Mapping Gender Bias and Workplace Inclusion in Artificial Intelligence-Based Recruitment: A Bibliometric Co-Occurrence

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

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.