OIT - AI Systems at Work

X AI Systems at Work A Changing Psychosocial Work Environment Author / Tahmina Karimova

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Authorization for publication: Caroline Fredrickson, Director, Research Department ILO Working Papers can be found at: www.ilo.org/research-and-publications/working-papers Suggested citation: Karimova, T. 2026. AI Systems at Work: A Changing Psychosocial Work Environment, ILO Working Paper 170 (Geneva, ILO). https://doi.org/10.54394/0003409801 ILO Working Paper 170

Abstract The dominant framing of AI systems at work focuses on the opportunities that AI-based technologies offer to improve workplace safety and health. With few exceptions, little is done to map and understand the negative implications of these technologies. Nonetheless, there is a small but growing awareness of the need to critically review whether the preventive approach offered by existing occupational safety and health frameworks is fit for purpose when addressing risks associated with the deployment of AI-based systems in the world of work. The debate on this topic is active at the regulatory level. Various jurisdictions are developing general AI regulations that tend to classify the use of AI-based systems in employment settings as high-risk. This paper proposes to examine the health impacts of AI, focusing on its effects on mental and social well-being (known in the occupational safety and health discipline as the workplace psychosocial environment). The paper argues that to effectively address psychosocial risks arising from AI-based systems, policymakers should adopt an integrated approach that includes laws and policies on labour and employment, equality and non-discrimination, occupational safety and health, and privacy and data protection. About the authors Tahmina Karimova is a lawyer specialised in public international law, sustainable development, international labour standards, human rights law, and arms control. She is a Law Research

and policies on labour and employment, equality and non-discrimination, occupational safety and health, and privacy and data protection. About the authors Tahmina Karimova is a lawyer specialised in public international law, sustainable development, international labour standards, human rights law, and arms control. She is a Law Research Specialist at the RESEARCH Department, ILO. Previously, she has worked as a Human Rights Officer in the Office of the High Commissioner for Human Rights, and prior to that as a Research Fellow at the Geneva Academy of the International Humanitarian Law and Human Rights/Law Faculty of the University of Geneva. She holds an LL.M from the University of Essex and a PhD in International Law from the University of Geneva and the Graduate Institute of International and Development Studies.02 ILO Working Paper 170 Abstract 01 About the authors 01 X Introduction 03 X 1 Setting the scene 05 AI systems and their uses at work 05 Health and safety in the AI-based workplace 06 X 2 Psychosocial risks and AI systems: interplay and data availability 09 Psychosocial factors at work 09 Psychosocial risks arising from AI-based technologies: current state of research 10 Focusing on new or “augmented” risks 12 Intensive/intrusive surveillance 13 Job autonomy (and dignity) 14 Excessive data collection and lack of transparency 16 X 3 Regulatory frameworks addressing AI associated PSRs 17 General AI regulations and the scope of OSH in them 19 OSH-related national regulatory responses to AI-based risks 24 The emergence of “new” OSH rights 28 X Conclusion 30 References 31 Table of contents03 ILO Working Paper 170 X Introduction The world of work and the nature of work are undergoing significant transformations as a result of digitalization, including AI-based technologies.1 Throughout workplaces, artificial intelligence (AI) is increasingly being used as a tool for the tasks that workers perform, but also for the entire cycle of the employment relationship, including recruitment, training, onboarding, monitoring

of digitalization, including AI-based technologies.1 Throughout workplaces, artificial intelligence (AI) is increasingly being used as a tool for the tasks that workers perform, but also for the entire cycle of the employment relationship, including recruitment, training, onboarding, monitoring and surveillance, remuneration, rewarding and career development, transfer and dismissal. This transformation is profoundly changing the composition of the workforce, the work environment, the equipment used, as well as the way work is designed and organized, leading to important impacts on workers’ health and well-being.2 As a result of these transformations, academics have begun to analyse the effects of digitalization and new AI technologies;3 policymakers and practitioners have made efforts to better understand how to govern technology at work; while regional institutions have developed campaigns and begun researching what is a digitalized world of work and what it means for workers. There is still limited research on how AI systems are integrated into core aspects of employment relationships, such as working time, wages, occupational safety and health (OSH), and other rights at work.4 To address this gap, we need to explore various questions, including the meaning of work, and whether policies created to ensure ‘humane conditions of work for all’ are fit to address challenges of this digital and AI-based world of work. At the moment, the discourse on AI-based technologies in the workplace is focused on the opportunities that these technologies offer, and with few exceptions, there is little systematized knowledge on the health impacts of AI-based technologies. The present paper will not, therefore, engage with the positive developments in the use of AI in the workplace or its benefits from an OSH perspective. Rather, it will focus on growing evidence and concerns regarding the effects of AI-based systems on health and well-being5 including the less understood aspects of workplace safety and health such as psychosocial risks – defined as “anything in the design or management of work that increases the risk of work-related stress”.6 This concern is reflected both within the broader context of active policymaking, related to general regulation of AI technologies, and in the sphere of new employment-specific standards such

