectives into the development and deployment of AI. This approach intends to mitigate concerns about AI potentially displacing workers and instead focuses on harnessing AI to augment worker productivity and safety.
The Impact and Potential
For Workers
The initiative marks the first formal collaboration between a major technology industry player and labor unions on AI. It represents a significant shift towards recognizing and addressing the concerns of frontline workers about how emerging technology, specifically AI, could impact their jobs. The alliance aims to empower workers with technology skills and provide labor leaders with insights into how AI could be leveraged to enhance, rather than replace, human labor.
For Microsoft
For Microsoft, this partnership reflects a proactive approach to addressing growing concerns about AI's role in the technology industry. By engaging directly with labor groups, Microsoft President Brad Smith demonstrates the company's commitment to responsible AI principles and to ensure that AI technology trends benefit the broader spectrum of society.
Broader Implications
This historic alliance sets a precedent for how tech giants can collaborate with labor organizations to address the challenges posed by emerging technologies. It underscores the importance of integrating AI principles prioritizing worker well-being and job security, fostering a more inclusive and sustainable technological future.
Conclusion
| 2023-04-01T00:00:00 |
https://www.neatprompts.com/p/microsoft-and-labor-unions-form-historic-alliance-on-ai
|
[
{
"date": "2023/04/01",
"position": 72,
"query": "AI labor union"
},
{
"date": "2023/08/01",
"position": 69,
"query": "AI labor union"
},
{
"date": "2024/04/01",
"position": 74,
"query": "AI labor union"
},
{
"date": "2024/06/01",
"position": 72,
"query": "AI labor union"
}
] |
|
Demystifying AI and Empowering Workers - UC Berkeley Labor Center
|
Demystifying AI and Empowering Workers
|
https://laborcenter.berkeley.edu
|
[] |
Employers are also using digital technologies to surveil and profile workers and prevent them from organizing unions. But technology and its impacts are not ...
|
What is AI, and why does it matter to workers? Employers are rapidly adopting new digital technologies that have significant impacts on workers – from work speed-up and deskilling to automation, discrimination, and hiring, firing, and management by algorithms. Employers are also using digital technologies to surveil and profile workers and prevent them from organizing unions. But technology and its impacts are not inevitable. How can we make sure that technology works for workers?
Join us for a three-part webinar series that will cut through the AI hype to help better understand digital technologies in the workplace and how to respond through collective bargaining and public policy.
Part 1: Demystifying AI and Other Digital Technologies
Wednesday, May 7, 10am–11am PT
This webinar with the UC Berkeley Labor Center’s Technology and Work Program, Tech Equity, and the Warehouse Workers Resource Center lays the groundwork for understanding digital technologies in the workplace and why they matter.
Part 2: Public Policy and Digital Technologies
Wednesday, May 28, 10am–11am PT
In this webinar, we will be joined by the California Federation of Labor Unions and members of the California Teachers Association to analyze the rapidly changing public policy landscape around technology and labor. It provides a framework for understanding key policy concepts and highlights important legislation and bills in California and across the country.
Part 3: Collective Bargaining and Digital Technologies
Wednesday, June 18, 10am–11am PT
This webinar with the Communication Workers of America and NewsGuild of New York explores how unions are addressing workplace technologies at the bargaining table. It features findings from the UC Berkeley Labor Center’s new research on technology agreements, highlights union experiences from the field, and provides concrete examples of bargaining strategies and contract provisions.
| 2023-04-01T00:00:00 |
https://laborcenter.berkeley.edu/event/demystifying-ai-and-empowering-workers/
|
[
{
"date": "2023/04/01",
"position": 87,
"query": "AI labor union"
},
{
"date": "2024/09/01",
"position": 39,
"query": "AI labor union"
}
] |
|
The future of labor unions in the age of automation and at the dawn ...
|
The future of labor unions in the age of automation and at the dawn of AI
|
https://ideas.repec.org
|
[
"Nissim",
"Gadi",
"Simon",
"Author",
"Listed"
] |
The COVID-19 crisis has accelerated an already-ongoing process of massive digitalization in economic production and services. AI and robotics are getting, ...
|
The COVID-19 crisis has accelerated an already-ongoing process of massive digitalization in economic production and services. AI and robotics are getting, for the first time, autonomous and self-learning, with human-like capabilities. The discussion about digitalization and the future of work has become even more imperative. So far, labor unions were the leading institutions representing employees. However, the rising possibility of human substitution by intelligent machines puts in question the feasibility of labor unions’ policies. This development undermines their traditional power sources, which depend on the membership of masses of paid workers and on their ability to stop production. In this context, this paper aims to discuss the challenges confronting unions in capitalist democracies. Most scholarly literature on labor relations has embraced the assumption that the digital revolution will eventually bring new, better jobs. We suggest considering an alternative scenario, namely, a digital revolution that causes mass replacement of human workers and structural, technological unemployment, which might expand our point of view, particularly for designing public policy. We suggest that unions now have two crucial roles. The first is to safeguard workers' rights and interests in the transition from an economy based on paid labor to an economy based on automated-autonomous production; and second, they should transform their primary calling from representing employees to representing the social rights of all citizens, and particularly the material interests of lay people.
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| 2021-07-14T00:00:00 |
2021/07/14
|
https://ideas.repec.org/a/eee/teinso/v67y2021ics0160791x21002074.html
|
[
{
"date": "2023/04/01",
"position": 90,
"query": "AI labor union"
},
{
"date": "2023/05/01",
"position": 85,
"query": "AI labor union"
},
{
"date": "2023/07/01",
"position": 87,
"query": "AI labor union"
},
{
"date": "2023/08/01",
"position": 90,
"query": "AI labor union"
},
{
"date": "2024/04/01",
"position": 90,
"query": "AI labor union"
},
{
"date": "2024/06/01",
"position": 91,
"query": "AI labor union"
},
{
"date": "2024/09/01",
"position": 82,
"query": "AI labor union"
},
{
"date": "2024/09/01",
"position": 81,
"query": "artificial intelligence labor union"
},
{
"date": "2025/02/01",
"position": 67,
"query": "artificial intelligence labor union"
},
{
"date": "2025/03/01",
"position": 67,
"query": "artificial intelligence labor union"
},
{
"date": "2025/05/01",
"position": 68,
"query": "artificial intelligence labor union"
}
] |
CA Senate Approves McNerney's 'No Robo Bosses Act' to Ensure ...
|
CA Senate Approves McNerney’s ‘No Robo Bosses Act’ to Ensure Human Oversight of AI in Workplace
|
https://sd05.senate.ca.gov
|
[] |
Press Release
CA Senate Approves McNerney’s ‘No Robo Bosses Act’ to Ensure Human Oversight of AI in Workplace
The California state Senate on Monday evening approved Senator Jerry McNerney’s SB 7, the “No Robo Bosses Act” — groundbreaking legislation that would require human oversight of artificial intelligence systems in the workplace to help prevent abuses.
SB 7 would bar California employers from relying primarily on AI systems, known as automated decision-making systems (ADS), to make promotion, discipline, or termination decisions without human oversight. The legislation would also prohibit the use of ADS systems that use personal information of workers to “predict” what they’ll do in the future.
“AI can boost productivity in the workplace, yet there are no safeguards in place to prevent machine algorithms from unjustly or illegally impacting workers’ livelihoods,” said Sen. McNerney, D-Pleasanton. “The Senate’s passage of SB 7 today sends a strong message: The use of AI in the workplace needs human oversight to ensure that California businesses are not operated by robo bosses. AI must remain a tool controlled by humans, not the other way around.”
SB 7 is sponsored by the California Federation of Labor Unions, AFL-CIO. If signed into law, the No Robo Bosses Act would be the first such law in the nation.
“No worker should have to answer to a robot boss when they are fearful of getting injured on the job, or when they have to go to the bathroom or leave work for an emergency,” said Lorena Gonzalez, President of the California Federation of Labor Unions, AFL-CIO, representing over 1,300 unions with 2.3 million union members. “When it comes to decisions that most impact our jobs, our safety and our families, we need human oversight.”
SB 7 won approval in the Senate on a vote of 26-10, and now goes to the Assembly for consideration.
SB 7 establishes necessary safeguards for AI in the workplace by:
Requiring human oversight and independent verification for promotion, demotion, firing, and disciplinary decisions.
Barring ADS systems from obtaining or inferring a worker’s immigration status; veteran status; ancestral history; religious or political beliefs; health or reproductive status, history, or plan; emotional or psychological state; neural data; sexual or gender orientation’ disability; criminal record; credit history’ or any other statuses protected state law.
Prohibiting the use of ADS for predictive behavior analysis based on personal information collected on workers that results in adverse action against a worker for what the AI predicts the worker will do.
Creating a process for workers to appeal to a human for decisions made by ADS.
SB 7 is co-authored by Assemblymembers Sade Elhawary, D-South Los Angeles, and Isaac Bryan, D-Los Angeles.
In Congress, McNerney co-founded and co-chaired the Artificial Intelligence Caucus and authored the AI in Government Act.
For more information on SB 7, click here.
Sen. Jerry McNerney is chair of the Senate Revenue and Taxation Committee and his 5th Senate District includes all of San Joaquin County and Alameda County’s Tri-Valley.
| 2023-04-01T00:00:00 |
https://sd05.senate.ca.gov/news/ca-senate-approves-mcnerneys-no-robo-bosses-act-ensure-human-oversight-ai-workplace
|
[
{
"date": "2023/04/01",
"position": 94,
"query": "AI labor union"
},
{
"date": "2025/06/01",
"position": 49,
"query": "AI employers"
},
{
"date": "2025/06/01",
"position": 8,
"query": "artificial intelligence labor union"
}
] |
||
A Look Ahead: AI's Influence on Labor in the Entertainment Industry
|
A Look Ahead: AI’s Influence on Labor in the Entertainment Industry
|
https://www.loeb.com
|
[
"Dimitry Krol"
] |
Dimitry Krol, Loeb & Loeb's Entertainment senior counsel, discusses how AI has reshaped negotiations and contracts throughout the guild, union and production ...
|
In a continuation of our artificial intelligence (AI) series, Dimitry Krol, Loeb & Loeb’s Entertainment senior counsel, discusses how AI has reshaped negotiations and contracts throughout the guild, union and production space in the entertainment industry.
While the strikes in Hollywood have subsided with temporary agreements in place, they shed light on the growing apprehension among talent regarding the integration of AI in the creation of creative works. The prevalence of generative AI, which is capable of precisely generating sounds and imagery and even of replicating actors, raises questions about the protections needed to allow cutting-edge technology and human talent to harmonize. Below, Dimitry explores the intricacies of AI provisions in labor agreements, addressing concerns through the lens of both talent and studios, and provides insight into the latest legislative and regulatory developments.
In this discussion, when referring to AI, we include both generative and artificial technologies.
Tell us about your practice and the type of entertainment labor matters you generally handle.
My practice focuses on helping clients navigate the entertainment labor landscape, which encompasses guild and union matters as well as labor and employment issues. I advise clients on risks and legal concerns that may arise during all phases of theatrical, television, digital, new media, interactive and commercial projects. This includes handling guild and union negotiations with the WGA, SAG-AFTRA, DGA, AFM, IATSE and Teamsters as well as any mediations, arbitrations and NLRB proceedings.
What current trends have you observed with the integration of AI within the entertainment industry?
AI in the entertainment industry is certainly a hot topic, especially when it comes to generative AI. This was, and is, a major negotiation point for the production companies and unions alike. SAG-AFTRA, WGA and DGA addressed AI in their 2023 collective bargaining agreements. The overarching concern among the writers, directors and actors is their potential replacement by such technology. However, they were also careful not to preclude its use. Rather, the goal was to strike a balance where their members would continue to work and be compensated, but also be able to use this burgeoning technology. The recent strikes highlighted the need to establish guidelines for using AI in content creation, emphasizing consent, knowledge and compensation.
How has AI altered negotiations between talent and studios?
Effective communication in the context of contracts, especially in the entertainment industry, is crucial to ensure clarity about the terms and usage of AI.
Collective bargaining agreements set parameters about AI use. Rights in the collective bargaining agreement cannot be waived without union permission. Addressing these parameters in contracts is crucial to ensure both parties have a clear understanding of their obligations.
It is not just about creation; considerations also extend to the type of consent provided and revoking or altering usage terms, including with third-party AI vendors. Concerns about motion capture content and the ability to control or limit usage are already surfacing. Therefore, discussions about not only creating and utilizing content but also adjusting terms should be part of the contractual discourse.
Are there any evolving regulatory or legislative developments that may impact the entertainment industry?
There are several legislative developments in the United States that could impact the entertainment industry. Currently, there is the NO FAKES Act and the No AI FRAUD Act, both of which seek to protect the voice and visual likeness of all individuals from AI use while also providing a recourse for any violation.
In the negotiations with the International Alliance of Theatrical Stage Employees currently taking place, it’s clear that they’ve been closely observing the SAG-AFTRA, WGA and DGA negotiations as well as legislative action. While challenges are anticipated, these past strikes hopefully have highlighted the importance of communication and fostering trust during negotiations, allowing everyone to continue to work without pause.
Looking forward, what strategies can legal counsel provide to clients in the entertainment space to navigate these ongoing agreement changes?
The application of AI provisions within collective bargaining agreements is one area where we are regularly conducting presentations. Our goal is to guide our clients to receive maximum protections while avoiding guild and union issues.
We’re also supporting clients by addressing concerns about potential reuse of content. Navigating pre-existing and prospective contracts involves a case-by-case approach tailored to each client’s needs. It’s important that we’re carefully examining the specific goals of the client as well as those of talent, writers, directors and actors.
Above all, the top priorities for clients involve navigating the evolving landscape of AI utilization while being mindful of pre-existing agreements and union obligations. It’s crucial to adapt to new provisions and for clients to remain informed about legislation for a comprehensive approach to safely navigating the ever-changing technological landscape from both the production and legal aspects.
How is Loeb a leader in the space?
At Loeb, we have a dedicated AI Industry group and extensive guild, union and business affairs experience. Our team excels in contract negotiation and compliance with relevant laws. Clients understand the complexity of these issues when reading through agreements and we help them navigate concerns in a way that is aligned with their goals.
In addition to individual client presentations, our firm regularly speaks on the topic of AI, including at our annual IP/Entertainment conferences. Our deep entertainment industry background combined with our experience advising on AI-related matters allows us to share valuable insights and address queries to keep clients prepared in the evolving AI landscape.
| 2024-06-13T00:00:00 |
2024/06/13
|
https://www.loeb.com/en/insights/publications/2024/03/a-look-ahead-ais-influence-on-labor-in-the-entertainment-industry
|
[
{
"date": "2023/04/01",
"position": 95,
"query": "AI labor union"
},
{
"date": "2023/05/01",
"position": 97,
"query": "AI labor union"
},
{
"date": "2023/07/01",
"position": 83,
"query": "AI labor union"
},
{
"date": "2024/03/01",
"position": 92,
"query": "AI labor union"
},
{
"date": "2024/03/21",
"position": 7,
"query": "artificial intelligence labor union"
},
{
"date": "2024/04/01",
"position": 99,
"query": "AI labor union"
},
{
"date": "2024/06/01",
"position": 79,
"query": "AI labor union"
},
{
"date": "2024/09/01",
"position": 84,
"query": "AI labor union"
},
{
"date": "2025/05/01",
"position": 88,
"query": "AI labor union"
},
{
"date": "2025/06/01",
"position": 93,
"query": "AI labor union"
}
] |
Microsoft to Lay Off About 9,000 Employees - The New York Times
|
Microsoft to Lay Off About 9,000 Employees
|
https://www.nytimes.com
|
[
"Karen Weise"
] |
Executives at a number of other tech companies have hinted that they expect A.I. to replace some of their workers. Andy Jassy, Amazon's chief ...
|
Microsoft said on Wednesday that it would lay off roughly 4 percent of its work force, or about 9,000 people, in another indication of the tightening job market at big technology companies.
The layoffs follow a reduction of about 6,000 positions in May. Microsoft had 228,000 employees at the end of June 2024, its most recent disclosure.
Though the outlook for the economy has been shaky in recent months, Microsoft has continued to produce multibillion-dollar quarterly profits. Its last earnings report showed unexpected strength, and investors have driven its market valuation up to almost $3.7 trillion. But Microsoft is in the middle of an expensive investment in artificial intelligence, including spending billions to lease and build data centers to support the demand for cloud computing and A.I.
The cuts were also a sign that Microsoft’s A.I. development may be having an impact on the size of its own work force. Microsoft’s A.I. product for coding and software development, Github Copilot, now has more than 15 million users, and executives have marveled publicly about how effective it has become.
| 2025-07-02T00:00:00 |
2025/07/02
|
https://www.nytimes.com/2025/07/02/technology/microsoft-layoffs-ai.html
|
[
{
"date": "2023/04/01",
"position": 42,
"query": "AI layoffs"
},
{
"date": "2023/05/01",
"position": 36,
"query": "AI layoffs"
},
{
"date": "2023/06/01",
"position": 42,
"query": "AI layoffs"
},
{
"date": "2023/07/01",
"position": 33,
"query": "AI layoffs"
},
{
"date": "2023/09/01",
"position": 39,
"query": "AI layoffs"
},
{
"date": "2023/11/01",
"position": 36,
"query": "AI layoffs"
},
{
"date": "2024/01/01",
"position": 39,
"query": "AI layoffs"
},
{
"date": "2024/05/01",
"position": 34,
"query": "AI layoffs"
},
{
"date": "2024/06/01",
"position": 36,
"query": "AI layoffs"
},
{
"date": "2024/07/01",
"position": 35,
"query": "AI layoffs"
},
{
"date": "2024/08/01",
"position": 38,
"query": "AI layoffs"
},
{
"date": "2024/12/01",
"position": 38,
"query": "AI layoffs"
}
] |
Report: Microsoft mandated 2025 layoffs from the top for a huge ...
|
Report: Microsoft mandated 2025 layoffs from the top for a huge (and desperate) $80B AI infrastructure investment News
|
https://www.resetera.com
|
[] |
https://www.windowscentral.com/microsoft/report-microsofts-2025-layoffs-revolve-around-its-desperate-usd80-billion-ai-infrastructure- ...
|
All these tech companies are spending a lot of money pushing for something not many people actually want or use, but if they don't they risk losing the "arms race." Except this race has no clear goal, there seem to be dozens of competitors, and despite pushing it so hard on the public, is probably going to massively flop, meaning all that money probably would've gone to better use being burned in a pit.
A lot of the NFT bros seemed to have moved on to AI, so if what happened to that is any indication, I wouldn't be surprised to see an AI crash on the horizon.
| 2025-07-05T00:00:00 |
2025/07/05
|
https://www.resetera.com/threads/report-microsoft-mandated-2025-layoffs-from-the-top-for-a-huge-and-desperate-80b-ai-infrastructure-investment.1236402/
|
[
{
"date": "2023/04/01",
"position": 54,
"query": "AI layoffs"
},
{
"date": "2023/05/01",
"position": 65,
"query": "AI layoffs"
},
{
"date": "2023/06/01",
"position": 59,
"query": "AI layoffs"
},
{
"date": "2023/07/01",
"position": 57,
"query": "AI layoffs"
},
{
"date": "2023/09/01",
"position": 63,
"query": "AI layoffs"
},
{
"date": "2023/11/01",
"position": 65,
"query": "AI layoffs"
},
{
"date": "2024/01/01",
"position": 57,
"query": "AI layoffs"
},
{
"date": "2024/05/01",
"position": 63,
"query": "AI layoffs"
},
{
"date": "2024/06/01",
"position": 58,
"query": "AI layoffs"
},
{
"date": "2024/08/01",
"position": 62,
"query": "AI layoffs"
}
] |
ChatGPT Is Doing Performance Reviews and Deciding Layoffs Now
|
ChatGPT Is Doing Performance Reviews and Deciding Layoffs Now
|
https://www.goingconcern.com
|
[
"Going Concern News Desk"
] |
According to a recent Resume Builder survey that tapped 1,342 US managers with direct reports to find out how many of them are using AI to ...
|
According to a recent Resume Builder survey that tapped 1,342 US managers with direct reports to find out how many of them are using AI to determine who gets promoted and fired, 6 in 10 of these managers “rely on” AI to make decisions about their underlings.
The key findings before we get deep into the muck:
A majority of these managers use AI to determine raises (78%), promotions (77%), layoffs (66%), and even terminations (64%)
More than 1 in 5 frequently let AI make final decisions without human input
Two-thirds of managers using AI to manage employees haven’t received any formal AI training
Nearly half of managers were tasked with assessing if AI can replace their reports
Of the 65% of respondents who use AI tools at work, 94% are using them to determine what to do with their underlings. What exactly are these managers using AI to do when it comes to personnel?
97% use it to create training materials
94% to build employee development plans
91% to assess performance
88% to draft performance improvement plans (PIPs)
78% to determine raises
77% to determine promotions
66% to determine layoffs
64% to determine terminations
The numbers look quite different when you pull back from only the managers using these tools but you see assessing performance and creating PIPs are quite popular as people management tasks being offloaded to AI all the time or often.
As for the tools themselves, ChatGPT is getting the most use as robot HR with 53% of the managers using it to perform the tasks listed above. Coming in second is Copilot with 29% and bringing up the rear is Gemini with 16%.
Here’s the part we should be worried about (unless you’re someone who thinks machines are better than humans at decisions like these as they’re not so bogged down with emotions and petty office politics):
Among managers who use AI to help manage their teams, a majority (71%) express confidence in AI’s ability to make fair and unbiased decisions about employees. A notable share of managers let AI operate with limited oversight. More than 20% say they allow AI to make decisions without human input either all the time (5%) or often (16%), while another 24% sometimes do. However, nearly all managers say they are willing to step in if they disagree with an AI-driven recommendation.
And this part:
Only one-third (32%) of managers using AI to manage people say they’ve received formal training on ethically using AI in managing people, while 43% have received informal guidance. Nearly one in four (24%) say they’ve received no training at all.
“It’s essential not to lose the ‘people’ in people management,” said Stacie Haller, chief career advisor at Resume Builder, of the survey results. “While AI can support data-driven insights, it lacks context, empathy, and judgment. AI outcomes reflect the data it’s given, which can be flawed, biased, or manipulated. Organizations have a responsibility to implement AI ethically to avoid legal liability, protect their culture, and maintain trust among employees.”
“Organizations must provide proper training and clear guidelines around AI, or they risk unfair decisions and erosion of employee trust,” she said.
| 2025-07-08T00:00:00 |
2025/07/08
|
https://www.goingconcern.com/chatgpt-is-doing-performance-reviews-and-deciding-layoffs-now/
|
[
{
"date": "2023/04/01",
"position": 55,
"query": "AI layoffs"
},
{
"date": "2023/05/01",
"position": 63,
"query": "AI layoffs"
},
{
"date": "2023/06/01",
"position": 58,
"query": "AI layoffs"
},
{
"date": "2023/07/01",
"position": 61,
"query": "AI layoffs"
},
{
"date": "2023/09/01",
"position": 62,
"query": "AI layoffs"
},
{
"date": "2023/11/01",
"position": 63,
"query": "AI layoffs"
},
{
"date": "2024/01/01",
"position": 58,
"query": "AI layoffs"
},
{
"date": "2024/05/01",
"position": 62,
"query": "AI layoffs"
},
{
"date": "2024/06/01",
"position": 62,
"query": "AI layoffs"
},
{
"date": "2024/07/01",
"position": 53,
"query": "AI layoffs"
},
{
"date": "2024/08/01",
"position": 63,
"query": "AI layoffs"
},
{
"date": "2024/12/01",
"position": 54,
"query": "AI layoffs"
}
] |
Microsoft Is Quietly Replacing Developers With AI—And the Layoffs ...
|
Microsoft Is Quietly Replacing Developers With AI—And the Layoffs Are Just Beginning
|
https://thephrasemaker.com
|
[
"Sahara Quinn",
"Ben Gryce",
"Koichi Jansson",
"Monica Lorde",
".Wp-Block-Post-Author-Name Box-Sizing Border-Box",
".Wp-Block-Post-Author-Biography Box-Sizing Border-Box"
] |
Halo's latest cuts highlight a bigger shift inside one of tech's most powerful companies On July 2, Microsoft cut roughly 9000 jobs ...
|
Halo’s latest cuts highlight a bigger shift inside one of tech’s most powerful companies
On July 2, Microsoft cut roughly 9,000 jobs globally, amounting to about 4% of its workforce. The official reason? A standard bit of corporate jargon: “organizational and workforce changes.” But inside the company—particularly in the Xbox division—employees tell a much more specific story: Microsoft is betting big on AI, and it’s already replacing people with it.
Among those hit were at least five employees at Halo Studios (formerly 343 Industries), including developers working on the next mainline Halo installment. The mood inside the studio is tense, with one insider telling Engadget that the studio is in “crisis” on at least one project, and that “nobody is really happy about the quality of the product right now.”
Behind the scenes, many believe this round of layoffs is about more than streamlining. “They’re trying their damndest to replace as many jobs as they can with AI agents,” one Halo developer said.
AI at the Center of the Storm
Microsoft’s shift to AI is no secret. CEO Satya Nadella said this spring that 30% of the company’s code is now written by AI, and internally, the use of tools like GitHub Copilot has become mandatory. AI now touches everything from engineering to documentation—and, increasingly, staffing decisions.
The result is a quieter kind of disruption. Microsoft is still hiring—but often in areas that support or expand its AI platforms, not the traditional teams that built its reputation in gaming, productivity, or even sales.
Halo Is Hurting. Again.
For Halo fans, the latest cuts are unsettling. The studio was already rattled by layoffs in 2023, including longtime creative lead Joe Staten, and since then has shifted toward relying on external studios and short-term contractors. It’s a model more common to Call of Duty or Battlefield, and while it can accelerate content production, it’s also led to instability—and visible gaps.
Halo Infinite, for instance, hasn’t delivered major narrative content in years.
Now, morale is reportedly low. “There’s been a lot of tension and pep talks trying to rally folks to ship,” one developer said. The studio is expected to reveal more about its work at this year’s Halo World Championship in October, but what shape that reveal takes is anyone’s guess.
It’s Not Just Xbox
While Halo Studios got the headlines, the layoffs affected a wide swath of Microsoft.
Sales and marketing teams were hit hard, especially middle managers and field staff.
were hit hard, especially middle managers and field staff. The King division in Barcelona, best known for Candy Crush, lost 10% of its staff—about 200 people.
in Barcelona, best known for Candy Crush, lost 10% of its staff—about 200 people. Rare’s Everwild and The Initiative’s Perfect Dark reboot have been canceled entirely.
and reboot have been canceled entirely. Turn 10 Studios , which makes Forza Motorsport, reportedly lost the “vast majority” of its team.
, which makes Forza Motorsport, reportedly lost the “vast majority” of its team. ZeniMax Online , Raven , Sledgehammer Games , and Blizzard were all affected.
, , , and were all affected. Longtime execs like ZeniMax president Matt Firor and Rare creative lead Gregg Mayles are both reportedly out.
Even Warcraft Rumble is being sunset by Blizzard—a quiet end for a once-hyped mobile spinoff.
The pattern is hard to ignore: Microsoft is shrinking some of its most iconic teams while AI takes center stage.
A Company Profiting While It Shrinks
If this sounds like crisis management, Microsoft’s financials tell a different story. The company reported nearly $26 billion in net income last quarter, with $70 billion in revenue. Xbox content and services are up 8% year-over-year.
And yet, those same teams are being downsized or shuttered.
That contradiction wasn’t lost on employees. Phil Spencer, head of Xbox, sent a company-wide email celebrating Xbox’s “most profitable year ever”—the same email that announced the layoffs. “I wasn’t sure what part of that I was supposed to be proud about,” said one affected developer.
The Bigger Bet
Microsoft says the changes are designed to “position the company for success in a dynamic marketplace,” and the tech giant is far from the only company leaning into AI right now. But the scale and pace of the shift—combined with deep cuts across beloved franchises—suggest something larger than typical restructuring.
This isn’t just about efficiency. It’s about redefinition.
So as Microsoft ramps up its AI development tools, trims down its legacy game studios, and cancels projects midstream, one thing becomes clear: the company is not waiting for the industry to change—it’s forcing the change itself.
Whether that future includes the creative teams behind franchises like Halo, Perfect Dark, or Forza is still uncertain. But it will definitely include AI. Lots of it.
| 2025-07-03T00:00:00 |
2025/07/03
|
https://thephrasemaker.com/2025/07/03/microsoft-is-quietly-replacing-developers-with-ai-and-the-layoffs-are-just-beginning/
|
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After 9,000 layoffs at Microsoft, Xbox exec tells fired employees to go ...
|
After 9,000 layoffs at Microsoft, Xbox exec tells fired employees to go and talk to AI to reduce job loss pain
|
https://www.indiatoday.in
|
[] |
After Microsoft's latest round of 9000 layoffs, an Xbox executive suggested that affected employees seek support and job search help from AI ...
|
As Microsoft did yet another round of mass layoffs last week, firing around 9,000 of its employees, one executive producer from Xbox Game Studios at the company has offered an unusual piece of advice to those affected: turn to AI chatbots for support. Matt Turnbull, an executive producer at Xbox, suggested that employees grappling with job loss might find relief and guidance by using large language models (LLMs) like ChatGPT or Microsoft’s own Copilot. His comments, made in a now-deleted LinkedIn post, came just days after Microsoft cut 9,000 roles across the company – marking its biggest wave of layoffs in 2025.
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“These are really challenging times, and if you’re navigating a layoff or even quietly preparing for one, you’re not alone and you don’t have to do it alone,” Turnbull wrote. “I’ve been experimenting with ways to use LLM AI tools to help reduce the emotional and cognitive load that comes with job loss.”
He acknowledged that people have mixed feelings about AI tools, especially amid concerns that automation is a major factor behind recent job cuts. Still, he maintained that tools like ChatGPT can help job seekers move forward faster and with more clarity. “No AI tool is a replacement for your voice or your lived experience,” Turnbull said. “But at a time when mental energy is scarce, these tools can help get you unstuck.”
In the same post, Turnbull listed a variety of AI prompts he found helpful – from generating resume bullet points to writing outreach messages and even reframing self-doubt after a layoff. For example, he suggested asking a chatbot to act as a career coach or help rework LinkedIn bios to highlight leadership and project experience.
His advice was shared with sincerity, but not everyone was impressed. The idea of using AI for emotional clarity, especially as AI itself is seen as contributing to job losses, struck a nerve with some. The post was eventually deleted, though it was first captured by Aftermath.
Microsoft, meanwhile, continues to defend the layoffs as part of broader organisational restructuring. In a company email, executives said the changes are necessary to “position the company and its teams for success in a dynamic marketplace.” While they confirmed that the gaming division was affected, the company claimed most of that unit remained intact.
Phil Spencer, CEO of Microsoft Gaming, addressed the issue in a memo to staff, saying the goal was to streamline teams and refocus on strategic areas. “To position Gaming for enduring success... we will end or decrease work in certain areas of the business and follow Microsoft’s lead in removing layers of management to increase agility and effectiveness,” Spencer wrote.
The recent 9,000 job cuts are part of a larger trend. Microsoft had already eliminated more than 6,000 roles in May, followed by smaller cuts in June. In 2023, the company laid off around 10,000 staff. And this is not unique to Microsoft – other tech giants are undergoing similar transformations. Meta cut around 5 per cent of its workforce as part of performance reviews earlier this year, while Google’s parent company Alphabet has laid off hundreds in its transition to more AI-focused work. Amazon, too, has slashed thousands of roles across various departments, including its books and devices divisions.
- Ends
| 2025-07-08T00:00:00 |
2025/07/08
|
https://www.indiatoday.in/technology/news/story/after-9000-layoffs-at-microsoft-xbox-exec-tells-fired-employees-to-go-and-talk-to-ai-to-reduce-job-loss-pain-2752440-2025-07-08
|
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] |
Companies aren't doing layoffs because of AI, it's because they have ...
|
Companies aren't doing layoffs because of AI, it's because they have too many people, says Jim Cramer
|
https://www.cnbc.com
|
[
"Jim Cramer"
] |
'Mad Money' host Jim Cramer talks the recent string of corporate layoff announcements.
|
'Mad Money' host Jim Cramer talks the recent string of corporate layoff announcements.
Share Share Article via Facebook Share Article via Twitter Share Article via LinkedIn Share Article via Email
| 2025-07-02T00:00:00 |
2025/07/02
|
https://www.cnbc.com/video/2025/07/02/companies-arent-doing-layoffs-because-of-ai-its-because-they-have-too-many-people-says-jim-cramer.html
|
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Tech layoffs show AI's impact extends beyond entry-level roles
|
Tech layoffs show AI's impact extends beyond entry-level roles
|
https://www.techspot.com
|
[
"Skye Jacobs"
] |
In a nutshell: The rapid advance of artificial intelligence is redrawing the boundaries of white-collar employment, leaving both novice and ...
|
In a nutshell: The rapid advance of artificial intelligence is redrawing the boundaries of white-collar employment, leaving both novice and seasoned professionals uncertain about their future in the workforce. However, experts are divided over which group faces the greatest risk.
Some within the industry, like Dario Amodei of Anthropic, argue that entry-level positions are most susceptible because their tasks are more easily automated. Amodei said that AI could "cannibalize half of all entry-level white-collar roles within five years." Rising unemployment among recent college graduates has added fuel to these concerns, though the causes remain debated.
Others see a different threat emerging for more experienced workers. Brad Lightcap, chief operating officer of OpenAI, told The New York Times that AI could challenge "a class of worker that I think is more tenured, is more oriented toward a routine in a certain way of doing things." The implications of this shift are significant: if mid- and late-career professionals are displaced, the effects could ripple through the economy and even destabilize political systems.
Data from sectors that have already embraced AI suggest that entry-level workers are feeling the brunt of the change. Payroll processor ADP reports that employment for workers with fewer than two years of tenure in computer-related fields peaked in 2023 and has since declined by about 20 to 25 percent. Customer service roles show similar patterns. Yet, according to Stanford researcher Ruyu Chen, employment for workers with greater tenure has increased in these same sectors.
Research also indicates that AI is transforming the nature of jobs, sometimes to the advantage of more experienced staff. When Italy temporarily banned ChatGPT in 2023, researchers found that while junior coders used AI to complete tasks more quickly, midlevel coders leveraged it to support their teams and manage projects in unfamiliar programming languages. "When people are really good at things, what they end up doing is helping other people as opposed to working on their own projects," said Sarah Bana, one of the study's authors. She noted that AI amplified this tendency, potentially leading companies to hire fewer junior coders but more midlevel ones.
Still, the risk to experienced workers is real. Danielle Li, an economist at MIT, explained that AI can "untether valuable skills from the humans who have traditionally possessed them. That state of the world is not good for experienced workers. You're being paid for the rarity of your skill, and what happens is that AI allows the skill to live outside of people." Li also suggested that the rise in unemployment among new graduates may reflect employers' expectations of needing fewer workers overall, not just at the entry level.
Some law firms and technology companies have already reduced their reliance on experienced professionals. Robert Plotkin, a partner at a law firm specializing in intellectual property, said his firm now uses about half as many contract lawyers as before the advent of generative AI. "I've become very efficient at using AI as a tool to help me draft applications in a way that's reduced our need for contract lawyers," Plotkin said.
Major technology firms have also made cuts that affect experienced employees. Google, Meta, and Microsoft have all conducted layoffs since 2022, with Microsoft's recent rounds including many middle managers and software developers. "Anything that is administrative, spreadsheet-related, where there's an email trail, a document-management type activity, AI should be able to perform fairly easily, freeing up time for managers to do more mentoring," said David Furlonger, a vice president at Gartner. "CEOs are implying in the data that we don't need as many of them as we did previously," he said.
The motivations behind these layoffs are multifaceted. Gil Luria, an equity analyst at D.A. Davidson, said companies are cutting costs to maintain profit margins while investing heavily in AI infrastructure. He noted that software engineers at all levels are vulnerable, particularly those with higher salaries who resist adapting to new technologies. "There are senior people who have figured out how to get leverage out of AI and senior people who are insistent that AI can't write code," Luria said.
Harper Reed, chief executive of 2389 Research, said that experienced coders with higher salaries and a reluctance to embrace AI are at risk. "How you decrease cost is not by firing the cheapest employees you have," Reed said. "You take the cheapest employee and make them worth the expensive employee." Studies suggest this is possible: recent research found that AI coding assistants increased the productivity of junior developers more than that of their experienced colleagues.
Reed explained that it may soon be financially logical for companies to hire junior employees who use AI to perform what was once mid-level work, with a handful of senior staff overseeing them and almost no middle-tier employees. That, he said, is essentially how his company is structured.
| 2025-07-08T00:00:00 |
2025/07/08
|
https://www.techspot.com/news/108593-who-faces-greater-risk-ai-novices-or-experienced.html
|
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AI will wipe out jobs: CEOs start saying the quiet part out loud
|
AI will wipe out jobs: CEOs start saying the quiet part out loud
|
https://www.team-bhp.com
|
[] |
1) Globally, 20 million manufacturing jobs could be replaced by automated tools by 2030. 2) By 2030, 14% of employees will have been forced to ...
|
NomadSK BHPian
Join Date: May 2023 Location: Riyadh Posts: 704 Thanked: 5,318 Times
Re: AI will wipe out jobs: CEOs start saying the quiet part out loud Doomers think AI is an existential threat that should be stopped.
Gloomers believe it’s an inevitable march toward job loss and human displacement.
Zoomers are excited and want to hit the gas pedal, full speed ahead.
Bloomers are cautiously optimistic, driving forward while tapping the brakes.
