Responsible AI governance in human resource management: A systematic literature review of ethics, trust, and algorithmic decision-making
Keywords:
algorithmic decision-making, artificial intelligence governance, employee trust, ethical artificial intelligence, strategic human resource managementAbstract
Artificial intelligence (AI) has increasingly transformed human resource management (HRM) through automated recruitment, workforce analytics, predictive decision-making, and digital talent management. However, the rapid integration of AI within organizational environments has also generated governance challenges related to ethics, employee trust, transparency, accountability, and algorithmic decision-making. This study aims to examine AI governance in HRM by focusing on the relationship between ethical challenges, trust formation, and governance mechanisms in AI-driven organizational systems. This study employed a systematic literature review (SLR) approach following the PRISMA 2020 framework. Data were collected from the Scopus database, resulting in 20 peer-reviewed studies published between 2020 and 2026. The findings indicate that AI governance in HRM has evolved into a multidimensional organizational issue extending beyond technological implementation. Ethical concerns such as algorithmic bias, surveillance, discrimination, and limited transparency significantly influence employee trust and organizational legitimacy. Furthermore, responsible AI implementation requires governance mechanisms emphasizing transparency, explainability, ethical auditing, and human oversight. This study contributes to the AI governance literature by providing an integrated understanding of ethics, trust, and algorithmic decision-making within HRM contexts while offering practical implications for responsible AI implementation in workforce management.
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