Abstract

The integration of artificial intelligence into human resource management has fundamentally revolutionised
recruitment by improving efficiency, scalability, and decision-making. Nonetheless,
AI-driven recruitment systems may perpetuate systemic prejudices if not ethically constructed and
meticulously overseen. This study examines Fair Hiring Algorithms, integrating ethical frameworks
with technical bias mitigation measures to enhance equitable talent acquisition. Utilising
interdisciplinary insights from computer science, organisational behaviour, and HRM ethics, we
analyse primary sources of bias—data-driven, design-based, and contextual—and advocate for a
multi-faceted strategy incorporating fairness-aware machine learning, inclusive training datasets,
and algorithmic transparency. Methods like pre-processing bias correction, in-processing fairness
limitations, and post-processing adjustments are examined, along with the function of explainable
AI (XAI) in promoting responsibility and confidence. Continuous audits, stakeholder involvement,
and regulatory compliance are highlighted as vital measures. This study gives useful advice for
HR professionals, AI developers, and policymakers who want to make hiring more fair and ethical
by combining new technology with moral principles.