Mckinsey report on AI, some interesting snippets;
Quote: Almost all companies invest in AI, but just 1 percent believe they are at maturity. Our research finds the biggest barrier to scaling is not employees—who are ready—but leaders, who are not steering fast enough. Quote: AI is becoming far more intelligent. One indicator is the performance of LLMs on standardized tests. OpenAI’s Chat GPT-3.5, introduced in 2022, demonstrated strong performance on high-school-level exams (for example, scoring in the 70th percentile on the SAT math and the 87th percentile on the SAT verbal sections). However, it often struggled with broader reasoning. Today’s models are near the intelligence level of people who hold advanced degrees. GPT-4 can so easily pass the Uniform Bar Examination that it would rank in the top 10 percent of test takers and it can answer 90 percent of questions correctly on the US Medical Licensing Examination. Quote: As an example, in 2023, an AI bot could support call center representatives by synthesizing and summarizing large volumes of data—including voice messages, text, and technical specifications—to suggest responses to customer queries. In 2025, an AI agent can converse with a customer and plan the actions it will take afterward—for example, processing a payment, checking for fraud, and completing a shipping action. Quote: Google’s Gemini Live has improved audio quality and latency and can now deliver a human-like conversation with emotional nuance and expressiveness. Quote: AI is gradually becoming less risky, but it still lacks greater transparency and explainability. Both are critical for improving AI safety and reducing the potential for bias, which are imperative for widescale enterprise deployment. There is still a long way to go, but new models and iterations are rapidly improving. Stanford University’s Center for Research on Foundation Models (CRFM) reports significant advances in model performance. Its Transparency Index, which uses a scale of 1 to 100, shows that Anthropic’s transparency score increased by 15 points to 51 and Amazon’s more than tripled to 41 between October 2023 and May 2024. Quote: AI technology is advancing at record speed. ChatGPT was released about two years ago; OpenAI reports that usage now exceeds 300 million weekly users13 and that over 90 percent of Fortune 500 companies employ its technology. The internet did not reach this level of usage until the early 2000s, nearly a decade after its inception. Quote: Different industries have different AI investment patterns. Within the top 25 percent of spenders, companies in healthcare, technology, media and telecom, advanced industries, and agriculture are ahead of the pack (Exhibit 12). Companies in financial services, energy and materials, consumer goods and retail, hardware engineering and construction, and travel, transport, and logistics are spending less. The consumer industry—despite boasting the second-highest potential for value realization from AI—seems least willing to invest, with only 7 percent of respondents qualifying in the top quartile, based on self-reported percentage of revenue spend on gen AI. That hesitation may be explained by the industry’s low average net margins in mass-market categories and thus higher confidence thresholds for adopting costly organization-wide technology upgrades. Quote: It concludes that employees are ready for AI. The biggest barrier to success is leadership. Mckinsey Report Link
And now some interesting stats;
1) Globally, 20 million manufacturing jobs could be replaced by automated tools by 2030.
2) By 2030, 14% of employees will have been forced to change their career because of AI.
3) Wall Street expects to replace 200,000 roles with AI in the next 3 to 5 years.
4) 75% of CEOs think generative AI will significantly change their business within the next three years.
5) 80% of the US workforce could have at least 10% of their tasks impacted by large language models.
6) More than 7.5 million data entry jobs will be lost by 2027.
7) 41% of employers worldwide intend to reduce their workforce because of AI in the next five years.
8) 47% of US workers are at risk of losing their jobs to automation over the coming decade.
9) It will take at least 20 years to automate just half of current worldwide work tasks.
10) 30% of US companies have replaced workers with AI tools like ChatGPT.
11) From January to early June 2025, 77,999 tech job losses were directly linked to AI.
12) 40% of companies that are adopting AI are automating rather than augmenting human work.
13) Since 2000, automation has resulted in 1.7 million manufacturing jobs being lost.
14) 13.7% of US workers report having lost their job to a robot.
15) Two-thirds of all jobs in the US and Europe are exposed to automation.
16) 40% of jobs worldwide are exposed to AI.
17) 60% of jobs in advanced economies could be impacted by AI.
18) 19% of workers are employed in the jobs most exposed to AI.
19) 27% of workers with a bachelor’s degree or higher are employed in jobs most exposed to AI.
20) Widespread adaptation of current automation tech could affect half of the world economy.
21) 34% of all business-related tasks are already performed by machines.
22) Technology and machines play at least some role in 53% of work tasks.
23) 25% of all work tasks could be done by AI.
24) AI can only economically replace vision-based tasks accounting for just 0.4% of the total wages earned in the US.
25) Just 26% of jobs in low-income countries are exposed to AI.
More you can read in the below link;
https://explodingtopics.com/blog/ai-replacing-jobs think AI is an existential threat that should be stopped.believe it’s an inevitable march toward job loss and human displacement.are excited and want to hit the gas pedal, full speed ahead.are cautiously optimistic, driving forward while tapping the brakes.Mckinsey report on AI, some interesting snippets;And now some interesting stats;1) Globally, 20 million manufacturing jobs could be replaced by automated tools by 2030.2) By 2030, 14% of employees will have been forced to change their career because of AI.3) Wall Street expects to replace 200,000 roles with AI in the next 3 to 5 years.4) 75% of CEOs think generative AI will significantly change their business within the next three years.5) 80% of the US workforce could have at least 10% of their tasks impacted by large language models.6) More than 7.5 million data entry jobs will be lost by 2027.7) 41% of employers worldwide intend to reduce their workforce because of AI in the next five years.8) 47% of US workers are at risk of losing their jobs to automation over the coming decade.9) It will take at least 20 years to automate just half of current worldwide work tasks.10) 30% of US companies have replaced workers with AI tools like ChatGPT.11) From January to early June 2025, 77,999 tech job losses were directly linked to AI.12) 40% of companies that are adopting AI are automating rather than augmenting human work.13) Since 2000, automation has resulted in 1.7 million manufacturing jobs being lost.14) 13.7% of US workers report having lost their job to a robot.15) Two-thirds of all jobs in the US and Europe are exposed to automation.16) 40% of jobs worldwide are exposed to AI.17) 60% of jobs in advanced economies could be impacted by AI.18) 19% of workers are employed in the jobs most exposed to AI.19) 27% of workers with a bachelor’s degree or higher are employed in jobs most exposed to AI.20) Widespread adaptation of current automation tech could affect half of the world economy.21) 34% of all business-related tasks are already performed by machines.22) Technology and machines play at least some role in 53% of work tasks.23) 25% of all work tasks could be done by AI.24) AI can only economically replace vision-based tasks accounting for just 0.4% of the total wages earned in the US.25) Just 26% of jobs in low-income countries are exposed to AI.More you can read in the below link; Last edited by NomadSK : 8th July 2025 at 10:20 .
| 2023-04-01T00:00:00 |
https://www.team-bhp.com/forum/shifting-gears/296318-ai-will-wipe-out-jobs-ceos-start-saying-quiet-part-out-loud-3.html
|
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{
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"position": 50,
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},
{
"date": "2025/07/04",
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{
"date": "2025/07/04",
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{
"date": "2025/07/04",
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|
These Companies Are Cutting Jobs Because Of AI - AOL.com
|
These Companies Are Cutting Jobs Because Of AI
|
https://www.aol.com
|
[
"Aol Staff",
"Douglas A. Mcintyre",
"June",
"At Pm"
] |
AI could replace as many as hundreds of thousands of jobs. Goldman Sachs put that figure even higher. In a recent report, it said AI would “displace” as ...
|
AI could replace as many as hundreds of thousands of jobs. Goldman Sachs put that figure even higher. In a recent report, it said AI would “displace” as many as 300 million jobs worldwide. AI-driven positions might replace some, but not enough to fill the employment crater that would otherwise exist.
Several companies have already started layoffs. And, most are in the tech industry. Financial services are close behind.
IBM (NYSE: IBM) was one of the first companies to say AI would be much more efficient than people. It said 8,000 people would be fired. The first wave of these will be in HR and other support functions that can be “automated.” There was a twist. IBM said it would add workers in other parts of the company that involved what it designated as “skilled workers.”
Goldman Sachs (NYSE: GS) has not only made forecasts of job cuts. It will likely be one of the companies that will lay off employees as AI advances. A Bloomberg Intelligence analysis of 93 banks said job cuts in the sector would reach 200,000. Tomasz Noetzel, the BI senior analyst who helped write the report, said, “Any jobs involving routine, repetitive tasks are at risk." Specifically, the report pointed to Citigroup (NYSE: C), JPMorgan Chase & Co.(NYSE: JPM), and Goldman. Chase and Citigroup have tens of thousands of people who work at branches.
Microsoft has been open about its cuts. It recently announced layoffs of 6,500. It has cut out another 10,000. These are primary in sales, marketing, and software development. Microsoft (NASDAQ: MSFT) said AI already write as much as 30% of its code.
Amazon (NASDAQ: AMZN) CEO Andy Jassy said he could cut jobs as AI does them better. He wrote to employees, “As we roll out more Generative AI and agents, it should change the way our work is done. We will need fewer people doing some of the jobs that are being done today, and more people doing other types of jobs."
Meta (NASDAQ: META) CEO Mark Zuckerberg said his company would lay off 3,600 people who managers thought were underperforming expectations. Some might be replaced by workers who have strong AI skills. Dario Amodei, CEO of Anthropic, maker of the chatbot Claude, said that AI would replace large segments of the workforce. According to The Washington Post, he "predicted last month that AI may eliminate half of all white-collar entry-level jobs within five years.
The announced layoffs at these companies are barely the tip of the iceberg.
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The post These Companies Are Cutting Jobs Because Of AI appeared first on 24/7 Wall St..
| 2023-04-01T00:00:00 |
https://www.aol.com/companies-cutting-jobs-because-ai-171901527.html
|
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|
Does AI Mean the End of Entry-Level Jobs? - Unite.AI
|
Does AI Mean the End of Entry-Level Jobs?
|
https://www.unite.ai
|
[
"Gary Espinosa"
] |
AI isn't just replacing repetitive manual labor. It's automating decision-making, content creation, customer interaction, and basic analytical ...
|
The question isn’t whether AI is changing the job market—it’s how deep the impact will go. For young people just entering the workforce, AI isn’t just a background shift; it’s a tidal wave.
The systems being deployed today are smarter, faster, and more capable than ever, raising real concerns about whether the traditional entry-level job has a future. As automation spreads from factory lines to office desks, the ground is shifting beneath white collar entry level positions we’ve been taking for granted for a long time.
The Disappearing On-Ramp: Entry-Level Jobs Under Siege
The entry-level job has long been the first rung on the ladder of career growth. But what happens when that first rung disappears? With AI advancing at a breakneck pace, warnings from industry leaders like Anthropic CEO Dario Amodei don’t sound like distant hypotheticals anymore. Amodei has predicted that AI could replace up to 50% of entry-level white-collar jobs by 2030. That’s just five years away.
Even today, the signs are ominous. McDonald’s in Australia has begun rolling out fully automated outlets. No more cashiers, no more fry cooks – just kiosks and robotic arms. Amazon warehouses increasingly rely on robotic systems for packaging and sorting. Chatbots have become the first point of contact in customer service, displacing call center trainees. And tools like GPT-4 and Claude are already replacing junior copywriters, analysts, and even paralegals.
This isn’t just about automation. It’s about a massive transformation of the entry-level landscape, creating an uncertain future for millions of young people trying to enter the workforce. Is this the beginning of the end for traditional first jobs?
What AI Is Already Replacing — And Why That Matters
AI isn’t just replacing repetitive manual labor. It’s automating decision-making, content creation, customer interaction, and basic analytical tasks — all of which have historically been entry-level roles. Think junior financial analysts running Excel reports. Now, a trained AI model can handle those spreadsheets in seconds. First-year associates pulling case law? Generative AI can produce case summaries faster and often with fewer errors. Behind the scenes, cloud automation is streamlining these processes even further, handling document retrieval, formatting, and workflow routing without human oversight.
And it doesn’t stop at white-collar sectors. Fast food chains are introducing robotic fryers and burger-flippers. Retail stores now install self-checkouts to reduce headcount. These are proof-of-concept deployments turning into cost-cutting strategies. Companies have every incentive to replace entry-level workers with AI: it’s cheaper, faster, and doesn’t call in sick.
The implications are stark. Entry-level jobs aren’t just disappearing; they’re being redefined in real time. What used to be your first job might now require managing the AI instead of doing the task yourself. That might sound like a step up, but for people without experience or technical training, it’s actually a barrier.
Is AI a Job Creator? The Reality Behind the Rhetoric
Tech evangelists love to say, “AI won’t destroy jobs, it will create them.” That may be true in the aggregate, but the details matter. Yes, we need more prompt engineers, AI ethicists, and data annotators. But those jobs aren’t entry-level. They require highly specialized skills or deep domain knowledge.
According to a recent report by the World Economic Forum, while AI is expected to create 97 million new roles by 2025, it will simultaneously eliminate 85 million. That’s a net gain, but not necessarily for those just entering the workforce. A college student applying for a call center job won’t be transitioning to a machine learning ops engineer overnight.
The real issue is timing and skill mismatch. The jobs being lost today are easy to get, while the jobs being created require years of training. There’s a gap that no amount of motivational optimism can bridge quickly. In practice, AI is creating roles for the already-employed and highly skilled, not the inexperienced worker looking for their first paycheck.
What This Means for the Future Workforce
If entry-level jobs disappear, we’re not just looking at short-term unemployment. We’re risking a long-term stall in professional development. Entry-level positions aren’t just about income; they teach soft skills, provide mentorship, and build professional networks. Without them, young people may find it harder to develop the competencies they need to move up.
Even putting together a basic application has changed. You now need a resume tailored to AI-augmented roles, which often feels out of reach for those without prior guidance or experience.
There’s also a psychological toll. If society no longer offers meaningful work opportunities to new entrants, what message does that send? It may deepen generational inequality, fuel resentment, and damage social cohesion. Young people could face a cruel paradox: living in the most technologically advanced age yet feeling economically excluded by it.
We may also see the rise of underemployment. Individuals trained for one set of tasks might end up in gig work, freelancing, or patching together temporary roles while more and more industries turn to AI. Instead of stepping into careers, many will remain stuck in limbo.
Think about it. Someone born in 2040, with the most amazing visual AI models available, won’t be as motivated to go to art school as someone in the 2000s.
Conclusion
The end of entry-level jobs isn’t inevitable, but it is dangerously close. If trends continue unchecked, we risk creating a society where only the already-skilled have a place in the labor market, while everyone else is left behind.
This isn’t just an economic challenge; it’s a cultural one. The first job is a rite of passage, a training ground, and often the launchpad for long-term success. AI should not take that away. Instead, we must build systems that help the next generation use AI as a stepping stone, not a stumbling block.
The future of work isn’t pre-written by code. It will be shaped by the choices we make today — in policy, in education, and in how we design the relationship between humans and machines. Let’s not wait until entry-level jobs become a relic of the past. Let’s innovate to keep them relevant, rewarding, and real.
| 2025-07-03T00:00:00 |
2025/07/03
|
https://www.unite.ai/does-ai-mean-the-end-of-entry-level-jobs/
|
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Impact of AI on Tech Jobs: Replacement or Augmentation?
|
Impact of AI on Tech Jobs: Replacement or Augmentation?
|
https://www.analyticsinsight.net
|
[
"Harpreet Singh Kapula"
] |
Currently, 14% of workers have experienced job displacement due to AI, yet 170 million new jobs are projected to emerge by 2030. The question ...
|
Behind the alarming statistic that 26% of programming jobs have disappeared lies a crucial distinction most miss: AI isn't eliminating programmers randomly. It's specifically targeting what industry insiders call ‘specification workers,’ programmers who work from detailed requirements rather than creating them.
This creates the ‘specification ceiling effect.’ While 67% of sales representative tasks face automation risk, only 21% of sales manager tasks do. The pattern repeats across tech: AI excels at executing predefined instructions but struggles with ambiguous, creative problem-solving that requires human judgment.
The entry-level massacre reveals another hidden truth: companies aren't just cutting costs, they're restructuring the traditional career ladder. The ‘apprenticeship gap’ emerges when junior roles disappear faster than senior mentorship capacity can adapt. This creates what economists call the ‘missing middle,’ a generation of professionals who never learned to bridge specification work with strategic thinking.
Geographic data shows the ‘Silicon Valley spillover effect.’ AI adoption doesn't spread uniformly; it follows venture capital networks. British Telecom's 10,000-person reduction follows patterns established by portfolio companies, suggesting AI displacement propagates through business relationships rather than technology readiness.
| 2023-04-01T00:00:00 |
https://www.analyticsinsight.net/artificial-intelligence/impact-of-ai-on-tech-jobs-replacement-or-augmentation
|
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|
Why AI Won't Take Your Job — But Someone Who Knows AI Will
|
Why AI Won’t Take Your Job — But Someone Who Knows AI Will
|
https://medium.com
|
[
"Swagoto Chatterjee"
] |
Picture this: You're scrolling through LinkedIn, and yet another headline screams about AI replacing millions of workers.
|
Why AI Won’t Take Your Job — But Someone Who Knows AI Will Swagoto Chatterjee 6 min read · 5 days ago 5 days ago -- Share
The robots aren’t coming for your job. Your AI-savvy colleague is
The Ultimate Guide to Thriving in the AI Revolution
Picture this: You’re scrolling through LinkedIn, and yet another headline screams about AI replacing millions of workers. Your heart skips a beat. Will ChatGPT make you obsolete? Is your career destined for the digital graveyard?
Here’s the plot twist nobody talks about: AI won’t take your job, but someone who knows how to wield AI like a superpower absolutely will.
The difference between career survival and career suicide in 2025 isn’t about fighting the machines — it’s about learning to dance with them. While everyone else panics about robot overlords, smart professionals are quietly becoming AI whisperers, multiplying their productivity and making themselves irreplaceable.
Let’s shatter some myths and reveal the real story behind AI and your career.
The History Lesson Your Career Needs: Why Technology Creates More Jobs Than It Destroys
Remember when everyone thought computers would eliminate office jobs? Or when the internet was going to destroy traditional retail? Spoiler alert: The opposite happened.
The Industrial Revolution created factory jobs. The computer age birthed entire…
| 2025-07-09T00:00:00 |
2025/07/09
|
https://medium.com/@swagoto365/why-ai-wont-take-your-job-but-someone-who-knows-ai-will-7c75073b18d0
|
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] |
AI in the Workplace: Top Use Cases You Need To Know
|
AI in the Workplace: Top Use Cases You Need To Know
|
https://smartdev.com
|
[
"Dung Tran"
] |
Intelligent virtual assistants (IVAs) are conversational AI tools embedded into digital work platforms to support employees with requests ...
|
Introduction
The modern workplace is evolving rapidly—shaped by hybrid workforces, rising expectations for personalized employee experiences, and a relentless need for productivity. Amid this transformation, Artificial Intelligence (AI) is emerging as a strategic enabler, automating routine tasks, uncovering actionable insights, and enhancing decision-making across departments.
This guide explores the most impactful AI use cases in the workplace, from HR and operations to IT and internal communications—revealing how businesses are using AI not just to optimize, but to reimagine work itself.
What is AI and Why Does It Matter in the Workplace?
1. Definition of AI and Its Core Technologies
Artificial Intelligence (AI) refers to the ability of machines to perform tasks that typically require human intelligence—such as recognizing patterns, making decisions, and learning from data. Core AI technologies include machine learning (ML), natural language processing (NLP), and computer vision. These technologies are already embedded in tools many businesses use daily, from voice assistants to data analytics dashboards (IBM definition).
In the workplace, AI takes on a very practical role. It automates repetitive processes, predicts business trends, powers virtual assistants, personalizes learning and development, and enhances collaboration through smart tools. From an HR chatbot answering onboarding questions to an IT helpdesk ticket routed by AI, these technologies are quietly—and profoundly—reshaping how work gets done.
Want to explore how AI can transform your sector? Discover real-world strategies for deploying smart technologies in airline systems. Visit How to Integrate AI into Your Business in 2025 to get started today and unlock the full potential of AI for your business!
2. The Growing Role of AI in Transforming the Workplace
AI is being deployed to support hybrid and remote work by optimizing meeting scheduling, summarizing discussions, and recommending follow-ups based on email and chat content. These tools are particularly valuable for distributed teams that need to stay aligned across time zones and platforms.
HR departments are leveraging AI for talent acquisition and retention. From resume parsing to candidate ranking and even cultural fit analysis, AI models help speed up hiring while minimizing human bias. AI also powers employee sentiment analysis, giving managers real-time insights into morale and engagement trends.
Across functions, AI augments decision-making by turning data into actionable recommendations. Marketing teams use it to prioritize leads, IT teams deploy it to detect anomalies in network traffic, and finance departments use AI to spot irregular spending patterns. The result is faster, data-informed decision-making that supports agility and growth.
3. Key Statistics or Trends in AI Adoption
According to PwC, 86% of CEOs say AI is a “mainstream technology” in their offices in 2024, up from 62% in 2020 (PwC Global AI Study). This reflects a growing acceptance of AI not just in IT, but across people management, customer experience, and operations.
IBM’s 2023 Global AI Adoption Index found that 35% of businesses are already using AI in at least one function, and an additional 42% are exploring its use. Key motivators include improving employee productivity, increasing data-driven decisions, and enhancing service delivery (IBM AI Index).
The market for workplace AI solutions is expected to surpass $37 billion by 2030, driven by increased demand for intelligent automation, virtual agents, and AI-enhanced collaboration tools (Fortune Business Insights).
Business Benefits of AI in the Workplace
AI is no longer experimental—it’s delivering real value by addressing long-standing challenges in workforce productivity, communication, and resource planning. Here are five specific benefits where AI is helping businesses rethink the workplace.
1. Improved Employee Productivity
AI boosts productivity by handling routine administrative tasks like scheduling, data entry, and status reporting. This frees up employees to focus on higher-value activities, from strategy development to creative problem-solving.
Smart assistants embedded in tools like Microsoft 365 and Google Workspace can now draft emails, summarize documents, and even suggest follow-up actions. These time-savers add up across the organization, especially for knowledge workers managing high information volumes.
2. Smarter Talent Management
Recruiting the right talent has always been a challenge. AI is streamlining hiring by automating resume screening, ranking candidates based on skills and experience, and even predicting cultural fit based on behavioral data.
Beyond hiring, AI supports learning and development by recommending personalized training paths based on performance metrics, job role, and future skill demand. This enables companies to continuously reskill their workforce in alignment with evolving business goals.
3. Enhanced Employee Experience
AI is being used to personalize the employee journey—from onboarding to career development. Chatbots assist new hires with FAQ-style queries, while virtual onboarding coaches guide them through tools, policies, and training schedules.
Real-time sentiment analysis via AI scans communication platforms for engagement signals, allowing HR teams to respond proactively to morale dips. These tools help foster more empathetic, responsive workplace cultures.
4. Predictive Operational Efficiency
AI helps identify process inefficiencies and optimize resource allocation. Facilities teams use AI to manage energy usage, cleaning schedules, and desk occupancy based on real-time utilization patterns.
In IT, AI predicts system downtimes and flags anomalies before they become service disruptions. These insights help organizations minimize downtime and reduce response times—driving both cost savings and user satisfaction.
Want to see how predictive maintenance is revolutionizing uptime and cutting costs? Read our deep dive on AI-driven maintenance in manufacturing and discover how you can move from reactive fixes to intelligent foresight.
5. Automated Compliance and Risk Management
Compliance and security are core concerns in today’s data-driven workplace. AI-powered monitoring tools analyze communication logs, access records, and transactions to detect compliance breaches or risky behaviors.
AI also supports data privacy by identifying and redacting personally identifiable information (PII) from unstructured data sources, which is especially critical for GDPR and HIPAA compliance in industries like healthcare and finance.
Challenges Facing AI Adoption in the Workplace
Despite its promise, integrating AI into workplace systems presents a number of organizational and technical hurdles. Below are five key challenges that businesses must address to successfully deploy AI at scale.
1. Data Silos and Fragmented Infrastructure 1. Data Silos and Fragmented Infrastructure
Many organizations store data in disconnected systems—HR tools, CRM platforms, Slack, emails—making it difficult for AI to gain a unified view. This fragmentation limits the effectiveness of AI models, especially those reliant on contextual understanding.
Solving this issue requires robust integration layers and a unified data governance strategy. Investing in middleware and cross-platform APIs is a practical first step toward creating a data environment AI can learn from.
Building responsible AI starts with awareness. Learn how to tackle real-world bias in our guide on AI fairness and ethical strategies.
2. Bias and Fairness in AI Models
AI is only as unbiased as the data it’s trained on. When historical hiring, promotion, or communication data reflects bias, AI models can perpetuate those inequities. This is a critical concern for HR applications, where fairness is paramount.
To mitigate risk, organizations must adopt explainable AI models, continuously monitor outcomes, and train models with diverse and representative data sets. Involving legal and ethics teams in AI development is also essential for trustworthy implementation.
For those navigating these complex waters, a business-oriented guide to responsible AI and ethics offers practical insights on deploying AI responsibly and transparently, especially when public trust is at stake.
3. Change Management and Employee Resistance
Introducing AI into workplace routines can trigger anxiety about job displacement or loss of autonomy. Employees may view AI as a surveillance tool rather than a productivity enhancer.
Addressing this challenge requires clear communication about AI’s role as an augmenting—not replacing—force. Engaging employees early, offering training, and demonstrating value in everyday workflows can build trust and support adoption.
4. Inadequate AI Literacy and Skills Gaps
AI systems can only deliver value when users understand how to work with them. Many teams lack the analytical or technical skills required to interpret AI-generated insights or monitor performance effectively.
Upskilling programs, AI literacy workshops, and collaborative interfaces can help bridge this gap. Business leaders must also invest in building cross-functional teams where technical and domain expertise can collaborate effectively.
5. Privacy and Security Concerns
Using AI to monitor employee performance or analyze communications raises sensitive privacy issues. Without clear boundaries, such systems risk violating employee trust or running afoul of data protection regulations.
To address this, businesses must implement transparent usage policies, anonymize data wherever possible, and prioritize security in all AI deployments. Partnering with privacy officers and external counsel can help balance innovation with responsibility.
Specific Applications of AI in the Workplace
AI is rapidly transforming work environments by automating routine tasks, enhancing collaboration, and improving productivity. These six applications illustrate the most impactful ways businesses are leveraging machine intelligence today.
1. Automated Talent Sourcing & Screening 1. Automated Talent Sourcing & Screening
Automated talent sourcing and screening use AI-powered tools to parse resumes, evaluate candidate profiles, and identify top matches based on skills and job requirements. These systems solve the industry-wide problem of sifting through thousands of applicants manually—a process that is time-consuming and prone to bias. By using machine learning algorithms trained on historical hiring data, these platforms can identify patterns that correlate with successful hires, recommending candidates who may not have traditional resumes but possess the right skills.
These tools work by harvesting resumes and job descriptions into data lakes, then applying NLP models to classify skills, experiences, and cultural fit. The AI integrates into applicant tracking systems (ATS), flagging high-potential candidates for recruiters to evaluate further. Human reviewers then assess shortlists, ensuring qualitative judgment complements quantitative filtering. Technical considerations include ensuring algorithmic fairness, while data security protocols prevent leakage of candidate personal data.
The strategic impact lies in drastically reducing cost-per-hire and time-to-fill metrics, while improving candidate quality. Automated screening helps overcome anecdotal limitations in human judgment and opens pipelines to more diverse talent pools. However, organizations must maintain transparency, audit model behavior for bias, and ensure compliance with privacy regulations like GDPR and CCPA.
Real-World Example:
Unilever deployed Pymetrics’ AI-powered platform to screen candidates via gamified challenges and predictive analytics. They complemented it with HireVue’s video-interview AI tools that analyze communication skills. This approach cut time-to-hire by 75% and doubled the number of interviews processed per recruiter.
2. Intelligent Virtual Assistants for Internal Support
Intelligent virtual assistants (IVAs) are conversational AI tools embedded into digital work platforms to support employees with requests ranging from IT help to HR queries. They solve bottlenecks in ticket-based systems and email trails by providing instant answers and automated workflows. By enhancing responsiveness and standardizing internal support, these tools reduce friction across teams and enable staff to focus on value-added tasks.
IVAs rely on NLP and dialog management engines that convert employee questions into intents and entities. They pull from knowledge bases—such as policy documents, troubleshooting guides, or SaaS APIs—to offer real-time guidance or automatically open service tickets. When unresolved queries arise, requests are escalated to human agents, with full context passed along to improve case resolution speed. Privacy is critical, so data encryption, access controls, and audit logs are essential to maintain compliance.
These systems improve efficiency by deflecting low-value queries and reducing resolution times by up to 60%, according to industry benchmarks. They provide consistent support regardless of shift or location, helping distributed teams stay productive. For successful adoption, companies must ensure the VA is regularly updated with evolving document libraries and supported by robust governance to build trust with users.
Real-World Example:
Siemens integrated IBM Watson Assistant into its internal service systems to handle IT, HR, and facilities inquiries. The assistant resolved roughly 55% of employee questions without human intervention. Siemens achieved a 40% reduction in support tickets and saw a 30% uplift in user satisfaction.
3. Predictive Workforce Planning
Predictive workforce planning uses AI to forecast staffing needs, talent gaps, and workforce attrition by analyzing historical data, market trends, and business objectives. It solves a critical problem for HR and operations leaders who struggle to align workforce supply with future demand—particularly in industries affected by seasonal variation or volatile project pipelines. With accurate forecasts, organizations can make informed decisions on hiring, training, and succession planning before problems arise.
AI models for workforce planning use time-series forecasting, regression models, and classification algorithms trained on employee tenure, performance, exit interviews, and project data. These models predict churn, estimate ramp-up times, and assess internal mobility patterns. Integrated into HRIS or ERP platforms, AI gives managers proactive dashboards to guide workforce allocation and scenario planning.
Strategically, predictive workforce planning supports agility and cost control. It enables leaders to shift from reactive headcount adjustments to proactive talent strategies that reduce turnover and ensure skill readiness. Key considerations include ensuring data privacy, handling sensitive workforce information ethically, and avoiding algorithmic assumptions that penalize non-linear career paths.
Real-World Example:
Royal Dutch Shell implemented AI models using Workday Prism and custom analytics to forecast skills gaps across its global workforce. The company used predictions to adjust its hiring roadmap and upskilling initiatives. Shell saw a 16% improvement in project staffing efficiency and reduced short-term contractor spend by 12%.
4. AI-Powered Employee Sentiment Analysis
Employee sentiment analysis leverages AI to monitor morale, engagement, and emerging workplace issues through analysis of communication platforms, survey responses, and pulse checks. It addresses the challenge of blind spots in leadership awareness and the delayed response to cultural or productivity issues. By analyzing tone, keyword frequency, and behavioral signals, AI helps HR teams act on concerns before they become crises.
These models use NLP and sentiment classification algorithms trained on annotated corpora to extract emotional cues and satisfaction markers. Platforms like Slack, Microsoft Teams, and internal forums are scanned (with user consent and anonymization) for patterns indicating burnout, dissatisfaction, or disengagement. Results feed into dashboards accessible to HR and team leaders, often segmented by department, region, or tenure group.
The operational value lies in enabling faster, more targeted interventions—whether it’s leadership check-ins, training refreshers, or policy reviews. Sentiment data complements traditional performance indicators and helps create a more responsive, transparent culture. Ethical use requires employee awareness, data minimization, and governance policies that balance insight with privacy.
Real-World Example:
Cisco deployed AI-powered sentiment analysis via its internal “People Insights” platform, built on Qualtrics and NLP layers. It helped identify burnout signals and engagement dips during the shift to remote work. As a result, the company improved its well-being initiatives and saw a 14% increase in employee satisfaction over two quarters.
5. Personalized Learning & Development (L&D)
Personalized L&D platforms use AI to recommend training programs tailored to an employee’s role, skills, learning style, and career goals. Traditional L&D programs often fail to engage learners because they rely on generic content and rigid pathways. AI solves this by delivering targeted content that adapts over time—boosting both engagement and effectiveness.
Recommendation engines are at the core of these platforms. They apply collaborative filtering, skill-matching, and behavioral analytics to suggest content from internal libraries or third-party MOOCs (e.g., Coursera, Udemy). As employees complete modules, the system refines future suggestions based on success metrics, knowledge gaps, and user feedback. L&D administrators receive cohort-wide insights to improve program design and ROI.
The strategic benefit is scalable workforce development that aligns with individual aspirations and organizational needs. By automating training curation and sequencing, HR teams can close skills gaps faster and at a lower cost. Considerations include ensuring equal access, content quality control, and integration with performance review systems.
Real-World Example:
Accenture uses its internal platform, “MyLearning,” powered by AI and integrated with Workday, to deliver personalized training paths. The system analyzes project assignments, career goals, and feedback to adapt content dynamically. The company reported a 24% increase in training completion rates and a 30% improvement in internal mobility.
6. Intelligent Document Management & Knowledge Retrieval
AI-enabled document management systems transform how employees find, access, and extract value from internal documentation. The traditional problem is information sprawl—documents buried in intranets, cloud drives, and email threads, slowing decision-making and collaboration. AI solves this by indexing content, summarizing key points, and surfacing relevant documents contextually.
These platforms use NLP, semantic search, and deep learning-based summarization to tag and retrieve information based on intent rather than keyword matches. For example, an employee looking for “latest expense policy” would receive the updated PDF, a TL;DR summary, and related Slack conversations. AI integrates with Microsoft SharePoint, Confluence, and Google Workspace to offer cross-platform relevance.
This capability enhances operational speed, reduces duplication, and improves knowledge transfer across distributed teams. It also supports compliance by ensuring staff always access the latest documents and policies. However, care must be taken to secure sensitive documents and validate AI-generated summaries for accuracy.
Real-World Example:
Deloitte implemented an AI-powered document intelligence platform based on Microsoft Syntex and Azure Cognitive Search. It helped employees surface the right policies and client files in seconds, rather than minutes. Deloitte estimated a 22% reduction in time spent on information retrieval across key business functions.
| 2025-07-08T00:00:00 |
2025/07/08
|
https://smartdev.com/ai-in-the-workplace-top-use-cases-you-need-to-know/
|
[
{
"date": "2023/04/01",
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"query": "AI replacing workers"
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{
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},
{
"date": "2023/09/01",
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},
{
"date": "2023/10/01",
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},
{
"date": "2023/11/01",
"position": 90,
"query": "AI replacing workers"
},
{
"date": "2023/11/01",
"position": 38,
"query": "AI workforce transformation"
},
{
"date": "2023/12/01",
"position": 87,
"query": "AI replacing workers"
},
{
"date": "2023/12/01",
"position": 68,
"query": "workplace AI adoption"
},
{
"date": "2024/01/01",
"position": 85,
"query": "AI replacing workers"
},
{
"date": "2024/01/01",
"position": 68,
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},
{
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"position": 96,
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},
{
"date": "2024/03/01",
"position": 85,
"query": "AI replacing workers"
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{
"date": "2024/03/01",
"position": 63,
"query": "workplace AI adoption"
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{
"date": "2024/04/01",
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{
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{
"date": "2024/05/01",
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{
"date": "2024/06/01",
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{
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{
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{
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{
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"position": 90,
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},
{
"date": "2024/10/01",
"position": 60,
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},
{
"date": "2024/11/01",
"position": 93,
"query": "AI replacing workers"
},
{
"date": "2024/11/01",
"position": 59,
"query": "AI workforce transformation"
},
{
"date": "2024/11/01",
"position": 60,
"query": "workplace AI adoption"
},
{
"date": "2024/12/01",
"position": 45,
"query": "AI workforce transformation"
},
{
"date": "2024/12/01",
"position": 61,
"query": "workplace AI adoption"
},
{
"date": "2025/01/01",
"position": 89,
"query": "AI replacing workers"
},
{
"date": "2025/01/01",
"position": 65,
"query": "workplace AI adoption"
}
] |
Three Things – ZIRP, US Debt & more AI - Discipline Funds
|
Three Things – ZIRP, US Debt & more AI
|
https://disciplinefunds.com
|
[
"Cullen Roche"
] |
Here are some things I think I am thinking about: 1) The Return of ZIRP (zero interest rate policy) ...
|
Here are some things I think I am thinking about:
1) The Return of ZIRP (zero interest rate policy).
The Fed released an interesting study this week saying there was about a 9% chance of the Fed cutting interest rates to 0% again.
That’s an interesting one to me because I would say that there’s about a 100% chance the Fed will get back to 0% at some point. This is probably the biggest and boldest macro view I maintain. I have no idea over what time horizon that will occur, but my basic thinking is that AI is going to crush services inflation. Then robots using AI will crush goods inflation. Then the government will respond with a ZIRP at some point because any recession that coincides with such an environment will very likely coincide with very high unemployment and very low inflation.
Said differently, I think we’re on the precipice of an era of unprecedented abundance. There is a flood of supply coming into the global economy because AI is going to make everything so accessible. And when robots start mass producing physical things using that technology we’ll see a collapse in goods inflation as physical things become increasingly abundant. We’ve already seen a microcosm of this play out over the last 30 years as technology has created an abundance of many things. But this is about to go into hyperdrive at some point in the next 5, 10 or 20 years. Again, I don’t know the exact time horizon because I don’t think the robotic technology is there yet, but if I had to pick year 20 or year 0 I’d say we’re closer to this happening now than any 20 year prediction assumes.
In short, I think the Fed’s 9% probability is way too low. ZIRP will come back during my lifetime. It’s just a matter of when.
2) AI Will Make Govt Will Get MUCH Bigger.
Someone wrote me a critical email last week because I was critical of Zohran Mamdani and Democratic Socialists. Look, I am not really that critical of government intervention in the economy and I wouldn’t place Democratic Socialists anywhere near actual Socialists. They’re just not the same things. At the same time, I am, as my first book stated, a “Pragmatic” Capitalist and I consider that to be someone who acknowledges that Capitalism is great, but also that Capitalism needs a certain level of oversight and government intervention. Having sensible rules, a government safety net and some level of countercyclical policy makes a lot of sense in my opinion. It is just practical Capitalism. And in many ways helps Capitalism from eating itself. Where I disagree with a lot of Democratic Socialists is about their hatred of Capitalism. My view is that their movement would make a lot more sense if it was something more aligned with Democratic Capitalism. In other words, you can be a Capitalist and also understand that Capitalism allows you to have a much bigger government than you otherwise would (because Capitalism is so good at creating the very resources that allow the government to spend so much). Capitalism doesn’t need to be at odds with many of the ideals that a Democratic Socialist might hold.
