Documents Friedrich Ebert Stiftung Competence Centre on the Future of Work 6 results

"Gender bias in AI systems poses significant challenges to achieving equality. AI, dependent on data for training and decision-making, often perpetuates gendered inequalities due to biased datasets that reinforce stereo types and exclude diverse experiences. This impacts women's access to healthcare, employment and education, compounding structural inequities. Addressing these biases requires integrating gender-sensitive data throughout AI development to ensure fairness and equ ty. Barriers include technical opacity, binary gender norms and patriarchal structures, particularly in the Global South. Solutions involve algorithmic transparency, inclusive data frameworks, cross-sectoral collaboration and feminist ethics. Embedding gender data in AI is essential to empower marginalised groups and pro mote social justice..."

"This paper explores Feminist Artificial Intelligence (FAI), a framework leveraging intersectional feminism to address biases and inequities in AI systems. FAI emphasises interdisciplinary collaboration, systemic power analysis and iterative theory-practice loops. By embed ding feminist values – equity, freedom and justice – FAI seeks to transform AI development, ensuring inclusivity and social sustainability. Practical applications include initiatives such as FemAI's advocacy for feminist perspectives in the EU AI Act and the MIRA diagnostic platform, which aligns AI tools with social justice goals. FAI marks a critical departure from traditional AI by tackling structural inequalities and promoting accountable and equitable AI solutions"

"This paper provides a feminist analysis of the European Union's Artificial Intelligence Act (hereinafter the “AI Act”), assessing its capacity to address gender inequities and structural power imbalances in AI systems. Drawing on feminist theories, the paper evaluates the AI Act's limitations in mitigating gender biases that might disproportionately impact marginalised groups, particularly women of colour and women from marginalised communities. Through case studies in recruitment and employment, healthcare, border management control and predictive policing in domestic violence cases, the report highlights how AI applications can rein force gender disparities. A detailed examination of specific provisions within the AI Act reveals critical gaps in addressing systemic discrimination and bias in AI governance. To promote a more equitable AI landscape, the report recommends integrating intersectional, feminist-informed revisions that prioritise interdisciplinarity, collective instead of individual approaches and strong oversight mechanisms that are oriented by human rights – including feminist – values. The proposed recommendations focus on strengthening the AI Act's framework to better safeguard marginalised communities and to ensure a regulatory approach that reflects the diverse experiences of all individuals."

"As artificial intelligence (AI) systems increasingly influence critical sectors such as healthcare, employment, education and law enforcement, concerns around bias – especially gender bias – have come to the forefront. Gender bias in AI not only reflects but can escalate existing inequalities, raising significant ethical, legal and societal is sues. This policy paper examines the impact of gender bias in AI systems and presents comprehensive guide lines for addressing it through a socio-technical lens. By focusing on different stages of the AI lifecycle, the paper provides actionable recommendations for various stake holders, including developers, deployers, users and regulators. Therefore, the aims of this document are to: → raise awareness about the escalation of gender bias when using AI systems in the decision-making process; → outline the challenges and opportunities of incorporating a socio-technical approach to tackle gender bias issues in AI systems; → provide a set of recommendations to key stakeholders from a socio-technical perspective on how to identify and prevent the reproduction of gender bias..."

"This paper examines platform workers' experience with unpaid labour by examining the constraints they face and the ways in which they intentionally use unpaid labour to navigate these constraints. Based on a qualitative study exploring online freelancing, food delivery and domestic work platforms in Belgium, we shed light on the intentional actions by workers, explaining how they use different resources to engage in unpaid labour and how this helps them to navigate constraints such as non-transparent work assignments and rating systems. We also show how workers face financial, private, physical, mental and career-related limits to their engagement in unpaid labour which can cause them to reduce their engagement or to exit the platform. Our aim is to contribute to an understanding of how the prevalence of unpaid labour on platforms fundamentally relates to workers' actions and the resources at their disposal."

"Rapid developments in artificial intelligence (AI) and automation technologies are set to transform the nature of work and the workplace itself. Advanced software systems with high degrees of autonomy are expected to perform a broad range of functions that are carried out by humans today. While the adoption of AI at workplaces is expected to positively impact the organisation of work by increasing operational efficiency, enabling informed decisions to be made faster while speeding up product and service innovation, new technologies can also have a negative impact on working conditions and the well-being of million of workers around the globe. Emerging evidence suggest that AI has the potential to contribute to an erosion of the middle class and a ‘job polarisation' that may inexorably lead to a greater economic disparity between highly skilled jobs and low-skilled jobs which would be too costly to automate. This current publication is based on three student papers as part of the PhD Summer School 2023 – AI and the Transformation of Work: Employment, Skills and Job Quality. The summer school was organised by the FES Competence Centre on the Future of Work in Berlin in cooperation with the Weizenbaum Institute for the Networked Society..."