“anything in the design or management of work that increases the risk of work-related stress”.6 This concern is reflected both within the broader context of active policymaking, related to general regulation of AI technologies, and in the sphere of new employment-specific standards such as the prohibition of certain artificial intelligence (AI)-based forms of workplace surveillance, and ‘new’ or ‘enhanced-old’ rights claimed in the context of AI-based algorithmic management (AM) (e.g. the right to transparency on the deployment of AI technologies, the right to human review of decisions taken by AI-based AM, etc.). This research paper examines whether there is a need to adapt existing legal frameworks to the new health risks associated with deployment of the AI in the workplace, and what those adaptations should be. It, therefore, examines how digital working environments trigger psychosocial 1 According to the ILO, while “digitalization encompasses all types of digital applications in the workplace from the simple use of desktop computers to advanced robotics and virtual/augmented reality, since the mid-2010s attention has focused on artificial intelligence, particularly machine learning”. In: ILO, Challenges and opportunities of digitalization, GB.350/HL/1(Rev.1), §5. 2 C. de Tecco, B. Persechino, S. Iavicoli, Psychosocial Risks in the Changing World of Work: Moving from the Risk Assessment Culture to the Management of Opportunities, Med Lav., 2023, 114(2). 3 A. Aloisi & V. De Stefano, Your Boss Is an Algorithm: Artificial Intelligence, Platform Work and Labour (Bloomsbury Publishing, 2022). 4 See e.g. EU-OSHA’s Healthy Workplaces Campaign: Safe and Healthy Work in the Digital Age, 2023 - 2025. 5 M. Jarota, Artificial Intelligence in the Work Process. A Reflection on the Proposed European Union Regulations on Artificial Intelligence from an Occupational Health and Safety Perspective, Computer Law & Security Review, Volume 49, 2023. 6 ILO, Psychosocial risks and stress at work, 10 November 2022.04 ILO Working Paper 170

from an Occupational Health and Safety Perspective, Computer Law & Security Review, Volume 49, 2023. 6 ILO, Psychosocial risks and stress at work, 10 November 2022.04 ILO Working Paper 170 hazards and whether these hazards require additional and/or different safeguards to protect workers' health and safety.7 In so doing, the paper will contribute to addressing gaps in knowledge of known and emerging risk factors, to help ensure safety, health, and well-being in workplaces and help enterprises better understand the specific risks associated with AI technologies, in an effort to advance decent work. The study proceeds first by setting the context of AI technologies in workplaces and work processes and introduces the types of AI technologies examined in this paper (section 1). Against this background, the repercussions of these technologies on safety and health at work are analysed. Section 2 then addresses the new and emerging health and safety risks associated with these AI technologies, focusing only on psychosocial risks posed by AI-based technologies. The paper then examines the role of international labour standards and regional and national labour regulatory trends (in section 3). The argument is made for the need for an integrated regulatory approach that combines preventive approaches with emerging regulatory interventions to address psychosocial risks associated with AI-based systems. This paper focuses on one specific aspect of workplace safety and health – PSRs – and will not address physical health and safety issues. 7 F. Chirico, The Forgotten Realm of the New and Emerging Psychosocial Risk Factors. J Occup Health, 59(5)(2023):433–435.05 ILO Working Paper 170 X 1 Setting the scene

AI systems and their uses at work Before addressing the factual interplay of AI-based technologies with the health and safety of workers, it is important to define what AI is and how it is used in the workplace and work processes. There are various ways in which the scientific literature defines the term “artificial intelligence”. In simple terms, the notion has been defined as “the ability of machines to think, learn and adapt” and importantly AI is “[n]o longer confined to routine tasks, AI now tackles complex challenges

