Fairness in AI-Driven Recruitment: Challenges, Metrics, Methods, and Future Directions

TL;DR

This paper reviews the challenges of ensuring fairness in AI-driven recruitment, analyzing biases, fairness metrics, mitigation methods, and future research directions to promote equitable hiring practices.

Contribution

It systematically categorizes biases, fairness metrics, and mitigation techniques in AI recruitment and highlights gaps and future directions for research and practice.

Findings

Biases are prevalent in AI recruitment systems.

Existing fairness metrics have limitations.

Future research should focus on developing robust auditing methods.

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.