Mapping Gender Bias and Workplace Inclusion in Artificial Intelligence-Based Recruitment: A Bibliometric Co-Occurrence
TL;DR
The results indicate a strong conceptual relationship between AI, gender bias, recruitment decision-making, and inclusion, where gender bias acts as a mediating mechanism influencing recruitment decisions and subsequently determining workplace inclusion outcomes.
Abstract
This study aims to systematically map the research landscape of gender bias and inclusion in Artificial Intelligence (AI)-based recruitment by identifying dominant themes, conceptual relationships, and existing research gaps. It addresses the fragmentation of prior studies that often examine AI, bias, and inclusion as separate domains. Using a bibliometric approach with keyword co-occurrence analysis, data were collected from Scopus-indexed journal articles published between 2016 and 2025 and analyzed using VOSviewer to visualize keyword networks, identify thematic clusters, and examine research trend evolution. The results indicate a strong conceptual relationship between AI, gender bias, recruitment decision-making, and inclusion, where gender bias acts as a mediating mechanism influencing recruitment decisions and subsequently determining workplace inclusion outcomes. This study contributes theoretically by proposing an integrated conceptual framework that positions AI-based recruitment as a socio-technical system connecting technological processes, algorithmic bias, and inclusion outcomes.