There are various ways in which the scientific literature defines the term “artificial intelligence”. In simple terms, the notion has been defined as “the ability of machines to think, learn and adapt” and importantly AI is “[n]o longer confined to routine tasks, AI now tackles complex challenges once exclusive to human intelligence”.8 Regulatory frameworks have likewise attempted to define AI. For example, the recently adopted EU AI Act, defines an AI system as “a machine-based system that is designed to operate with varying levels of autonomy and that may exhibit adaptiveness after deployment, and that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments”.9 AI-based technologies – a term that requires its own definition – are a subset of a broader workplace digitalization process encompassing a wide variety of technologies such as AI, advanced robotics, technology used for monitoring remote working, the internet of things (IoT), big data, wearables, and online platforms, among others.10 This paper focuses on the following types of technologies: advanced robotics, AI-based AM (see box). A very short introduction to advanced robotics, AI-based AM and smart digital systems Advanced robotics (included in AI-enabled systems)11 or smart robots are “technologies designed to perform tasks requiring high precision, adaptability and autonomy … [such as] industrial robots, … robotic arms used for repetitive and hazardous tasks, as well as modern innovations such as autonomous mobile robots, drones, exoskeletons and collaborative robots (“cobots”)”.12 These AI-based robots are increasingly being used in advanced manufacturing. AI-based AM – also referred to as ‘algorithmic management of work’ – can be defined as the use of algorithmic procedures for the coordination of labour input in an organization,13 but it can also be defined as the delegation of managerial functions to algorithms.14 AIbased AM collects data from workspaces, workers and their activities. These data are then processed by AI-based systems “to make automated or semi-automated decisions, or to

but it can also be defined as the delegation of managerial functions to algorithms.14 AIbased AM collects data from workspaces, workers and their activities. These data are then processed by AI-based systems “to make automated or semi-automated decisions, or to 8 ISO, What is artificial intelligence (AI)? See also Oxford English Dictionary that defines AI as “The capacity of computers or other machines to exhibit or simulate intelligent behaviour” in OED, Artificial Intelligence. 9 EU, AI Act, 13 March 2024, Art. 3. 10 EU-OSHA, Digital Technologies at Work and Psychosocial Risks: Evidence and Implications for Occupational Safety and Health, 2024. 11 J. T. Licardo, M. Domjan, T. Orehovački, Intelligent Robotics – A Systematic Review of Emerging Technologies and Trends, Electronics 13 (3). 12 ILO, Revolutionizing health and safety: The role of AI and digitalization at work, 2025, p. 6. 13 S. Baiocco, E. Fernandez-Macias, U. Rani and A. Pesole, The Algorithmic Management of Work and its Implications in Different Contexts, (ILO, 2022), p. 5. 14 MIT Sloan Management Review, Algorithmic Management: The Role of AI in Managing Workforces, April 2023.06 ILO Working Paper 170 provide information to decision-makers, such as human resources managers, employers and sometimes workers themselves”.15 Smart digital systems can also incorporate AI-based technologies. In the workplace context, these include “systems that use a range of digital technologies – such as sensor-based devices, AI, IoT, wearables, wireless technologies, augmented reality, virtual reality and drones – to monitor, analyse, and manage workplace safety and health risks, including physical, ergonomic, chemical, biological and psychosocial, associated with various factors such as workers' activities or tasks, equipment, workplace layout and work organization ”.16 While there is a great deal of overlap between these technologies, AI-based technologies lie at the core of each. For this reason, the literature classifies them according to how they transform the

