Ethical Challenges in AI Recruitment: Beyond Technical Bias to the Reproduction of Social Inequalities

Abstract

Recruitment processes play a central role in shaping access to employment and social mobility. The increasing use of artificial intelligence in these processes is beginning to change how candidates are evaluated, raising questions about whether regulatory frameworks are sufficient to address emerging challenges. Current regulatory approaches remain focused on the technical aspects of AI, while giving less weight to the social conditions that shape access to employment. The article employs qualitative analysis to examine how AI-driven recruitment processes, though often presented as neutral, are influenced by candidates' social and economic backgrounds. It examines how existing patterns of human decision-making can be reproduced through AI systems, particularly as these models rely on historical data and extend existing selection practices. The analysis draws on qualitative data on retraining trajectories and career transitions in the Romanian IT sector. It further shows that access to employment is strongly shaped by differences in access to resources, enabling certain candidates to bypass selection processes more effectively than others. AI systems tend to reinforce these patterns by operating within opaque decision-making structures, thereby contributing to the persistence of unequal outcomes. The findings indicate that screening based on easily measurable criteria is central to hiring practices, whether carried out by human recruiters or AI systems, often limiting candidates' ability to demonstrate their full potential. By bringing together social and technical perspectives, the article highlights how these disadvantages remain embedded in recruitment processes, even when criteria appear neutral. The paper argues for greater regulatory involvement in guiding AI toward more robust and fair evaluation practices and suggests a shift in regulatory approaches toward addressing broader inequalities, enhancing transparency, and promoting more meaningful forms of evaluation in recruitment.