Anyhow, I’ll get off my political soapbox because I think the Democratic Socialists will be happy to hear that I believe point #1 means the government is going to get much bigger than it already is. Well, I should be clear. If I worked in a department like a building department at a government I would be very terrified about my employment because AI can automate away tens of thousands of jobs like that. But therein lies the paradox of AI and employment. If AI is going to be able to replace a lot of those jobs then that means more and more people are likely to need government assistance. So, AI is a bit of a Capitalism paradox in that it is likely to create an abundance of stuff, which will create a shortage of jobs required to consume that stuff, which will result in an increase in the demand for government assistance.
Again, I don’t know the time horizon of any of this. And I could be wildly too optimistic about what I think AI is capable of, but if I am right then we’re on the precipice of the golden age of Capitalism, which will, ironically, make Democratic Socialists much more influential.
3) How Big Will the Government Get?
Speaking of much bigger government – I joined Michael Batnick for a segment of What Are Your Thoughts? We talked about many of the issues I’ve debunked over the years. I think you’ll enjoy the segment as we jump into many concerns on people’s minds these days including the risk of exploding government debt, rising interest payments, the collapse of the Dollar and the Dollar’s recent relative forex decline.
And please remember, all hate mail at this website goes to Jerome Powell at [email protected] – I am nothing more than an apolitical and totally unbiased market analyst. Thanks for reading and remember not to let your politics (or my political rants) get in the way of pragmatic and disciplined investing.
| 2025-07-08T00:00:00 |
2025/07/08
|
https://disciplinefunds.com/2025/07/08/three-things-zirp-us-debt-more-ai/
|
[
{
"date": "2023/04/01",
"position": 75,
"query": "AI unemployment rate"
},
{
"date": "2023/05/01",
"position": 96,
"query": "AI unemployment rate"
},
{
"date": "2023/08/01",
"position": 98,
"query": "AI unemployment rate"
},
{
"date": "2023/09/01",
"position": 96,
"query": "AI unemployment rate"
},
{
"date": "2023/12/01",
"position": 98,
"query": "AI unemployment rate"
},
{
"date": "2024/05/01",
"position": 95,
"query": "AI unemployment rate"
},
{
"date": "2024/09/01",
"position": 96,
"query": "AI unemployment rate"
}
] |
Hotter than a GPU in July: some tech jobs skyrocket, unemployment ...
|
Hotter than a GPU in July: some tech jobs skyrocket, unemployment rate slides
|
https://www.computerworld.com
|
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"More This Author",
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] |
1 day ago · US tech hiring jumped in June, adding 90000 jobs and cutting the industry unemployment rate to 2.8%. AI demand is growing, employers are ...
|
US employers added more than 90,000 tech workers in June, pushing the industry’s unemployment rate down from 3.4% to 2.8%, according to a CompTIA analysis of the latest Bureau of Labor Statistics (BLS) data.
The Computing Technology Industry Association (CompTIA) reported that tech unemployment remains well below the national average of 4.1%. “Tech employment showed surprising strength for the month given recent expectations,” said Tim Herbert, CompTIA’s chief research officer. “It’s worth pointing out there is more to tech hiring than AI. The data continues to confirm employer hiring activity across many tech talent domains.”
Even so, CompTIA’s AI Hiring Intent Index showed a 153% year-over-year increase in jobs requiring AI skills, with demand rising for AI specialists such as architects and engineers. Those gains remained concentrated among select employers.
| 2023-04-01T00:00:00 |
https://www.computerworld.com/article/4018280/hotter-than-a-gpu-in-july-some-tech-jobs-skyrocket-unemployment-rate-slides.html
|
[
{
"date": "2023/04/01",
"position": 88,
"query": "AI unemployment rate"
},
{
"date": "2023/04/01",
"position": 71,
"query": "generative AI jobs"
},
{
"date": "2023/05/01",
"position": 73,
"query": "generative AI jobs"
},
{
"date": "2023/06/01",
"position": 70,
"query": "generative AI jobs"
},
{
"date": "2023/07/01",
"position": 76,
"query": "generative AI jobs"
},
{
"date": "2023/09/01",
"position": 89,
"query": "AI job losses"
},
{
"date": "2023/09/01",
"position": 70,
"query": "generative AI jobs"
},
{
"date": "2023/10/01",
"position": 76,
"query": "generative AI jobs"
},
{
"date": "2023/11/01",
"position": 76,
"query": "generative AI jobs"
},
{
"date": "2023/12/01",
"position": 72,
"query": "generative AI jobs"
},
{
"date": "2024/01/01",
"position": 75,
"query": "generative AI jobs"
},
{
"date": "2024/02/01",
"position": 74,
"query": "generative AI jobs"
},
{
"date": "2024/03/01",
"position": 75,
"query": "generative AI jobs"
},
{
"date": "2024/07/01",
"position": 74,
"query": "generative AI jobs"
},
{
"date": "2024/08/01",
"position": 77,
"query": "generative AI jobs"
},
{
"date": "2024/09/01",
"position": 73,
"query": "generative AI jobs"
},
{
"date": "2024/11/01",
"position": 73,
"query": "generative AI jobs"
},
{
"date": "2025/01/01",
"position": 77,
"query": "generative AI jobs"
},
{
"date": "2025/02/01",
"position": 67,
"query": "generative AI jobs"
},
{
"date": "2025/03/01",
"position": 65,
"query": "generative AI jobs"
},
{
"date": "2025/04/01",
"position": 90,
"query": "AI unemployment rate"
},
{
"date": "2025/04/01",
"position": 69,
"query": "generative AI jobs"
},
{
"date": "2025/06/01",
"position": 66,
"query": "generative AI jobs"
}
] |
|
Recent Graduates Face Rising Unemployment as AI Displaces Entry ...
|
Recent Graduates Face Rising Unemployment as AI Displaces Entry-Level Jobs
|
https://news.ssbcrack.com
|
[
"News Desk"
] |
In a significant shift, the unemployment rate for recent college graduates has surpassed that of the general population for the first time ...
|
In a significant shift, the unemployment rate for recent college graduates has surpassed that of the general population for the first time in three years, as highlighted in a report by Oxford Economics. The report, released in May, indicates that entry-level positions are increasingly being displaced by advancements in artificial intelligence (AI). Notably, graduates with degrees in programming and technology are facing particular challenges in the job market.
Various factors contribute to this emerging trend. Recent statements by Amazon’s CEO, Andy Jassy, emphasize the company’s transition to greater AI utilization, which he acknowledged would necessitate a reduction in the workforce. Dario Amodei, CEO of the AI firm Anthropic, echoed similar sentiments, forecasting that AI could lead to the elimination of half of all white-collar jobs.
Brooke DeRenzis, head of the National Skills Coalition, characterized the arrival of AI as a pivotal moment for the middle class, coining it a “jump ball.” While AI might create new opportunities and enhance existing roles, the net effects on employment remain uncertain. DeRenzis advocates for significant investment in training programs aimed at equipping workers with the skills necessary to thrive alongside AI. She also stresses the importance of developing a robust social safety net that extends beyond traditional unemployment insurance, particularly for those in sectors facing complete displacement.
DeRenzis warned that without proactive measures, the widening gap in inequality could become a significant issue. “We can shape a society that supports our workforce in adapting to an AI economy in a way that can actually grow our middle class,” she stated.
Despite the rapid advances in AI technology, researchers caution against overestimating its current capabilities. Morgan Frank, a professor at the University of Pittsburgh specializing in the impact of AI on jobs, noted that many existing AI applications remain error-prone and are not yet equipped to entirely replace human labor in numerous tasks. He suggested that while many tech company leaders highlight the potential job losses due to AI, they may also be responding to the fallout from excessive hiring during the pandemic.
Frank pointed out that while there may not be an immediate catastrophe on the horizon, individuals entering the workforce today seem to lack the opportunities previously available. He emphasized the transient nature of the current job landscape, stating, “The way AI operates and the way that people use it is constantly shifting, and we’re just in this transitory period…. The frontier is moving.”
| 2025-07-09T00:00:00 |
2025/07/09
|
https://news.ssbcrack.com/recent-graduates-face-rising-unemployment-as-ai-displaces-entry-level-jobs/
|
[
{
"date": "2023/04/01",
"position": 90,
"query": "AI unemployment rate"
}
] |
Helpful AI prompts for your next job search - Yahoo
|
Helpful AI prompts for your next job search
|
https://www.yahoo.com
|
[] |
...
|
Looking for a new job is a full-time job in itself, and one that can test your nerves. But this is where AI has become a valuable companion, helping you save time on your job hunt.
Indeed, AI tools like ChatGPT, Gemini, Copilot and Perplexity can be cleverly used to simplify the job search process, says Guido Sieber, managing director at a Germany-based recruitment agency.
1. Finding the right job vacancies
One way to use AI is for job searching. There are plenty of job platforms, but going through each one individually to find suitable vacancies takes time.
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This is where AI chatbots can help. Sieber advises starting with precise job queries, such as: "Find current job offers for financial accountants in X city with a remote working option." The more specific the query, the better the results are likely to be.
For those wanting to learn more about employers in their desired industry, Sieber suggests trying prompts like: "List the top five employers for IT security in X country."
According to the recruitment expert, it is important to refine all queries during a chat session with the AI. "The first answer is rarely perfect," Sieber says.
AI can also be used to improve application documents. In the next step, AI tools can help optimize CVs and tailor them to the desired job. Suitable prompts include: "What skills are currently most frequently sought in job advertisements for UX designers?"
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This can help identify trends in the targeted field and align applications with the requirements. "Draft a cover letter for a junior controller position based on this job advert. Highlight my experience with SAP and Excel."
2. Adapt your cover letter to the job
By providing the job advert to the AI chatbot, applicants can improve their cover letters with the response.
"Analyse my CV for potential red flags that HR managers might view negatively." This allows the AI to check the application for possible weaknesses.
However, Sieber notes that overly general queries, such as "Improve my CV," provide too little context to be helpful.
3. Use AI to prepare for your interview
AI can also assist in preparing for job interviews, and chatbots can serve as effective training partners for interviews. Sieber suggests prompts such as:
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"What questions are frequently asked in interviews for data analysts?"
"Simulate an interview for a position in human resources with questions about my recruiting experience."
"How can I convincingly answer a question about my salary expectations?"
The AI can also provide feedback on the applicant's responses upon request.
Sieber says that AI should only be used as a tool in the application process. All suggestions must be critically reviewed and adapted to your personal style, as HR managers are quick to discard generic documents.
Additionally, you may want to check the data protection policies and options for limiting data usage of the AI tool you've chosen.
Sensitive data, as well as complete application documents, should not be entered into the chat. It is better to work with snippets and anonymised versions.
| 2023-04-01T00:00:00 |
https://www.yahoo.com/lifestyle/articles/helpful-ai-prompts-next-job-164734763.html
|
[
{
"date": "2023/04/01",
"position": 97,
"query": "AI unemployment rate"
},
{
"date": "2023/05/01",
"position": 70,
"query": "AI unemployment rate"
},
{
"date": "2023/07/01",
"position": 75,
"query": "AI unemployment rate"
},
{
"date": "2023/08/01",
"position": 72,
"query": "AI unemployment rate"
},
{
"date": "2023/09/01",
"position": 71,
"query": "AI unemployment rate"
},
{
"date": "2023/11/01",
"position": 73,
"query": "AI unemployment rate"
},
{
"date": "2023/12/01",
"position": 72,
"query": "AI unemployment rate"
},
{
"date": "2024/05/01",
"position": 68,
"query": "AI unemployment rate"
},
{
"date": "2024/08/01",
"position": 71,
"query": "AI unemployment rate"
},
{
"date": "2024/09/01",
"position": 70,
"query": "AI unemployment rate"
}
] |
|
Unemployment surges as employers replace new hires with AI
|
Unemployment surges as employers replace new hires with AI
|
https://www.leadstory.com
|
[] |
The unemployment rate for new college graduates has recently surged. Economists say businesses are now replacing entry-level jobs with ...
|
Unemployment surges as employers replace new hires with AI CBS America · 9 days ago CBS America 9 days ago
The unemployment rate for new college graduates has recently surged. Economists say businesses are now replacing entry-level jobs with artificial intelligence. Ali Bauman has the story.
Breakdown Unemployment among recent college graduates has risen to 6.6 percent, surpassing the national average. 49s
Unemployment among recent college graduates has risen to 6.6 percent, surpassing the national average. Economists say businesses are eliminating entry-level jobs and replacing them with AI, especially in tech sectors. 1m 10s
Economists say businesses are eliminating entry-level jobs and replacing them with AI, especially in tech sectors. Experts predict AI-driven workforce disruption could last up to 15 years and affect both entry-level and repetitive jobs. 1m 28s
Experts predict AI-driven workforce disruption could last up to 15 years and affect both entry-level and repetitive jobs. Education leaders recommend focusing on lifelong learning and adaptability to remain competitive in the job market. 2m 22s
Education leaders recommend focusing on lifelong learning and adaptability to remain competitive in the job market. Workers who innovate and collaborate with AI are expected to have better employment prospects. 2m 40s
| 2025-07-06T00:00:00 |
2025/07/06
|
https://www.leadstory.com/v/unemployment-surges-as-employers-replace-new-hires-with-ai-20257611
|
[
{
"date": "2023/04/01",
"position": 98,
"query": "AI unemployment rate"
},
{
"date": "2025/07/05",
"position": 84,
"query": "AI unemployment rate"
}
] |
Philippines: Scale AI creating 'race to the bottom' as outsourced ...
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Philippines: Scale AI creating ‘race to the bottom’ as outsourced workers face ‘digital sweatshop’ conditions incl. low wages & withheld payments
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https://www.business-humanrights.org
|
[] |
Philippines: Scale AI creating 'race to the bottom' as outsourced workers face 'digital sweatshop' conditions incl. low wages & withheld payments. See all tags.
|
Article Philippines: Scale AI creating ‘race to the bottom’ as outsourced workers face ‘digital sweatshop’ conditions incl. low wages & withheld payments
"Behind the AI boom, the armies of overseas workers in ‘digital sweatshops’", 30 August 2023
In dingy internet cafes, jam-packed office spaces or at home, they annotate the masses of data that American companies need to train their artificial intelligence models. The workers differentiate pedestrians from palm trees in videos used to develop the algorithms for automated driving; they label images so AI can generate representations of politicians and celebrities; they edit chunks of text to ensure language models like ChatGPT don’t churn out gibberish.
More than 2 million people in the Philippines perform this type of “crowdwork”, according to informal government estimates, as part of AI’s vast underbelly. While AI is often thought of as human-free machine learning, the technology actually relies on the labour-intensive efforts of a workforce spread across much of the global south and is often subject to exploitation.
The mathematical models underpinning AI tools get smarter by analysing large data sets, which need to be accurate, precise and legible to be useful. Low-quality data yields low-quality AI. So click by click, a largely unregulated army of humans is transforming the raw data into AI feedstock.
In the Philippines, one of the world’s biggest destinations for outsourced digital work, former employees say that at least 10,000 of these workers do this labour on a platform called Remotasks, which is owned by the $7bn San Francisco start-up Scale AI.
Scale AI has paid workers at extremely low rates, routinely delayed or withheld payments and provided few channels for workers to seek recourse, according to interviews with workers, internal company messages and payment records, and financial statements. Rights groups and labour researchers say Scale AI is among a number of American AI companies that have not abided by basic labour standards for their workers abroad.
Of 36 current and former freelance workers interviewed, all but two said they’ve had payments from the platform delayed, reduced or cancelled after completing tasks. The workers, known as “taskers,” said they often earn far below the minimum wage – which in the Philippines ranges from $6 to $10 a day depending on region...
In a statement, Anna Franko, a Scale AI spokesperson, said the pay system on Remotasks “is continually improving” based on worker feedback and that “delays or interruptions to payments are exceedingly rare”.
But on an internal messaging platform for Remotasks...notices of late or missing payments from supervisors were commonplace. On some projects, there were multiple notices in a single month. Sometimes, supervisors told workers payments were withheld because work was inaccurate or late. Other times, supervisors gave no explanation. Attempts to track down lost payments often went nowhere, workers said – or worse, led to their accounts being deactivated...
In enlisting people in the Global South as freelance contractors, micro-tasking platforms like Remotasks sidestep labour regulations – such as a minimum wage and a fair contract - in favour of terms and conditions they set independently, said Cheryll Soriano, a professor at De La Salle University in Manila who studies digital labour in the Philippines...
Dominic Ligot, a Filipino AI ethicist, called these new workplaces “digital sweatshops”...
...government officials in the Philippines...admitted they weren’t sure how to regulate the platform. The Department of Information and Communications Technology...Data annotation is an “informal sector,” said department head Ivan John Uy. “Regulatory protective mechanisms are not there.”...
Initially, taskers said, they could earn as much as $200 in a week. Then in 2021, around the time Remotasks expanded to India and to Venezuela, pay rates plunged...Filipino freelancers went from earning $10 per task on some projects to less than 1 cent, according to a former SEPI staff employee...
By auctioning off work globally, Remotasks has created a “race to the bottom” for wages, said the owner of an outsourcing firm that has worked with SEPI. “It’s vicious competition,”...
In its terms and conditions, Remotasks says it “reserves the right” to withhold payment, remove freelancers from projects or deactivate their accounts for work deemed inaccurate. This “non-specified” set of rules, Valente said, lets the company decide if and when it wants to pay them for work even after it’s already been done...
For young people in places like Mindanao struggling to find work, there are few alternatives. Scale AI can exploit Filipino workers, said Philip Alchie Elemento, 37, an ex-tasker, “because they know we don’t have a choice”.
| 2023-04-01T00:00:00 |
https://www.business-humanrights.org/en/latest-news/philippines-scale-ai-creating-race-to-the-bottom-as-outsourced-workers-face-poor-conditions-in-digital-sweatshops-incl-low-wages-withheld-payments/
|
[
{
"date": "2023/04/01",
"position": 80,
"query": "AI wages"
},
{
"date": "2023/08/01",
"position": 73,
"query": "AI wages"
},
{
"date": "2024/01/01",
"position": 91,
"query": "AI wages"
},
{
"date": "2024/03/01",
"position": 91,
"query": "AI wages"
},
{
"date": "2024/05/01",
"position": 71,
"query": "AI wages"
},
{
"date": "2024/07/01",
"position": 83,
"query": "AI wages"
},
{
"date": "2024/09/01",
"position": 81,
"query": "AI wages"
}
] |
|
Welcome - 2025 Dice Tech Salary Report - Tech Professionals
|
2025 Dice Tech Salary Report
|
https://www.dice.com
|
[] |
We're tracking how AI expertise can command an almost 18% salary premium, analyzing why nearly half of employed tech professionals are job hunting.
|
Survey and Report Methodology
Collection of Data
The 2024 Dice Salary Survey was administered online by Dice.com among registered Dice job seekers and site visitors between August 30, 2024, and November 6, 2024. Respondents were invited to participate in the survey in two ways: 1) via an email invitation to Dice’s registered (searchable) database members and 2) through a notification via website banner on Dice.com user profile page. A total of 2,835 completed surveys are represented in this report (this number excludes unemployed respondents, students, incomplete responses and those who work outside of the U.S.).
| 2023-04-01T00:00:00 |
https://www.dice.com/technologists/ebooks/tech-salary-report/
|
[
{
"date": "2023/04/01",
"position": 84,
"query": "AI wages"
},
{
"date": "2024/01/01",
"position": 79,
"query": "AI wages"
},
{
"date": "2024/03/01",
"position": 83,
"query": "AI wages"
},
{
"date": "2024/07/01",
"position": 88,
"query": "AI wages"
},
{
"date": "2024/09/01",
"position": 71,
"query": "AI wages"
}
] |
|
ON ALGORITHMIC WAGE DISCRIMINATION - Columbia Law Review
|
ON ALGORITHMIC WAGE DISCRIMINATION
|
https://www.columbialawreview.org
|
[
"Columbia Law Review",
"Veena Dubal"
] |
One such example, used to specify what defines unsafe and ineffective automation in the workplace, involves an unnamed company that has installed AI-powered ...
|
INTRODUCTION
Over the past two decades, technological developments have ushered in extreme levels of workplace monitoring and surveillance across many sectors. These automated systems record and quantify workers’ movement or activities, their personal habits and attributes, and even sensitive biometric information about their stress and health levels. Employers then feed amassed datasets on workers’ lives into machine learning systems to make hiring determinations, to influence behavior, to increase worker productivity, to intuit potential workplace problems (including worker organizing), and, as this Article highlights, to determine worker pay.
To date, policy concerns about growing technological surveillance in the workplace have largely mirrored the apprehensions articulated by consumer advocates. Scholars and advocates have raised concerns about the growing limitations on worker privacy and autonomy, the potential for society-level discrimination to seep into machine learning systems, and a general lack of transparency on workplace rules. For example, in October 2022, the White House Office of Science and Technology Policy released a non-legally-binding handbook identifying five principles that “should guide the design, use, and deployment of automated systems to protect the American public in the age of artificial intelligence.” These principles called for automated systems that (1) were safe and effective, (2) protect individuals from discrimination, (3) offer users control over how their data is used, (4) provide notice and explanation that an automated system is being used, and (5) allow users access to a person who can remedy any problems they encounter. The Blueprint for an AI Bill of Rights (hereinafter Blueprint) specified that these enumerated rights extended to “[e]mployment-related systems [such as] . . . workplace algorithms that inform all aspects of the terms and conditions of employment including, but not limited to, pay or promotion, hiring or termination algorithms, virtual or augmented reality workplace training programs, and electronic workplace surveillance and management systems.”
Under each principle, the Blueprint provides “illustrative examples” of the kinds of harms that the principle is meant to address. One such example, used to specify what defines unsafe and ineffective automation in the workplace, involves an unnamed company that has installed AI-powered cameras in their delivery vans to monitor workers’ driving habits, ostensibly for “safety reasons.” The Blueprint states that the system “incorrectly penalized drivers when other cars cut them off . . . . As a result, drivers were incorrectly ineligible to receive a bonus.” Thus, the specific harm identified is a mistaken calculation by an automated variable pay system developed by the company.
What the Blueprint does not specify, however, is that the company in question—Amazon—does not directly employ the delivery workers. Rather, the company contracts with Delivery Service Providers (DSPs), small businesses that Amazon helps to establish. In this putative nonemployment arrangement, Amazon does not provide to the DSP drivers workers’ compensation, unemployment insurance, health insur-ance, or the protected right to organize. Nor does it guarantee individual DSPs or their workers minimum wage or overtime compensation. Instead, DSPs receive a variable hourly rate based on fluctuations in demand and routes, along with “bonuses” based on a quantified digital evaluation of on-the-job behavior, including “service, safety, [and] client experience.” DSPs, while completely reliant on Amazon for business, must hire a team of drivers as employees. These Amazon-created and -controlled small businesses rely heavily on their automated “bonuses” to pay for support, repairs, and driver wages. As one DSP owner–worker complained to an investigator, “Amazon uses these [AI surveillance] cameras allegedly to make sure they have a safer driving workforce, but they’re actually using them not to pay [us] . . . . They just take our money and expect that to motivate us to figure it out.”
Presented with this additional information, we should ask again: What exactly is the harm of this automated system? Is it, as the Blueprint states, the algorithm’s mistake, which prevented the worker from getting his bonus? Or is it the structure of Amazon’s payment system, rooted in evasion of employment law, data extraction from labor, and digitalized control?
Amazon’s automated control structure and payment mechanisms represent an emergent and undertheorized firm technique arising from the logic of informational capitalism: the use of algorithmic wage discrimination to maximize profits and to exert control over worker behavior. “Algorithmic wage discrimination” refers to a practice in which individual workers are paid different hourly wages—calculated with ever-changing formulas using granular data on location, individual behavior, demand, supply, or other factors—for broadly similar work. As a wage-pricing technique, algorithmic wage discrimination encompasses not only digitalized payment for completed work but, critically, digitalized decisions to allocate work, which are significant determinants of hourly wages and levers of firm control. These methods of wage discrimination have been made possible through dramatic changes in cloud computing and machine learning technologies in the last decade.
Though firms have relied upon performance-based variable pay for some time (e.g., the use of bonuses and commission systems to influence worker behavior), my research on the on-demand ride hail industry suggests that algorithmic wage discrimination raises a new and distinctive set of concerns. In contrast to more traditional forms of variable pay, algorithmic wage discrimination—whether practiced through Amazon’s “bonuses” and scorecards or Uber’s work allocation systems, dynamic pricing, and wage incentives—arises from (and may function akin to) the practice of “price discrimination,” in which individual consumers are charged as much as a firm determines they may be willing to pay. As a labor management practice, algorithmic wage discrimination allows firms to personalize and differentiate wages for workers in ways unknown to them, paying them to behave in ways that the firm desires, perhaps for as little as the system determines that the workers may be willing to accept. Given the information asymmetry between workers and firms, companies can calculate the exact wage rates necessary to incentivize desired behaviors, while workers can only guess how firms determine their wages.
The Blueprint example underscores how algorithmic wage discrimination can be “ineffective” and rife with calculated mistakes that are difficult to ascertain and correct. But algorithmic wage discrimination also creates a labor market in which people who are doing the same work, with the same skill, for the same company, at the same time may receive different hourly pay. Digitally personalized wages are often determined through obscure, complex systems that make it nearly impossible for workers to predict or understand their constantly changing, and frequently declining, compensation.
Drawing on anthropologist Karl Polanyi’s notion of embeddedness—the idea that social relations are embedded in economic systems —this Article excavate the norms around payment that constitute what one might consider a moral economy of work to help situate this contemporary rupture in wages. Although the United States–based system of work is largely regulated through contracts and strongly defers to the managerial prerogative, two restrictions on wages have emerged from social and labor movements: minimum-wage laws and antidiscrimination laws. Respectively, these laws set a price floor for the purchase of labor relative to time and prohibit identity-based discrimination in the terms, con-ditions, and privileges of employment, requiring firms to provide equal pay for equal work. Both sets of wage laws can be understood as forming a core moral foundation for most work regulation in the United States. In turn, certain ideals of fairness have become embedded in cultural and legal expectations about work. Part I examines how recently passed laws in California and Washington State, which specifically legalize algorithmic wage discrimination for certain firms, compare with and destabilize more than a century of legal and social norms around fair pay.
Part II draws on first-of-its-kind, long-term ethnographic research to understand the everyday, grounded experience of workers earning through and experiencing algorithmic wage discrimination. Specifically, Part II analyzes the experiences of on-demand ride-hail drivers in California before and after the passage of an important industry-initiated law, Proposition 22, which legalized this form of variable pay. This Part illuminates workers’ experiences under compensation systems that make it difficult for them to predict and ascertain their hourly wages. Then, Part II examines the practice of algorithmic wage discrimination in rela-tionship to workers’ on-the-job meaning making and their moral interpretations of their wage experiences. Though many drivers are attracted to on-demand work because they long to be free from the rigid scheduling structures of the Fordist work model, they still largely conceptualize their labor through the lens of that model’s payment structure: the hourly wage. Workers find that, in contrast to more standard wage dynamics, being directed by and paid through an app involves opacity, deception, and manipulation. Those who are most economically dependent on income from on-demand work frequently describe their experience of algorithmic wage discrimination through the lens of gambling. As a normative matter, this Article contends that workers laboring for firms (especially large, well-financed ones like Uber, Lyft, and Amazon) should not be subject to the kind of risk and uncertainty associated with gambling as a condition of their work. In addition to the salient constraints on autonomy and threats to privacy that accompany the rise of on-the-job data collection, algorithmic wage discrimination poses significant problems for worker mobility, worker security, and worker collectivity, both on the job and outside of it. Because the on-demand workforces that are remunerated through algorithmic wage discrimination are primarily made up of immigrants and racial minority workers, these harmful economic impacts are also necessarily racialized.
Finally, Part III explores how workers and worker advocates have used existing data privacy laws and cooperative frameworks to address or at least to minimize the harms of algorithmic wage discrimination. In addition to mobilizing against violations of minimum-wage, overtime, and vehicle reimbursement laws, workers in California—drawing on the knowledge and experience of their coworkers in the United Kingdom—have developed a sophisticated understanding of the laws governing data at work. In the United Kingdom, a self-organized group of drivers, the App Drivers & Couriers Union, has not only successfully sued Uber to establish their worker status but also used the General Data Protection Regulation (GDPR) to lay claim to a set of positive rights concerning the data and algorithms that determine their pay. As a GDPR-like law went into effect in California in 2023, drivers there are positioned to do the same. Other workers in both the United States and Europe have responded by creating “data cooperatives” to fashion some transparency around the data extracted from their labor, to attempt to understand their wages, and to assert ownership over the data they collect at work. In addition to examining both approaches to addressing algorithmic wage discrim-ination, this Article argues that the constantly changing nature of machine learning technologies and the asymmetrical power dynamics of the digitalized workplace minimize the impact of these attempts at trans-parency and may not mitigate the objective or subjective harms of algorithmic wage discrimination. Considering the potential for this form of discrimination to spread into other sectors of work, this Article proposes instead an approach that addresses the harms directly: a narrowly structured, nonwaivable peremptory ban on the practice.
While this Article is focused on algorithmic wage discrimination as a labor management practice in “on-demand” or “gig work” sectors, where workers are commonly treated as “independent contractors” without protections, its significance is not limited to that domain. So long as this practice does not run afoul of minimum-wage or antidiscrimination laws, nothing in the laws of work makes this form of digitalized variable pay illegal. As Professor Zephyr Teachout argues, “Uber drivers’ experiences should be understood not as a unique feature of contract work, but as a preview of a new form of wage setting for large employers . . . .” The core motivations of labor platform firms to adopt algorithmic wage discrimination—labor control and wage uncertainty—apply to many other forms of work. Indeed, extant evidence suggests that algorithmic wage discrimination has already seeped into the healthcare and engineering sectors, impacting how porters, nurses, and nurse practitioners are paid. If left unaddressed, the practice will continue to be normalized in other employment sectors, including retail, restaurant, and computer science, producing new cultural norms around compensation for low-wage work. The on-demand sector thus serves as an important and portentous site of forthcoming conflict over longstanding moral and political ideas about work and wages.
| 2023-04-01T00:00:00 |
https://www.columbialawreview.org/content/on-algorithmic-wage-discrimination/
|
[
{
"date": "2023/04/01",
"position": 85,
"query": "AI wages"
},
{
"date": "2024/07/01",
"position": 89,
"query": "AI wages"
},
{
"date": "2024/09/01",
"position": 88,
"query": "AI wages"
}
] |
|
Upwork: Highly productive AI employees are more likely to be burnt ...
|
Upwork: Highly productive AI employees are more likely to be burnt out
|
https://www.unleash.ai
|
[
"Lucy Buchholz",
"Senior Journalist",
"Lucy Buchholz Is An Experienced Business Reporter",
"She Can Be Reached At Lucy.Buchholz Unleash.Ai."
] |
AI has generated a 40% increase in productivity, which is largely down to employees having the time to experiment with the tool (30%), continued ...
|
Trust is imperative to the success of any organization – but as AI is becoming more integrated in the modern-day workplace, is it starting to hold more value than human colleagues?
According to Upwork’s study From Tools to Teammates: Navigating the New Human-AI Relationship, 64% of full-time employees who report the highest productivity gains from AI say they have a better relationship with AI than with their human colleagues. The survey included 2,500 global workers and C-suite executives
Additionally, more than two-thirds of high-performing AI users admitted to trusting AI more than their co-workers.
High-performing AI users state that this is because “AI is more polite and empathetic” than their colleagues, causing them to re-evaluate what “teamwork” looks like.
To discover how this is impacting the workplace, UNLEASH spoke exclusively to Dr. Gabby Burlacu, Senior Research Manager at the Upwork Research Institute.
The pros and cons of AI’s productivity boosts
AI has generated a 40% increase in productivity, which is largely down to employees having the time to experiment with the tool (30%), continued product enhancements (25%), self-directed upskilling (22%), and employer-supported training (22%).
As a result, more than three-quarters (77%) of C-suite leaders report noticing these productivity gains.
However, 88% of employees who experience a productivity gain through AI also state that they are burnout.
This has caused many to feel disconnected, with 62% unable to see how the daily use of AI aligns with their company goal. As a result, these employees were found to be more likely to quiet quit than less productive workers who do not use AI regularly.
“AI is delivering real productivity gains, but our research reveals a more complex picture under the surface,” Dr Burlacu explains.
We found that workers who are highly productive with AI are also the most likely to report signs of burnout and disconnection. This tells us that the conversation can’t stop at efficiency.”
As a result, Dr Burlacu urges leaders to “rethink” how teams and metrics are designed for success in an AI-augmented environment.
She continues: “Productivity and wellbeing can’t be treated as trade-offs. Organizations that thrive in this new era will be the ones that take a more holistic approach to AI adoption, one that prioritizes sustainable performance and human connection.”
Dr Burlacu continues to explain how AI is helping freelancers thrive, with nine in 10 reporting that the technology has had a positive impact on their work and 90% saying it helps them learn new skills.
“One area we’re watching closely is how freelancers are adapting to AI,” Dr Burlacu shares. “Many are using it to augment their skill sets and manage their workloads more effectively, often with greater autonomy and flexibility than traditional employees.
| 2025-07-09T00:00:00 |
2025/07/09
|
https://www.unleash.ai/artificial-intelligence/upwork-highly-productive-ai-employees-are-more-likely-to-be-burnt-out/
|
[
{
"date": "2023/04/01",
"position": 31,
"query": "AI workers"
},
{
"date": "2023/04/01",
"position": 37,
"query": "future of work AI"
},
{
"date": "2023/05/01",
"position": 32,
"query": "AI workers"
},
{
"date": "2023/06/01",
"position": 33,
"query": "AI workers"
},
{
"date": "2023/09/01",
"position": 29,
"query": "AI workers"
},
{
"date": "2023/10/01",
"position": 11,
"query": "AI workers"
},
{
"date": "2023/11/01",
"position": 32,
"query": "AI workers"
},
{
"date": "2024/01/01",
"position": 29,
"query": "AI workers"
},
{
"date": "2024/02/01",
"position": 32,
"query": "AI workers"
},
{
"date": "2024/03/01",
"position": 30,
"query": "AI workers"
},
{
"date": "2024/03/01",
"position": 69,
"query": "artificial intelligence workers"
},
{
"date": "2024/05/01",
"position": 32,
"query": "AI workers"
},
{
"date": "2024/06/01",
"position": 33,
"query": "AI workers"
},
{
"date": "2024/11/01",
"position": 30,
"query": "AI workers"
},
{
"date": "2024/12/01",
"position": 31,
"query": "AI workers"
},
{
"date": "2025/02/01",
"position": 40,
"query": "AI workers"
},
{
"date": "2025/05/01",
"position": 40,
"query": "AI workers"
}
] |
Community Providers: Cloudflare Workers AI - AI SDK
|
Cloudflare Workers AI
|
https://ai-sdk.dev
|
[] |
Cloudflare Workers AI workers-ai-provider is a community provider that allows you to use Cloudflare's Workers AI models with the AI SDK.
|
Community Providers Cloudflare Workers AI
workers-ai-provider is a community provider that allows you to use Cloudflare's Workers AI models with the AI SDK.
The Cloudflare Workers AI provider is available in the workers-ai-provider module. You can install it with:
pnpm npm yarn pnpm add workers-ai-provider
Then, setup an AI binding in your Cloudflare Workers project wrangler.toml file:
wrangler.toml [ ai ] binding = "AI"
To create a workersai provider instance, use the createWorkersAI function, passing in the AI binding as an option:
import { createWorkersAI } from 'workers-ai-provider' ; const workersai = createWorkersAI ( { binding : env . AI } ) ;
To create a model instance, call the provider instance and specify the model you would like to use as the first argument. You can also pass additional settings in the second argument:
import { createWorkersAI } from 'workers-ai-provider' ; const workersai = createWorkersAI ( { binding : env . AI } ) ; const model = workersai ( '@cf/meta/llama-3.1-8b-instruct' , { safePrompt : true , } ) ;
You can use the following optional settings to customize:
safePrompt boolean Whether to inject a safety prompt before all conversations. Defaults to false
You can use Cloudflare Workers AI language models to generate text with the generateText or streamText function:
import { createWorkersAI } from 'workers-ai-provider' ; import { generateText } from 'ai' ; type Env = { AI : Ai ; } ; export default { async fetch ( _ : Request , env : Env ) { const workersai = createWorkersAI ( { binding : env . AI } ) ; const result = await generateText ( { model : workersai ( '@cf/meta/llama-2-7b-chat-int8' ) , prompt : 'Write a 50-word essay about hello world.' , } ) ; return new Response ( result . text ) ; } , } ;
import { createWorkersAI } from 'workers-ai-provider' ; import { streamText } from 'ai' ; type Env = { AI : Ai ; } ; export default { async fetch ( _ : Request , env : Env ) { const workersai = createWorkersAI ( { binding : env . AI } ) ; const result = streamText ( { model : workersai ( '@cf/meta/llama-2-7b-chat-int8' ) , prompt : 'Write a 50-word essay about hello world.' , } ) ; return result . toTextStreamResponse ( { headers : { 'Content-Type' : 'text/x-unknown' , 'content-encoding' : 'identity' , 'transfer-encoding' : 'chunked' , } , } ) ; } , } ;
Some Cloudflare Workers AI language models can also be used with the generateObject function:
| 2023-04-01T00:00:00 |
https://ai-sdk.dev/providers/community-providers/cloudflare-workers-ai
|
[
{
"date": "2023/04/01",
"position": 34,
"query": "AI workers"
},
{
"date": "2023/05/01",
"position": 35,
"query": "AI workers"
},
{
"date": "2023/06/01",
"position": 35,
"query": "AI workers"
},
{
"date": "2023/10/01",
"position": 61,
"query": "AI workers"
},
{
"date": "2023/11/01",
"position": 35,
"query": "AI workers"
},
{
"date": "2024/02/01",
"position": 34,
"query": "AI workers"
},
{
"date": "2024/05/01",
"position": 35,
"query": "AI workers"
},
{
"date": "2024/06/01",
"position": 38,
"query": "AI workers"
},
{
"date": "2024/12/01",
"position": 34,
"query": "AI workers"
}
] |
|
Despite Enthusiasm, Employees Underwhelmed About AI's Potential
|
Despite Enthusiasm, Employees Underwhelmed About AI’s Potential
|
https://www.planadviser.com
|
[
"Yasin Mohamud"
] |
According to two different surveys, employers must go beyond just providing access to AI and do more to train their employees.
|
Even with widespread eagerness about the potential for artificial intelligence to have a positive impact on workers’ productivity, most employees believe “they were overpromised on its potential,” according to a new report from cloud communications and IT company GoTo.
