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Artificial intelligence, decoloniality and epistemic justice in the South African public sector: Implications for human resource management in Industry 5.0

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  • Monument Thulani Bongani Makhanya

    (University of Zululand)

Abstract

In the context of Industry 5.0, the convergence of Artificial Intelligence (AI), decoloniality, and epistemic justice presents both opportunities and challenges for Human Resource Management (HRM) in the South African public sector. This paper emanates because of the possibility that AI-driven HRM practices could reinforce colonial legacies, repeat systemic bias, and prolong epistemic exclusion if not led by justice-oriented perspectives. This paper aims to explore how concepts of ethical, decolonial, and epistemic justice might inform the use of AI in HRM to promote inclusivity and fairness. This paper employs bibliometric analysis to map and evaluate scholarly discourse on AI, decoloniality, and HRM, aiming to uncover research trends, gaps, and theoretical intersections. Findings indicate that although AI can be efficient and predictive, its application in HRM frequently ignores ethical requirements and local knowledge systems. To protect equity, the study recommends integrating decolonial ideas, participatory methods, and epistemic justice frameworks into HRM technology. This paper concludes that the South African public sector may reconcile technological innovation with transformation and social justice imperatives by ethically aligning AI in Industry 5.0. Key Words:Industry 5.0 Artificial Intelligence (AI), Epistemic Justice, Decoloniality, Human Resource Management (HRM)

Suggested Citation

  • Monument Thulani Bongani Makhanya, 2025. "Artificial intelligence, decoloniality and epistemic justice in the South African public sector: Implications for human resource management in Industry 5.0," International Journal of Business Ecosystem & Strategy (2687-2293), Bussecon International Academy, vol. 7(5), pages 165-176, December.
  • Handle: RePEc:adi:ijbess:v:7:y:2025:i:5:p:165-176
    DOI: 10.36096/ijbes.v7i5.1030
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