such as workers' activities or tasks, equipment, workplace layout and work organization ”.16 While there is a great deal of overlap between these technologies, AI-based technologies lie at the core of each. For this reason, the literature classifies them according to how they transform the world of work. Discussions distinguish two main applications of AI technology in the workplace:  “The first is aimed at automating tasks that workers perform, especially routine and repetitive tasks that can be efficiently handled by machines. The second is to use AI-based analytics and algorithms to replace or augment management functions: hiring, monitoring, supervising and training workers, as well as scheduling hours and breaks – or what is commonly referred to as “algorithmic management”. Both have implications for job quantity (the number of jobs) and job quality, including respect for fundamental principles and rights at work”.17 Existing literature further complements the scope of AI in the workplace by documenting other applications of AI-based technologies in the workplace, such as the use of AI technologies in assessing employee performance,18 monitoring and predicting employee health,19 and even directly improving employee health.20 These examples illustrate the existence of previously unknown relationships between workers and AI. This study uses “AI systems” and “AI technologies” interchangeably to describe the three types of technologies examined (i.e. advanced robotics, AI-based AM and smart digital systems). Health and safety in the AI-based workplace Researchers in the field of digitalization of workplaces find that AI-based technologies offer a range of opportunities for workers (e.g. automating routine and repetitive tasks, reduced physical risks), for employers (e.g. higher productivity and efficiency) and opportunities to improve workplace safety.21 These opportunities, however, also come with a broad range of risks to workers’ safety, health and well-being, including physical, mental, psychosocial, economic, and ethical risks.22 With that said, as noted, this study will only address the PSRs. As AI-based technologies are becoming highly integrated across industries and span the entire range of employer/

ers’ safety, health and well-being, including physical, mental, psychosocial, economic, and ethical risks.22 With that said, as noted, this study will only address the PSRs. As AI-based technologies are becoming highly integrated across industries and span the entire range of employer/ managerial responsibilities, one of their direct impacts on workplace health will be felt at the 15 EU-OSHA, Digital Technologies at Work and Psychosocial Risks, p. 24. 16 EU-OSHA, Digital Technologies at Work and Psychosocial Risks, p. 19. 17 ILO, Challenges and opportunities of digitalization, §7. 18 A. Varma, V. Pereira & P . Patel, Artificial Intelligence and Performance Management, Organizational Dynamics Volume 53:1 (2024). 19 El-Helaly M. Artificial Intelligence and Occupational Health and Safety, Benefits and Drawbacks, Med Lav. 115(2)(2024). 20 ILO, Revolutionizing health and safety: The role of AI and digitalization at work, 2025, p. 9. 21 For a broad overview see ILO, Revolutionizing health and safety: The role of AI and digitalization at work. 22 J. Howard & P . Schulte, Managing Workplace AI Risks and the Future of Work, Am J Ind Med., 67(11)(2024), p.4.07 ILO Working Paper 170 level of psychosocial factors. Second, PSRs have been less studied and, thus, are relatively less well understood. Cobots, for example, working alongside workers or sharing a task with the worker, may create a risk of traumatic injury or cognitive overload and work intensification for the worker working in close proximity.23 Work intensification, for example, can result from “mismatch between a human worker's physical or cognitive capabilities and a cobot's AI‐enabled pacing”.24 Above all, a general fear of job loss and job insecurity has been a frequent concern for workers “collaborating” with the advanced digital technologies. 25 The use of AI-based analytics and algorithms often results in a continuous interaction between