“The Pulse of Work in 2025: Trends, Truths, and the Practicality of AI”, completed in partnership with research firm Workplace Intelligence, found that 62% of workers believe there has been too much hype around AI. The report examined the findings of a survey of 2,500 global employees and IT leaders about AI use and sentiment.
Employees’ feelings about the “overhype” around AI is likely because they are not prepared for “making the most of what these tools have to offer,” according to the report. Most of the respondents (86%) admitted to not using AI tools to their full potential and not being very familiar with how they can deploy them in their daily tasks (82%).
Employees also said they estimate spending 2.6 hours per day (13 hours per week) on tasks that AI could do. This means that in the U.S. alone, businesses are potentially not taking advantage of more than $2.9 trillion annually in efficiency, according to the report.
Although many workers recognize AI’s value, they still feel underwhelmed by “the revolutionary change they were promised,” said Rich Veldran, GoTo’s CEO, in a statement.
“The solution is clear: companies must go beyond just providing access to AI by ensuring employees have both the right tools and the right education,” said Veldran in the statement, noting that in practice, this means teams should be equipped with effective training and clear guidelines.
Use and Misuse
According to the report, employees are already using AI for some tasks, just not the ones for which their managers believe they are using. Instead of using the tool as a time-saver, 54% of employees reported that they’ve used it for “sensitive tasks” or “high-stakes decision-making.”
These tasks include ones that require emotional intelligence (29%), tasks impacting safety (26%) and ethical or sensitive personnel actions (16%). When prompted if they regret using AI for these tasks, 77% of workers said they did not.
The survey also found mistrust of the tools among employees: 86% of workers said they are not confident in its accuracy and reliability, and 76% reported that AI often produces outputs that need to be revised by users.
Predictably, when it comes to who is at the forefront of AI use, smaller companies are already behind. At the smallest companies (50 employees or fewer), just 59% of workers use AI and 46% said they do not know how to use it to save time or improve their work, according to the report.
‘Proficiency Has Flatlined’
Although enterprises are investing in AI, Section Inc.—a company dedicated to AI transformation and upskilling—found that workforce proficiency “is still in neutral,” raising reservations about return on investment.
According to the report, since September 2024, employees’ general AI proficiency has “flatlined,” with only 10% of the workforce scoring as AI-proficient.
The report’s survey examined 5,013 knowledge workers across the U.S., U.K. and Canada, including individual contributors and C-suite executives, measuring workers’ knowledge, usage and skill with generative AI tools.
A key reason for the lack of proficiency in enterprise organizations is because there is a lack of “wide-spread deployment,” according to Greg Shove, Section’s CEO.
“Our research echoes what we hear from enterprise organizations: they’ve rolled out ChatGPT to leadership or a few groups and stopped there,” Shove said in a statement. “Without widespread deployment, AI vendors will start seeing churn, CEOs will get frustrated by lack of ROI and workers will be left to figure it out for themselves.”
| 2025-07-07T00:00:00 |
2025/07/07
|
https://www.planadviser.com/despite-enthusiasm-employees-underwhelmed-ais-potential/
|
[
{
"date": "2023/04/01",
"position": 54,
"query": "AI workers"
},
{
"date": "2023/05/01",
"position": 52,
"query": "AI workers"
},
{
"date": "2023/06/01",
"position": 52,
"query": "AI workers"
},
{
"date": "2023/09/01",
"position": 50,
"query": "AI workers"
},
{
"date": "2023/10/01",
"position": 23,
"query": "AI workers"
},
{
"date": "2023/11/01",
"position": 51,
"query": "AI workers"
},
{
"date": "2024/01/01",
"position": 50,
"query": "AI workers"
},
{
"date": "2024/02/01",
"position": 51,
"query": "AI workers"
},
{
"date": "2024/03/01",
"position": 50,
"query": "AI workers"
},
{
"date": "2024/03/01",
"position": 43,
"query": "artificial intelligence workers"
},
{
"date": "2024/05/01",
"position": 51,
"query": "AI workers"
},
{
"date": "2024/06/01",
"position": 57,
"query": "AI workers"
},
{
"date": "2024/11/01",
"position": 50,
"query": "AI workers"
},
{
"date": "2024/12/01",
"position": 50,
"query": "AI workers"
}
] |
AI proposed to replace American workers - Steam Community
|
AI proposed to replace American workers :: Off Topic
|
https://steamcommunity.com
|
[] |
Elon Musk is planning to replace tens of thousands of sacked American workers with artificial intelligence tools, according to US officials.
|
Hozzászólás jelentése
INDOK
Megjegyzés: Ez CSAK spam, hirdetés és problémás (zaklatás, veszekedés vagy illetlenség) bejegyzések bejelentésére használandó.
| 2023-04-01T00:00:00 |
https://steamcommunity.com/discussions/forum/12/595140510531775477/?l=hungarian&ctp=2
|
[
{
"date": "2023/04/01",
"position": 58,
"query": "AI workers"
}
] |
|
AI proposed to replace American workers :: Off Topic
|
AI proposed to replace American workers :: Off Topic
|
https://steamcommunity.com
|
[
"Αναρτήθηκε Αρχικά Από"
] |
Elon Musk is planning to replace tens of thousands of sacked American workers with artificial intelligence tools, according to US officials.
|
Αρχή νέας συζήτησης
Αναφορά ανάρτησης
ΛΟΓΟΣ
Σημείωση: Η αναφορά χρησιμοποιείται ΜΟΝΟ για spam, διαφημίσεις και προβληματικές αναρτήσεις (παρενοχλήσεις, διενέξεις και αγένεια).
| 2023-04-01T00:00:00 |
https://steamcommunity.com/discussions/forum/12/595140510531775477/?l=greek
|
[
{
"date": "2023/04/01",
"position": 71,
"query": "AI workers"
},
{
"date": "2024/01/01",
"position": 38,
"query": "AI replacing workers"
},
{
"date": "2024/03/01",
"position": 38,
"query": "AI replacing workers"
},
{
"date": "2025/05/01",
"position": 34,
"query": "AI replacing workers"
}
] |
|
Leveraging AI for Employee Engagement - SpringerLink
|
Leveraging AI for Employee Engagement
|
https://link.springer.com
|
[
"Majumder",
"Future Institute Of Engineering",
"Misra",
"Techno International New Town",
"Department Of Business Administration",
"Management",
"Kolkata",
"West Bengal",
"Soumi Majumder",
"Department Of Computer Science"
] |
AI has become a game changer in the modern workplace in regard to engagement with employees. Vast amounts of data can be analysed using AI ...
|
AI has become a game changer in the modern workplace in regard to engagement with employees. Vast amounts of data can be analysed using AI technologies such as machine learning algorithms and natural language processing, which can provide valuable information. This allows organisations to gain a better understanding of the behaviour, preferences and needs of their staff. Using artificial intelligence, organisations can identify patterns, anticipate outcomes and adapt their engagement strategies according to them (Hughes et al. in Managing technology and middle-and low-skilled employees. Emerald Publishing Limited, pp. 61–68, 2019 [1]). The impact of AI on employees is multifaceted, ushering in transformative changes across various aspects of the professional landscape. First, AI has substantially altered the way natural language processing is carried out at work through automated tasks that allow workers to focus their attention on more complicated and exciting aspects of their jobs (Lourens et al. in Role of artificial intelligence in formative employee engagement. IEEE, pp. 936–941, 2022 [2]). As employees undertake higher value tasks, this transition improves job satisfaction and gives them a chance to improve their skills.
| 2025-07-14T00:00:00 |
2025/07/14
|
https://link.springer.com/chapter/10.1007/978-981-96-4496-4_7
|
[
{
"date": "2023/04/01",
"position": 74,
"query": "AI workers"
},
{
"date": "2023/05/01",
"position": 69,
"query": "AI workers"
},
{
"date": "2023/06/01",
"position": 69,
"query": "AI workers"
},
{
"date": "2023/09/01",
"position": 73,
"query": "AI workers"
},
{
"date": "2023/10/01",
"position": 92,
"query": "AI workers"
},
{
"date": "2023/11/01",
"position": 68,
"query": "AI workers"
},
{
"date": "2024/01/01",
"position": 73,
"query": "AI workers"
},
{
"date": "2024/02/01",
"position": 67,
"query": "AI workers"
},
{
"date": "2024/03/01",
"position": 74,
"query": "AI workers"
},
{
"date": "2024/05/01",
"position": 69,
"query": "AI workers"
},
{
"date": "2024/06/01",
"position": 74,
"query": "AI workers"
},
{
"date": "2024/11/01",
"position": 72,
"query": "AI workers"
},
{
"date": "2024/12/01",
"position": 68,
"query": "AI workers"
}
] |
Create Your Own AI Employee in Just 5 Minutes (No Code)
|
Create Your Own AI Employee in Just 5 Minutes (No Code)
|
https://www.geeky-gadgets.com
|
[
"Julian Horsey"
] |
Discover how to create an AI employee in just 5 minutes using tools like Zapier and Replit. Automate tasks and transform your workflows ...
|
Imagine having an employee who never sleeps, never takes a coffee break, and can handle repetitive tasks with flawless precision—all set up in just five minutes. It might sound like a scene from a sci-fi movie, but thanks to advancements in AI and automation platforms, this is no longer a distant dream. Tools like Zapier, Replit, and Cursor 1.2 are transforming how we approach work, making it possible to create an AI-powered “employee” that can manage customer inquiries, streamline workflows, and even develop apps—all without requiring a degree in computer science. The question is no longer whether AI can integrate into your daily operations, but how quickly you can make it happen. And the answer? Faster than you might think.
In this guide by Creator Magic, discover the surprisingly simple steps to build your own AI employee, using innovative tools that are as accessible as they are powerful. From automating customer interactions with Zapier to simplifying app development with Replit, you’ll explore how these platforms are breaking down barriers to innovation. Along the way, you’ll discover how AI can take over time-consuming tasks, allowing you to focus on what truly matters—whether that’s scaling your business or reclaiming hours in your day. By the end, you’ll not only understand how to set up your AI assistant but also gain insight into the broader implications of integrating AI into your workflow. After all, the future of work isn’t just coming—it’s already here, waiting for you to take the reins.
AI Automation Made Simple
TL;DR Key Takeaways : AI-powered tools like Zapier and Replit enable the creation of an “AI employee” in just five minutes, automating repetitive tasks and enhancing productivity without requiring technical expertise.
Zapier allows users to design workflows for tasks such as responding to YouTube comments, managing customer inquiries, and maintaining 24/7 engagement through AI-driven automation.
Replit simplifies mobile app development with AI-powered templates and assistants, making app creation accessible to both beginners and experienced developers.
Cursor 1.2 enhances coding efficiency with features like a built-in to-do list and mobile/web capabilities, streamlining workflows for developers and non-coders alike.
AI tools are providing widespread access to technology by using natural language processing (NLP), allowing non-technical users to automate tasks, develop applications, and innovate with minimal barriers to entry.
How Zapier Automates AI Employee Tasks
Zapier has transformed automation by allowing you to design workflows that handle tasks such as responding to YouTube comments or managing customer inquiries. With its latest updates, you can set up an AI “employee” capable of monitoring comments, identifying specific keywords, performing web searches, and generating thoughtful replies. This capability ensures consistent engagement with your audience while saving time and effort.
Here’s how you can set up a workflow using Zapier:
Define a YouTube trigger to detect relevant comments or interactions.
Use AI orchestration to gather information or craft personalized responses.
Integrate APIs to execute the workflow seamlessly and efficiently.
For example, you can configure the AI to answer frequently asked questions, provide humorous replies, or direct users to helpful resources. This automation allows you to maintain 24/7 responsiveness, freeing you to focus on higher-priority tasks without compromising on audience engagement.
Replit: Simplifying Mobile App Development
Replit has made mobile app development accessible to everyone, regardless of their technical background. Its AI-powered templates and assistants enable you to build functional apps quickly and efficiently. For instance, you can create a Tide Times app by selecting a pre-designed template, integrating APIs for real-time data, and refining the design using natural language prompts.
The platform’s AI assistant simplifies the development process by automating repetitive coding tasks and offering suggestions to enhance functionality. Whether you are a beginner or an experienced developer, Replit accelerates the development cycle, allowing you to bring your ideas to life with minimal effort. By reducing the complexity of app creation, Replit enables you to focus on innovation and user experience.
Build an AI Employee in 5 Minutes
Uncover more insights about AI automation in previous articles we have written.
Cursor 1.2: Enhancing AI Coding Efficiency
Cursor 1.2 introduces features designed to optimize coding workflows and minimize errors. A built-in to-do list helps you organize tasks, making sure that no step is overlooked during the development process. Additionally, enhanced mobile and web capabilities allow you to code from virtually anywhere, making it easier to stay productive on the go.
These updates highlight how AI tools are becoming increasingly user-friendly, catering to both professional developers and non-coders. By simplifying complex tasks, Cursor 1.2 enables you to focus on creativity and problem-solving rather than technical details. This tool exemplifies how AI-driven platforms are reshaping the way coding and development are approached, making them more efficient and accessible.
How AI Tools Are Transforming Accessibility
AI tools are rapidly evolving, making automation and development more accessible than ever before. Natural language processing (NLP) allows you to interact with these tools conversationally, significantly reducing the learning curve for non-technical users. For example, you can describe your desired outcome, and the AI will generate the necessary code or workflow to achieve it.
This widespread access of technology enables individuals and businesses to build AI-driven systems without requiring prior technical expertise. Whether it’s automating customer interactions, managing administrative tasks, or developing applications, AI tools are reshaping how problems are solved. By lowering barriers to entry, these tools enable a broader audience to harness the power of AI for innovation and efficiency.
Future Implications of AI in Workflows
The potential of AI to transform workflows is immense. By automating repetitive or time-consuming tasks, AI systems free up your time for strategic decision-making and creative problem-solving. This capability is particularly valuable for businesses looking to scale operations without increasing overhead costs.
As AI tools continue to advance, they will become even more integral to both personal and professional workflows. Embracing these technologies now can help you stay ahead of the curve, unlocking new opportunities for growth and efficiency. By integrating AI into your daily operations, you can position yourself or your business to thrive in an increasingly automated world.
Media Credit: Creator Magic
Latest Geeky Gadgets Deals
Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy
| 2025-07-06T00:00:00 |
2025/07/06
|
https://www.geeky-gadgets.com/create-your-own-ai-employee-in-just-5-minutes-no-code/
|
[
{
"date": "2023/04/01",
"position": 75,
"query": "AI workers"
},
{
"date": "2023/05/01",
"position": 71,
"query": "AI workers"
},
{
"date": "2023/06/01",
"position": 71,
"query": "AI workers"
},
{
"date": "2023/10/01",
"position": 60,
"query": "AI workers"
},
{
"date": "2023/11/01",
"position": 69,
"query": "AI workers"
},
{
"date": "2024/02/01",
"position": 68,
"query": "AI workers"
},
{
"date": "2024/05/01",
"position": 70,
"query": "AI workers"
},
{
"date": "2024/06/01",
"position": 76,
"query": "AI workers"
},
{
"date": "2024/12/01",
"position": 69,
"query": "AI workers"
}
] |
The personal AI gap: Employees move fast, companies lag behind
|
The personal AI gap: Employees move fast, companies lag behind.
|
https://atos.net
|
[] |
While employees use AI in their personal lives, they often lack access to similar tools at work, creating a technological gap.
|
I spoke to some experts and gathered the following key insights on the topic.
Personal AI gap: A growing divide
AI has become a part of our daily lives, reshaping how we live. Live translations, photo editing and smart message suggestions are just a few examples of AI-powered features available on our mobile phones.
However, in the workplace, this shift introduces both advantages and challenges.
"The upside is that people’s awareness of automation is growing, and adoption becomes natural. Personal AI accelerates the embrace of AI tools," explained Janusz Marcinkowski, digital workplace innovation consultant, Atos.
However, here’s the downside: companies are lagging behind. The AI features available on personal devices are often not reflected in workplace tools. Additionally, many organizations restrict the use of private devices with AI features at work, widening the technological gap.
Bilyana Lyubomirova, the global head of career management at Atos said, "People often use AI tools (breaking the rules) because they optimize their work and offer new creative solutions. Organizations need to focus on balancing innovation with regulation."
The concerns of companies are understandable. Using unauthorized tools poses risks of data leakage or copyright infringement. However, banning these tools is not an effective solution. If AI can help people perform their tasks more quickly or efficiently, they will use it. Excessive control does not eliminate the problem – it merely drives it underground, where companies lose control.
"There is a tendency to forbid what cannot be controlled, but that’s not the solution. The best approach is to help people use AI responsibly," added Date Reitsema, employee experience expert.
Cultural shift: It's leaders, not employees, who block change
A prohibition-based approach does not work, and the rigid approach of companies stifles experimentation. Instead, organizations should create a space for secure and informed use of AI. "Companies often push for innovation without providing the right environment. It is not just about strategy but also about ensuring the right tools and mindset are in place," Lyubomirova added.
Another downside to this is that many good ideas die a quick and quiet death — not implemented, even if they are valuable. This is demotivating for employees. According to Lyubomirova, employees often pay out-of-pocket for tools only to be told they can’t use them due to company policy. This can stifle engagement.
A recent McKinsey report indicates that leaders are unaware of the extent to which employees use AI. ‘’Three times more employees use AI for a third or more of their work than leaders realize. While 92% of companies plan to invest in AI over the next three years, only 1% of leaders believe their organizations have reached maturity in integrating AI into work processes.”
| 2023-04-01T00:00:00 |
https://atos.net/en/blog/the-personal-ai-gap-employees-move-fast-companies-lag-behind
|
[
{
"date": "2023/04/01",
"position": 80,
"query": "AI workers"
},
{
"date": "2023/05/01",
"position": 74,
"query": "AI workers"
},
{
"date": "2023/06/01",
"position": 73,
"query": "AI workers"
},
{
"date": "2023/10/01",
"position": 55,
"query": "AI workers"
},
{
"date": "2023/11/01",
"position": 74,
"query": "AI workers"
},
{
"date": "2024/02/01",
"position": 73,
"query": "AI workers"
},
{
"date": "2024/05/01",
"position": 67,
"query": "AI workers"
},
{
"date": "2024/06/01",
"position": 72,
"query": "AI workers"
},
{
"date": "2024/12/01",
"position": 66,
"query": "AI workers"
}
] |
|
And Why AI Needs to Meet Workers Where They Are | By Kris Ballew
|
The Problem With "Just Ask a Manager" — And Why AI Needs to Meet Workers Where They Are
|
https://www.hospitalitynet.org
|
[] |
In hospitality, knowledge is power—but only if it's accessible. Too often, the people who need critical answers the most—the ones on the ...
|
In hospitality, knowledge is power—but only if it’s accessible. Too often, the people who need critical answers the most—the ones on the floor, in the kitchen, or starting a shift—are the least likely to find them quickly.
They’re told to check a binder that doesn’t exist, log into a portal they’ve never seen, or track down a manager during a rush. It’s not just inefficient—it’s a recipe for inconsistency, frustration, and support for tickets that didn’t need to happen.
Frontline Teams Deserve Instant Access, Not Corporate Runaround
Let’s face it: most hotels and resorts have spent years investing in systems that work well for the back office. But what about the staff who don’t sit at desks, don’t use email, and work across shifts, roles, and even properties?
These team members need fast, clear answers about policies, procedures, schedules, training, benefits, and more. But they don’t want to download five apps or remember six logins.
What they really want is simple:
"How do I request a shift change?"
"Where can I see my training progress?"
"What’s the SOP if a guest refuses housekeeping?"
When that info is hard to find, two things happen:
They ask their manager. Their manager doesn’t know either.
A Smarter Way: AI That Lives Inside the Flow of Work
What we’ve learned at Unifocus is that searching isn’t enough. People don’t want to search—they want answers. And they want them right where the work is happening, not buried in a portal.
That’s why we’re introducing AskAI: an embedded virtual assistant that instantly surfaces knowledge, policies, how-tos, and best practices in natural language. No hunting. No logging in. Just ask.
Here’s what makes it different:
Context-aware : It understands where the employee is (e.g., department, shift, system) and responds accordingly.
: It understands where the employee is (e.g., department, shift, system) and responds accordingly. Natural language : Ask questions like a human, get answers like a human.
: Ask questions like a human, get answers like a human. Multi-language support : So, everyone gets the help they need.
: So, everyone gets the help they need. Secure and controlled: Admins define what’s available and what’s not—it’s your data, on your terms.
AskAI is not some bolt-on chatbot. It’s a deeply integrated virtual assistant that draws from vetted content and FAQs across Unifocus tools—and any client-defined material as well.
From Training to Shift Ops, Knowledge Shouldn’t Be a Bottleneck
We’re seeing AskAI support:
Faster onboarding by answering role-specific questions
Reduced pressure on supervisors and HR
Lower support ticket volume
Increased system adoption because people understand how to use the tools
In one pilot, a property saw 28% fewer manager interruptions during busy check-in periods—just from staff getting faster answers through AskAI.
A Final Word: Empowerment Starts with Access
I’ve spent years building products for hospitality. And one truth keeps showing up: the best service starts with a confident, informed team. But that only happens when the answers are easy to find.
AskAI isn’t about replacing managers or removing human interaction. It’s about removing friction—so your people spend less time hunting for info and more time delivering great guest experiences.
Because in the end, smart tools don’t just help staff work better. They help them feel better. And that changes everything.
About Unifocus
Unifocus is the hospitality industry's most complete labor and operations platform, purpose-built to help hotel teams run leaner, act faster, and improve every shift. With core pillars in Workforce Management, Hotel Operations, and Communications, Unifocus connects planning, execution, and feedback in one seamless system.
Learn more at www.unifocus.com
About Unifocus
Unifocus is the hospitality industry's most complete labor and operations platform, purpose-built to help hotel teams run leaner, act faster, and improve every shift. With core pillars in Workforce Management, Hotel Operations, and Communications, Unifocus connects planning, execution, and feedback in one seamless system.
Learn more at www.unifocus.com
View source
| 2023-04-01T00:00:00 |
https://www.hospitalitynet.org/opinion/4128053.html
|
[
{
"date": "2023/04/01",
"position": 81,
"query": "AI workers"
},
{
"date": "2023/05/01",
"position": 75,
"query": "AI workers"
},
{
"date": "2023/06/01",
"position": 74,
"query": "AI workers"
},
{
"date": "2023/09/01",
"position": 75,
"query": "AI workers"
},
{
"date": "2023/10/01",
"position": 71,
"query": "AI workers"
},
{
"date": "2023/11/01",
"position": 73,
"query": "AI workers"
},
{
"date": "2024/01/01",
"position": 75,
"query": "AI workers"
},
{
"date": "2024/02/01",
"position": 72,
"query": "AI workers"
},
{
"date": "2024/03/01",
"position": 76,
"query": "AI workers"
},
{
"date": "2024/05/01",
"position": 74,
"query": "AI workers"
},
{
"date": "2024/06/01",
"position": 79,
"query": "AI workers"
},
{
"date": "2024/11/01",
"position": 73,
"query": "AI workers"
},
{
"date": "2024/12/01",
"position": 73,
"query": "AI workers"
}
] |
|
Workers absent from government's AI “strategy” - NZCTU
|
Workers absent from government’s AI “strategy”
|
https://union.org.nz
|
[
"Stella Whitfield"
] |
The New Zealand Council of Trade Unions Te Kauae Kaimahi is concerned that the artificial intelligence (AI) “strategy” document released ...
|
The New Zealand Council of Trade Unions Te Kauae Kaimahi is concerned that the artificial intelligence (AI) “strategy” document released today by the Government ignores impacts on working people and replicates the corporate hype of Microsoft and other tech giants.
“It is crucial that no workers are left behind as AI usage increases, and so it is deeply concerning that workers are absent from the document released by the Government today,” said NZCTU President Richard Wagstaff.
“AI technologies do provide opportunities for improving productivity and the quality of service. But this will only happen if workers are actively engaged on the implementation and governance of these technologies.
“Workers also need to be properly trained on how to use AI safely and productively, but the strategy released today fails to set out a coherent plan for achieving this.
“Some workers, particularly in clerical and administrative roles, are at a high risk of being displaced by AI. We need to deliver a just transition for any workers negatively affected by AI by supporting them to retrain and find good work.
“The strategy also skates over the very real risks that AI technologies pose for workers. This includes the severe health and safety risks associated with AI surveillance systems, productivity monitoring, and automated management.
“The “light touch” approach proposed by the Government will do nothing to protect New Zealand workers from the serious risks posed by AI,” said Wagstaff.
| 2025-07-08T00:00:00 |
2025/07/08
|
https://union.org.nz/workers-absent-from-governments-ai-strategy/
|
[
{
"date": "2023/04/01",
"position": 83,
"query": "AI workers"
},
{
"date": "2023/05/01",
"position": 76,
"query": "AI workers"
},
{
"date": "2023/06/01",
"position": 75,
"query": "AI workers"
},
{
"date": "2023/11/01",
"position": 75,
"query": "AI workers"
},
{
"date": "2024/02/01",
"position": 74,
"query": "AI workers"
},
{
"date": "2024/05/01",
"position": 75,
"query": "AI workers"
},
{
"date": "2024/06/01",
"position": 80,
"query": "AI workers"
},
{
"date": "2024/12/01",
"position": 74,
"query": "AI workers"
},
{
"date": "2025/02/01",
"position": 62,
"query": "AI workers"
},
{
"date": "2025/05/01",
"position": 62,
"query": "AI workers"
}
] |
Agentic AI: what HR leaders need to know - Raconteur
|
Agentic AI: what HR leaders need to know
|
https://www.raconteur.net
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Are your employees ready to become 'agent bosses'? Agentic AI systems offer human-like intuition and problem-solving skills, leading many to ...
|
While many organisations are still grappling with the widespread adoption of generative AI, the next leap forward in the technology is coming fast – and it promises to usher in even more disruption for managers and HR departments.
Agentic AI broadly describes enabling AI-powered systems to speak to each other in a common language, so they can execute entire end-to-end processes that require collaboration and the application of logic.
While the parameters and outcomes might be defined by humans, AI ‘agents’ will make decisions and carry out transactions on their own initiative, leading Microsoft to claim employees will effectively be ‘bosses’ commanding a small army of bots. Others predict all-human workforces will soon be consigned to the history books, as smart AI does the grunt work in everything from financial services to retail.
How companies are already using agentic AI
Examples of agentic AI already taking place include mortgage lenders using agents to summarise applicants’ credentials and make recommendations on whether to lend to them; agents monitoring multiple CCTV feeds and deciding when to escalate incidents; and AI ‘personal shoppers’ who find the right online products based on customers’ preferences and budgets.
Documented users of agentic AI include everyone from Renault Group and Freshfields, a law firm, to Bayer and Gymshark. What such agents all have in common is the ability to use tightly honed intuition to improve their decision-making. By bringing together information from different sources and making value judgments on it, they are more like a human than an existing model, such as ChatGPT, which answers more linear questions.
Historically, people have been rewarded for management complexity but we will see a move towards skills-based organisations
“If there’s a piece of work human beings can do, even when it is working across myriad different systems or even physical pieces of paper, agentic gives software agency to do that,” says Prasun Shah, global CTO and AI lead in workforce consulting for PwC.
The logical next step, says Shah, is for such agents to interact more deeply with humans. He predicts the rise of bespoke personal assistants who complete everyday tasks on behalf of employees with minimal input. Shah adds that some businesses are also experimenting with digital twins, which shadow staff in their everyday tasks to learn how to operate on their behalf.
Microsoft, perhaps predictably, is even more evangelical about the technology. Spokespeople for the company have stated that they believe humans will “amplify their impact” and “think like a CEO” by becoming agent-bosses directing AI systems to do their bidding. As one spokesperson put it: “Agent-bosses don’t just do work – they orchestrate it”.
The dangers of moving too fast on agentic AI
Most businesses today are a long way from that point. According to a recent report from Mclean & Company, an HR research consultancy, only 7% of HR leaders globally say their business has a documented AI strategy and many are struggling to work out when and if staff should use the technology. In many cases, piecemeal adoption of AI technology is causing internal schisms.
Despite these issues, agentic AI is on the march. By 2030, says Accenture, more AI agents will be using ERP software than humans. “We are definitely expecting 2026 to be the year of agentic,” says Shah. “But it won’t stop there because models will evolve and you will see a move away from big, fat ERPs and see agentic-led business models emerge. More and more sophisticated AI employees will be created in a way whereby you can assemble and disassemble them to create more complicated AI personas.”
Shah says one of his clients is developing an agentic negotiating panel for its sales teams, where AI plays the role of the procurement professionals they’ll be pitching to, enabling them to hone their strategies with counterparts who react in real time to new information.
There is a danger of moving too fast in this area, however. In early 2024, payment provider Klarna said its new AI-powered assistant was doing the work of 700 customer service staff. In May 2025, it said it was hiring again because customers found the AI interactions unsatisfactory. Deployed correctly, however, the appeal of turbocharging human performance is irresistible. By 2030, a Salesforce study says 80% of business leaders believe they will have an AI agent in their ranks. Salesforce’s UK CEO Zahra Bahrololoumi has said: “I believe business leaders today are the last generation that will lead an all-human workforce.”
The impact of agentic AI on HR
This has multiple implications for HR. Kirk Chang, professor of human resource management and technovation at the University of East London, says human employees will be required to define boundaries, set goals and monitor AI outputs. They will focus on mentoring and innovation, while AI does the analysis in the background.
“To harness agentic AI’s full potential, HR must ask critical questions about ethics, readiness and governance and invest in upskilling and structural adaptation to ensure AI augments rather than undermines core HR functions,” he adds. “With thoughtful implementation and a focus on ethical governance, agentic AI can help HR leaders reimagine their function and drive organisational success.”
With thoughtful implementation, agentic AI can help HR leaders reimagine their function
Chang says HR is a powerful test case for agentic AI since so many of its core functions, including performance management and recruitment, are underpinned by data-driven processes. But HR must also be involved in any agentic AI rollout across the organisation: he advises HR leaders to audit the current readiness of their business to adopt new technology both technically and culturally, to get advice on the new skills and competencies required within HR teams to manage, interpret and oversee AI-driven processes. They should also consider the governance structures needed to monitor AI decisions and intervene when necessary.
Senior leaders, including in the HR function, should practice using AI tools, he adds, since many are less well-versed in the practicalities than their team members. Shah, meanwhile, says it’s time to consider the type of structure and future staffing you’ll need when agentic AI joins the workforce: “When work is taken over by agentic AI, inevitably you will see organisational structures being flattened and the focus will shift to deep specialisms and skills inside an enterprise. Historically, people have been rewarded for management complexity but we will see a move towards skills-based organisations.”
There will be winners and losers in the agentic AI revolution and there are plenty of different predictions for what happens to human workforces if adoption becomes widespread. Whether we will all be commanders of our own agentic armies very much remains to be seen.
| 2025-07-07T00:00:00 |
2025/07/07
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https://www.raconteur.net/talent-culture/agentic-ai-hr-leaders
|
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"date": "2024/12/01",
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AI is turbocharging worker productivity but it's also wreaking havoc ...
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AI is turbocharging worker productivity but it’s also wreaking havoc on their mental health
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https://fortune.com
|
[
"Brit Morse"
] |
But as workers rely more heavily on AI, new research highlights the potential impacts on their mental health. Employees that take advantage of ...
|
Good morning!
Companies are eager for employees to use AI at work, and multiple reports and case studies point to the productivity gains that come from adopting the technology. But as workers rely more heavily on AI, new research highlights the potential impacts on their mental health.
Employees that take advantage of the latest AI tools reported a 40% boost in their productivity, according to a new report from freelancing platform Upwork. C-suite leaders, for their part, are also noticing a difference—around 77% say they’ve observed productivity gains from AI adoption.
But even though workers are getting more done, they’re paying a high emotional toll. The majority (88%) of the most productive AI-enabled workers report feelings of burnout due to an increase in workload. And as a result, these workers are twice as likely to consider quitting their jobs, the report finds.
“That’s a flashing red light for CHROs: Productivity gains can disappear overnight if high performers walk out the door,” Kelly Monahan, Ph.D and managing director of Upwork’s research institute tells Fortune.
AI in the workplace is having other effects on workers than just burnout. Around 62% of workers say they’re not clear on how the technology contributes to their company’s bottom line. And more than two-thirds of employees who use AI the most say they have better relationships with it than their human colleagues, contributing to a sense of disconnect and alienation.
AI isn’t just changing what people do in the workplace, says Monahan. It’s changing the “social architecture” of the office.
“AI is increasingly becoming a teammate, and no longer just a background tool,” she says. “Forward-looking CHROs are redesigning workflows, roles, and even career paths, so that humans and AI agents complement, rather than cannibalize, each other’s strengths.”
Brit Morse
[email protected]
Around the Table
A round-up of the most important HR headlines.
Will AI lead to job cuts for the oldest or youngest generations? Experts weigh in on the potential outcomes. New York Times
People managers now oversee about twice as many workers as just five years ago, a new analysis finds. Axios
In an attempt to lure more shoppers, Amazon is turning its usual Prime Day of deals into an entire week, doubling the length of this year’s promotion. Wall Street Journal
Watercooler
Everything you need to know from Fortune.
AI in the classroom. As the new technology becomes more commonplace in higher education, students are raising concerns about professors’ use of it. —Beatrice Nolan
Holding back. Melinda French Gates may be one of the wealthiest women in the world and yet she’s not writing checks for her daughter’s new startup. —Orianna Rosa Royle
Helping out. Amazon is reportedly asking some workers to volunteer to assist with orders on Prime Day by asking office workers to help pick and pack orders. —Chris Morris
| 2025-07-09T00:00:00 |
2025/07/09
|
https://fortune.com/2025/07/09/ai-turbocharging-worker-productivity-wreaking-havoc-on-mental-health/
|
[
{
"date": "2023/04/01",
"position": 86,
"query": "AI workers"
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{
"date": "2023/04/01",
"position": 97,
"query": "future of work AI"
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{
"date": "2023/05/01",
"position": 81,
"query": "AI workers"
},
{
"date": "2023/06/01",
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{
"date": "2023/09/01",
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{
"date": "2024/01/01",
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{
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},
{
"date": "2024/03/01",
"position": 81,
"query": "AI workers"
},
{
"date": "2024/05/01",
"position": 83,
"query": "AI workers"
},
{
"date": "2024/06/01",
"position": 88,
"query": "AI workers"
},
{
"date": "2024/11/01",
"position": 79,
"query": "AI workers"
},
{
"date": "2024/12/01",
"position": 81,
"query": "AI workers"
},
{
"date": "2025/02/01",
"position": 79,
"query": "AI workers"
},
{
"date": "2025/05/01",
"position": 79,
"query": "AI workers"
}
] |
AI-Powered Analytics Uncover Hidden Patterns in Employee ...
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AI-Powered Analytics Uncover Hidden Patterns in Employee Engagement Data
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https://link.springer.com
|
[
"Majumder",
"Future Institute Of Engineering",
"Misra",
"Techno International New Town",
"Department Of Business Administration",
"Management",
"Kolkata",
"West Bengal",
"Soumi Majumder",
"Department Of Computer Science"
] |
It will help to understand the experiences and emotions of workers more deeply by analysing anonymised survey responses, identifying employees' ...
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Jha, N., Sareen, P., & Potnuru, R. K. G. (2019). Employee engagement for millennials: Considering technology as an enabler. Development and Learning in Organisations: An International Journal, 33(1), 9–11.
Garg, R., Kiwelekar, A. W., Netak, L. D., & Ghodake, A. (2021). i-Pulse: A NLP based novel approach for employee engagement in logistics organisation. International Journal of Information Management Data Insights, 1(1), 100011.
Golestani, A., Masli, M., Shami, N. S., Jones, J., Menon, A., & Mondal, J. (2018, December). Real-time prediction of employee engagement using social media and text mining. In 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 1383–1387). IEEE.
Nimmagadda, S., Surapaneni, R. K., & Potluri, R. M. (2024). Artificial intelligence in HR: Employee engagement using chatbots. In Artificial intelligence enabled management: An emerging economy perspective (p. 147).
Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093–1113.
Taboada, M. (2016). Sentiment analysis: An overview from linguistics. Annual Review of Linguistics, 2, 325–347.
Pasat, A., Birdici, A., & Pop, I. (2021). An internship campaign case study showing results of enhanced recruitment processes using NLP. In The International Scientific Conference eLearning and Software for Education (Vol. 2, pp. 222–231). “Carol I” National Defence University.
Strang, K. D., & Sun, Z. (2022). ERP staff versus AI recruitment with employment real-time big data. Discover Artificial Intelligence, 2(1), 21.
Laumer, S., & Morana, S. (2022). HR natural language processing—Conceptual overview and state of the art on conversational agents in human resources management. In Handbook of research on artificial intelligence in human resource management (pp. 226–242).
Hall, A. N. (2020). Predicting employee engagement: Machine learning applications to the personality-engagement link [Doctoral dissertation], Northwestern University.
Koo, B., Curtis, C., & Ryan, B. (2021). Examining the impact of artificial intelligence on hotel employees through job insecurity perspectives. International Journal of Hospitality Management, 95, Article 102763.
Dutta, D., Mishra, S. K., & Tyagi, D. (2023). Augmented employee voice and employee engagement using artificial intelligence-enabled chatbots: A field study. The International Journal of Human Resource Management, 34(12), 2451–2480.
Rane, N. (2023). Role and challenges of ChatGPT and similar generative artificial intelligence in human resource management. Available at SSRN 4603230.
