Addressing Algorithmic Bias and Data Privacy in Human Resource Management

Abstract

Artificial intelligence (AI) has transformed Human Resource Management (HRM) by automating recruitment, enhancing performance evaluation, and enabling data-driven workforce planning. However, its adoption raises critical concerns related to algorithmic bias, data privacy, and employee trust, creating a significant gap in understanding how these technical and ethical dimensions interact. This study aims to synthesize current evidence on the impact of AI on HRM functions, the challenges associated with fairness and privacy, and employee perceptions of AI-enabled HRM systems. A Systematic Literature Review (SLR) was conducted following PRISMA 2020 guidelines and structured using the PICOC framework. Searches across major scientific databases identified 1,042 records, of which 35 peer-reviewed studies published between 2020 and 2025 met all eligibility criteria. The findings show that AI enhances HRM efficiency and decision quality but presents recurring risks of algorithmic bias, opaque decision-making, and weak data governance. Employee perceptions of fairness, transparency, and privacy strongly influence trust and acceptance of AI-based HRM systems. The review concludes that effective AI adoption requires socio-technical integration combining algorithmic capability with robust governance and ethical safeguards. The study introduces an integrated conceptual model linking AI capabilities, HRM functions, data governance, employee trust, and organizational outcomes—representing a key theoretical contribution and a novel synthesis of previously fragmented research.

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