man worker's physical or cognitive capabilities and a cobot's AI‐enabled pacing”.24 Above all, a general fear of job loss and job insecurity has been a frequent concern for workers “collaborating” with the advanced digital technologies. 25 The use of AI-based analytics and algorithms often results in a continuous interaction between a worker and an AI-enabled system affecting all aspects of work and above all in overseeing and managing everyday tasks.26 These systems are no longer experimental but are being integrated across traditional industries.27 A survey of mid-level managers in six high-income countries indicates that out of the 6,047 managers surveyed about their firms’ use of AM tools, 74 per cent indicated that their firms use at least one tool to instruct, monitor or evaluate employees.28 AI-based AM assumes managerial authority29 substantially changing the traditional employer and employee relationship but also carrying with it potential detrimental effects to safety and health work. Emerging evidence suggests that surveillance capabilities of AI-based AM have been associated with a variety of adverse psychosocial and health effects on workers. They can have “disempowering and inequitable impacts on worker health, job quality, and the employer–employee relationship”.30 The aforementioned survey of managers in six high income countries also found that 27 per cent of mid-level managers were concerned that employees’ physical and mental health are often not adequately protected.31 Evidence is emerging that AM tools negatively impact workers in the areas of job satisfaction, trust, workloads, motivation, and stress levels. Thus, another survey, conducted in the United States, using data from 1,273 respondents (workers), found that 46 per cent of workers who said that their productivity was monitored “all the time” (i.e. using AM and surveillance technologies) agreed that they worked too fast, compared to just 15 % of workers who said that their productivity was never monitored electronically. 32 According to the same survey, 53 per cent of workers who said that their productivity was monitored “all the time” agreed that they felt anxious at work all or some of the time. This anxiety could be related to the fact that data colproductivity was never monitored electronically. 32 According to the same survey, 53 per cent of workers who said that their productivity was monitored “all the time” agreed that they felt anxious at work all or some of the time. This anxiety could be related to the fact that data collected and fed into AI does not consider contextual information or co-relate the data points that would allow sound conclusions on workers’ productivity and habits. As noted in a submission to Australian Parliament’s Standing Committee on Employment, Education and Training, excessive 23 K. Jung & J.-S. Yang, Mitigating Safety Challenges in Human-Robot Collaboration: the Role of Human Competence, Technological Forecasting and Social Change, Volume 213 (2025). 24 J. Howard & P . Schulte, Managing Workplace AI Risks and the Future of Work, p.4. 25 L. Tomidei et al., Beyond Pure Technology – The Cognitive and Organisational Impacts of Cobots, 12 December 2022; EU-OSHA, Digital Technologies at Work and Psychosocial Risks, p. 15. 26 J. Howard & P . Schulte, Managing Workplace AI Risks and the Future of Work, p.4. 27 U. Rani, A. Pesole, and I. Gonzalez Vazquez, Algorithmic Management practices in regular workplaces: case studies in logistics and healthcare, (ILO, 2024). T. Cox and G. R. Oosterwijk, Algorithmic Management in the Workplace: Case Studies on the Impact of Algorithmic Technologies in Seven Sectors in the Nordics, Policy Study, October 2024. 28 OECD, Algorithmic Management in the Workplace: New Evidence from an OECD Employer Survey, Artificial Intelligence Papers, 31(2025).

At the country level: in “United States, 90% of managers indicate that their firms provide at least one algorithmic management tool,

while prevalence rates in European countries (France, Germany, Italy, and Spain) range between 76% (Italy) and 81% (France)”, p. 19. 29 GuruLink, Algorithmic Management: The Rise of AI Middle Management. 30 J. Howard & P . Schulte, Managing Workplace AI Risks and the Future of Work, p.5. 31 OECD, Algorithmic Management in the Workplace: New Evidence from an OECD Employer Survey. 32 A. Hertel-Fernandez, Estimating the Prevalence of Automated Management and Surveillance Technologies at Work and their Impact on Workers’ Well-Being, 2024.08 ILO Working Paper 170 monitoring, like key-stroke monitoring, serving “as a ‘proxy’ for worker productivity does not tell ‘a complete story’”. 33 The evidence presented above shows that AI is responsible for challenges at the level of job satisfaction, trust, workloads, and motivation. Digitalization of the employment relationship takes place in a context where workplace PSRs are often overlooked by workplace safety and health regulations, with a considerable amount of legislation across the globe focusing on the physical aspects of workplace safety rather than mental and social health. 33 Australia, House Standing Committee on Employment, Education and Training of the Parliament, The Future of Work, 2025, p. 46.09 ILO Working Paper 170 X 2 Psychosocial risks and AI systems: interplay and data availability

Psychosocial factors at work A report of the Joint ILO/WHO Committee on Occupational Health defines psychosocial factors at work as “interactions between and among work environment, job content, organizational conditions and workers’ capacities, needs, culture, personal extra-job considerations that may, through perceptions and experience, influence health, work performance and job satisfaction”.34 This definition serves as a reference point to encapsulate the dynamic interrelationship between the work environment/conditions in general and subjective elements such as individual mental and health capacities. Any aspect in the design or management of work that increases the risk of work-related stress can be understood as a psychosocial hazard.35 In other words, the workthe work environment/conditions in general and subjective elements such as individual mental and health capacities. Any aspect in the design or management of work that increases the risk of work-related stress can be understood as a psychosocial hazard.35 In other words, the workplace factors that may cause stress are defined as psychosocial hazards.36 The terms PSRs and hazards are often used interchangeably. Psychosocial factors, also known as those workplace factors that can cause stress, and therefore, become psychosocial hazards at work include37: ● job content/task design (lack of variety in the work; under-use of skills or lack of appropriate skills for work); ● workload and work pace (long or unsocial work hours; shift work; inflexible hours); ● job control (lack