Budhwar, P., Chowdhury, S., Wood, G., Aguinis, H., Bamber, G. J., Beltran, J. R., … Varma, A. (2023). Human resource management in the age of generative artificial intelligence: Perspectives and research directions on ChatGPT. Human Resource Management Journal, 33(3), 606–659.
Brynjolfsson, E., Li, D., & Raymond, L. R. (2023). Generative AI at work (No. w31161). National Bureau of Economic Research.
| 2025-07-14T00:00:00 |
2025/07/14
|
https://link.springer.com/chapter/10.1007/978-981-96-4496-4_8
|
[
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"date": "2023/04/01",
"position": 88,
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"query": "AI workers"
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{
"date": "2023/05/01",
"position": 77,
"query": "artificial intelligence employment"
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{
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{
"date": "2024/05/01",
"position": 82,
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},
{
"date": "2024/06/01",
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{
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{
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{
"date": "2024/10/01",
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{
"date": "2024/11/01",
"position": 69,
"query": "artificial intelligence employment"
},
{
"date": "2024/12/01",
"position": 80,
"query": "AI workers"
}
] |
Algorithmic Management: The Role of AI in ...
|
Algorithmic Management: The Role of AI in Managing Workforces
|
https://sloanreview.mit.edu
|
[
"Mohammad Hossein Jarrahi",
"Mareike Möhlmann",
"Min Kyung Lee",
"Massachusetts Institute Of Technology",
"About The Authors"
] |
by MH Jarrahi · 2023 · Cited by 38 — Algorithmic management promises to make work processes more effective and efficient. For example, algorithms can speed up hiring by filtering through large ...
|
Carolyn Geason-Beissel/MIT SMR | Getty Images
With the help of digital technology, complex managerial tasks, such as the supervision of employees and assessment of job candidates, can now be taken over by machines. While still in its early stages, algorithmic management — the delegation of managerial functions to algorithms in an organization — is becoming a key part of AI-driven digital transformation in companies.
Algorithmic management promises to make work processes more effective and efficient. For example, algorithms can speed up hiring by filtering through large quantities of applicants at relatively low costs.1 Algorithmic management systems can also allow companies to understand or monitor employee productivity and performance.2 However, ethical challenges and potential negative downsides for employees must be considered when implementing algorithmic management. In the case of hiring, AI-enabled tools have faced heavy criticism due to harmful biases that can disfavor various groups of people, resulting in efforts to create guidelines and regulations for ethical AI design.
In this article, we build on our years of research on algorithmic management and focus on how it transforms management practices by automating repetitive tasks and enhancing the role of managers as coordinators and decision makers. However, the introduction of algorithms into management functions has the potential to alter power dynamics within organizations, and ethical challenges must be addressed. Here we offer recommendations for how managers can approach implementation using new skill sets.
Profit From Scale and Efficiency While Improving Workforce Well-Being
Algorithms can enhance the scale and efficiency of management operations. In the gig economy, algorithmic systems coordinate and organize work at an unprecedented scale — think about the number of matching riders and drivers using Uber or Lyft at any one time across the globe. Likewise, standards organizations have already taken advantage of the increased accuracy of algorithmic processing to manage both tasks and workers. UPS equips trucks with sensors that monitor drivers’ every move to increase efficiency. Similarly, Amazon heavily relies on algorithms to track workers’ productivity and even generate the paperwork for terminating employment if they fail to meet targets.
However, our research suggests that focusing solely on efficiency can lower employee satisfaction and performance over the long term by treating workers like mere programmable “cogs in a machine.�
About the Authors Mohammad Hossein Jarrahi is an associate professor at the UNC School of Information and Library Science. Mareike Möhlmann is assistant professor in the Information and Process Management department at Bentley University. Min Kyung Lee is an assistant professor in the School of Information at the University of Texas at Austin.
References 1. U. Leicht-Deobald, T. Busch, C. Schank, et al., “The Challenges of Algorithm-Based HR Decision-Making for Personal Integrity,” Journal of Business Ethics 160, no. 2 (December 2019): 377-392. 2. M. Möhlmann, L. Zalmanson, O. Henfridsson, et al., “Algorithmic Management of Work on Online Labor Platforms: When Matching Meets Control,” MIS Quarterly 45, no. 4 (December 2020): 1999-2022. 3. M.K. Lee, “Understanding Perception of Algorithmic Decisions: Fairness, Trust, and Emotion in Response to Algorithmic Management,” Big Data & Society 5, no. 1 (January-June 2018). 4. M.H. Jarrahi, G. Newlands, M.K. Lee, et al. “Algorithmic Management in a Work Context,” Big Data & Society 8, no. 2 (July 2021). 5. M. Möhlmann and O. Henfridsson, “What People Hate About Being Managed by Algorithms, According to a Study of Uber Drivers,” Harvard Business Review, Aug. 30, 2019, https://hbr.org. 6. A. Zhang, A. Boltz, C.W. Wang, et al., “Algorithmic Management Reimagined for Workers and by Workers: Centering Worker Well-Being in Gig Work,” CHI Conference on Human Factors in Computing Systems, April 29-May 5. 2022: 1-20. 7. M.H. Jarrahi, “Artificial Intelligence and the Future of Work: Human-AI Symbiosis in Organizational Decision-Making,” Business Horizons 61, no. 4 (July-August 2018): 577-586. 8. M.H. Jarrahi, S. Kenyon, A. Brown, et al., “Artificial Intelligence: A Strategy to Harness Its Power Through Organizational Learning,” Journal of Business Strategy, forthcoming. 9. P.R. Daugherty and H. James Wilson, “Human + Machine: Reimagining Work in the Age of AI” (Cambridge, Massachusetts: Harvard Business Press, 2018). 10. R. Courtland, “Bias Detectives: The Researchers Striving to Make Algorithms Fair,” Nature, June 20, 2018, www.nature.com. 11. M. Möhlmann, C. Salge, and M. Marabelli, “Algorithm Sensemaking: How Platform Workers Make Sense of Algorithmic Management,” Journal of the Association for Information Systems 24, no. 1 (May 2022). 12. K. Martin, “Ethical Implications and Accountability of Algorithms,” Journal of Business Ethics 160 (December 2019): 835-850. 13. Möhlmann, “What People Hate About Being Managed.”
| 2023-04-05T00:00:00 |
2023/04/05
|
https://sloanreview.mit.edu/article/algorithmic-management-the-role-of-ai-in-managing-workforces/
|
[
{
"date": "2023/04/01",
"position": 15,
"query": "AI workforce transformation"
}
] |
A Pathway to Achieving Industry 5.0 Sustainability Goals
|
Generative Artificial Intelligence in Adaptive Social Manufacturing: A Pathway to Achieving Industry 5.0 Sustainability Goals
|
https://www.mdpi.com
|
[
"Jourabchi Amirkhizi",
"Pedrammehr",
"Pakzad",
"Shahhoseini",
"Parisa Jourabchi Amirkhizi",
"Siamak Pedrammehr",
"Sajjad Pakzad",
"Ahad Shahhoseini"
] |
by P Jourabchi Amirkhizi · 2025 · Cited by 3 — ... workforce adaptation, ethical AI governance, and adoption barriers, ultimately facilitating the transition toward Industry 5.0's sustainability goals.
|
Following the thematic analysis and concept mapping using Leximancer, a validation phase was conducted to ensure that the sustainability dimensions were clear, practically relevant, and applicable to adaptive social manufacturing. This phase involved 15 industry experts specializing in advanced manufacturing and sustainability consultancy, all of whom had extensive experience in Industry 5.0-related sustainability practices. The validation process consisted of three structured sessions, each lasting one hour, where experts critically assessed the sustainability dimensions identified through content analysis. In the first session, experts were provided with detailed descriptions of each sustainability dimension, including definitions and conceptual justifications derived from the thematic analysis. They were asked to evaluate the clarity, industry relevance, and practical applicability of each dimension, providing written feedback that was later coded into thematic categories. In the second session, expert feedback from the first session was analyzed using open and axial coding, identifying recurring themes and concerns related to overlapping, ambiguous, or redundant dimensions. Experts were then engaged in a structured discussion, where they collectively assessed dimensions that needed refinement, merging, or redefinition. Through this collaborative process, some sustainability dimensions were consolidated to remove redundancies, while others were expanded or reworded to improve conceptual clarity. In the third and final session, experts reviewed the revised sustainability dimensions and provided quantitative validation through a five-point Likert scale, rating the importance and clarity of each dimension. To ensure inter-expert agreement, a Cohen’s Kappa test was conducted, yielding a high consensus score (Kappa = 0.80), indicating strong alignment among experts. The final validated framework consisted of nine sustainability dimensions, which had been refined through expert-driven evaluation and systematic feedback analysis. This validation process ensured that the finalized sustainability dimensions were both theoretically sound and practically applicable, providing a robust foundation for assessing sustainability in Industry 5.0. The validated framework reflects expert consensus, making it a reliable reference for sustainable industrial practices within adaptive social manufacturing ( Table 2 ).
The analysis identified nine primary themes, each representing a distinct aspect of sustainability within Industry 5.0. Environmental sustainability, addressed in 25 articles, emphasizes carbon emission reduction, resource recycling, reuse, and supply chain management, underscoring Industry 5.0’s commitment to minimizing environmental impact. Social sustainability, supported by 17 articles, focuses on enhancing employee welfare, promoting social responsibility, and supporting community development. Economic sustainability, noted in 10 articles, emphasizes reducing costs, increasing productivity, and fostering flexible business models. Ethical sustainability, examined in 10 articles, addresses equitable resource distribution and transparency in decision making. Technological sustainability, referenced in eight articles, highlights the role of advanced technologies, such as the Internet of Things and big data, in enhancing efficiency and reducing environmental impact. Cultural sustainability, covered in eight articles, emphasizes preserving cultural identity and local values within production processes. Supply chain sustainability, discussed in eight articles, focuses on transparency within supply chains and managing product life cycles. Human sustainability, examined in eight articles, pertains to improving employee health and safety and creating secure work environments. Lastly, managerial sustainability emerges in 10 articles, highlighting the development of sustainable management strategies and international cooperation across supply chains. To examine the interactions and relationships among these themes, the Leximancer software (LexiDesktop5) was employed, producing a concept map based on term frequency and co-occurrence analysis. The resulting map, shown in Figure 2 , visually illustrates the primary clusters identified, such as Social Welfare and Inclusivity, Ethical AI Integration, and Environmental Efficiency. The map also highlights a strong link between environmental sustainability and technological sustainability, underscoring the importance of green and intelligent technologies in Industry 5.0 for minimizing environmental impact.
The purpose of this content analysis was to identify the sustainability dimensions that uniquely characterize Industry 5.0, setting it apart from the efficiency-focused paradigm of Industry 4.0. Through a rigorous thematic analysis of selected scholarly articles, this study aimed to reveal the specific sustainability goals that align with Industry 5.0’s human-centric, ethical, and ecological principles. Utilizing Leximancer for concept mapping, this analysis offers a structured and objective view of the sustainability themes essential to Industry 5.0’s framework. The content analysis began with a systematic literature review conducted across major academic databases, including Scopus and Web of Science, with a focus on peer-reviewed publications from the past seven years. A total of 104 articles were selected based on search terms explicitly related to Industry 5.0 and sustainability, such as “Industry 5.0 sustainability goals”, “human-centric manufacturing”, and “ethical AI in manufacturing”. Each article was reviewed for relevance to Industry 5.0’s distinct sustainability framework.
Predictive Maintenance (PM): This uses real-time data from sensors and IoT devices to monitor the condition of equipment and predict potential failures. By analyzing patterns in equipment behavior, it identifies anomalies and forecasts maintenance needs, reducing unplanned downtime and preventing catastrophic failures. PM extends the equipment lifespan and reduces material waste, aligning with environmental sustainability. It also minimizes costs, contributes to economic sustainability, and supports managerial sustainability by improving operational planning.
By integrating a multi-stage expert discussion approach, coupled with qualitative thematic coding and quantitative statistical validation, this study ensured that the final 17 AI functions were identified based on scientific rigor, industry consensus, and empirical validation. The expert panel consisted of 130 specialists with 2–10 years of experience in applied AI for industrial sustainability, ensuring a comprehensive and balanced perspective. These experts, actively engaged in GAI-driven solutions for real-world manufacturing environments, provided critical insights into the intersection of GAI, circular economy principles, and adaptive production systems. Their inclusion ensured that the study captured not only the technical potential of AI but also its feasibility and impact on sustainable industrial ecosystems. The recruitment strategy, based on an Australian-based professional forum, ensured that regionally relevant sustainability challenges were incorporated while still benefiting from the global expertise and interdisciplinary knowledge of participants.
The inter-rater agreement results obtained in this study, specifically the Cohen’s Kappa values ranging from 0.625 to 0.733, indicate substantial agreement among expert panels. These values are consistent with findings in the recent literature evaluating expert consensus on AI applications in sustainability-focused domains. For instance, ref. [ 75 ] reported similar Kappa coefficients in assessing GAI’s role in the construction industry using multi-criteria decision-making techniques, reinforcing the validity of structured expert involvement. Additionally, ref. [ 76 ] emphasized the importance of expert evaluations in the sustainable energy sector, where GAI-driven resource optimization required cross-disciplinary validation. These parallels confirm that the levels of agreement in our study align with established practices in AI-related sustainability assessments. Moreover, the observed consistency reflects the robustness of our methodological approach, including structured FGDs and multi-stage validation processes, similar to those advocated in broader Industry 5.0 research [ 77 79 ]. This comparison with the existing literature substantiates the reliability of the findings presented in Table 3 and reinforces their generalizability across industrial contexts.
This study employed a FGD approach with 130 industry experts across three structured three-hour sessions to identify and validate 17 key GAI functions for Industry 5.0 sustainability. In the first session, experts were divided into five subgroups, where they discussed sustainability challenges and the role of AI using digital whiteboards. This was followed by open coding, where AI functions were categorized based on similarities. The second session focused on reviewing and refining the preliminary list, eliminating redundancies, and conducting a five-point Likert scale assessment to evaluate each function’s impact on nine sustainability dimensions. In the final session, aggregated ratings informed the prioritization of 20 AI functions, from which the 17 most impactful and strategically relevant functions were selected through an iterative consensus-building process. To ensure the reliability of expert judgments and inter-group agreement, a Cohen’s Kappa test was conducted, confirming a high level of consensus among expert groups. Additionally, MANOVA was applied to quantify the statistical significance of expert ratings, ensuring that the final selection was not only consensus-driven but also statistically validated. The observed agreement percentages ranged between 85% and 92%, while the Kappa coefficient (κ) values fell between 0.625 and 0.733, indicating substantial inter-rater reliability across all groups. Given that κ values above 0.61 denote substantial agreement, these results confirm the robustness of expert evaluations. The standard error remained below 0.045 in all cases, and 95% confidence intervals further validated the reliability of these findings ( Table 3 ).
5.3. Evaluation Framework and Statistical Analysis
The descriptive statistics in Table 4 assess the performance and variability of 17 GAI functions in adaptive social manufacturing, emphasizing their roles in achieving Industry 5.0 sustainability goals. The mean values reveal the central tendency of expert evaluations, with BMT as the highest-rated function (mean = 3.54), reflecting its strong perceived importance across multiple sustainability dimensions, particularly ethical and managerial sustainability. In contrast, WETP and IBM, with lower mean scores of 3.00 and 2.81, respectively, suggest a more moderate influence in the evaluated contexts.
Variability in ratings, represented by standard deviation (SD), highlights differences in expert opinions. AT and DSCM exhibit high SD values (1.529 and 1.551, respectively), indicating diverse perspectives, possibly due to varying industry applications or levels of familiarity. Conversely, CPSI demonstrates lower variability (SD = 1.126), suggesting a stronger consensus on its impact. These variations in consensus reflect the evolving maturity and contextual relevance of different AI functions in adaptive manufacturing systems. Skewness and kurtosis further refine the analysis by indicating the shape of rating distributions. Negative skewness in functions like ROA (skewness = −0.482) suggests a concentration of higher ratings, reflecting optimism about their sustainability contributions. Conversely, positive skewness, as seen in SSS (skewness = 0.237), indicates a tendency toward lower evaluations. Most kurtosis values hover around 1, denoting moderate peakedness and balanced distributions, reinforcing the reliability of expert assessments. The analysis of individual functions reveals key trends. BMT’s high mean and moderate SD underscore its widely acknowledged role in fostering fairness, inclusivity, and ethical decision making. DSCM, with a balanced mean of 3.09 but high variability, reflects divergent perspectives, likely influenced by sector-specific challenges or varying levels of technological adoption. WETP, despite its moderate mean score, shows high variability (SD = 1.479), suggesting polarized views, potentially due to differences in organizational integration. SMD, with a mean score of 3.12 and low skewness, signifies its specialized but critical role in advancing environmental and technological sustainability through innovative materials research.
The normality tests using the Kolmogorov–Smirnov and Shapiro–Wilk methods ( Table 5 ) revealed that the data did not strictly follow a normal distribution, as indicated by p-values consistently below 0.05 across all variables. This suggests that the assumption of perfect normality, essential for certain parametric analyses, is not fully met. However, skewness and kurtosis values, ranging between −2 and +2, indicate approximate normality, ensuring the robustness of subsequent multivariate analyses. The Kolmogorov–Smirnov test, which assesses the goodness of fit of a sample distribution against normality, consistently rejected the null hypothesis. Similarly, the Shapiro–Wilk test, particularly sensitive to departures from normality in small to medium samples, confirmed this finding. Despite these results, skewness and kurtosis analysis provided additional reassurance about the dataset’s integrity. Negative skewness in variables such as ROA and CPSI suggests a slight bias toward higher ratings, whereas positive skewness in SSS indicates a prevalence of lower scores. These trends are typical in complex, multidimensional datasets, where minor departures from symmetry are common.
p -value below 0.05, confirm statistically significant differences among the AI functions. These findings underscore their diverse roles in achieving Industry 5.0 sustainability objectives, demonstrating tailored impacts on environmental, social, and economic goals. The low Wilks’ Lambda value highlights the strong discriminatory capability of these functions across sustainability metrics, while the F-statistic quantifies their substantial differences. The p-value further reinforces the statistical reliability of these findings, indicating that the observed variations are meaningful rather than incidental. Additional test measures, such as Hotelling’s Trace and Largest Root Effect, provide further granularity. While functions like DSCM and ES exhibit broad impacts across multiple dimensions, others like SSS and CPD show more specialized contributions. This differentiation highlights the strategic alignment of each AI function with specific sustainability goals, offering actionable insights for their prioritization and implementation. The significant MANOVA results reveal systemic interactions between AI functions and sustainability objectives. Functions such as DSCM and ROA emerge as pivotal for enhancing resource efficiency and system resilience in environmental and managerial dimensions. Similarly, BMTs significantly contributes to ethical and social sustainability by promoting fairness and inclusivity in decision making. These findings suggest that generative AI is not merely a collection of isolated technologies but an interconnected framework driving Industry 5.0’s multidimensional sustainability agenda. The MANOVA analysis in Table 6 examines variations in the impacts of the 17 generative AI functions across nine sustainability dimensions. The results, with a Wilks’ Lambda value of 0, an F-statistic of 832.631, and a-value below 0.05, confirm statistically significant differences among the AI functions. These findings underscore their diverse roles in achieving Industry 5.0 sustainability objectives, demonstrating tailored impacts on environmental, social, and economic goals. The low Wilks’ Lambda value highlights the strong discriminatory capability of these functions across sustainability metrics, while the F-statistic quantifies their substantial differences. The p-value further reinforces the statistical reliability of these findings, indicating that the observed variations are meaningful rather than incidental. Additional test measures, such as Hotelling’s Trace and Largest Root Effect, provide further granularity. While functions like DSCM and ES exhibit broad impacts across multiple dimensions, others like SSS and CPD show more specialized contributions. This differentiation highlights the strategic alignment of each AI function with specific sustainability goals, offering actionable insights for their prioritization and implementation. The significant MANOVA results reveal systemic interactions between AI functions and sustainability objectives. Functions such as DSCM and ROA emerge as pivotal for enhancing resource efficiency and system resilience in environmental and managerial dimensions. Similarly, BMTs significantly contributes to ethical and social sustainability by promoting fairness and inclusivity in decision making. These findings suggest that generative AI is not merely a collection of isolated technologies but an interconnected framework driving Industry 5.0’s multidimensional sustainability agenda.
Correlation analysis further supports these insights by illustrating how GAI functions contribute to different sustainability goals. Functions optimizing supply chains and resource management show strong correlations with environmental sustainability, while those enhancing workforce engagement and fairness align more with social and ethical dimensions. These results highlight the interconnected nature of GAI-driven manufacturing and emphasize the need for a holistic approach to sustainability in Industry 5.0. Moreover, the analysis captures the nuances of GAI integration into adaptive social manufacturing. While high-performing functions contribute broadly across sustainability goals, specialized functions serve niche but essential roles. For example, DSCM enhances supply chain adaptability and environmental efficiency, whereas SSSs focus on workplace safety, aligning with human sustainability. These distinctions underscore the importance of a balanced deployment strategy that integrates both versatile and specialized GAI functions for optimal collective impact.
The MANOVA findings in Table 6 provide a robust statistical foundation for understanding the differential impacts of generative GAI in social manufacturing. Correlation analysis further explores interdependencies, revealing strong positive correlations (r > 0.6) between DSCM, ROAs, and environmental/economic sustainability, while BMTs and CETs exhibit higher correlations (r > 0.5) with ethical and social sustainability. These results reinforce the evaluation findings and offer strategic guidance for aligning AI deployment with Industry 5.0 sustainability objectives. By highlighting the diverse contributions and interactions among GAI technologies, the analysis underscores the critical role of targeted AI integration in fostering resilient, inclusive, and sustainable manufacturing ecosystems.
The Friedman test results in Table 7 provide a robust ranking of the 17 generative AI functions based on their median impact across Industry 5.0’s nine sustainability dimensions. As a non-parametric method, the Friedman test effectively evaluates ordinal data, identifying relative differences in AI contributions to various sustainability goals. The rankings reveal clear distinctions, with DSCM and ES consistently achieving top positions due to their significant influence, particularly in environmental and managerial sustainability. Conversely, functions such as SPT and SSS ranked lower, reflecting their more specialized roles within the broader sustainability framework. DSCM emerged as a standout performer, excelling in supply chain sustainability and environmental efficiency, where its adaptability to dynamic market conditions proved invaluable. Its top rankings across multiple dimensions underscore its systemic importance in sustainability strategies. Similarly, ES demonstrated a high impact in human and social sustainability by optimizing workplace ergonomics and fostering inclusive environments. In contrast, lower-ranked functions like SPT and SSS had narrower applications. While SPT supports long-term strategic planning and resilience building, its influence across cultural or ethical sustainability was limited. SSS, primarily focused on workplace safety, had a localized impact, contributing significantly to human sustainability but offering less value in other dimensions. These findings highlight the differentiated roles of generative AI functions, where some have broad, cross-dimensional impacts while others serve highly specialized purposes.
The Friedman test rankings also revealed key patterns among other AI functions. BMT ranked prominently in ethical and managerial sustainability, emphasizing its role in ensuring fairness and inclusivity in decision making. ROA stood out for balancing environmental and economic objectives, contributing to both efficiency and cost-effectiveness. Meanwhile, PM and CPD ranked moderately, reflecting their contributions to technological and cultural sustainability, respectively. The mean rankings and statistical measures accompanying the Friedman test results further validate these insights. For example, DSCM’s high mean ranking confirms its systemic significance, while the lower mean scores of SPT and SSS reinforce their more targeted applications. From a strategic perspective, the Friedman test results provide actionable guidance for AI deployment in adaptive social manufacturing. High-ranking functions like DSCM and ES should be prioritized for their broad-based impacts, while lower-ranked yet specialized functions such as SPT and SSS can be integrated to address specific challenges. This ensures an optimized balance between versatility and niche functionality.
Partial eta squared (η2) values were calculated to measure the effect size for each sustainability dimension. The high η² values (all above 0.7) indicate a substantial influence of sustainability dimensions on GAI effectiveness, highlighting the systemic interplay between technology and sustainability goals. The analysis identified DSCM and ES as the most impactful functions, particularly excelling in environmental and managerial sustainability. These functions significantly enhance resource efficiency, reduce waste, and improve human–machine collaboration. Moderately impactful functions, such as AT, WETP, and AIDSS, were also noted for their contributions to social and human sustainability by fostering collaboration, enhancing decision making, and supporting workforce development, critical elements of Industry 5.0. Conversely, functions such as SSS and CPD ranked lower in overall sustainability impact. While still important, they were considered less versatile compared to top-performing applications. The evaluation framework, which integrated multivariate analysis with expert assessments, effectively quantified the contributions of GAI functions to Industry 5.0’s sustainability dimensions. This comprehensive approach not only prioritized the most impactful functions but also provided insights into their interactions and roles in achieving sustainability objectives. By combining theoretical insights from content analysis and expert interviews with empirical data, the framework offers a robust, data-driven understanding of GAI’s role in sustainable manufacturing. These findings establish a foundation for the next phase of the study, which will explore the dynamic inter-relationships among AI functions using system dynamics modeling. The statistical insights gained here will inform the development of feedback loops, identify leverage points, and guide strategies for optimizing GAI implementation in adaptive social manufacturing. This ensures alignment with Industry 5.0’s overarching goals of sustainability, adaptability, and innovation.
| 2025-04-14T00:00:00 |
2025/04/14
|
https://www.mdpi.com/2227-9717/13/4/1174
|
[
{
"date": "2023/04/01",
"position": 74,
"query": "AI workforce transformation"
}
] |
The Future of Work: How AI is Transforming the Workplace
|
The Future of Work: How AI is Transforming the Workplace
|
https://www.funnelwebboss.com
|
[] |
AI is transforming the workplace in numerous ways, from automation and predictive analytics to personalization, collaboration, and skills development.
|
Artificial intelligence (AI) is transforming the workplace and has the potential to revolutionize the way we work. In this blog article, we'll explore how AI is transforming the future of work and what it means for businesses and employees.
Automation
One of the most significant impacts of AI on the workplace is automation. AI-powered machines and robots are increasingly taking on tasks that were previously performed by humans. For example, factories are using AI-powered robots to assemble products, while retail stores are using self-checkout machines to process purchases. As automation becomes more prevalent, it's expected to replace many jobs, particularly those that involve repetitive tasks.
However, automation doesn't necessarily mean that humans will become obsolete in the workplace. Instead, it's likely that humans will be required to work alongside machines to perform more complex tasks that require human judgement, creativity, and problem-solving skills.
Predictive Analytics
Another area where AI is transforming the workplace is in predictive analytics. AI-powered algorithms can analyze large amounts of data to identify patterns and trends that would be difficult for humans to detect. For example, banks are using predictive analytics to identify customers who are at risk of defaulting on their loans, while retailers are using it to forecast demand for products.
The use of predictive analytics is expected to increase in the coming years, particularly in industries such as healthcare, finance, and retail. By using AI-powered algorithms to identify patterns and trends, businesses can make better decisions, reduce costs, and improve efficiency.
Personalization
AI is also transforming the workplace by enabling greater personalization. AI-powered tools can analyze data about individuals to provide personalized recommendations and experiences. For example, online retailers can use AI to provide personalized product recommendations to customers based on their previous purchases, while healthcare providers can use it to develop personalized treatment plans based on patients' medical histories.
The use of personalization is expected to grow in the coming years, particularly in industries such as healthcare, retail, and marketing. By providing personalized experiences, businesses can improve customer satisfaction and loyalty, leading to increased revenue and growth.
Collaboration
AI is also transforming the way we collaborate in the workplace. AI-powered tools can facilitate communication and collaboration between individuals and teams, regardless of their location. For example, video conferencing tools can allow teams to collaborate in real-time, while project management tools can help teams track progress and communicate about project milestones.
The use of collaboration tools is expected to grow in the coming years, particularly as more businesses adopt remote and hybrid work models. By using AI-powered collaboration tools, businesses can improve efficiency, reduce costs, and improve communication and teamwork among employees.
Skills Development
As AI becomes more prevalent in the workplace, it's expected to change the skills that are required for many jobs. For example, jobs that involve repetitive tasks are likely to be replaced by automation, while jobs that require human judgement, creativity, and problem-solving skills are likely to become more important.
In response, businesses and employees will need to focus on developing new skills that are relevant to the changing workplace. For example, employees will need to develop skills in data analysis, digital communication, and critical thinking, while businesses will need to focus on developing AI-related skills such as programming and machine learning.
Ethical Considerations
As AI becomes more prevalent in the workplace, it's important to consider the ethical implications of its use. For example, AI-powered algorithms may perpetuate biases and discrimination if they are trained on biased data. It's important for businesses to consider the ethical implications of their use of AI and take steps to ensure that their algorithms are fair and unbiased.
Another ethical consideration is the impact of AI on jobs. While automation may lead to job displacement in some industries, it's important for businesses to consider how they can retrain and reskill their workforce to adapt to the changing job market.
conclusion
In conclusion, AI is transforming the workplace in numerous ways, from automation and predictive analytics to personalization, collaboration, and skills development. While the use of AI has the potential to improve efficiency, reduce costs, and improve decision-making, it's important to consider the ethical implications of its use, particularly in terms of bias and job displacement. As AI becomes more prevalent in the workplace, businesses and employees will need to adapt and develop new skills to stay competitive. By embracing AI and taking steps to ensure its ethical use, businesses can create a more productive, efficient, and innovative workplace for the future.
Discover the transformative power of the AI revolution and unlock new opportunities for your business success. Sign up for our newsletter to learn more!
| 2023-04-01T00:00:00 |
https://www.funnelwebboss.com/blog/the-future-of-work-how-ai-is-transforming-the-workplace
|
[
{
"date": "2023/04/01",
"position": 88,
"query": "AI workforce transformation"
}
] |
|
[2304.11771] Generative AI at Work
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[2304.11771] Generative AI at Work
|
https://arxiv.org
|
[
"Brynjolfsson",
"Li",
"Raymond"
] |
by E Brynjolfsson · 2023 · Cited by 1474 — We study the staggered introduction of a generative AI-based conversational assistant using data from 5,172 customer support agents. Access to AI assistance ...
|
arXiv Is Hiring a DevOps Engineer
Work on one of the world's most important websites and make an impact on open science.
| 2023-04-01T00:00:00 |
https://arxiv.org/abs/2304.11771
|
[
{
"date": "2023/04/01",
"position": 100,
"query": "AI workforce transformation"
}
] |
|
How AI is accelerating innovation and what should ...
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How AI is accelerating innovation and what should business leaders do about it.
|
https://www.linkedin.com
|
[
"Pronix Inc",
"Phillip Swan",
"Vivek Gururani"
] |
Senior executives should ensure that AI is used in ways that are transparent, accountable, and aligned with organizational values and social norms.
|
Artificial intelligence (AI) has emerged as a powerful force in today's business landscape, transforming the way we work and interact with technology. From healthcare to finance, AI is accelerating innovation in many industries, helping organizations to achieve new levels of efficiency, productivity, and growth. Let’s have a look at how AI is driving innovation, and what business leaders can do to harness its potential.
What is AI?
AI refers to a set of technologies and techniques that enable machines to perform tasks that would normally require human intelligence, such as visual perception, speech recognition, decision-making, and natural language processing. AI systems can analyze vast amounts of data, identify patterns and insights, and make predictions and recommendations based on that data.
How is AI accelerating innovation?
AI is transforming many industries by enabling organizations to leverage the power of data and automation to create new products and services, streamline operations, and enhance the customer experience. Here are some examples of how AI is driving innovation in different industries:
Healthcare: AI is being used to develop new drugs, improve diagnostics, and personalize treatment plans. AI-powered tools can analyze medical images, genetic data, and other patient information to identify disease risk factors and recommend personalized interventions.
Finance: AI is transforming the finance industry by enabling organizations to make better investment decisions, detect fraud, and reduce risk. AI-powered systems can analyze market data, identify trends, and make predictions about stock prices and other financial indicators.
Manufacturing: AI is helping manufacturers to optimize production processes, reduce waste, and improve quality control. AI-powered systems can analyze sensor data from production equipment, identify patterns of machine failure, and predict maintenance needs.
Retail: AI is enhancing the customer experience by enabling retailers to offer personalized recommendations, improve inventory management, and automate customer service. AI-powered chatbots can interact with customers, answer questions, and make purchase recommendations based on their preferences.
What can business leaders do about it?
To harness the potential of AI and accelerate innovation in their organizations, executives should consider the following strategies:
Develop an AI strategy: Senior executives should develop a clear vision and strategy for how AI can be used to drive innovation in their organizations. This should include a roadmap for identifying and prioritizing AI use cases, evaluating AI vendors and technologies, and developing the necessary skills and capabilities to implement AI effectively.
Build a data-driven culture: To leverage the full potential of AI, organizations need to build a culture that values data and analytics. Senior executives should encourage data-driven decision-making and ensure that employees have access to the data and tools they need to drive innovation.
Invest in AI talent: AI requires specialized skills and expertise, and organizations need to invest in recruiting and developing talent in this area. Senior executives should consider partnering with universities and other organizations to develop training programs and create a pipeline of skilled AI professionals.
Prioritize ethical considerations: AI raises a number of ethical and social issues, and organizations need to prioritize these considerations in their AI strategies. Senior executives should ensure that AI is used in ways that are transparent, accountable, and aligned with organizational values and social norms.
| 2023-04-01T00:00:00 |
https://www.linkedin.com/pulse/how-ai-accelerating-innovation-what-should-business-do-munir-phd
|
[
{
"date": "2023/04/01",
"position": 9,
"query": "artificial intelligence business leaders"
}
] |
|
Mastering AI-Infused Leadership: Essential Insights and ...
|
Mastering AI-Infused Leadership: Essential Insights and Actions to Drive Success
|
https://www.linkedin.com
|
[
"European Leadership",
"John Maeda",
"Fabio Moioli",
"Tom Würzburg",
"Marketing Professional"
] |
AI-driven leadership is a style of leadership that embraces and harnesses the power of AI to achieve your objectives.
|
I have a confession to make. I’m not an AI-driven leader. In fact, I’m not even sure what that means. But I’m curious. And I’m not alone. Many people are wondering how AI is changing the world of work and what it means for leaders. How can we use AI to make better decisions, improve productivity, delight customers, and innovate faster? How can we develop new skills, mindsets, and strategies to thrive in the AI era? How can we avoid the pitfalls and ethical dilemmas of using AI for our business goals?
TL;DR: AI is changing the world of work and leadership. To succeed in the AI era, you need to become an AI-driven leader. This means learning the technologies , establishing clear business objectives , preparing people for the journey, and experimenting and scaling. It also means leading effectively in an AI environment. This means being customer-centric, partner-friendly, competitor-aware, regulator-compliant, and society-responsible. And it means joining a community of AI-driven leaders who can support you, challenge you, and inspire you.
In this highly experimental guide, I will try to answer these questions and more. I will explore what AI-driven leadership is, why it matters, and how you can become an AI-driven leader. I will also share some best practices and tools to use AI for your leadership goals.
But before we dive into the details, let me tell you a story.
What is AI-Driven Leadership?
A few years ago, I attended a conference on AI and leadership. The keynote speaker was a famous CEO of a tech company that claimed to be “AI-first” or “AI-driven”. He talked about how his company was using AI to transform every aspect of its business, from product development and marketing to customer service and operations. He showed impressive charts and graphs that demonstrated the impact of AI on his company’s performance and growth. He also shared some anecdotes and examples of how AI helped him and his team make better decisions, improve productivity, delight customers, and innovate faster.
I was impressed. And so were most of the people in the audience. We all wanted to learn more about how he did it. How did he become an AI-driven leader? What were his secrets? What were his challenges?
Unfortunately, he didn’t tell us much. He said that becoming an AI-driven leader was not easy. It required a lot of learning, experimentation, and adaptation. It also required a lot of courage, vision, and trust. He said that he had to learn the technologies, establish clear business objectives, prepare people for the journey , and experiment and scale .
That sounded reasonable. But it was also vague. What did he mean by learning the technologies? Which technologies? How did he learn them? How did he establish clear business objectives? What were they? How did he measure them? How did he prepare people for the journey? Who were they? How did he communicate with them? How did he experiment and scale? What did he test? What did he measure? What did he learn? What did he scale?
He didn’t answer any of these questions. He just smiled and thanked us for our attention.
I was disappointed. And so were most of the people in the audience. We all wanted to know more about how to become AI-driven leaders. But we didn’t get any practical advice or guidance.
That’s why I decided to write this guide. I wanted to find out what it really means to be an AI-driven leader. And I wanted to share what I learned with you.
So what is AI-driven leadership?
AI-driven leadership is a style of leadership that embraces and harnesses the power of AI to achieve your objectives. AI-driven leaders are not only aware of the potential and limitations of AI, but also actively use it to enhance their own and their team’s performance.
AI-driven leadership involves four key aspects:
Learning the technologies
Establishing clear business objectives
Preparing people for the journey
Experimenting and scaling
Why Does AI-Driven Leadership Matter?
You may be wondering why you should care about AI-driven leadership. After all, you’re doing fine without it, right? You have a successful business, a loyal team, and happy customers. Why fix something that isn’t broken?
Well, let me tell you something. AI is not a fad. It’s not a hype. It’s not a buzzword. It’s a reality. And it’s here to stay. And if you don’t pay attention to it, you may soon find yourself out of business.
Don’t believe me? Just look at the facts. According to a report by McKinsey & Company, by 2030, up to 70% of companies could adopt at least one type of AI technology. The same report estimates that AI could add up to $13 trillion to the global economy by 2030.
That’s a lot of money. And a lot of opportunity. And a lot of competition.
“The playing field is poised to become a lot more competitive, and businesses that don’t deploy AI and data to help them innovate in everything they do will be at a disadvantage.” - Paul Daugherty , chief technology and innovation officer, Accenture
AI can offer many benefits for leaders and organizations, such as:
Improved decision-making: AI can help you analyze large amounts of data, identify patterns and insights, generate predictions and recommendations, and reduce biases and errors.
