Ethical Artificial Intelligence in human resource management for sustainable workplaces a bibliometric and literature review

This study explores the evolving role of Human Resource Management (HRM) in managing ethical Artificial Intelligence (AI) to support the achievement of Sustainable Development Goals (SDGs) in workplace environments. A mixed-methods approach combining bibliometric and literature review was applied to Scopus-indexed literature. The bibliometric component analyzed publication trends, country and institutional contributions, keyword co-occurrence, and co-authorship networks, while the literature review examined conceptual insights and practical directions from the most relevant scholarly publications. The findings revealed an increasing academic interest in the intersection of HRM, AI ethics, and sustainability, with significant contributions from India, China, the United States, the United Kingdom, and Romania. Leading institutional affiliations included the Symbiosis International Deemed University and the University of Delhi. The co-occurrence of key terms highlighted central themes, including human resource management, artificial intelligence, decision-making, ethical technology, and predictive analytics. Co-authorship analysis revealed moderate research collaboration, with emerging networks indicating potential for global cooperation. Literature review of the top-cited papers indicated that HRM is evolving from an administrative function to a strategic player in guiding the ethical deployment of AI technologies in recruitment, performance management, and organizational development. Based on these insights, theoretical and practical recommendations were proposed for HR professionals, organizations, employees, and policymakers. HRM should act as an ethical gatekeeper by establishing robust AI governance policies and promoting employee AI literacy and awareness. Organizations are encouraged to embed ethical considerations in AI implementation, ensure inclusivity, and align AI use with sustainability objectives. Policymakers should establish regulatory frameworks that require transparency and accountability in AI systems that impact workplace decisions. The study has limitations. It relies solely on Scopus-indexed publications, potentially omitting relevant studies from other sources or non-English literature. Moreover, although comprehensive, the bibliometric approach does not capture qualitative dimensions, such as contextual or industry-specific challenges, in the adoption of ethical AI. Future research should incorporate broader datasets and empirical case studies to deepen the understanding of ethical AI practices across diverse organizational contexts.