and monitoring emotions. These tools also claim to en­hance workers wellbeing. While the aim is to enhance efficiency, they can unintentionally cause harm, amplify workplace inequalities, and may discriminate women due to biased data. This can occur during recruitment, daily tasks, and emotional assessments, leading to un­fair judgments and reinforcing biases. AI and robotics technologies do not simply support work; they shape it. They structure workplace condi­tions, influence employee behaviour, and directly affect both physical and mental health. Identifying OSH risks can help build workplaces that are healthier, more equi­table, and more sustainable for all, as well as ensuring a better environment for innovation. The use of AI requires the implementation of proactive policies that address the new OSH-risks associated with digitalisation and automation. A Gender Perspective on Occupational Safety and Health(OSH) In recent years, there has been growing—but still limit­ed—attention from international organisations, research institutes, and policymakers to the gendered implica­tions of AI in employment and workplace dynamics. Re­search highlights the persistent underrepresentation of women in AI professions, the prevalence of algorithmic bias in hiring and promotion, and the critical importance of gender-sensitive data for equitable AI deployment (FES Future of Work 2025). AI-driven automation can both reinforce and mitigate in­equalities, depending on how data and design decisions are handled; exclusion from data can entrench discrimi­natory outcomes, while inclusive practices can help close the gap. However, left unchecked, gender bias in AI can reinforce stereotypes and create hazardous or unfair workplaces, affecting everything from job assignments to promotion decisions and disciplinary actions and severely affect workers’ safety and wellbeing. A gendered perspective in occupational safety and health(OSH) is essential because algorithmic manage­ment creates risks that affect men and women different­ly. Traditional OSH frameworks focus on physical haz­ards or general psychosocial risks but often overlook how biased algorithms, surveillance systems, and digital tools can exacerbate discrimination, harassment, and exclusion in the workplace. Without integrating gender, OSH policies fail to address structural and indirect harm, from biased performance monitoring to online harass­ment. Updating OSH to include gendered risks ensures safer, more equitable workplaces and equips organisa­tions to mitigate both physical and psychosocial harm caused by emerging technologies. Legislation addressing emerging technologies already ex­ists at the national level and through national applica­tions of EU law(Eurofound 2024), such as AI and algo ­rithmic management, and collective bargaining in rela­tion to these technologies, while some collective agreements in Europe now explicitly include provisions on the issue(UNI Europa 2024). At the same time, un ­ions, experts, and political actors are calling for a strong­er reaffirmation of collective bargaining rights at the EU level. The AI Act Regulation(2024) established a legal framework obliging developers and AI system users to address bias, increase transparency, and ensure account­ability. For a closer examination of the AI Act and its ca­pacity to address gender inequities, see Karagianni 2025 (Karagianni 2025). The EU AI-act is a step in the right direction. The first­in-the world law on artificial intelligence may help iden­tify gender bias and ensure that workers’ safety and well-being is not negatively affected However, rather than passively wait for regulations to apply, affected 1 parties, employers, employees, organisations and unions can and should take steps to reduce gender-specific risks of AI. A report from the international Labor Organization(ILO) points out regulatory gaps in the managing of OSH-risks related to automation(ILO 2025). Broad conventions pro ­vide a foundation for the right to a safe and healthy work­place, but there is a lack of more concrete regulations that specify how to secure this right in the workplace AI-systems are never neutral but depend on various choices, from how the model is trained, to how data is labelled by people, and which technologies are chosen, from testing to deployment. As Johannes Anttila, policy advisor in the European Par ­liament, has mentioned in his work, and during the con­ference workshop, these systems are“sociotechnical”: They involve people, culture and context. It should not come as a surprise that AI-systems will tend to reflect existing structural inequalities and bias related to gen­der. Anttila has argued that what we need to do is bring back context, transparency, and worker agency, all the while making sure that fundamental rights are protect­ed. Transparency is one step in this, but what is needed is reflexivity, a purpose and intent in the design and the deployment phases. 1  There is a concern that the Digital Omnibus Package will undermine the protections the Act initiated as protecting privacy, discrimination, transparency, fairness and work­ers’ rights, which could adversely affect women and other vulnerable groups(Del Castillo 2025). 4 Friedrich-Ebert-Stiftung e.V.