Increased productivity: AI can help you automate repetitive and mundane tasks, optimize workflows and processes, enhance efficiency and quality, and free up time for more creative and strategic work.
Enhanced customer experience: AI can help you personalize products and services, anticipate customer needs and preferences, provide faster and better support, and increase customer loyalty and satisfaction.
Faster innovation: AI can help you generate new ideas, explore new possibilities, experiment with different solutions, and create new value propositions.
But AI also poses some challenges for leaders and organizations, such as:
Skills gap: AI requires you to develop new skills or upgrade existing ones to work effectively with AI. These skills include technical skills (such as data literacy), cognitive skills (such as critical thinking), social skills (such as emotional intelligence), and ethical skills (such as responsible use of AI).
Change management: AI requires you to manage the change that comes with adopting new technologies. This includes addressing the fears and resistance of employees who may feel threatened or displaced by AI; ensuring the alignment of organizational culture, structure, and processes with AI; and balancing the expectations and interests of different stakeholders.
Ethical dilemmas: AI requires you to consider the ethical implications of using AI for your business goals. This includes ensuring the fairness, transparency, accountability, and security of AI systems; protecting the privacy and dignity of human beings; and preventing the harmful or unintended consequences of AI.
So what does this mean for you? It means that you need to be prepared for the AI revolution. You need to be proactive, not reactive. You need to be strategic, not tactical. You need to be visionary, not myopic.
You need to be an AI-driven leader.
But how do you become one? That’s what I’m going to tell you in the next section.
How to Become an AI-Driven Leader
So you want to be an AI-driven leader. Good for you. But how do you do it? Do you just buy some fancy software, hire some nerdy data scientists, and hope for the best? Do you just follow some generic best practices and copy what others are doing? Do you just wing it and see what happens?
No, no, and no. Becoming an AI-driven leader is not something that happens by accident. It requires a mindset shift, a skillset upgrade, and a strategy execution. It requires intentionality, integration, implementation, and indication. It requires you to be smart, brave, and humble.
Here are some steps you can take to become an AI-driven leader today:
Learn the technologies: You don’t need to be a technical expert, but you need to know the basics of AI technologies and how they work. You also need to stay updated on the latest trends and developments in the field. Read books and articles, take online courses, attend webinars and events, and join online communities and networks related to AI. Learn from your data scientists and engineers, if you have them. Ask questions, listen to explanations, and understand their challenges and opportunities. Admit what you don’t know and learn from others.
Establish clear business objectives: Know what you want to achieve with AI and how it fits your overall strategy. Set SMART goals for your AI initiatives and measure your progress and results. Conduct a SWOT analysis, identify your key use cases and pain points, define your success metrics and KPIs, and create a roadmap and action plan for your AI projects. Communicate your objectives to your stakeholders, including employees, customers, partners, and investors. Explain why AI matters for your business, what benefits it will bring, and what challenges it will entail.
Prepare people for the journey: Tell your stakeholders, including employees, customers, partners, and investors, what AI can do for them and what challenges it will bring. Make your team a place where people learn, collaborate, and trust each other and give them the training and support they need to work with AI. Create a compelling story and vision for AI, address the fears and resistance of employees, provide learning opportunities and incentives, create cross-functional teams and partnerships, and ensure ethical and responsible use of AI. Empower your people to use AI for their own tasks and goals, encourage them to experiment and innovate, and celebrate their successes and failures.
Experiment and scale: Use an agile and iterative approach to implement AI solutions. Test your hypotheses, measure your results, learn from your failures, and scale your successes. Monitor and evaluate the impact of AI on your business outcomes and ethical values. Follow the lean startup methodology, use data-driven decision making, apply design thinking and user feedback, leverage cloud-based platforms and tools, and establish governance and accountability mechanisms.
By following these steps, you can become an AI-driven leader who can leverage AI for your leadership goals. But becoming an AI-driven leader is not enough. You also need to lead in an AI-driven world. That means adapting to the changes and challenges that AI brings to your organization and society.
By 2030, up to 70% of companies could adopt at least one type of AI technology, and AI could add up to $13 trillion to the global economy by 2030 (McKinsey & Company).
More than 90% of surveyed companies report some ethical issues with AI, such as bias, privacy, security, and accountability (Harvard Business Review).
AI-driven leaders are twice as likely to report outsize business results than their peers who are not using AI (McKinsey & Company).
Almost 100 million people will work in the AI space by 2025, and the demand for AI skills has increased by 4.5 times since 2013 (Tidio).
About 62% of consumers are willing to submit data to AI to improve their experience, and 63% of consumers prefer messaging an AI bot to communicate with a business (Tidio).
How to Lead an AI-Driven Team
Leading a team is hard enough. Leading an AI-driven team is even harder. You have to deal with not only human beings, but also machines. You have to balance the needs and expectations of both. You have to manage the collaboration and communication between them. And you have to ensure that they work together to achieve your objectives.
But don’t worry. It’s not impossible. In fact, it can be rewarding and exciting. You can unleash the power of AI to enhance your team’s performance, creativity, and innovation. You can also empower your team members to grow and learn with AI.
Here are some tips on how to lead an AI-driven team effectively:
Define your vision and goals: Know what you want to achieve with AI and how it fits your overall strategy as a leader. Set SMART goals for your AI initiatives and measure your progress and results. Make sure your team members know and share your vision and goals, and let them participate in the planning and decision-making process. Ask for their feedback and input often.
Choose the right technologies: Learn the basics of AI technologies and how they work as a leader. Stay updated on the latest trends and developments in the field. Pick the right technologies for your objectives and use cases, and weigh the benefits and risks of using AI for your team. Think about the technical feasibility, business viability, user desirability, and ethical acceptability of your AI solutions.
Train and support your team: Your team members need to learn and grow with AI. See what they know and what they need to know. Help them improve their technical, cognitive, social, and ethical skills. Offer them learning opportunities and incentives. Provide them with the right tools and platforms to access, analyze, and use data and AI systems. Create a team culture of learning, collaboration, and trust.
Experiment and iterate: AI changes your team’s outcomes and values. Keep an eye on that. Encourage your team members to experiment and innovate with AI, learn from their mistakes, and share their best practices. Celebrate their achievements and contributions.
Lead by example: Teach your team how to use AI for your goals. Use AI for your own tasks and decisions. Collaborate and communicate with both humans and machines. Be transparent and accountable for your AI actions and outcomes. Be ethical and responsible for your AI impact on your team and society. Motivate your team to follow your lead and adopt AI-driven behaviors and mindsets.
Now you can lead an AI-driven team effectively. You can harness the power of AI to boost your team’s performance, creativity, and innovation. You can also empower your team members to grow and learn with AI. In the next section, I will explore how you can lead effectively in an AI environment.
“We look at technology to help bolster the teams and intelligence that we already have.” - Beth Mach Mach, chief data officer, Publicis Media
How to Lead Effectively in an AI Environment
So you’ve become an AI-driven leader. Congratulations. You’ve learned the technologies, established clear business objectives, prepared people for the journey, and experimented and scaled. You’ve embraced AI as a tool, fostered a culture of innovation, developed a data-driven mindset, built a diverse and inclusive team, invested in employee development, and prioritized ethics and responsibility.
You’re awesome. But you’re not done yet.
You see, AI is not only changing your organization. It’s also changing your environment. It’s changing the way you interact with your customers, partners, competitors, regulators, and society at large. It’s changing the expectations, demands, opportunities, and risks that you face as a leader.
And you need to adapt to these changes. You need to lead effectively in an AI environment.
But how do you do that? How do you navigate the complexities and uncertainties of an AI-driven world? How do you balance the opportunities and challenges of AI for your organization and society? How do you stay ahead of the curve and maintain your competitive edge?
Here are some tips to help you lead effectively in an AI environment:
Be customer-centric: AI can help you improve your customer experience by personalizing products and services, predicting customer needs and preferences, providing faster and better support, and increasing customer loyalty and satisfaction. But AI can also cause new problems for your customer relationships, such as privacy concerns, trust issues, and ethical dilemmas. As a leader, you need to be customer-centric and put your customers’ interests first. Know your customers’ needs, wants, values, and expectations. Tell your customers clearly and honestly how you use AI and how it benefits them. Respect your customers’ privacy and dignity and protect their data. Make sure your AI systems are fair, transparent, accountable, and secure. Listen to your customers’ feedback and complaints and address them quickly and effectively.
Be partner-friendly: Collaborating with other organizations that complement your capabilities and offerings can create new value propositions and business models with AI. However, partner relationships can also face new problems with AI, such as compatibility issues, coordination problems, and power imbalances. As a leader, you should seek win-win solutions with your partners and be partner-friendly. Find potential partners that share your vision, values, and goals. Clarify roles, responsibilities, and expectations with your partners. Ensure that your incentives and interests are aligned with your partners. Communicate effectively and frequently with your partners. And handle any conflicts or disputes with your partners amicably and fairly.
Be competitor-aware: With AI at your fingertips, you have the power to stay one step ahead of your rivals. You can improve your products, make smarter decisions, streamline your business processes, and spark innovation faster than ever. But don't forget, AI can also give your competition an edge, leading to unexpected entrants, fast-paced changes, and increased threats. To keep your business on top, you need to be competitor-savvy. Take a close look at your rivals, assessing their strengths, weaknesses, opportunities, and threats. Predict their next moves, and differentiate yourself by offering unique value propositions and unforgettable customer experiences.
Be regulator-compliant: AI is a powerful tool for staying compliant with existing regulations. It can automate compliance tasks, enhance accuracy, and minimize risks. But beware, AI can also bring about new challenges for your regulatory environment, such as ambiguous rules, changing standards, and ethical dilemmas. As a leader, it's your responsibility to be regulation-ready. Follow the laws and regulations that apply to your industry and region. Understand the legal implications of using AI to achieve your business goals. Engage with regulators proactively and constructively to clarify any uncertainties or ambiguities. Adhere to ethical principles and values when implementing AI, and demonstrate accountability and responsibility for the outcomes and impacts of your AI systems.
Be society-responsible: With the help of AI, you can create social value, solve societal problems, and advance human welfare. AI can also pose challenges for your societal environment, such as social inequalities, job displacements, and environmental impacts. As a leader, you must be socially conscious and considerate of the broader impact of your AI initiatives on society. Align your AI goals with the United Nations Sustainable Development Goals or other relevant frameworks. Collaborate with civil society organizations, NGOs, academia, and media to raise awareness and address any concerns or issues related to AI. Support social causes and initiatives that use AI for good. And advocate for ethical and responsible AI policies and regulations at the local, national, and global levels.
With this, you can lead effectively in an AI environment. You can balance the opportunities and challenges of AI for your organization and society. You can stay ahead of the curve and maintain your competitive edge.
But you can’t do it alone. You need to collaborate with other leaders who share your vision and values. You need to join a community of AI-driven leaders who can support you, challenge you, and inspire you.
That’s why I invite you to join me in this journey. Let’s learn from each other. Let’s share our experiences and insights. Let’s create a network of AI-driven leaders who can shape the future of work and society.
Are you with me? If so, please leave a comment below or contact me directly. I’d love to hear from you.
And if you enjoyed this article, please share it with your friends and colleagues. Let's pave the way for a new era of leadership, one that embraces the power of AI and the potential it holds for a brighter future.
| 2023-04-01T00:00:00 |
https://www.linkedin.com/pulse/mastering-ai-infused-leadership-essential-insights-actions-w%C3%BCrzburg
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[
{
"date": "2023/04/01",
"position": 23,
"query": "artificial intelligence business leaders"
}
] |
|
How do companies actually use AI? : r/ProductManagement
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The heart of the internet
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https://www.reddit.com
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[] |
Many businesses use AI chatbots to handle customer queries and free up human agents for more complex issues. In marketing, AI analyzes data to craft ...
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Hello, I'd like to start a discussion on AI - specifically, for all the hype, I can't find many companies actually using it.
If you've worked on a AI project / product, or if you've heard about it, could you please let us know (roughly) how AI was used?
| 2023-04-01T00:00:00 |
https://www.reddit.com/r/ProductManagement/comments/132ra4f/how_do_companies_actually_use_ai/
|
[
{
"date": "2023/04/01",
"position": 57,
"query": "artificial intelligence business leaders"
},
{
"date": "2023/04/01",
"position": 21,
"query": "artificial intelligence employers"
}
] |
|
How do companies actually use AI? : r/ArtificialInteligence
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The heart of the internet
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https://www.reddit.com
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[] |
Companies use AI to transform operations by automating routine tasks, analyzing complex data, and enhancing decision-making. AI applications include customer ...
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Hello, sorry if this is a stupid question but I'm a bit confused - for all the current hype about AI, I can't find many companies actually using it.
If you've worked on a AI project / product, or if you've heard about it, could you please let us know (roughly) how AI was used?
| 2023-04-01T00:00:00 |
https://www.reddit.com/r/ArtificialInteligence/comments/132rfgc/how_do_companies_actually_use_ai/
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[
{
"date": "2023/04/01",
"position": 64,
"query": "artificial intelligence business leaders"
}
] |
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AI Considerations for Teaching and Learning
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AI Considerations for Teaching and Learning
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https://teaching.resources.osu.edu
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This Teaching Topic will help you make informed choices about AI use in your course by providing background on generative AI tools, an overview of their ...
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Artificial intelligence (AI) is all around us. If you have used a mobile phone, driven a car with navigation, or asked a virtual assistant to complete a task, chances are you’ve recently used or encountered some form of AI-assisted technology. The rapid emergence of applications that leverage AI to gather information, organize ideas, draft prose, and generate media has prompted special consideration for how educators can support both student learning and academic integrity in a world where these tools are increasingly available.
The introduction of ChatGPT, Google Bard, and similar AI chatbots has prompted varied responses from educators and a deluge of resources on how we should respond to this new technology. While some see potential in embracing AI, others express concern about its implications for academic integrity. For example, how should we address AI’s ability to replicate student responses to writing prompts, perform certain kinds of information analysis (e.g., summarizing), and convincingly fabricate research? These questions extend beyond student writing and research as well, since AI can be used to generate code, prep for exams, read complex texts, create art, and more.
It's helpful to remind ourselves that the dynamic of technological change in education is not new. We frequently adapt our instruction to integrate new teaching tools and address an ever-changing digital landscape. With each new advancement, it’s wise to engage in dialogue around its implications for teaching and learning. While scholarship on AI and education is still emerging, we can rely upon existing evidence-based approaches to guide decisions around whether to fully embrace, cautiously integrate, or carefully limit AI-powered technologies in our courses.
This Teaching Topic will help you make informed choices about AI use in your course by providing background on generative AI tools, an overview of their benefits and limitations, and practical guidance for integrating them into your teaching.
The insights and guidance provided below will evolve as new information arises around generative AI tools and their impact on teaching and learning.
What is Artificial Intelligence?
Artificial intelligence (AI) is “the capability of a machine to imitate intelligent human behavior” (Merriam-Webster). Advancements in AI have transformed the way we live, including how we teach and learn. Think about some examples of AI-assisted technology you might encounter in education today, including calculators, automated grading tools, text editors, transcription programs, and assistive technology. You may even remember the first iterations of some of these technologies and the conversations about benefits and challenges that followed.
Generative AI applications can generate content, rather than merely analyze existing data, by utilizing Large Language Model (LLM) technology. Many of these applications function as AI-powered chatbots—in other words, users submit a prompt and content is generated in real time in response to that prompt.
One of the more widely known and discussed AI-powered chatbots is ChatGPT. Developed by tech company OpenAI, its large language model was trained using very large datasets, codes, and texts, and it pulls from all this data to generate responses. Using predictive technology, it can “create or revise written products of all kinds, including essays, computer code, lesson plans, poems, reports, and letters” (University of Toronto, n.d.). It’s likely OpenAI is also utilizing user prompts and ChatGPT responses to train the model as the company collects data from users and continues to modify and improve the tool.
While ChatGPT is well-known, it is far from the only generative AI system. In fact, the range of AI tools available is expanding on an almost weekly basis as companies develop their own versions. Educators may be primarily focused on AI’s ability to generate text, but it is worth noting that ChatGPT and many other AI applications can also create code, images, music, and other media.
Some additional examples of generative AI applications include:
Microsoft Copilot and Adobe Firefly are currently the only robust generative AI tools that have been vetted and approved for use at Ohio State. Copilot is an AI-powered chatbot that draws from public online data, giving you access to better answers and greater efficiency, but with additional security measures in place. Adobe Firefly is a generative AI engine that aims to support and augment your creative work. You can use Firefly to generate and enhance images, edit objects, and more.
Learn more about approved AI tools at Ohio State.
Generative AI: Benefits and Limitations
As with any emergent technology, generative AI comes with both benefits and limitations. Some of these are still being discovered through user experimentation and updates to the technology. Though much of what we know about generative AI applications and AI language models will shift over time, we share some important considerations below.
Potential Benefits
It is helpful for educators to consider that generative AI:
Provides learners a tool for generating rough drafts, outlines, and brainstorming notes. Generative AI applications like ChatGPT can assist students during the earliest stages of the writing process, auto-generating text for learners who are either stymied by writer’s block or stuck during the brainstorming process. When prompted, these systems can produce reams of raw content (of varying levels of quality and accuracy) that students can further evaluate, interrogate, and research. Using AI tools in this fashion can help students during the difficult preliminary stages of the composition process, creating a workable path toward their own inquiries and investigations (Gero, 2022; Krause, 2022; Weissman, 2023).
Summarizes and clarifies longer or potentially difficult texts. AI chatbots can also condense and summarize longer texts with only moderate error, potentially aiding students during the reading and research process. They may help in clarifying and explaining daunting or challenging texts in simple, digestible language. This function might potentially help learners (especially English language learners) gain a deeper comprehension of dense academic materials by making obscure prose and concepts more approachable and accessible (Anson and Straume, 2022; Warner, 2022).
Assists learners with automated grammatical assistance and language acquisition. When prompted, generative AI applications like ChatGPT can provide direct and immediate automated assistance for students struggling with grammar, mechanics, and syntax. They can identify, explain, and even correct basic grammatical mistakes. Additionally, AI language systems can function as a fluent conversation partner for informal language practice. This might be of particular benefit for English language learners and multilingual students who are still learning the basic mechanics of writing in English (Warner, 2022).
Promotes wider classroom discussion around rhetoric, style, and AI literacy. Generative AI applications also provide an avenue for discussing various facets of rhetoric, authorship, and academic integrity with students. They can function as the focal point of a broader conversation about the ethical questions posed by AI language systems, especially as their continued use and development alters our understanding of plagiarism and cheating. Using generative AI, we can help students develop their own style, skill, and voice as authors, particularly when we ask them to review and discuss their work in contrast to machine-generated texts (Fyfe, 2022; Grobe, 2023; Anson and Straume, 2022).
Known Limitations
Educators should also be aware that generative AI:
Generates incomplete, inaccurate, or false information. Although they draw from vast datasets of text, AI-powered chatbots remain limited to the information available to them at the time of their training. In other words, they cannot access or consult with external sources of information, nor can they self-correct or fill knowledge gaps with correct information. For example, ChatGPT often punctuates its responses with obvious fabrications, failing to maintain accuracy when tasked with generating knowledge outside its dataset. Users can also prompt ChatGPT to churn out obvious misinformation and nonsense, making it generate “garbage output” that is presented credibly and uncritically. In particular, it struggles when prompted to generate text about current events or recent developments, particularly on anything that has occurred after 2021 (Fyfe, 2022; Schatten, 2022; Grobe, 2023).
Creates inaccurate or fabricated citations. In addition to a penchant for generating misinformation, generative AI tools are incapable of conducting research and substantiating claims with credible evidence. When prompted to conduct research or cite secondary sources, for example, ChatGPT often fabricates research references and riddles the text with plausible sounding but entirely false or made-up claims, quotations, and scholars (Fyfe, 2022; Krause, 2022).
Includes plagiarized text without proper attribution. Generative AI’s understanding of American academic integrity and copyright standards is virtually nonexistent. Texts generated by language models have consistently committed frequent and flagrant acts of plagiarism, from direct, word-for-word plagiarism to misrepresenting others’ ideas as their own (Tutella, 2023).
Reiterates biases and is prone to discriminatory language. Generative AI applications can sometimes employ biased or discriminatory language or repeat extreme or controversial viewpoints. Even when safeguards are added to filter out some of the more extreme or discriminatory positions, AI language systems are still prone to generating text that reinforces certain stereotypes, biases, and belief systems (Hutson, 2021).
Replicates, but cannot replace, human agency and expression. Generative AI applications use predictive algorithms to generate text based on user input. Despite their relative fluency and adaptability, they cannot comprehend the meaning behind their words or exhibit human-like levels of critical thinking. This disconnect sometimes leads to text that sounds stilted, makes insubstantial claims, and lacks the subtle intricacy of human expression. AI can also commit rhetorical errors with relative frequency, pepper its texts with meaningless filler phrases, and over-rely on certain writing formulas. For instance, ChatGPT has a strong preference for five paragraph essays with short, three-sentence paragraphs and often overuses single word modifiers and transitions (Grober, 2023; University of Central Florida, 2023).
Additional Considerations
Educators may also want to think about the following murky areas related to generative AI applications.
Generative AI may harvest and share student data. ChatGPT and other tools are third-party software systems that can track, collect, and share data from their registered users. For example, OpenAI’s privacy policy claims that it reserves the right to harvest and share user data but does not clarify for whom and for what purpose. This brings up potential concerns regarding privacy and security (Caines, 2023), and some students may be uncomfortable using or creating accounts with generative AI applications. Therefore, exercise caution around requiring students to use AI applications and consider alternatives for supporting student learning and meeting your instructional goals.
The copyright status of AI-generated works remains unclear. Questions regarding the intellectual ownership of AI-generated texts remain contentious, with no foreseeable resolution in sight. Users should err on the side of caution when it comes to claiming outright authorship of anything created by ChatGPT or other AI language systems (McKendrick, 2022). There is also a lack of clear guidance on how to cite material generated by AI in any of the major citation styles such as MLA and APA.
AI platforms may transition into subscription-based and pay-to-use services. As with many free applications, we should consider that generative AI applications could someday require payment or offer “premium” services to paying users, bringing about potential equity and access issues. OpenAI has already introduced ChatGPT Plus, a pay-to-use service.
Explore strategies for having open discussions with students about AI concerns, including helpful conversation starters and class activities, in AI Teaching Strategies: Having Conversations with Students.
Teaching and AI: Strategies and Examples
When considering adoption of any new technology or teaching strategy, begin by reflecting on course goals and learning outcomes. Then decide whether integrating or allowing the technology in your course can enhance learning and support specific assessments and activities (Wiggins & McTigue, 2005). Activities and assignments designed to support students’ self-directed learning or develop their skills in leveraging the latest technologies for professional practice could potentially require different approaches to AI than those focused on fostering creativity or reflective practice.
Each section below offers suggestions for supporting your students to develop knowledge and skills around AI while maintaining an evidence-based lens on instruction.
Align to Learning Goals and Outcomes
Reflecting on your course goals prior to deciding if and how to integrate generative AI technology will help you clarify learning outcomes, facilitate alignment to activities and assessments, and support student success. Activities and assignments that scaffold the process of learning, as opposed to those that assess the product of learning (e.g., student-developed artifacts such as written assignments, code, or media), may be well-suited to the integration of generative AI applications. When using potentially transformative technologies such as generative AI in your course, strive to create learning experiences that enable students to practice what Bloom (1956) considers, "… the more complex classes of intellectual abilities and skills," such as applying, analyzing, evaluating, and creating.
In the table below, consider how the use of generative AI in each learning activity supports the example learning outcome.
Learning Outcome AI-Supported Learning Activity In Nursing, students should be able to summarize research behind evidence-based practice. Students work in groups to examine and critique AI output of a literature summary vs. human-based summaries and consider the implications for how they inform a specific clinical practice scenario. In Sociology, students must be able to examine literature reviews to establish the background for proposed research. Using relevant disciplinary databases and Google Scholar, students track down the citations (students might need some guidance here) in an AI-generated literature review to evaluate 1) whether the citations exist and 2) how relevant they are to the proposed research. In a Biology lab experience, students must be able to articulate valid experimental methods that contribute something novel to scientific knowledge. Putting themselves in the position of scientific peer reviewers, students evaluate and critique AI output of a methods statement vs. human-based statement for validity and for how it articulates its contribution to the science. In an Academic English Writing Program course, multilingual and international students must identify rhetorical patterns in a range of genres in American academic contexts. Students use AI to generate three different passages paraphrasing a key source for a paper making an argument, note patterns among the different passages (how the passages represent the author’s points, what is emphasized, their accuracy, critique potential bias in point of view or language), and in an annotation, choose which passage is most useful for their argument and explain how they would revise it for their paper.
Students may need additional guidance when using new technologies. Use the following suggestions to plan how you will support student learning during AI-related learning activities.
Model motivation and excitement about AI as a potential learning tool, when appropriate.
Outline strategies that are critical to success when using AI tools in your course. For example, guide students toward appropriate approaches to evaluating AI-generated output, such as identifying false claims, logical fallacies, fabricated evidence, and unacknowledged biases.
Provide actionable feedback to students on their use of these strategies and on their performance in AI-supported learning activities.
Create opportunities for students to reflect on their use of AI and explain how it impacted their learning.
Gauge students’ interests and comfort with using AI applications and offer alternatives when possible. For example, if students have privacy or security concerns around setting up an account in a particular app, you could provide them with pre-generated AI output instead.
Prepare ancillary resources to help students navigate any unfamiliar AI tools they are required to use.
Explore more ideas in Universal Design for Learning: Planning with All Students in Mind and Supporting Student Learning and Metacognition.
UX Tip Active Learning AI-supported learning activities are a great opportunity to use active learning strategies to foster engagement and create a student-centered experience. Active learning can be broadly described as “any instructional method that engages students in the learning process" (Prince, 2004). Recent meta-analyses have established its value both in terms of student learning (Freeman et al., 2014) and equity (Theobald et al., 2020). You might intersperse small-scale activities like Think-Pair-Share and polling during lecture, reference AI for low-stakes brainstorming or Writing-to-Learn activities, or plan more structured and time-intensive activities that utilize AI output such as case studies, student-led discussions, debates, and peer review.
Explore how you can use technology to support active learning strategies in your course by viewing this recording of Active Learning with Technology.
Design for Transparency
A growing body of research suggests transparency of instruction is important for enhancing the student experience and supporting academic success. Studies have shown that intentional design of instruction for transparency contributes to greater learning outcome achievement (Winkelmes et al., 2016; Howard, Winkelmes, and Shegog, 2020) and in teaching large-enrollment classes (Winkelmes, 2013). This notion is further supported by research on the use of explicit assessment criteria, which has been found to support student self-regulation (Balloo et al., 2018).
When considering AI in teaching and learning, the following transparency-related considerations are important to note.
Set clear expectations for students’ use of AI in your syllabus and discuss them openly. Communicate in your syllabus about expectations for students’ course-related uses of AI (Wheeler, Palmer, and Aneece, 2019). Set tone, routines, and guidelines early in the term and engage students in openly discussing the opportunities and limitations of AI, as well as what represents misuse of AI in your course or for specific assignments and activities. See AI Teaching Strategies: Crafting Your GenAI Syllabus Statement for detailed guidance and sample syllabus statements.
Design transparent assignments and activities, and share explicit assessment criteria for them. Apply the Transparency in Learning and Teaching (TILT) framework to communicate the purpose, task, and criteria for success for each of your course activities, assignments, and assessments. Are AI-related skills relevant? How will they help students meet learning outcomes? Use this exercise to explicitly state why, when, and how AI can or should be used or prohibited in your course (Winkelmes et al., 2016). Visit http://www.tilthighered.com for TILT templates, examples, and resources. Provide a rubric—or work with students to co-develop a rubric—for all assignments and assessments. To support student understanding of assignment expectations, you could even have them use the rubric to evaluate AI-generated responses to the assignment prompt.
Involve students in decision-making to give them ownership in AI policies and assessment criteria for your course. Avoid transactional engagements with students when addressing AI—do not simply provide policies about AI technologies in your course or recite assignment instructions. Instead, adopt transformative approaches that involve students in the review, feedback, and decision-making around your policies and assessment criteria. For example, you might craft a discussion exercise in which students examine the grading rubric, ask questions, offer feedback, and practice applying it to sample work. They can then reflect upon the rationale for the inclusion or exclusion of AI tools in the given assignment (Balloo et al., 2018).
Discuss the ethical implications of AI in real-world contexts beyond the classroom. Discuss with students the current and emerging roles of AI in real-world professional and practice settings and incorporate these connections into assignments when relevant. How might AI-related skills benefit students in their studies or careers beyond your course? Speak candidly about the ethical considerations of organizations and professionals using AI to inform decision-making, policy setting, and other aspects of work. Conversations can stay within the confines of your field of study or extend to broader contexts with which students have contact, including education, finance, government, healthcare, and law (Villasenor, 2023).
Learn more about designing assignments that use AI in AI Teaching Strategies: Transparent Assignment Design.
Support Academic Integrity
When we think about academic integrity, we often default to its negative connotations and imagine punitive measures and prohibitions. But academic integrity in practice is more nuanced. It is meant to support ethical behavior, not just to punish students for cheating or other forms of misconduct. Being intentional and thoughtful about your approach will help you maintain a culture of honesty and trust in your class, even when new technologies or tools are introduced.
When it comes to generative AI technology in particular, outright bans would be unproductive and difficult to enforce consistently. They could potentially result in what John Villasenor describes as “the injustice of false positives and false negatives,” where some students are able to circumvent our prevention efforts and others are falsely accused and unjustly penalized for using AI to helm their compositions (2023). And while it may be tempting to move to only in-class exams and writing assignments, or to use timed online quizzes with AI-detection tools activated, you can’t rely solely on emerging technologies to detect all AI-generated responses. Moreover, these strategies for preventing AI use (or misuse) could have serious implications for equity, inclusion, and accessibility in your course.
We recommend a more positive and proactive approach to AI and academic integrity.
First, communicate transparent expectations and policies around academic integrity (and specific AI-related considerations) to your students.
Second, design or re-design your assessments proactively to promote academic integrity.
Finally, know that employing AI-detection technologies should be your last resort for deterring academic misconduct.
Read more in A Positive Approach to Academic Integrity.
Whether you decide to actively use AI in your teaching, design assignments to minimize the use of AI, or take a more prohibitive approach, the following strategies can help you reduce misconduct and support your students to understand academic integrity in the context of their own work.
Define Clear Expectations for Integrity
It's important to establish expectations for academic integrity—both in general and specific to AI use—early in the term. Be prepared to provide the rationale for policies when discussing your syllabus with students and before major assignments and assessments throughout the term.
Include university policies for academic integrity in your syllabus. Openly communicating the university’s policies for academic integrity in your syllabus, as well as your own policy for GenAI use, will level set expectations for your course as well as for students’ academic careers at the university. Lead a conversation around university expectations at the beginning of the term and, as needed, during your course. Ohio State currently has a definition of academic integrity. Additionally, students are required to abide by the university’s Code of Student Conduct, which includes academic misconduct. It’s always best practice to include the university’s policy on academic misconduct in your syllabus.. Discuss with students how the use of AI fits within the university’s policies on academic integrity and misconduct. For example, students may have the misconception that since a chatbot is not a real person, they can use the text it generates without plagiarizing or committing misconduct. Prompt them to consider the nuances of using AI-generated works and related ethical considerations to help them understand what is permitted and prohibited at the university and in your course.
Communicate your course-specific policies for academic integrity and AI use in your syllabus. Beyond university policy, you may have additional expectations for academic integrity and GenAI use in your course. Your GenAI course policy should be clearly communicated and use inclusive, student-centered language. Include a statement in your syllabus and have a conversation around these expectations at the start of term. Your GenAI course policy can address: specific GenAI applications permitted for use
the activities for which they are allowed or encouraged (for example, generating ideas, developing outlines, creating rough drafts, revising, and so on )
how students should cite their use of AI
the consequences for misusing GenAI
who students should contact if they have questions If you decide to use any AI-detection tools in your course, clearly communicate how and when you will use them via multiple outlets, such as your syllabus statement, class discussions, Carmen announcements, in-class reminders, and assignment instructions. Explain your rationale for using them to help students see the broader context of the affordances, limitations, and ethical implications of using AI.
Share specific guidelines for AI use and integrity for individual assignments and assessments, as needed. There may be specific assignments or assessments in your course for which you definitely want students to use or not use AI applications. Beyond your syllabus statement, remind students of these expectations before any relevant course activities, assignments, or assessments. Include explicit recommendations or restrictions in prompts and directions, and clarify them in open discussions with students.
Using the Carmen course template can help you set clear expectations for each assignment in your course. With the assignment template, utilize the bullet points and icons in the Academic Integrity section to communicate your expectations for using generative AI applications. Use the other sections (Directions, Resources) to provide more detailed information. For example, if you are requiring or recommending that students use a specific GenAI application for an assignment, include information about the tool and additional guidance for technology requirements, troubleshooting, account setup, and student privacy. You might also remind students of how to provide proper attribution and use your preferred citation style for all resources they use, including generative AI applications.
Learn more in AI Teaching Strategies: Crafting Your GenAI Syllabus Statement and AI Teaching Strategies: Transparent Assignment Design.
Create or ADAPT ASSIGNMENTS TO PROMOTE INTEGRITY
Carefully consider how each assignment or assessment in your course can best support student learning as well as academic integrity. This vital strategy aligns with many of the best practices for designing assessments of student learning, such as connecting assignments to learning outcomes, providing authentic learning experiences, and creating achievable assignments.
The following strategies will support students to engage meaningfully in the work they author and produce in your course, whether you permit the use of AI tools or not.
Leverage multimodal assignments. Creating assignments that cannot be completed solely by AI-assisted technology can help to minimize student reliance on it. For instance, instead of having students write a policy brief, ask students to create a presentation outlining their research findings and policy recommendations for a specific intended audience. You can still be flexible in terms of presentation tools and format (e.g., recorded voiceover PowerPoint, Flip, Adobe Express) to give students agency of choice. Having students articulate their learning in multiple formats helps them reframe their learning and gives them rhetorical flexibility to communicate in a range of contexts (Selfe, 2007). Explore the Ohio State Toolset and Additional Tools as you consider the variety of technologies and formats students can use to present their work.
Emphasize the process, not the product. When given an input, generative AI applications produce an output. But learning is a process that is not just about generating a product, and this is an important distinction for students who feel the pressure of high-stakes assignments and grades. Incorporate scaffolded assignments that build on one another and include different types of tasks (e.g., proposal, outline, literature synthesis, rough draft, peer review, final draft). By breaking down a larger assignment into smaller chunks, and having more frequent lower-stakes assignments, students experience the process while recognizing the connection between components. Scaffolded assignments also provide ample opportunities for feedback and revision, which allow students to refine their thinking and learning (Bean, 2011).
Prompt student reflection and metacognition. New AI chatbots may sound more human than earlier versions. Nonetheless, having students include personal reflections or connect to their lived experience for your assignments will bring in a human element that chatbots cannot adequately replicate. Students might reflect on personal and professional experiences, their growth in your course, and why course content is valuable to them. You can also ask students to describe their writing process and reflect upon the steps they took. Creating intentional opportunities for students to reflect on their learning strategies can help them become more successful, self-directed learners (Ambrose et al., 2010).
Connect to current events or build upon in-class comments. Generative AI applications like ChatGPT are trained on an enormous amount of data, but they have limitations that you can leverage. For example, most of the data ChatGPT was trained on is from before 2022 (at present). Therefore, you could ask students to relate their learning to a current event for which ChatGPT may not yet have enough information to properly generate a response. Guiding students to connect and apply their learning to current events can help them see the value of their learning, improve their engagement and motivation, and apply their learning to relevant real-world contexts (Ambrose et al., 2010).
Create authentic assignments with real-world value. Students who see value in and feel connected to what they are learning may be less likely to rely on AI-generated support to complete an assignment. Consider how you can integrate real-world content (such as case studies) and authentic tasks (such as project proposals or practice client sessions), to increase students’ motivation on assignments. As with connecting activities to current events, engaging students in tangible real-world tasks allows them to translate key course concepts into meaningful practice.
Consider workload. Students may be more likely to resort to generative AI tools if their workload is heavy or their assignments and deadlines feel unmanageable. Take stock of the amount, length, and sequence of assignments in your course to mitigate any unnecessary pressure on students. It can be helpful to estimate the workload in comparison to your course’s number of credit hours.
If, despite your efforts to apply an integrated approach to academic integrity, you suspect a student has used generative AI to commit misconduct, the case can be submitted to the Committee on Academic Misconduct.
Reflect on Teaching
The need to address the issues and opportunities created by AI is now a reality for educators. While the above recommendations for addressing AI in your teaching are informed by evidence-based approaches, the effects of AI on student learning and experience will not be immediately clear. This is further complicated by the fact that AI technologies are still emerging and evolving, making their effects difficult to pin down. As such, understanding the impact of your own course redesign efforts or AI-related teaching strategies will require intentional reflection and evaluation.
Below are a few suggestions for reflecting on your teaching, with a particular focus on AI.
Review summative assessment data. Analyze data for any summative assessments (such as exams, essays, projects, or presentation) in your course that aligned to instruction that leveraged or limited AI. What insights on your approach to AI can you glean from students’ performance?
Review formative assessment data. Consider data and observations from formative assessments related to AI, including quizzes, in-class practice activities, and student reflections. What insights on your approach to AI can you glean from students’ engagement and performance?
Collect and consider student feedback about AI use in your course. Plan how you will collect student feedback about the use (or non-use) of AI in your course. You can survey students about your instructional approaches, any barriers they encountered in your course, and what elements most supported their learning and success. You might also revisit any informal written reflections students submitted about their use of AI during the term. The Small Group Instructional Diagnosis (SGID) is a service offered by the Drake Institute for Teaching and Learning that provides instructors with valuable student feedback through a focus-group style evaluation conducted by an instructional consultant. Information is collected on supports and barriers to student learning, as well as ideas for positive change. Your consultant can gather student feedback on specific areas of interest, such as your approach to AI in your instruction.
