Abstract

The integration of Artificial Intelligence (AI) into talent acquisition processes promises efficiency gains and data-driven decision-making. However, this technological advancement also presents significant ethical challenges, particularly concerning algorithmic bias and fairness. This paper explores the complex landscape of AI-driven talent acquisition, examining the potential for bias to perpetuate existing inequalities in hiring practices. It reviews relevant literature on algorithmic bias, fairness metrics, and explainable AI (XAI) techniques. The study then presents a novel methodology for identifying and mitigating bias in AI recruitment systems, focusing on pre-processing techniques, in-processing constraints, and post-processing adjustments. The results demonstrate the effectiveness of the proposed methodology in improving fairness metrics without significantly compromising predictive accuracy. The paper concludes by discussing the implications of these findings for HR professionals and policymakers, emphasizing the need for a proactive and ethical approach to AI implementation in talent acquisition. The importance of continuous monitoring, auditing, and human oversight to ensure fair and equitable outcomes is also highlighted.