Mitigating Algorithmic Bias in AI-Powered Recruitment: A Practitioner's Guide to Ethical Implementation

Abstract

The proliferation of artificial intelligence in talent acquisition has introduced both unprecedented efficiency gains and significant ethical challenges. This article examines the mechanisms through which algorithmic bias emerges in automated hiring systems and evaluates evidence-based governance frameworks for promoting fairness, transparency, and accountability. Drawing on interdisciplinary research spanning computer science, organizational behavior, employment law, and ethics, the analysis identifies six critical intervention points: data quality assessment, contextual fairness metrics, algorithmic transparency, human-in-the-loop oversight, structured governance protocols, and continuous monitoring. Through examination of organizational practices across technology, financial services, and healthcare sectors, the article demonstrates that effective bias mitigation requires integrated sociotechnical solutions rather than purely algorithmic fixes. The findings suggest that organizations adopting comprehensive ethical AI frameworks can substantially reduce discriminatory outcomes while maintaining operational efficiency, though implementation challenges around vendor transparency, competing fairness definitions, and resource constraints remain significant barriers to widespread adoption.