Artificial Intelligence (AI) in human resource management (HRM): a systematic review of its dual impact on diversity, equity, and inclusion (DEI)

Abstract

Artificial Intelligence (AI) is transforming human resource management (HRM), introducing new efficiencies in recruitment, evaluation, and decision-making. However, its effect on diversity, equity, and inclusion (DEI) remains debated. This systematic literature review (SLR) compiles findings from 43 peer-reviewed articles published between 2016 and 2024 to critically assess AI’s dual role in HRM as both a potential promoter of fairness and a source of embedded bias. Rooted in ethical principles like fairness and accountability, organizational viewpoints such as HRM implementation challenges and best practices, and technological factors including algorithmic transparency and data quality, this review highlights four main themes: (1) AI’s ability to improve standardization, objectivity, and accessibility in HR processes; (2) risks related to algorithms and data that could perpetuate systemic bias and lessen accountability; (3) the human, data, and algorithmic origins of these issues; and (4) strategies for mitigation including participatory design, explainability, human oversight, and ethical governance. Despite growing interest in AI integration within HRM, previous studies have mostly treated fairness and effectiveness as separate issues, providing limited insight into how AI simultaneously impacts DEI outcomes. Additionally, the current literature often neglects the practical difficulties of implementing ethical principles, leaving HR professionals with scattered guidance. This review addresses these gaps by providing a timely, interdisciplinary overview that connects academic discussions with the urgent need for ethically responsible AI use in real-world HR environments. Practical implications are provided for HR professionals, developers, and organizational leaders, highlighting the importance of transparent implementation and inclusive design. Additionally, the review highlights theoretical and methodological gaps, suggesting that future research should focus on employee perceptions, contextual moderators, and the long-term effects of AI in various organizational settings. By presenting a comprehensive, multidisciplinary synthesis, this study advances the ongoing discussion on the ethical integration of AI in HRM. It provides practical guidance on aligning technological progress with inclusive organizational values. JEL Codes: M12; O33; J71; M14