Bias in AI-Driven Recruitment: An Ensemble-Based Community Detection Bibliometric Analysis of Challenges in Hiring and Employment

Carlo Drago, Francesca Valentina Giglio Moro () and Angelo Leogrande ()
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Francesca Valentina Giglio Moro: Università Niccolò Cusano
Angelo Leogrande: LUM University Giuseppe Degennaro

Chapter Chapter 3 in Human Resource Development for Sustainability and Social Responsibility, 2026, pp 33-42 from Springer

Abstract: Abstract At the intersection between artificial intelligence (AI), algorithmic bias, and hiring practices, challenges arise in terms of ethical, technical, and managerial issues. By applying advanced bibliometric techniques—networks of keywords, multiple correspondence analysis (MCA), and ensemble community detection — the study systematically maps the evolving academic discourse on AI-driven recruitment. The findings reveal three broad conceptual clusters: (1) productivity and workforce analytics, (2) algorithmic bias and human resource management, and (3) fairness, transparency, and privacy in the workplace. The findings highlight the dual nature of using AI in recruitment: while providing operational efficiencies, automation can reproduce human biases embedded in historical training data, raising serious ethical concerns. In this study, transparency, diversity, and ethical governance are highlighted as key factors for AI systems for recruitment, as well as other emerging issues including Corporate Digital Responsibility (CDR). Through a multidisciplinary perspective, the paper calls for a robust ethical framework that balances innovation and fairness, especially when AI systems are integrated into employee decision-making. This work advances a methodological approach in bibliometric research within AI-based recruitment by identifying key issues in the field. Additionally, it offers a solid foundation for developing research agendas related to responsible AI use in HR.

Keywords: Recruitment; AI-driven recruitment; Bibliometric analysis (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-032-09683-8_3

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DOI: 10.1007/978-3-032-09683-8_3

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