allowing them to reap AI’s promised rewards. In con­trast, lower earners and women are being shut out from AI opportunities, which impacts their skill development, job satisfaction, and time savings, both personally and professionally(Human Rights Press Releases 2025). Emerging evidence suggests that there is a considerable AI-literacy gap that needs to be bridged. Inclusive up­skilling initiatives are essential to give women equal op­portunities to build competencies in AI use and to close adoption gaps. Trade Unions and Capacities to Act More can be done at the workplace to protect workers from bias, exclusion, and harassment associated with the introduction of AI-system. A start is to acknowledge that bias in data and algorithms is a major gender-spe­cific risk. AI systems trained on historical datasets may perpetuate discriminatory hiring practices or amplify ste­reotypes, leading to unfair evaluations or exclusion in re­cruitment and performance assessments. For example, algorithmic scheduling—common in fe­male-dominated sectors like retail and hospitality—allows shifts to be allocated with little notice, increasing work and income uncertainty and stress for women workers. Advocacy groups, NGOs, but in particular trade unions, play a vital role in spreading knowledge about these specific risks. Workers themselves should also be able to discern when, for example, algorithmic tools used at the workplace are having gendered effects and be aware of their overall right for redress. Transparent reporting requires employers to explain AI decision processes, publish the results of audits, and maintain open channels for complaints. Raising aware­ness begins with regular transparency measures such as bias audits of AI systems and communicating findings openly to affected employees. Organisations should be encouraged to publish easy-to-understand reports on the risks identified for specific groups and describe how these risks are being addressed so workers become con­scious of the underlying issues Furthermore, there are many other ways in which organisa­tions can address the gendered impacts of AI. Workshops and training programs could help highlight potential risks and safeguards, ensuring both workers and managers un­derstand how technology can reinforce or reduce bias. Transparency is a first step, but more importantly, work­ers must be able to influence and negotiate how algo­rithms are developed and deployed. Involving unions and women’s groups in the rollout of new technologies ensures that diverse perspectives and worker voices are integrated into the process. Worker’s involvement is a necessary requirement for being able to discern when, for example algorithmic tools used at the workplace are having gendered effects or of their overall rights, for in­stance of co-determination. It also means involving affected workers in the design and rollout of workplace AI systems. When platforms and algorithms are developed by diverse teams and with meaningful input from women, they are less likely to embed harmful stereotypes. Studies have shown that bi­ases often creep in“by design,” mirroring societal preju­dices unless deliberately countered by participatory pro­cesses. Workplace committees, user panels, or feedback forums can ensure that the experience and concerns of women are recognised. This not only raises awareness but leads to more inclusive and equitable technologies. Much of this debate focuses on workers’ rights to trans­parency, participation, negotiation, and redress. Yet, the discussion above also highlights another important as­pect: bargaining over algorithms is not only about over­sight but also about co-design—ensuring that these sys­tems function as intended. This should be a shared re­sponsibility of both employers and workers, anchored in transparent and well-defined rules(Enriquez/Anttila). Conclusion In this paper, it is argued that AI systems in the work­place must be proactively monitored and regulated to prevent gender bias, protect worker safety and well-be­ing, and ensure fair treatment, especially as AI and ro­botics become more prevalent. As outlined in the Norwegian guidelines for non-discrimi­natory AI, monitoring should start at the early phase of planning and development of the AI-tools. Equally im­portant, monitoring must be in place in the implementa­tion phase to protect workers from OSH-risks and active­ly involve workers and other stakeholders in the process. Increasing awareness of gender-specific AI risks in the workplace demands systemic change—through education, transparency, regulation, stakeholder involvement, and organisational culture. Unions have a particularly impor­tant role to play in empowering workers – and especially women – to recognise risks, advocate for improvement, and participate in shaping fair and responsible use of AI. Unions may also help raise critical questions about own­ership and control. Who develops these technologies, and what biases are embedded in the data they rely on? De­pending on these choices, AI systems can either perpetu­ate, widen, or help reduce existing gender inequalities. Conclusion 7