Track your own observations and reflections about AI use in your course. Your personal reflections on the teaching and learning process, particularly around new uses of AI and specific AI-related learning activities, will be helpful in determining what to maintain or change for the next iteration of your course.
Reflection is only as valuable as the work you do afterward… Each of the above components can provide meaningful insights as you iterate and improve upon your instruction to better support student learning.
Find Help
Thoughtfully considering your course goals, learning outcomes, and uses of technology may enhance and transform your teaching practice (Hilton, 2016). Changes that are a fundamental departure from your current practice could mean major adjustments to the learning activities, assessments, and instructor and student roles in your course. Sometimes these shifts may even require a complete redesign of your course.
If you are unsure where to begin or need guidance along the way, a number of units across Ohio State are available to support you. To find assistance with course and assignment redesign, beyond the support provided by your department, browse our Teaching Support Forms.
| 2023-04-01T00:00:00 |
https://teaching.resources.osu.edu/teaching-topics/ai-considerations-teaching-learning
|
[
{
"date": "2023/04/01",
"position": 5,
"query": "artificial intelligence education"
}
] |
|
How do companies actually use AI? : r/artificial
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The heart of the internet
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https://www.reddit.com
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[] |
Human Resources: AI can be used to automate tasks such as resume screening, scheduling interviews, and onboarding new employees. AI can also be used to analyze ...
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Hello, sorry if this is a stupid question but I'm a bit confused - for all the current hype about AI, I can't find many companies actually using it.
If you've worked on a AI project / product, or if you've heard about it, could you please let us know (roughly) how AI was used?
| 2023-04-01T00:00:00 |
https://www.reddit.com/r/artificial/comments/132rccs/how_do_companies_actually_use_ai/
|
[
{
"date": "2023/04/01",
"position": 10,
"query": "artificial intelligence employers"
}
] |
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Am I crazy or some people are out of their minds ...
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The heart of the internet
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https://www.reddit.com
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[] |
If your value lies in being a problem solver, then AI is another tool that will accelerate a lot of the more tedious aspects of design (finding reference photos ...
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Have you seen the leap that ai took in few years? Recent months? It's a massive snowball going downhill that no one can stop. https://youtu.be/twKgWGmsBLY
I'm very worried about the future (two years maybe based on crazy rate of advancement...)
I read a lot about the Ai in art and graphic design fields. It's spreading everywhere. The most common thing I get from these articles and videos is that "ai is just a tool", "just adapt", "it won't take away graphic designer job", "they were also scarred when digitial art emerged". Funny enough many people saying it are already set good in industry, praise Ai just so they can benefit from selling shitty courses or people that are not even in any art field just newly find passion for design with help of midjourney.
Can someone please explain to me this mindset? I guess it could be considered a tool if it would act as sort of aid to making a final project, not making it final by itself.
I look around at freelance websites and people submit Ai generated images to clients straight out of midjourney with that square containing 4 different images not giving a single fuck. Art contests filled with ai images. I look on instagram where a guy posts a 5 hyperrealistic images a day which might take months to make "traditionally/digitaly" and gets thousands of likes. Adobe Stock filled with Ai images and there will be even more of it since its "a grind game" now for all sigmas and alphas. So many youtube videos making tutorials on it as a easy way to make money made by not sketchy at all entrepreneurs who also promote shitcoins for pump and dump, sell dropshipped items as the real ones and are all made with ai voice.
How is a young designer supposed to build own brand and portfolio when their work is just burried under pile of ai generated images? Fine, use ai to speed up some parts of workflow but it still will never be as fast and as efficient as people spamming midjourney images burring your artwork.
How exactly do people imagine adapting? One day in near future when a client says they want a logo, are we supposed to invite them to our studios, sit at the desk togheter and just type in stuff they want into a free software and expect to get paid for that? For transcribing their words as they watch us do it? Oh wait, there is very good voice recognition now so what exactly are we supposed to do?
Are people really expecting big brands and corporations who would fire hundreds of people just to save mere pennies to not use Ai?
Are people really expecting other freelancers and competition not to take massive shortcuts in their work to the point of generating image and calling it a day?
I knew a guy in collage who was simply horrible at design. All he did was tracing images found on the internet and he wasn't even good at it but the way he talked about the projects "he made" made it seem like it was as hard as climbing Everest. Now that guy can hide his lack of creativity by tracing ai images so no one can find the original source of "his projects". And here I am wasting hours pulling my hair out, trying to be original and thinking out of box while he would be able to just jerk off in that time.
How the fuck am I even supposed to compete with this?
Some people on twitter describe themselves as Ai artists now and even take comissions. I saw people selling courses on midjourney and their prompts on patreon.
Like I'm sorry to all of you ai artists but when it comes to the way these things are monetized now I can not think of bigger rip off than the one that you are doing to your clients. Midjourney courses? I legit saw someone calling setting up a discord account hard step in a overview of 400$ course 💀💀💀 Prompts? It takes one second on midjourney discord to notice other users using set of prompts that enhance the image greatly and these mf are selling them.
I think that ai is incredible for reference and making moodboards, fleshing it out from a blurry concept. And that's where I set the limit for myself when it comes to adapting it. I want all of my artworks to be original, creative and to be able to call it mine. I feel like I have a set of rules hardcoded in my head that just block me from ripping stuff and people off and I absolutely hate myself for it. So many chances wasted because I didn't want to take the easy way. Always thought the hard work will be rewarding. Feels like it doesn't matter anymore to anyone.
I don't deny new technology. I know that this post might seem like a massive rant on Ai but i just stopped holding back long time ago and I'm just saying how i feel about it whereas I can't help but feel like some other people are just hoping for the best and pretending everything will turn out to be ok. I really just want you to help me find a way of using it as a tool and not feeling like a total douchbag for charging clients while I barely would do any work and they could have done it themselves in the first place.
I know that the world is moving fast and you got to adapt but with such a fast and huge leap in tech will there be anything left to adapt to? Unless by adapting you mean just dumping this industry, wasting years learning it and finding something else that is safe from ai (for now)
| 2023-04-01T00:00:00 |
https://www.reddit.com/r/graphic_design/comments/12z7zol/am_i_crazy_or_some_people_are_out_of_their_minds/
|
[
{
"date": "2023/04/01",
"position": 4,
"query": "artificial intelligence graphic design"
}
] |
|
AI in Healthcare: Faster Diagnoses, Personalized Care - Hone Health
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AI in Healthcare: Faster Diagnoses, Personalized Care
|
https://honehealth.com
|
[
"Bill Stump",
"Bill Stump Is A Content Creator",
"Journalist",
"Brand Strategist Specializing In Health",
"Fitness",
"Sports. A Former Editorial Executive At Men S Health",
"Women S Health",
"Prevention",
"He Now Runs Well Made Creative",
"A Branded Content Studio Helping Health"
] |
AI is assisting healthcare providers in practical and often invisible ways: speeding up diagnoses, sorting through the flood of data modern ...
|
At my most recent doctor’s appointment, my physician surprised me. “Is it okay if AI listens and takes notes?” he asked, gesturing toward his tablet.
I said yes, then something rare happened: he leaned back, looked me in the eye, and we had the most present, personal, and human conversation about my health I’ve ever had in a clinical setting.
That experience captures a subtle but profound shift around AI in healthcare. By taking care of administrative tasks, AI is making doctor-patient interactions more focused, personal, and human.
At the same time, AI in healthcare is expanding what’s possible at the cutting edge of medicine, analyzing massive datasets to help detect rare diseases, uncover overlooked treatments, and reveal new ways to diagnose conditions earlier and more accurately.
These breakthroughs are beginning to shape everyday healthcare, from interpreting complex biomarker panels to tracking symptoms and surfacing insights your doctor can act on.
While AI isn’t replacing your healthcare provider, it can help them get a clearer view of your health so that you can make smarter decisions together to protect and improve it.
AI-Powered Diagnostics and Imaging
AI is assisting healthcare providers in practical and often invisible ways: speeding up diagnoses, sorting through the flood of data modern medicine generates, and flagging risks before they become serious problems.
According to a 2025 American Medical Association survey, two-thirds of physicians use AI tools in their practice, a 78% increase from the year before.
“There are now over 1,000 FDA-approved AI tools in healthcare,” says James Zou, Ph.D., a Stanford professor who studies medical AI.
One standout example is EchoNet, an AI system Zou helped develop that analyzes cardiac ultrasound videos to assess heart function. In clinical trials, its evaluations were as accurate as those of experienced sonographers.
Systems like this are part of a larger movement of using AI to power personalized medicine and patient care. By spotting subtle anomalies earlier and more consistently, AI enables healthcare providers to tailor follow-up testing and interventions based on a person’s specific physiology, not population-wide guidelines.
This kind of precision problem-solving is being replicated across healthcare. AI Imaging tools like Aidoc help radiologists detect brain bleeds and blood clots faster. Pathology platforms like PathAI flag early signs of cancer. Large language models, such as Google’s Med-PaLM 2, can help clinicians interpret medical questions and lab results with clinician-level accuracy, answer patient questions, summarize clinical notes, or explain test results in simple language.
AI & Personalized Medicine
These types of diagnostic breakthroughs—faster scans, earlier pattern recognition, more accurate reads—are laying the foundation for individualized medicine, where a person’s care plan isn’t shaped by symptoms, but by signals from their unique biology.
Today, patients are awash in data. Comprehensive biomarker tests can determine hormone levels and inflammation markers. Continuous glucose monitors (CGMs) identify blood sugar trends. Wearable devices keep tabs on your HRV and resting heart rate.
For clinicians, parsing what matters in that tidal wave of information can be daunting. But this is where AI shines. It can analyze data to highlight the most relevant health information for each patient, flagging patterns that align with clinical risks or opportunities, and prioritizing insights that warrant action.
Imagine a middle-aged patient with a family history of heart disease who logs meals, wears a fitness tracker, and gets regular blood work. AI might notice their ApoB, cholesterol, and inflammation markers spike when their sleep and activity decline. It could then surface those findings to the physician and suggest a personalized exercise and stress reduction plan to lower the markers before things escalate.
And that’s just the beginning.
“Soon enough, AI could look at 20,000 biomarkers and, based on millions of cases, recommend personalized interventions,” says Valter Longo, Ph.D., professor of gerontology and biological sciences at USC. “It could recommend healthy actions based on biological age, hormones, and other factors.”
While those healthcare insights identified by AI would be delivered to the physician, not directly to you, they point to care that’s shaped by real-time analysis of your own biology, rather than static population norms.
“AI can turn the overwhelming flood of biomarker and wearable data into actionable, personalized insights,” says Zou.
The Limitations of AI in Healthcare
Even the most advanced AI model can’t build trust, show empathy, or understand the full complexity of a person’s life. That’s why the future of AI in healthcare depends on doctors who are AI-literate, able to ask the right questions, interpret the data, and apply it in the context of real human care, Longo says.
Still, people are already using generative AI tools in healthcare, using tools like ChatGPT to answer medical questions. “It’s quite good at answering the common [questions],” says Zou. “But for complex issues, it lacks the full clinical context to answer correctly, and that can lead to mistakes.”
The best care now comes from professionals who combine clinical expertise, data fluency, and human understanding. As Longo puts it: “Right now [AI] is helpful but can be unreliable in certain cases. It can help me put things together and give me possibilities, but it doesn’t replace human intelligence and decision-making.”
AI in Drug Development & Discovery
AI is also opening new doors in medical research, especially for people with rare or hard-to-diagnose illnesses.
Physician-scientist David Fajgenbaum, M.D., who nearly died from a rare disease called Castleman’s, founded Every Cure, a nonprofit using AI to identify existing drugs that could treat rare or overlooked conditions.
When Every Cure’s AI helped uncover a hidden treatment option for Castleman’s disease, it didn’t just save Fajgenbaum’s life; it showed how data-driven pattern recognition can bring hope to the hardest cases.
This same approach could accelerate longevity science, helping identify drugs that slow biological aging by targeting mechanisms like autophagy, mitochondrial resilience, or cellular senescence.
“It’s quite promising,” says Zou. “AI can detect early signs of aging-related conditions and generate new hypotheses for prevention.”
Proceed with Promise—and Caution
Despite the promise that AI eliminates bias, it often inherits new ones, especially when trained on flawed data. If a dataset underrepresents women or people of color, for example, the AI may make less accurate recommendations for those groups.
Privacy is another concern. Healthcare data is sensitive, and there’s growing scrutiny over how it’s used by AI and who gets to see it. Groups like the FDA and AMA are pushing for clearer standards and better safeguards.
“Even when trained, AI gets too much wrong,” says Longo, comparing its potential to nuclear power: transformative, but not without risk. Like nuclear energy, AI offers enormous promise, but national leaders need to consider not just what it can do, but what it might do if left unchecked, Longo says, adding, “It has to be regulated carefully.”
| 2025-07-08T00:00:00 |
2025/07/08
|
https://honehealth.com/edge/ai-in-healthcare/?srsltid=AfmBOooIq_mkqHaBUyEy3VGulT9lQFjJ3KagBaTie68_Hn7QkxooAdgX
|
[
{
"date": "2023/04/01",
"position": 60,
"query": "artificial intelligence healthcare"
}
] |
MONAI - Medical Open Network for AI
|
Medical Open Network for AI
|
https://monai.io
|
[
"Project Monai"
] |
MONAI is the leading open-source framework for healthcare imaging AI, trusted by researchers and clinicians worldwide. Build, train, and deploy medical AI ...
|
Contributors
Over the last three years our community has expanded rapidly! But it takes a community to build out the success of Project MONAI, which is why we want to highlight contributing organizations. Below, you'll find contributors organizations who have dedicated resources to actively contributing back to Project MONAI.
| 2023-04-01T00:00:00 |
https://monai.io/
|
[
{
"date": "2023/04/01",
"position": 62,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/09/01",
"position": 66,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/11/01",
"position": 63,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/12/01",
"position": 63,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/02/01",
"position": 65,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/03/01",
"position": 59,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/05/01",
"position": 65,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/06/01",
"position": 67,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/10/01",
"position": 66,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/01/01",
"position": 66,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/02/01",
"position": 74,
"query": "AI healthcare"
},
{
"date": "2025/02/01",
"position": 68,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/03/01",
"position": 67,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/04/01",
"position": 73,
"query": "AI healthcare"
},
{
"date": "2025/04/01",
"position": 66,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/06/01",
"position": 74,
"query": "AI healthcare"
}
] |
|
Societal impact of AI and how it's helped communities - Google AI
|
Societal impact of AI and how it's helped communities
|
https://ai.google
|
[] |
Access · Climate · Economic opportunity · Government innovation · Healthcare · Learning and education · Science.
|
Monk Skin Tone
In partnership with Harvard professor and sociologist Dr. Ellis Monk, Google released a new skin tone scale designed to be more inclusive of the spectrum of skin tones we see in our society. The Monk Skin Tone Scale will help us and the tech industry at large build more representative datasets so we can train and evaluate AI models for fairness, resulting in features and products that work better for people of all skin tones.
| 2023-04-01T00:00:00 |
https://ai.google/societal-impact/
|
[
{
"date": "2023/04/01",
"position": 76,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/09/01",
"position": 79,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/11/19",
"position": 82,
"query": "AI job creation vs elimination"
},
{
"date": "2024/11/19",
"position": 93,
"query": "future of work AI"
},
{
"date": "2025/02/01",
"position": 91,
"query": "AI healthcare"
},
{
"date": "2025/02/01",
"position": 93,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/03/01",
"position": 93,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/04/01",
"position": 91,
"query": "AI healthcare"
},
{
"date": "2025/04/01",
"position": 95,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/06/01",
"position": 91,
"query": "AI healthcare"
}
] |
|
Regulatory and Compliance Considerations for Using AI in Health ...
|
Part 1: Regulatory and Compliance Considerations for Using AI in Health Care: AI Federal Landscape - Legal and Regulatory Considerations
|
https://training.feldesman.com
|
[] |
Artificial Intelligence (AI) is rapidly transforming every facet of ... Goals: Discuss how AI is being used in healthcare settings; understand how ...
|
This webinar is part 1 of 2 in "Regulatory and Compliance Considerations for Using AI in Health Care" webinar series. To purchase the full series, click here.
Artificial Intelligence (AI) is rapidly transforming every facet of health care—from revenue cycle management to clinical decision-making. As AI becomes more deeply embedded in daily operations, health center leaders must not only understand its vast potential but also proactively manage the associated risks. This webinar will provide strategic guidance on the legal, compliance, professional liability, and privacy and security implications of implementing AI in health center environments. Designed for executive leadership, compliance, legal, and IT professionals, this session will help you navigate the complexities of AI adoption while safeguarding your organization’s mission and reputation.
Session 1: AI Federal Landscape - Legal and Regulatory Considerations
Goals: Discuss how AI is being used in healthcare settings; understand how federal and state legislation are taking shape with regard to AI in health care.
Agenda:
Discuss how AI is and may be used in healthcare. Key regulations affecting AI in health care (e.g., Executive Orders, HIPAA, FDA). Review of federal and state legislation highlighting areas of concern by policymakers. Private interests advocating for regulation of AI in health care. The intersection of AI, data privacy, security, and compliance.
The on-demand version of this webinar will be available for purchase after the conclusion of the live webinar.
| 2023-04-01T00:00:00 |
https://training.feldesman.com/content/part-1-regulatory-and-compliance-considerations-using-ai-health-care-ai-federal-landscape
|
[
{
"date": "2023/04/01",
"position": 79,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/07/01",
"position": 82,
"query": "AI healthcare"
},
{
"date": "2023/08/01",
"position": 83,
"query": "AI healthcare"
},
{
"date": "2023/09/01",
"position": 82,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/11/01",
"position": 79,
"query": "AI healthcare"
},
{
"date": "2023/11/01",
"position": 78,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/12/01",
"position": 75,
"query": "AI healthcare"
},
{
"date": "2023/12/01",
"position": 79,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/01/01",
"position": 81,
"query": "AI healthcare"
},
{
"date": "2024/02/01",
"position": 78,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/03/01",
"position": 79,
"query": "AI healthcare"
},
{
"date": "2024/03/01",
"position": 97,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/05/01",
"position": 80,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/06/01",
"position": 80,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/09/01",
"position": 77,
"query": "AI healthcare"
},
{
"date": "2024/10/01",
"position": 80,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/11/01",
"position": 76,
"query": "AI healthcare"
},
{
"date": "2025/01/01",
"position": 80,
"query": "artificial intelligence healthcare"
}
] |
|
A Call for Collaboration in AI Research in Healthcare
|
A Call for Collaboration in AI Research in Healthcare
|
https://communities.springernature.com
|
[
"Research Communities Springer Nature",
"Mohsen Khosravi",
"Assistant Professor",
"Birjand University Of Medical Sciences"
] |
This is a call for global collaboration among researchers to conduct studies on the performance of Large Language Models (LLMs) across ...
|
This is a call for global collaboration among researchers to undertake research projects on the performance of prominent Large Language Models (LLMs), such as ChatGPT, Copilot, Gemini, and others, across diverse contexts and settings in healthcare.
LLMs are gaining increasing significance worldwide, particularly within healthcare settings, as they provide valuable services to users in a personalized and efficient manner.
Publications in this area range from analyzing the performance of LLMs in answering questions, to evaluating their ability to rewrite and simplify complex content, and, more importantly, their capacity to assist in diagnosing diseases based on descriptive, quantitative, or imaging data provided by users. Such studies are novel within the literature and have garnered substantial attention, as evidenced by their publication in high-quality international journals on an unprecedented scale.
I would be pleased to collaborate with researchers globally who are interested in conducting studies in this field. Please feel free to contact me at your convenience via email.
Sincerely,
Mohsen Khosravi, Ph.D.
Assistant Professor, Social Determinants of Health (SDH) Research Center
Birjand University of Medical Sciences, Iran
Email: [email protected]
| 2025-07-09T00:00:00 |
2025/07/09
|
https://communities.springernature.com/posts/a-call-for-collaboration-in-ai-research-in-healthcare
|
[
{
"date": "2023/04/01",
"position": 82,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/07/01",
"position": 80,
"query": "AI healthcare"
},
{
"date": "2023/08/01",
"position": 77,
"query": "AI healthcare"
},
{
"date": "2023/09/01",
"position": 89,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/11/01",
"position": 85,
"query": "AI healthcare"
},
{
"date": "2023/11/01",
"position": 81,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/12/01",
"position": 70,
"query": "AI healthcare"
},
{
"date": "2023/12/01",
"position": 84,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/01/01",
"position": 86,
"query": "AI healthcare"
},
{
"date": "2024/02/01",
"position": 81,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/03/01",
"position": 83,
"query": "AI healthcare"
},
{
"date": "2024/03/01",
"position": 85,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/05/01",
"position": 83,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/06/01",
"position": 83,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/09/01",
"position": 85,
"query": "AI healthcare"
},
{
"date": "2024/10/01",
"position": 85,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/11/01",
"position": 82,
"query": "AI healthcare"
},
{
"date": "2025/01/01",
"position": 83,
"query": "artificial intelligence healthcare"
}
] |
Naviant Acquires Amitech Solutions to Expand Healthcare and ...
|
Naviant Acquires Amitech Solutions to Expand Healthcare and Intelligent Automation Capabilities
|
https://naviant.com
|
[
"Kara Martin"
] |
“By combining our innovative automation and AI solutions with our healthcare expertise and Naviant's scale and capabilities, we'll accelerate ...
|
July 8, 2025
Naviant, a recognized leader in intelligent automation and process consulting, announced today that it has acquired Amitech Solutions, an award-winning healthcare focused services firm specializing in intelligent automation, AI, agentic automation, RPA, and data analytics. Headquartered in St. Louis, MO, Amitech is also a top tier UiPath partner and a recognized leader in automation and AI for the healthcare industry.
With its deep industry expertise and tailored solutions, Naviant solves complex problems by harnessing intelligence and unlocking end-to-end automation. Michael Carr, President & CEO at Naviant, says, “This partnership represents a strategic move to enhance our capabilities in the intelligent automation industry. By integrating Amitech’s innovative team and solutions with our own, significant value can be unlocked to solve complex problems and deliver exceptional outcomes for our customers.”
“Amitech was named an Agentic Automation Fast Track Partner in 2025 due to their early mover position in building agentic automation workflows,” said Ashim Gupta, CFO and COO for UiPath.” They recognized the opportunity of agentic automation to truly transform the enterprise. This acquisition validates that position, and we look forward to supporting this partnership to accelerate the power of Agentic AI across both Naviant and Amitech’s customer base and beyond.”
“This partnership with Naviant marks a major milestone in our mission to redefine what’s possible with data, AI, and intelligent automation,” said Amit Bhagat, Founder and CEO of Amitech. “By combining our innovative automation and AI solutions with our healthcare expertise and Naviant’s scale and capabilities, we’ll accelerate impact for both of our customers and the broader market through a data-driven digital workforce that reduces administrative burden and maximizes human potential.”
Michael Carr, President & CEO at Naviant, says, “Amitech is a leader in driving significant value to their customers, and we are excited to merge our talented teams together to elevate everyone’s experience and customer success.”
About Naviant
Naviant, a recognized leader in intelligent automation and process consulting, helps organizations reimagine work. With their deep industry expertise and tailored solutions, Naviant solves complex problems by harnessing intelligence and unlocking end-to-end automation.
As their customer’s most trusted partner in automation, Naviant has over 35 years of experience delivering exceptional business outcomes. With a process-first approach, Naviant focuses on simplifying business operations prior to implementing technology. Their comprehensive suite of automation solutions includes artificial intelligence (AI), agentic automation, intelligent document processing (IDP), enterprise content management (ECM), business and process orchestration, content portals, process and task mining, and robotic process automation (RPA). Naviant’s flexible Managed Services empowers customers to focus on their business, while allowing Naviant to manage, enhance, or scale their automation solutions as needed.
For more information about Naviant, please visit naviant.com.
About Amitech
Amitech Solutions is an award-winning, healthcare-focused services firm specializing in intelligent automation, AI, and data analytics. Amitech is honored to have been named UiPath’s Partner of the Year for four consecutive years (2020-2023), and in 2025, UiPath’s Agentic Automation Fast Track Partner for its leading-edge expertise in Agentic Automation. We help healthcare organizations improve quality, reduce medical and administrative expenses, and address workforce challenges by leveraging automation, AI, and digital agents. By streamlining manual processes, we empower healthcare leaders and care teams to focus on delivering better experiences and outcomes, driving lasting transformation across healthcare.
| 2023-04-01T00:00:00 |
https://naviant.com/blog/amitech-acquisition/
|
[
{
"date": "2023/04/01",
"position": 94,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/02/01",
"position": 92,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/03/01",
"position": 91,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/05/01",
"position": 95,
"query": "artificial intelligence healthcare"
}
] |
|
Artificial Intelligence in Thoracic Surgery: Transforming Diagnostics ...
|
Artificial Intelligence in Thoracic Surgery: Transforming Diagnostics, Treatment, and Patient Outcomes
|
https://www.mdpi.com
|
[
"Lopes",
"Mascarenhas",
"Fonseca",
"Fernandes",
"Maria Gabriela O.",
"Leite-Moreira",
"Adelino F.",
"Sara Lopes",
"Miguel Mascarenhas",
"João Fonseca"
] |
Background/Objectives: Artificial intelligence is revolutionizing healthcare. In the recent years, AI tools have been incorporated by medical specialties ...
|
4.1. Challenges and Ethical Considerations
31,33,34,43, AI in TS holds significant promise for enhancing patient outcomes and operational efficiency. However, AI may also lead to job displacement, particularly in roles involving routine tasks, and presents several challenges with ethical considerations that need to be addressed [ 2 89 ]. The healthcare integration of AI technology, including SaMD, implies the insurance of patient safety, equity, and trust, on which privacy, data protection, data bias, explainability, and responsibility, rely [ 31 61 ] ( Figure 11 ).
33, Organizations like the ITU-WHO Focus Group on AI for Health are working to create benchmarking processes to assess AI’s accuracy and safety in healthcare [ 31 61 ]. The International Medical Device Regulators Forum (IMDR) is an international group working to harmonize SaMD regulation: they develop guidelines and ensure safety and effectiveness. In Europe, regulations from the Medical Device Coordination Group (MDCG) clarify SaMD by risk, requiring specific assessments according to the device. EUDAMED was also designed to implement diagnostic Medical Devices [ 31 62 ].
32,33,43,31, Despite the promise of ML in TS, issues around patient data privacy and AI decision-making transparency, remain unresolved [ 28 53 ]. Handling sensitive patient information necessitates stringent data protection measures, such as compliance with regulations like the General Data Protection Regulation (GDPR) [ 8 34 ]. SaMD may collect and store sensitive patient data, which are easy to reproduce and vulnerable to remote access and manipulation. Healthcare organizations are increasingly targeted by cyberattacks aiming to exploit vulnerabilities in data storage. Hence, robust cybersecurity measures must be implemented to protect patient data [ 31 33 ]. A survey in the UK estimated that 63 per cent of the population is uncomfortable with sharing their personal data to improve AI technology, reflecting widespread concerns about data privacy and misuse [ 33 ].
An accurate SaMD application must be standardized: it should produce consistent results when applied to similar datasets, regardless of the user or setting. This adaptability, however, increases the demands for compliance with data protection guidelines and adequate security measures [ 37 ]. The EMA (European Medicine Agency), the FDA (U.S. Food and Drug Administration), hospitals, and healthcare providers and manufacturers are all responsible for warranting that SaMD can work across systems. Adherence to the FAIR principles for data management is mandatory: accessibility, interoperability, findability, and reusability [ 64 ] ( Figure 12 ).
33,19,31, Specifically for SaMD intended for diagnosis, prevention, monitoring, and treatment, there is a need for clinical and real-world studies; everything must flow for human benefit, with clear and transparent algorithms—the way they reach decisions must be readily understood (explainability and transparency) [ 31 62 ]. The ‘black box’ is a major challenge in AI, especially with DL algorithms: to achieve trust and clinical adoption, developing AI systems with interpretable and understandable outputs is crucial to integration in clinical settings [ 13 33 ]. Concerns about reliability or a preference for established practices also make healthcare professionals hesitant in AI implementation.
31,33,31,33,63,69, AI systems may inadvertently perpetuate existing biases present in the training data, leading to disparities in care and compromising equitable healthcare delivery [ 8 116 ]. If minority populations are underrepresented in medical datasets, AI tools may be less accurate for these groups, exacerbating health inequities. Bias can be introduced into the clinical decision-making process during training or through decisions made during SaMD design [ 61 ]. Addressing this requires deliberate efforts to collect diverse and representative data and implement strategies that mitigate bias in AI development [ 12 89 ].
17, Determining responsibility for AI-driven decisions, particularly in surgical contexts, raises ethic–legal questions. Clear guidelines and informed consent when AI tools are involved in patient care are essential [ 33 ]. It is important to note that AI may misclassify nodules, leading to unnecessary biopsies or missed cancers [ 2 33 ]. Responsibility for AI-driven decisions (developers, healthcare providers, and institutions) becomes complex and involves multiple factors and stakeholders, especially when errors occur. Liability depends on the nature of the AI system, its integration into clinical practice, and the specific circumstances of the case. The clinician retains primary responsibility for patient outcomes when assistive-AI provides recommendations, because they ultimately make the final decisions. However, autonomous AI operates with minimal human intervention. When harm results from its use, liability may shift towards the developers or manufacturers, if the system was used as intended and adhered to regulatory standards. Healthcare institutions are responsible for properly integrating AI systems into their workflows, including adequate staff training, regular maintenance, and timely updates. Failure to do so could result in institutional liability. Manufacturers may be liable under product liability laws, if the AI system is found to be defective in design, manufacturing, or lacks proper instructions and warnings. The legal landscape for AI in healthcare is still developing.
32, Advanced AI-driven robotic surgery systems are not widely available: implementation can be expensive, limiting access in resource-constrained settings [ 2 33 ]. Integrating AI tools into clinical workflows and EHRs is crucial and can be technically challenging and resource-intensive. Implementing and maintaining AI systems require substantial financial investment, which may not be feasible for all healthcare institutions [ 31 37 ].
While AI holds transformative potential for healthcare, addressing these challenges and ethical considerations is imperative to ensuring that its integration promotes health equity, protects patient rights, and maintains public trust. Collaborative efforts to develop robust, transparent, and ethical AI solutions tailored to the unique demands of TS are a priority for AI to become trustworthy.
| 2025-01-14T00:00:00 |
2025/01/14
|
https://www.mdpi.com/2075-4418/15/14/1734
|
[
{
"date": "2023/04/01",
"position": 95,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/09/01",
"position": 99,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/11/01",
"position": 93,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/12/01",
"position": 94,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/02/01",
"position": 94,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/03/01",
"position": 86,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/05/01",
"position": 96,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/06/01",
"position": 95,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/10/01",
"position": 96,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/01/01",
"position": 96,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/02/01",
"position": 80,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/03/01",
"position": 81,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/04/01",
"position": 82,
"query": "artificial intelligence healthcare"
}
] |
Conversational artificial intelligence (AI) - RACGP
|
Conversational artificial intelligence (AI)
|
https://www.racgp.org.au
|
[] |
There is no doubt that conversational AI could revolutionise parts of healthcare delivery. GPs should be extremely careful, however, in using ...
|
‘Conversational artificial intelligence (AI)’ refers to technologies that can engage in natural and human-like conversations. Conversational AI encompasses tools such as advanced chatbots, virtual agents/assistants, and ‘embodied conversational agents’ (avatars) Prominent examples include OpenAI’s ChatGPT, Google Gemini, Microsoft Copilot, and Anthropic’s Claude.
Many conversational AI tools use generative AI techniques (creating new content) to engage in these conversations and can be considered Generative AI as well as Conversational AI. The focus of this fact sheet is on the conversational AI properties of these technologies. These AI technologies are distinct from AI scribes, which convert a conversation with a patient into a clinical note, summary, or letter that can be incorporated into the patient’s health record. The RACGP has a separate resource on AI scribes.
Conversational AI tools are trained on vast quantities of data from the internet, including articles, books. Unlike ‘simple’ chatbots that rely on pre-defined rules and scripts to respond to the user, ‘advanced’ conversational AI chatbots use large volumes of data together with AI technologies (such as machine learning, natural language processing, and automatic speech recognition). The current generation of conversational AI tools incorporate generative AI which is inherently probabilistic (subject to chance or variation) and can behave unpredictably1. These innovations mean that the tool can discern the intent of the user’s inputs and ‘learn’ from users’ behaviour over time.
There is no doubt that conversational AI could revolutionise parts of healthcare delivery. GPs should be extremely careful, however, in using conversational AI in their practice at this time. Many questions remain about patient safety, patient privacy, data security, and impacts for clinical outcomes.
| 2023-04-01T00:00:00 |
https://www.racgp.org.au/running-a-practice/technology/artificial-intelligence-ai/conversational-artificial-intelligence
|
[
{
"date": "2023/04/01",
"position": 96,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/09/01",
"position": 100,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/11/01",
"position": 95,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/12/01",
"position": 95,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/02/01",
"position": 96,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/03/01",
"position": 88,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/05/01",
"position": 97,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/06/01",
"position": 97,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/10/01",
"position": 97,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/01/01",
"position": 98,
"query": "artificial intelligence healthcare"
}
] |
|
Shining a Light: Safer Health Care Through Transparency
|
Shining a Light: Safer Health Care Through Transparency
|
https://www.ihi.org
|
[] |
Yes, I would like to receive promotional updates from the Institute for Healthcare Improvement. ... Patient Safety and Artificial Intelligence: Opportunities and ...
|
National Patient Safety Foundation’s Lucian Leape Institute. Shining a Light: Safer Health Care Through Transparency. Boston: National Patient Safety Foundation; 2015.
Defining transparency as “the free flow of information that is open to the scrutiny of others,” this report offers sweeping recommendations to bring greater transparency in four domains: between clinicians and patients; among clinicians within an organization; between organizations; and between organizations and the public.
The report, produced by the NPSF Lucian Leape Institute Roundtable on Transparency, makes the case that true transparency will result in improved outcomes, fewer medical errors, more satisfied patients, and lowered costs of care. Case studies are included to document how transparency is practiced in each of the domains.
| 2023-04-01T00:00:00 |
https://www.ihi.org/library/publications/shining-light-safer-health-care-through-transparency
|
[
{
"date": "2023/04/01",
"position": 99,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/11/01",
"position": 97,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/12/01",
"position": 98,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/02/01",
"position": 98,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/03/01",
"position": 96,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/06/01",
"position": 99,
"query": "artificial intelligence healthcare"
}
] |
|
Tempus | AI-enabled precision medicine
|
AI-enabled precision medicine
|
https://www.tempus.com
|
[] |
Tempus has built the world's largest library of clinical & molecular data and an operating system to make that data accessible and useful, starting with ...
|
Extensive molecular profiling combined with clinical data identifies targeted therapies and clinical trials for a large proportion of cancer patients, and paired tumor/normal plus transcriptome sequencing outperforms tumor-only DNA panel testing.
Nature Biotechnology Study Reveals that Tempus’ xT Platform Increases Cancer Patients’ Personalized Therapeutic Opportunities Extensive molecular profiling combined with clinical data identifies targeted therapies and clinical trials for a large proportion of cancer patients, and paired tumor/normal plus transcriptome sequencing outperforms tumor-only DNA panel testing.
Showcasing a robust pan-cancer tumor organoid (TO) platform, revealing genomic/transcriptome fidelity of TO culture from >1,000 patients. We demonstrate a neural-network-based high-throughput approach for label-free, light-microscopy-based drug assays capable of predicting patient-specific heterogeneity in drug responses with applicability across solid cancers.
A Pan-cancer Organoid Platform for Precision Medicine Showcasing a robust pan-cancer tumor organoid (TO) platform, revealing genomic/transcriptome fidelity of TO culture from >1,000 patients. We demonstrate a neural-network-based high-throughput approach for label-free, light-microscopy-based drug assays capable of predicting patient-specific heterogeneity in drug responses with applicability across solid cancers.
| 2023-04-01T00:00:00 |
https://www.tempus.com/?srsltid=AfmBOoqNq8LUKTFLY3qZzxdNAtfPf4NF4NnC_734QTPuqPGGoYVVhHtx
|
[
{
"date": "2023/04/01",
"position": 100,
"query": "artificial intelligence healthcare"
}
] |
|
[D] Struggling to Find Remote AI Jobs? Would You ...
|
The heart of the internet
|
https://www.reddit.com
|
[] |
AI-Jobs.net: AI-Jobs.net offers a curated list of remote AI-related job postings from around the world, covering various industries and positions.
|
Hey fellow Redditors!
Lately, I've been on the hunt for remote AI job opportunities, and I've found the process to be quite time-consuming and frustrating. I have a strong interest in AI and a desire for the flexibility of remote work, but I'm struggling to find job postings that cater to both of these needs.
The Problem:
There are numerous job boards specifically for AI positions and others dedicated to remote work opportunities. However, I haven't found a single platform that combines these two essential aspects for people like me. Going through multiple job boards and filtering out irrelevant posts is an exhausting process.
A Potential Solution:
I've been thinking about developing a web scraper to fetch AI-related job listings that allow remote work from various job boards. The idea is to aggregate all the results into a single website dedicated to remote AI job opportunities. Furthermore, I'm considering using AI to recommend more personalized job postings based on the user's profile, making the job search process even more efficient.
Here's what I envision the platform would offer:
A comprehensive list of remote AI job postings from multiple sources
AI-powered personalized job recommendations based on your profile
Customizable email alerts for new job postings that match your preferences
Tips, resources, and blog posts about AI, remote work, and career development
Your Thoughts:
Before I dive into building this platform, I want to gather your opinions on this idea. Would you find a website like this useful? Do you think it could help you and other AI professionals in your remote job search? What features would you like to see on the platform, and how do you feel about AI-powered personalized job recommendations?
Please share your thoughts, suggestions, and feedback in the comments below. If there's enough interest, I'll work on bringing this platform to life and keep you updated on its progress.
