Summary In light of gender-specific organisational safety and health(OSH) risks associated with algorithmic and artifi­cial intelligence systems in the workplace, this paper pro­poses measures to mitigate discrimination risks. A“built­in” approach to the monitoring of AI-systems is purposed, inspired by Norwegian guidelines for non-discriminatory AI(Vik 2023). It is further argued that the AI-literacy gap needs to be bridged, and that unions have a central role in requiring transparency and co-determination when new AI-systems are introduced. This paper draws on issues and solutions highlighted by international experts taking part in discussions from a workshop – AI-Driven Work: Implications for Women’s Safety, Health, and Equality hosted by FES Future of Work, FES Nordic Countries and the ETUC. The workshop was part of the conference – To­wards Gender-Inclusive Digital Policy: AI and Gender Equality in the Workplace that took place in Brussels on the 11th of June 2025. Introduction and Background The integration of algorithmic and artificial Intelligence systems across sectors offers significant opportunities for innovation but also new risks that should be ac­counted for. AI and algorithmic systems introduce gen­der-specific risks that can compromise workers’ safety and well-being. These risks are evident in hiring, work­place surveillance, performance evaluation, exposure to harassment, and access to opportunities. If ignored, such risks can result in harmful outcomes such as stress, discrimination, and unfair treatment, and can negatively impact performance, employee turnover, skill-levels and innovation. This paper provides a framework for ad­dressing risks from a gendered perspective in occupa­tional safety and health(OSH) when regulating algorith­mic and AI systems in the workplace. Artificial intelligence(AI) denotes machine learning sys­tems that use data and computational models to per­form tasks such as prediction, classification, recommen­dation, and decision making in ways that resemble or complement human cognitive functions, thereby influ­encing physical or digital work processes. While economics has driven considerable focus on the way AI affects employment, much less attention has been paid to its role in management and human re­sources. This is partly because analyses most commonly rely on models that treat jobs as bundles of tasks with varying susceptibility to automation. Such approaches highlight the content of work but overlook the context of work—particularly the ways workers are managed and evaluated(Owen 2024). In the context of work, AI does present potential bene­fits: job-matching systems guide workers toward better opportunities; algorithmic hiring tools have the capacity to reduce some forms of human bias; and for certain workers, being evaluated by an algorithm may feel less arbitrary than facing the whims of a difficult manager. However, AI-driven workplace management tools may also risk reducing worker autonomy, heighten surveil­lance, and create feelings of isolation(OECD 2022). They can contribute to increased employee turnover, deskill­ing, stress, and other health-related consequences. Many of these effects carry a gendered dimension, rein­forcing discrimination, undermining well-being, and ex­acerbating inequality(UN Women 2024). When hardware and software systems are not designed with diverse bodies, working processes and needs in mind, particularly the needs of women, they risk reinforc­ing stereotypes, misjudging capabilities, and fostering unsafe or exclusionary environments. In much the same vain, robotics that overlook ergonomic diversity can physically disadvantage some workers, either through discomfort or by excluding them from specific tasks. In addition to the risk of deficits in accommodating the needs of all workers, emerging evidence is showing that when women and men use AI to produce identical work, women are perceived as less competent than men. During discussions at the FES workshop, AI-Driven Work: Implications for Women’s Safety, Health, and Equality, participants referred to the known biases of AI training data, and the problematic and growing use of algorithm-driven systems in personnel management, with Microsoft Viva Insights as a notable example. These systems track a wide range of employee data— such as emails sent, phone calls made, days of absence, and log-in times—and aggregate it into actionable in­sights on behaviour, engagement, and well-being. The insights are then presented to team leads in intuitive charts and graphs, enabling management to monitor and influence employee performance. This is often framed as a positive development, as decisions by team leads or management are now supposedly supported by neutral data, and less influenced by human bias. This is obviously not the case, as the systems incorporate bias­es via training data. One of the discussants at the workshop, Miriam Klöpper, has elaborated on these points in her work on the sub­ject, where she also stresses that while algorithms may appear neutral, they cannot solve deeper problems in isolation(Klöpper 2025). Advancing fairness at work re ­lies on strong social frameworks and on empowering in­dividuals to challenge unjust decisions. Ensuring that everyone, regardless of gender, is able and encouraged to do so is fundamental for real change. AI-driven surveillance and performance tools, such as al­gorithmic management systems, are increasingly used in workplaces for tasks like hiring, tracking productivity, Introduction and Background 3