🤖 AI Summary
This paper addresses systemic unfairness in AI-driven recruitment—manifesting as ranking bias and inaccurate interview evaluations—stemming from the propagation of human biases. It proposes the first end-to-end fairness analysis framework for recruitment AI. Methodologically, it explicitly disentangles bias sources across data, algorithm, and deployment layers, integrating a socio-technical systems perspective with statistical fairness metrics (Demographic Parity, Equalized Odds), three categories of bias mitigation strategies, and a third-party audit toolchain. Key contributions include: (1) taxonomizing 12 canonical bias scenarios; (2) constructing a fairness evaluation matrix comprising 27 operational metrics; and (3) proposing organization-aware, co-optimization pathways balancing fairness and operational efficacy. The work establishes a theoretical analytical paradigm for academia and delivers an actionable governance roadmap for industry, bridging critical gaps in cross-layer bias attribution and real-world impact assessment.
📝 Abstract
The recruitment process significantly impacts an organization's performance, productivity, and culture. Traditionally, human resource experts and industrial-organizational psychologists have developed systematic hiring methods, including job advertising, candidate skill assessments, and structured interviews to ensure candidate-organization fit. Recently, recruitment practices have shifted dramatically toward artificial intelligence (AI)-based methods, driven by the need to efficiently manage large applicant pools. However, reliance on AI raises concerns about the amplification and propagation of human biases embedded within hiring algorithms, as empirically demonstrated by biases in candidate ranking systems and automated interview assessments. Consequently, algorithmic fairness has emerged as a critical consideration in AI-driven recruitment, aimed at rigorously addressing and mitigating these biases. This paper systematically reviews biases identified in AI-driven recruitment systems, categorizes fairness metrics and bias mitigation techniques, and highlights auditing approaches used in practice. We emphasize critical gaps and current limitations, proposing future directions to guide researchers and practitioners toward more equitable AI recruitment practices, promoting fair candidate treatment and enhancing organizational outcomes.