Looking forward to your input!
| 2023-04-01T00:00:00 |
https://www.reddit.com/r/MachineLearning/comments/12gvtk6/d_struggling_to_find_remote_ai_jobs_would_you/
|
[
{
"date": "2023/04/01",
"position": 43,
"query": "artificial intelligence hiring"
}
] |
|
The Pros and Cons of Using AI and Automation in ...
|
The Pros and Cons of Using AI and Automation in Recruitment
|
https://www.linkedin.com
|
[
"Apidel Technologies",
"Overture Partners",
"Anubrain Technology"
] |
Improved candidate matching: AI and automation can analyze large volumes of data to find candidates who match specific job requirements. This can help to reduce ...
|
As the world becomes increasingly digitized, more and more companies are turning to AI and automation to streamline their recruitment processes. But while these tools can certainly save time and improve efficiency, they also come with their fair share of challenges. In this article, we’ll explore the pros and cons of using AI and automation in recruitment.
Pros:
Time savings: One of the most significant benefits of using AI and automation in recruitment is the amount of time it can save. Automated processes can handle the tedious, repetitive tasks that recruiters would otherwise need to do manually, freeing up their time to focus on more important aspects of their jobs. Improved candidate matching: AI and automation can analyze large volumes of data to find candidates who match specific job requirements. This can help to reduce bias in the hiring process and increase the likelihood of finding the right candidate for the job. Better candidate experience: Automation can provide real-time feedback and updates to candidates, keeping them informed throughout the hiring process. This can lead to a more positive candidate experience, which can in turn improve the employer brand. Reduced cost per hire: Using AI and automation can reduce the cost per hire by reducing the need for human labor in the recruitment process.
Cons:
Limited human touch: While automation can save time and improve efficiency, it can also reduce the human touch in the recruitment process. This can make candidates feel like they are interacting with a machine rather than a person, which can be off-putting. Risk of bias: AI and automation can also introduce bias into the recruitment process if the algorithms used are not properly designed or trained. This can lead to discriminatory hiring practices and negatively impact diversity and inclusion efforts. Data privacy concerns: Automated recruitment processes can collect and process large amounts of personal data about candidates, which raises data privacy concerns. Companies need to be transparent about what data they are collecting and how they are using it. Cost of implementation: Implementing AI and automation in recruitment can be expensive, particularly for small businesses. The cost of software, hardware, and training can add up quickly, making it difficult for some companies to justify the investment.
Conclusion:
| 2023-04-01T00:00:00 |
https://www.linkedin.com/pulse/pros-cons-using-ai-automation-recruitment-karyarth-consultancy
|
[
{
"date": "2023/04/01",
"position": 47,
"query": "artificial intelligence hiring"
}
] |
|
How AI is changing the staffing and recruitment industry?
|
How AI is changing the staffing and recruitment industry?
|
https://www.linkedin.com
|
[
"Stack It Recruitment",
"Graham Townley"
] |
With AI, recruiters and staffing agencies can streamline their recruitment processes, increase efficiency, and find the best candidates for job openings.
|
Artificial Intelligence (AI) is changing the staffing and recruitment industry in significant ways. With AI, recruiters and staffing agencies can streamline their recruitment processes, increase efficiency, and find the best candidates for job openings. In this article, we will explore how AI is changing the staffing and recruitment industry and how it is shaping the future of hiring.
One of the significant ways that AI is transforming the recruitment industry is by automating time-consuming and repetitive tasks. Recruitment software powered by AI can now scan through hundreds of resumes in minutes, identifying the most qualified candidates based on their skills, experience, and education. This not only saves recruiters a considerable amount of time but also ensures that they are reviewing candidates who meet the job requirements.
Another significant advantage of using AI in recruitment is the ability to reduce bias. Human recruiters are susceptible to unconscious bias, which can lead to discriminatory hiring practices. AI-powered software, on the other hand, can eliminate bias by focusing solely on the candidate's qualifications and experience, rather than their personal characteristics such as their name or gender. This approach can help ensure that the best candidates are selected based on their skills, experience, and fit for the job.
AI also offers benefits for job seekers, as it can help them find job opportunities that match their skills and experience. Job search engines that use AI can scan through job postings and match candidates with job opportunities that are the best fit for their qualifications. This saves job seekers time and helps them find job openings that they may not have otherwise known about.
In addition to automating and streamlining recruitment processes, AI can also help recruiters and staffing agencies predict future hiring needs. AI algorithms can analyze data from past hiring patterns and market trends to identify which job roles will be in demand in the future. This can help recruiters stay ahead of the competition and ensure that they have the right talent available when needed.
Finally, AI can also help with employee retention by analyzing data on employee engagement and satisfaction. AI-powered software can analyze employee feedback and identify trends that may indicate potential issues that need to be addressed. This can help employers take proactive measures to retain their top talent and create a positive work environment.
| 2023-04-01T00:00:00 |
https://www.linkedin.com/pulse/how-ai-changing-staffing-recruitment-industry-sumit-sugathan
|
[
{
"date": "2023/04/01",
"position": 57,
"query": "artificial intelligence hiring"
}
] |
|
The Impact of Automation and AI on Hiring Practices: How ...
|
The Impact of Automation and AI on Hiring Practices: How to Incorporate New Technologies into Your Recruitment Strategy
|
https://medium.com
|
[
"Hassan Iftikhar"
] |
AI and automation can significantly improve recruitment by helping HR professionals find the right candidate quickly and efficiently. By utilizing AI in ...
|
The Impact of Automation and AI on Hiring Practices: How to Incorporate New Technologies into Your Recruitment Strategy Hassan Iftikhar 11 min read · Apr 10, 2023 -- Listen Share
Originally posted on the Remotebase blog.
Artificial intelligence is altering the recruitment industry rapidly. Check out this blog to learn about recent trends and practices to stay ahead!
The Impact of Automation and AI on Hiring Practices: How to Incorporate New Technologies into Your Recruitment Strategy
The recruitment process remains a significant challenge for HR teams in most companies, even in 2023, as the job market remains highly competitive and complex. With the rise of remote work and the gig economy, companies need more skilled candidates in many industries, making it difficult to fill open positions.
76% of hiring managers say that attracting quality candidates is the biggest challenge because candidates have higher expectations from their employers, such as work-life balance, flexible work arrangements, and a strong company culture, making it challenging for companies to attract and retain top talent. Moreover, new technologies and social media platforms are constantly emerging, changing how companies approach recruitment and making it necessary to stay up-to-date with the latest trends and tools.
So, what’s the way out?
The impact of Artificial Intelligence is everywhere, from the moment you think, type, and act. It’s everywhere. Hence, it must exist in the talent acquisition process too.
In this article, we’ll cover how automation and artificial intelligence can impact recruiting process and some proven strategies to incorporate to automate the hiring process.
The Impact of Automation and AI on the Recruitment Process
The impact of automation and AI on the recruitment process has been significant in recent years. Moreover, the trend is expected to continue as 88% percent of companies globally already use AI in some way for HR, according to SHRM.
Suppose you ask professionals in the field what effect AI technologies have had on recruitment technology. In that case, they will undoubtedly tell you that it has ushered in a new era of innovative companies that are being taken “to the next level” of modern recruiting thanks to technological advancements. The direct impact is the extent to which AI can be utilized to help businesses find the finest personnel in the least amount of time, which in turn can improve employee retention rates.
Integrating new AI technologies into existing recruiting systems is proving to be highly efficient in automating various repetitive and often performed procedures previously carried out manually by recruitment specialists. Recruiters and HR professionals are given the luxury of time by “machine learning” technology that automatically screens candidates, allowing them to focus on what they do best: hiring.
Here’s how artificial intelligence (AI) & automation are impacting recruiting process:
1. Augmented Writing
AI tools are helping recruitment professionals to create more effective job descriptions and other recruitment content. These tools use natural language processing and machine learning algorithms to analyze data and provide suggestions for improving the quality and effectiveness of recruitment content.
Improved Language and Tone
AI-powered recruiting tools can help recruitment professionals to improve the language and tone of their job descriptions and other recruitment content. These AI recruitment tools analyze large amounts of data to identify the most effective words and phrases to use in recruitment content in the most engaging tone and style.
Increased Inclusion and Diversity
Another benefit of incorporating AI in recruitment is the potential to increase inclusion and diversity in recruitment content. These AI tools can identify potentially discriminatory language and suggest more neutral alternatives. For example, a study by TalVista found that job descriptions edited using their AI-powered tool had a 25% increase in gender-neutral language and a 22% increase in underrepresented group-friendly language.
Read more on the power of diversity in building a strong development team.
Better Search Engine Optimization (SEO)
Jobvite found that job postings with SEO-optimized titles received 2.3 times more applications than those without optimized tags. In addition, the hiring manager can use AI capabilities to improve the SEO of their job descriptions and other recruitment content. These tools analyze search data to identify the most effective keywords and phrases to use in recruitment content, increasing the likelihood that the content will appear in relevant search results.
2. Sourcing Tools
AI-powered sourcing tools are helping recruitment professionals to find and engage with qualified candidates more efficiently and effectively. These tools use natural language processing and machine learning algorithms to analyze data and suggest the most effective recruitment strategies.
Here are some ways that AI is impacting hiring through sourcing tools:
More Targeted Sourcing
AI-powered tools can help to hire managers to identify the most qualified candidates for a particular role by analyzing their soft skills, experience, and qualifications through quality data while avoiding time-consuming tasks. These tools can search through large volumes of data from various sources, including job boards, social media platforms, and professional networking sites.
Improved Engagement
A study by Beamery found that personalized outreach using AI-powered sourcing tools can increase response rates from candidates by up to 75%. Another benefit of AI sourcing tools is the potential to improve candidate engagement by providing more personalized and relevant communication. These tools can analyze candidate preferences and behavior data to deliver tailored communication and recommendations for the most effective outreach strategies.
Better Predictive Analytics
AI in recruiting technology can provide human resources professionals with better predictive analytics to help identify the most qualified candidates for a particular role. Implementing AI in recruitment can analyze candidate data and identify patterns to predict their likelihood of success in a specific position, enabling recruitment professionals to make more informed hiring decisions.
3. Assessment Section
AI and automation significantly impact the recruiting process’s assessment section by providing more accurate, efficient, and objective candidate evaluation.
Here are some ways that AI and automation are impacting the assessment section of recruitment:
More Objective Assessment
AI-powered assessment tools can help recruitment professionals remove bias from the assessment process by objectively screening candidates and their qualifications. This tool can be incorporated into recruitment software to analyze data on candidate responses to interview questions, skills tests, and other evaluation criteria, providing a more standardized and objective evaluation process than a human recruiter.
Moreover, A study by Harvard Business Review found that AI-powered assessments can reduce human bias in the recruiting process by up to 42%.
Improved Efficiency
Automation can also improve the efficiency of the assessment process by automating tasks such as resume screening and skills testing, giving data-driven insights. Applying AI in recruiting can analyze candidate qualifications and experience data points to determine the culture fit for a particular role, reducing the time and resources required for manual evaluation.
Personalized Assessment
AI-powered assessment tools can provide a more personalized assessment of candidate skills and qualifications by tailoring evaluation criteria to each candidate. In addition, these tools can analyze candidate preferences and behavior data to deliver tailored evaluation criteria and recommendations for the most effective assessment strategies. A study by IBM found that personalized assessments using AI-powered tools can increase the accuracy of candidate predictions by up to 70%.
4. Chatbots
AI-powered chatbots are revolutionizing recruitment by providing a more efficient and engaging candidate experience, saving recruiters time. Chatbots can automate many routine tasks involved in the recruiting process, such as candidate screening, scheduling interviews, and answering frequently asked questions.
Here are some ways that AI and automation are helping through chatbots in the recruiting process:
Faster Response Time
Chatbots can provide candidates with instant responses to their queries, reducing the time required for manual communication and increasing the speed of the hiring process.
Improved Candidate Experience
Chatbots can provide candidates with a more engaging and personalized experience by tailoring communication and recommendations to each candidate, eliminating human bias. These tools can analyze candidate preferences and behavior data to provide customized communication and recommendations for the most effective outreach strategies. A study by Allegis Group found that candidates who interacted with chatbots reported higher satisfaction with the hiring process.
Reduced Workload
It can reduce the workload for recruitment professionals by automating many of the routine tasks involved in the hiring process. These tools can automate repetitive tasks such as resume screening, scheduling interviews, and sending follow-up emails, freeing recruitment professionals to focus on more strategic tasks.
How AI Benefits HR Professionals & Recruiters
1. Improved Candidate Sourcing
Sourcing AI-powered systems can identify candidates with the right qualifications and experience for a particular role more quickly and accurately than traditional methods. This can help to fill the top of the recruiting funnel with high-quality candidates, reducing the time and effort required to find the best candidates.
2. Streamlined Screening
AI-powered screening tools can auto-screen candidates by evaluating their resumes and applications, saving recruiters time and effort. In addition, these tools can analyze keywords, experience, and education to identify the most suitable candidates, reducing the time and resources required for manual screening.
3. Efficient Interviewing
AI is transforming the interviewing stage of recruitment by providing more accurate and objective evaluations of candidates, improving the candidate experience, and streamlining the interview process. One way that AI is helping in the interviewing stage is by using video and voice analysis to evaluate candidates’ responses. AI-powered tools can analyze factors such as tone of voice, facial expressions, and body language to assess candidates’ suitability for a particular role.
According to a survey by PwC, 67% of HR professionals believe that AI will help them to identify the right candidate more quickly and accurately. Another study by LinkedIn found that 58% of recruiters believe that AI can help to reduce bias in the hiring process.
4. Smooth Selection Process
Artificial intelligence is transforming the selection stage of recruitment by providing more accurate and objective evaluations of candidates, improving the candidate experience, and streamlining the selection process. As AI technology continues to advance, likely, these benefits will only become more significant in the future.
According to a survey by Deloitte, 33% of companies already use Artificial intelligence tools for candidate screening and assessment, and another 41% plan to do so in the next two years. Moreover, it can help recruiters to make more informed decisions about which candidates to select for a particular role.
How AI Helps in Talent Acquisition
Artificial Intelligence’s most crucial job in recruiting is to make an organization’s hiring process much more efficient, primarily through automation. When advances in AI make it possible for machines to handle the more repetitive tasks that HR professionals and recruiters usually have to do, it gives them more time to work on more critical studies.
To better comprehend the role of artificial intelligence in talent acquisition, here is how it helps business leaders fulfill their recruiter role and hire high-performing candidates:
1. Finding the Right Candidates
AI and automation can significantly improve recruitment by helping HR professionals find the right candidate quickly and efficiently. By utilizing AI in recruitment, talent acquisition specialists can screen resumes, quickly identify higher-quality candidates, and reduce the time spent reviewing resumes. In addition, AI-powered candidate sourcing tools can help HR professionals identify potential candidates from various online resources, including social media platforms, job boards, and professional networks.
Additionally, AI-powered interview scheduling tools can help automate the scheduling process and reduce scheduling conflicts, ensuring that interviews are conducted promptly and efficiently. Video interviewing d powered by artificial intelligence can also provide more accurate assessments of candidates by analyzing their facial expressions and body language.
2. Employee Retention
Artificial intelligence-powered analytics can help identify factors contributing to employee turnovers, such as poor management, inadequate training, or lack of career growth opportunities. By identifying these issues early on, HR professionals can take steps to address them and improve employee satisfaction and retention.
3. Skill Gap Analysis
AI-powered recruiting software can be crucial to understand the skill gaps through targeted training programs to upskill employees. HR professionals believe the skills gap is a significant issue in their organization. By identifying and addressing these gaps, HR professionals can improve employee performance and productivity, ultimately leading to better business outcomes.
4. Compliance and Risk Management
AI-powered tools can help HR professionals manage compliance and reduce risk by automating tasks like document management, background checks, and payroll processing. According to a study by Deloitte, 33% of HR professionals cite compliance and risk management as top priorities. By automating these tasks, HR professionals can reduce the risk of errors and ensure that their organization meets regulatory requirements.
5. Reduced Costs
AI in recruitment can also improve the efficiency of the interview process, reducing the number of interviews required to find the top talent, which can help save costs associated with scheduling, travel, and time spent by HR professionals. Overall, by automating many of the recruitment processes, AI can help to save costs, reduce the workload of HR professionals, and ensure that the recruitment process is more efficient and effective. For example, AI-powered resume screening tools can quickly and accurately analyze resumes, reducing the time HR professionals spend reviewing them manually. This can help to save costs associated with manual labor and reduce the risk of human error.
Artificial Intelligence Recruitment Tools
Now that you know how AI plays a vital role in recruitment, let’s check out how some of the AI recruiting technology tools help them serve as recruiting solutions for you:
1. Mya: A chatbot recruitment assistant that can help candidates apply for jobs, schedule interviews, and provide feedback.
2. HireVue: A video interviewing tool that uses AI to analyze a candidate’s facial expressions, tone of voice, and body language to assess their suitability for the role.
3. Entelo: A candidate sourcing tool that uses AI to search through social media and other online resources to identify potential candidates.
4. Textio: An AI-powered writing tool that analyzes job descriptions and provides suggestions to make them more inclusive and attractive to diverse candidates.
5. Workable: An applicant tracking system can help HR professionals manage job postings, resumes, and interview scheduling.
Bottom Line
The impact of automation and AI on hiring practices is undeniable. As technology continues to advance, companies must adapt to stay competitive in attracting and retaining top talent. But despite AI’s widespread adoption, some HR professionals remain wary of it.
But what if we tell you you don’t need to worry?
Remotebase is a platform that takes care of your recruitment needs while hiring the top talent for you. The candidates do not only go through a rigorous screening and assessment process but are thoroughly trained to work on Silicon Valley-caliber projects.
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FAQs
What is the effect of automation and AI on HR?
The effect of automation and AI on HR is that it can streamline many recruitment and HR processes, reducing the time and effort required by HR professionals.
Will automation and AI replace human recruiters?
While automation and AI can automate recruitment processes, they must maintain the human touch required to build relationships with candidates and make final hiring decisions.
What are some common challenges of incorporating automation and AI into a recruitment strategy?
Common challenges of incorporating automation and AI into a recruitment strategy include the cost of implementing new technologies, the need for training and upskilling HR professionals, and concerns about the impact on the candidate experience.
Want to build an expert tech team?
| 2023-04-10T00:00:00 |
2023/04/10
|
https://medium.com/tech-lead-hub/the-impact-of-automation-and-ai-on-hiring-practices-how-to-incorporate-new-technologies-into-your-6ef1f47ef1b2
|
[
{
"date": "2023/04/01",
"position": 67,
"query": "artificial intelligence hiring"
}
] |
AI in the hiring process
|
AI in the hiring process
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https://www.emerald.com
|
[] |
by E Hocken · 2023 · Cited by 3 — The purpose of this paper is to highlight the risks in using artificial intelligence (AI) as part of the hiring process, particularly focusing on the risks of ...
|
The approach taken was to draw upon current experiences of AI used in hiring processes through case studies and existing research and to discuss current and upcoming legislative measures in place within the UK and EU to extract key advice that companies can use to ensure AI is used as effectively and fairly as possible in the hiring process.
| 2023-04-01T00:00:00 |
https://www.emerald.com/insight/content/doi/10.1108/SHR-03-2023-0014/full/html
|
[
{
"date": "2023/04/01",
"position": 80,
"query": "artificial intelligence hiring"
}
] |
|
AI in Workforce Decisions: The Human Imperative - LNGFRM
|
AI in Workforce Decisions: The Human Imperative
|
https://lngfrm.net
|
[
"Lngfrm Team"
] |
While AI's growing influence in workforce decisions, particularly layoffs, is undeniable, its potential to foster fairer, more humane, and even ...
|
The headlines are stark, painting a picture of an automated future where algorithms, not human managers, wield the axe of redundancy. It’s a narrative that fuels widespread anxiety, with a recent Gallup survey revealing that a staggering 75% of Americans anticipate AI will significantly reduce U.S. jobs over the next decade.
Yet, as with most technological revolutions, the reality is far more nuanced than the fear-mongering suggests. While AI’s growing influence in workforce decisions, particularly layoffs, is undeniable, its potential to foster fairer, more humane, and even alternative outcomes remains largely untapped by leaders who often misinterpret its true capabilities.
The core issue, as articulated by C200 member Lauren Herring, CEO of IMPACT Group, isn’t AI itself, but rather how companies choose to implement it. Many stumble at the first hurdle, treating AI as an infallible oracle rather than a sophisticated tool.
The result is often a reinforcement of existing biases, a loss of trust, and a missed opportunity for more strategic workforce management.
One of the most critical missteps leaders make is basing monumental decisions on flawed foundations: inaccurate or incomplete data. AI, for all its computational prowess, is merely a reflection of the information it’s fed.
If the inputs—employee skills, performance histories, training records—are limited or corrupted, the output will inevitably be skewed. Gartner research, for instance, predicts that up to a third of generative AI projects will be abandoned precisely because of poor data quality. Gartner
It’s a digital axiom: garbage in, garbage out. Relying on such compromised insights for something as sensitive as job cuts isn’t just inefficient; it’s ethically perilous.
Equally problematic is the pervasive assumption that AI and analytical tools are inherently objective. The allure of a neutral, data-driven decision-maker is strong, but it’s a dangerous illusion. Bias, often baked into historical data sets, can be amplified by algorithms, leading to discriminatory outcomes. A Capterra survey found that while 98% of HR professionals plan to use AI for labor cost reduction, only half are confident in its unbiased recommendations.
This stark disconnect highlights the urgent need for human judgment and robust bias-detection tools to act as critical safeguards, preventing technology from inadvertently perpetuating systemic inequalities.
Furthermore, an over-reliance on AI, treating it as the sole arbiter of fate, strips away the vital human element from complex decisions. AI may excel at processing quantitative metrics, but it struggles with the qualitative nuances that define an individual’s value: their potential for growth, their unique contributions to company culture, their resilience in the face of challenges.
Such factors are often overlooked when algorithms dominate, potentially leading to the loss of invaluable talent. Beyond this, a blind faith in AI can lead to tangible compliance errors, from miscalculating severance packages to overlooking crucial notice periods, exposing companies to legal and reputational risks.
Perhaps the most glaring oversight is the failure to embrace transparency. As AI’s role in employment decisions expands, regulatory bodies are taking notice. New York, for example, is set to amend its Worker Adjustment and Retraining Notification (WARN) law, requiring employers to disclose when mass layoffs are linked to AI use. This emerging legislative landscape underscores a fundamental truth: employees and the public demand clarity and accountability when technology impacts livelihoods.
Shrouding AI-driven decisions in secrecy only breeds suspicion and erodes the trust essential for a healthy employer-employee relationship.
Yet, despite these pitfalls, AI holds immense promise when wielded responsibly. It can, paradoxically, be a powerful force for fairness and compassion in the difficult realm of workforce reduction.
One transformative application lies in identifying alternatives to layoffs. By analyzing vast employee data, AI can uncover opportunities for reskilling and redeployment, matching individuals with new roles within the organization rather than showing them the door.
Companies like IKEA and Deloitte have successfully leveraged this approach, demonstrating that AI can be a tool for retention and growth, not just reduction.
Beyond prevention, AI can dramatically improve the efficiency and personalization of the offboarding process. It can automate the complex calculations of severance packages and final paychecks, ensuring accuracy and timely communication.
More importantly, AI can personalize the exit experience, recommending tailored outplacement services based on an individual’s role, tenure, and location. While a Pew Research survey indicates public skepticism about AI in workplace decisions, its capacity to streamline and customize the transition for affected employees is a tangible benefit.
However, it is in the realm of outplacement where the delicate balance between AI’s utility and the irreplaceable human touch becomes most apparent. AI tools can undoubtedly empower job seekers: optimizing resumes and LinkedIn profiles, generating professional headshots, automating job searches, and providing AI-assisted interview preparation.
These functionalities enhance efficiency and provide data-driven insights. But, as Lauren Herring emphasizes, they are complements, not replacements, for human connection.
Herring points to the phenomenon of “employee zombies”—individuals who feel isolated and disconnected after a layoff—as a stark reminder of AI’s limitations. Surveys of outplacement participants consistently reveal that the relationship with a career coach is valued above all other program elements, even AI-assisted resume development.
In moments of profound emotional intensity, such as job loss, AI cannot replicate empathy, compassion, or the nuanced guidance that only an experienced human can provide.
The rising trend of “boomerang employees“—rehires who return to former employers, accounting for 35% of new hires in March 2025, according to ADP—underscores the long-term value of treating employees with dignity, even during separation.
Thoughtful AI use, combined with an unwavering commitment to the human side of the employer-employee relationship, is not just ethically sound; it’s a strategic imperative for attracting and retaining talent in a dynamic labor market.
Ultimately, the narrative around AI and layoffs must shift from one of fear and replacement to one of partnership and enhancement. Leaders who grasp this distinction, who understand that AI is a powerful tool to inform, streamline, and even humanize difficult decisions, rather than a substitute for leadership and empathy, will be the ones who navigate the future of work successfully, fostering trust and resilience in their organizations.
| 2025-07-08T00:00:00 |
2025/07/08
|
https://lngfrm.net/ai-in-workforce-decisions-the-human-imperative/
|
[
{
"date": "2023/04/01",
"position": 39,
"query": "artificial intelligence layoffs"
},
{
"date": "2023/05/01",
"position": 34,
"query": "artificial intelligence layoffs"
},
{
"date": "2023/06/01",
"position": 37,
"query": "artificial intelligence layoffs"
},
{
"date": "2023/07/01",
"position": 40,
"query": "artificial intelligence layoffs"
}
] |
Xbox projects cancelled amid Microsoft layoffs
|
Xbox projects cancelled amid Microsoft layoffs
|
https://dig.watch
|
[] |
At the same time, AI talent wars are heating up. Meta has reportedly offered huge bonuses to poach researchers, while Amazon's Andy Jassy said ...
|
6 Jul 2025
Xbox projects cancelled amid Microsoft layoffs
Microsoft has confirmed plans to cut up to 9,000 jobs—roughly 4% of its global workforce—in its latest round of redundancies this year. The company cited the need to adapt to a rapidly evolving market, while pressing ahead with major investments in artificial intelligence.
Although Microsoft did not specify which divisions will be affected, reports suggest its Xbox gaming unit will face significant cuts. According to internal emails, the reboot of Perfect Dark and the game Everwild have been cancelled, and The Initiative, the studio behind Perfect Dark, will shut down.
Additional layoffs are impacting other gaming studios, including Turn 10 and ZeniMax Online Studios. ZeniMax’s long-time director Matt Firor has announced his departure. Meanwhile, Ireland’s Romero Games has also been affected after funding for its project was pulled by a publisher.
The upcoming job cuts will mark Microsoft’s fourth round of layoffs in 2025. Over 800 affected roles are based in Washington state, including in Redmond and Bellevue, key Microsoft hubs. The company is currently investing $80bn in AI infrastructure, including data centres and chips.
Microsoft’s AI push has seen it hire AI pioneer Mustafa Suleyman to lead its Microsoft AI division and deepen ties with OpenAI. However, tensions have reportedly grown in that relationship. Bloomberg noted difficulty in selling Microsoft’s Copilot tool, as many users prefer ChatGPT.
At the same time, AI talent wars are heating up. Meta has reportedly offered huge bonuses to poach researchers, while Amazon’s Andy Jassy said last month that AI would eventually replace certain roles at his company.
Would you like to learn more about AI, tech and digital diplomacy? If so, ask our Diplo chatbot!
| 2025-07-06T00:00:00 |
2025/07/06
|
https://dig.watch/updates/xbox-projects-cancelled-amid-microsoft-layoffs
|
[
{
"date": "2023/04/01",
"position": 42,
"query": "artificial intelligence layoffs"
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"date": "2023/05/01",
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{
"date": "2023/06/01",
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"query": "artificial intelligence layoffs"
},
{
"date": "2023/07/01",
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"query": "artificial intelligence layoffs"
},
{
"date": "2023/11/01",
"position": 43,
"query": "artificial intelligence layoffs"
},
{
"date": "2024/04/01",
"position": 44,
"query": "artificial intelligence layoffs"
},
{
"date": "2024/06/01",
"position": 44,
"query": "artificial intelligence layoffs"
},
{
"date": "2024/08/01",
"position": 38,
"query": "artificial intelligence layoffs"
}
] |
Intel cuts more than 500 jobs in Oregon - OPB
|
Intel cuts more than 500 jobs in Oregon
|
https://www.opb.org
|
[] |
However, as artificial intelligence expands, Intel has lost ground in recent years to other major chip makers focused on creating AI-capable ...
|
Intel’s Jones Farm Campus in Hillsboro, Ore., July 8, 2025. Intel plans to cut over 500 Oregon employees as part of a layoff plan. Morgan Barnaby / OPB
UPDATE — July 11, 2025: Intel revised the number of layoffs in Oregon to 2,392 on Friday, higher than the 500 disclosed on Tuesday.
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Original story below:
One of Oregon’s largest private employers is permanently cutting over 500 jobs, according to a notice filed with the state.
Global computer chip firm Intel will lay off 529 engineers, technicians and other positions at its major campus in Hillsboro and another location in Aloha.
The move is part of Intel’s efforts to become a more efficient company, a spokesperson said in an email to OPB.
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“We are making these decisions based on careful consideration of what’s needed to position our business for the future,” the email said, “and we will treat people with care and respect as we complete this important work.”
The affected employees have been notified of the layoffs, or will be soon, according to the notice sent to Oregon officials. James Warner, director of corporate people movement at Intel, said in the required notice that layoffs will take place over a two-week period ending on July 15. Workers are given around four weeks notice, the filing said, and will get pay and other benefits for nine more weeks.
In this provided photo, Intel's High Numerical Aperture Extreme Ultraviolet lithography tool in Hillsboro, Ore., in April, 2024. The 165-ton tool is the first commercial lithography system of its kind in the world. Courtesy of Intel Corporation
Intel is one of the world’s biggest semiconductor companies with expertise in designing, developing and manufacturing the computer chips essential to modern day electronics. The chipmaker is headquartered in Santa Clara, California, but the Hillsboro campus acts as the company’s research and development hub.
However, as artificial intelligence expands, Intel has lost ground in recent years to other major chip makers focused on creating AI-capable semiconductors. In October, Intel cut 1,300 jobs in Oregon as part of a larger effort to trim 15% of the company’s global workforce.
Still, Intel remains among Oregon’s top employers with around 20,000 workers in the state.
In December, CEO Pat Gelsinger abruptly retired after spending four years running the semiconductor firm. The following month, Intel reported a nearly $19 billion loss in fiscal year 2024.
Semiconductor industry veteran Lip-Bu Tan took over as CEO in March, vowing to create a leaner company focused on innovation. In an April earnings call, Tan foreshadowed the layoffs confirmed in Tuesday’s official notice to the state.
“Organizational complexity and bureaucracies have been suffocating the innovation and agility we need to win,” Tan told investors on the April call. “It takes too long for decisions to get made. New ideas and people who generate them have not been given the room or resources to incubate and grow. The unnecessary silos have led to bad execution. I’m here to fix this.”
| 2025-07-08T00:00:00 |
2025/07/08
|
https://www.opb.org/article/2025/07/08/intel-oregon-job-cuts-more-than-500-employees/
|
[
{
"date": "2023/04/01",
"position": 45,
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"query": "artificial intelligence layoffs"
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{
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{
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{
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{
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"query": "artificial intelligence layoffs"
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{
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}
] |
Microsoft Announces Further Mass Layoffs: Is the AI Revolution ...
|
Microsoft Announces Further Mass Layoffs: Is the AI Revolution Slowing?
|
https://www.xtb.com
|
[] |
Microsoft has announced another round of layoffs, affecting approximately 9,000 employees, nearly 4% of its global workforce.
|
Microsoft has announced another round of layoffs, affecting approximately 9,000 employees, nearly 4% of its global workforce. This marks the second wave of job reductions in 2025, following earlier cuts in May (6,000 employees) and January (around 1% of the workforce). The layoffs span various departments, including sales, engineering, marketing, and gaming segments, as well as middle management. Officially, Microsoft attributes these decisions to the need to adapt its organizational structure to a dynamic market and to achieve strategic priorities, with artificial intelligence playing a key role.
Is the AI Transformation Too Costly?
The primary driver of these changes is a record investment in AI infrastructure, with Microsoft planning to allocate as much as $80 billion this fiscal year to expand data centers and develop AI-powered services. Such massive expenditures are intended to enable Microsoft to maintain its leadership position in the technological race but simultaneously exert pressure on the company's operating margins. Consequently, Microsoft is reorganizing resources, reducing the number of managers, and automating processes, partly through tools like Copilot.
The layoffs affect not only administrative and sales positions but also engineering teams, particularly those working on older technologies or projects unrelated to AI. A growing proportion of Microsoft's code is already being generated with the assistance of AI tools.
Does This Mean the AI Revolution Is Slowing Down?
Microsoft's actions do not necessarily signify a slowdown in the AI revolution, but rather a revolution brought about by these very tools.
Investment growth continues, but companies—including Microsoft—are beginning to optimize the pace of infrastructure expansion, shifting from the costly stage of model training to more efficient practical implementation. Microsoft aims to avoid overpaying in the current revolution, should it prove to be smaller than currently anticipated.
The pace of AI adoption in companies is decelerating; market data suggests that AI adoption among enterprises has already reached a high level, but the growth momentum has significantly slowed in recent quarters. Market leaders are experiencing stagnation, and new players are struggling to break through.
Experts and industry leaders predict that the "easy" gains in AI are over; further development will require breakthrough innovations, not merely increased computing power or data access. Data resources are increasingly limited, and infrastructure costs are rising.
The era of easy AI victories has concluded. Progress will now be more challenging and demand deeper breakthroughs.
The company's shares have returned to yesterday's closing price but remain below their historical daily peak of $500 per share, which was reached on Monday at the end of June. Since the beginning of this year, Microsoft shares have gained over 16%, a level significantly higher than the Nasdaq 100, which has risen by just over 7%. Source: xStation5
| 2023-04-01T00:00:00 |
https://www.xtb.com/en/market-analysis/microsoft-announces-further-mass-layoffs-is-the-ai-revolution-slowing
|
[
{
"date": "2023/04/01",
"position": 84,
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{
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{
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{
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"query": "artificial intelligence layoffs"
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{
"date": "2024/08/01",
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"query": "artificial intelligence layoffs"
}
] |
|
Behind Microsoft's layoffs: New attitude shaped by AI | Business
|
Behind Microsoft’s layoffs: New attitude shaped by AI
|
https://www.whitecountycitizen.com
|
[
"Alex Halverson The Seattle Times",
"Tns"
] |
The Redmond-based tech giant laid off more than 6,000 employees in May, followed up by an additional 305 in early June. The company kicked off ...
|
Searcy, AR (72143)
Today
Partly cloudy skies this evening. A few showers developing late. Low 72F. Winds SW at 5 to 10 mph. Chance of rain 40%..
Tonight
Partly cloudy skies this evening. A few showers developing late. Low 72F. Winds SW at 5 to 10 mph. Chance of rain 40%.
| 2023-04-01T00:00:00 |
https://www.whitecountycitizen.com/business/behind-microsoft-s-layoffs-new-attitude-shaped-by-ai/article_323c38cc-c385-4726-b2b4-363ca5c575be.html
|
[
{
"date": "2023/04/01",
"position": 86,
"query": "artificial intelligence layoffs"
},
{
"date": "2023/05/01",
"position": 70,
"query": "artificial intelligence layoffs"
},
{
"date": "2023/06/01",
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"query": "artificial intelligence layoffs"
},
{
"date": "2023/07/01",
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"query": "artificial intelligence layoffs"
},
{
"date": "2023/11/01",
"position": 87,
"query": "artificial intelligence layoffs"
},
{
"date": "2024/04/01",
"position": 92,
"query": "artificial intelligence layoffs"
},
{
"date": "2024/06/01",
"position": 90,
"query": "artificial intelligence layoffs"
},
{
"date": "2024/08/01",
"position": 77,
"query": "artificial intelligence layoffs"
}
] |
|
Intel to cut over 500 jobs in Oregon as layoffs expand nationwide
|
Intel to cut over 500 jobs in Oregon as layoffs expand nationwide
|
https://www.storyboard18.com
|
[] |
MIB launches 'Kalaa Setu' AI challenge; invites startups to build content-generation tools ... The Oregon layoffs follow a recent announcement of ...
|
Intel Corp. is set to lay off more than 500 employees in Oregon, marking a major step in its ongoing restructuring effort under newly appointed CEO Lip-Bu Tan, reports Bloomberg.
The company disclosed in a regulatory filing that 529 positions will be permanently eliminated from its Aloha and Hillsboro sites starting July 15, as part of a sweeping plan to reduce operating expenses and streamline operations, the report added.
The Oregon layoffs follow a recent announcement of 107 job cuts at Intel's Santa Clara headquarters in California.
While the company has yet to confirm the full scale of its workforce reduction, sources familiar with the matter estimate the cuts could affect more than 20% of Intel's global staff, according to the report.
In a statement, Intel said the job cuts are designed to help the company become "a leaner, faster and more efficient company."
The chipmaker added, "Removing organizational complexity and empowering our engineers will enable us to better serve the needs of our customers and strengthen our execution. We are making these decisions based on careful consideration of what's needed to position our business for the future."
The layoffs are part of CEO Lip-Bu Tan's aggressive plan to revive the Silicon Valley giant after years of losing ground to competitors in the semiconductor space. Once a dominant force in chip manufacturing, Intel has been outpaced by rivals such as Nvidia, which capitalized on the recent surge in AI computing demand.
Tan, who took the helm in April, has pledged to reinvigorate Intel's innovation engine while trimming excess layers of bureaucracy.
| 2023-04-01T00:00:00 |
https://www.storyboard18.com/how-it-works/intel-to-cut-over-500-jobs-in-oregon-as-layoffs-expand-nationwide-73655.htm
|
[
{
"date": "2023/04/01",
"position": 87,
"query": "artificial intelligence layoffs"
},
{
"date": "2023/05/01",
"position": 89,
"query": "artificial intelligence layoffs"
},
{
"date": "2023/06/01",
"position": 98,
"query": "artificial intelligence layoffs"
},
{
"date": "2024/08/01",
"position": 94,
"query": "artificial intelligence layoffs"
}
] |
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