Worker participation and representation: the impact on risk prevention of AI worker management systems
This EU-OSHA report analyses the challenges and opportunities posed by artificial intelligence-based worker management (AIWM) systems regarding occupational psychosocial risks. It examines the role of worker participation, industrial democracy, and collective bargaining in identifying, assessing, and mitigating these risks through a literature review and several company and regulatory case studies. The findings highlight that while AIWM can intensify surveillance and stress, worker involvement and supportive regulatory frameworks can help leverage technology for safer work design.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
Policymakers, employers, workers' representatives, and researchers seeking guidance on governing the occupational safety and health impacts of algorithmic management.
What the document contains
This report examines the definition and impacts of artificial intelligence-based worker management (AIWM) systems on occupational safety and health, focusing on psychosocial risks and the mediating role of worker participation and industrial democracy.
The report does not provide legally binding standards, technical source code, or a comprehensive assessment of physical workplace hazards unrelated to digital and algorithmic management.
- AI-based worker management systems present both risks and opportunities for occupational safety and health, particularly concerning psychosocial factors.
- Negative psychosocial impacts include heightened surveillance, eroded autonomy, work intensification, performance pressure, and schedule unpredictability.
- AIWM can also be used proactively to improve job design, task allocation, and burnout prevention when implemented transparently.
- Worker representation structures and industrial democracy play a crucial role in mitigating AI-related risks, but face obstacles such as technological opacity and power imbalances.
- Company and regulatory case studies from across Europe demonstrate that early worker involvement and supportive statutory rights improve the governance of algorithmic management.
Executive summary 1 Introduction 2 Objectives and methods 3 Conceptual framework 4 Review of the literature 5 Case examples of governance of psychosocial risks in the context of algorithmic management 6 Conclusions and policy pointers References
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Accountability
3/5Clear responsibility for AI outcomes
Opening the black box of algorithms is a pre-condition to ensure the accountability and regulation
4.2 RQ2: Which are the main obstacles facing worker representation structures to identify and prevent psychosocial risks stemming from AIWM?
screening score · 2026-10-25
Fairness
3/5Non-discrimination and equitable treatment
AIWM technologies that are used to monitor and evaluate performance can create performance pressure and are also associated with high stress levels among workers, particularly when they perceive the metrics and processes to be unfair.
Executive Summary
screening score · 2026-10-25
Human Dignity
1/5Respect for the inherent worth of persons
individualisation and dehumanisation of workers, creating an unhealthy, competitive environment
3.2 Psychosocial factors
screening score · 2026-10-25
Human Oversight
4/5Humans remain in control of consequential decisions
This agreement explicitly prohibited the use of the AIWM technology for monitoring employee performance or behaviour, or for making individual human resource decisions without human supervision.
5.2.1 Adaptation of worker participation structures in Germany
screening score · 2026-10-25
Privacy
4/5Protection of personal data and private life
Trade unions acknowledged the company' s aim to improve workplace safety, but they demanded the anonymisation of all personal data to prevent the monitoring of breaks or individual worker productivity.
5.1.1 A Swedish mining company
screening score · 2026-10-25
Transparency
4/5Openness about how AI systems work and are used
The main purpose of both regulatory initiatives is to improve algorithmic transparency which, as explained in previous sections, is a key obstacle that worker representation structures face
5.2.2 Practical guide on algorithmic management in Spain
screening score · 2026-10-25
Enforcement
4/5Consequences and compliance mechanisms with real force
When AI is used for human resource decisions – such as those involving recruitment, staff mobility and dismissals – the works council’s consent and verification are required.
5.2.1 Adaptation of worker participation structures in Germany
screening score · 2026-10-25
Impact Assessment
4/5Required assessment of an AI system's risks/impacts before or during use
Moreover, the Act includes obligations for employers using high-risk AI systems to establish risk management systems.
4.3 RQ3: How are governments and social partners regulating the use of AIWM technologies to prevent psychosocial risks derived from their use?
screening score · 2026-10-25