Balancing Efficiency, Ethics, and Employee Well-Being: An Integrative Review of AI-Driven Human Resource Management

  • Sudarwati Faculty of Economics and Business, Universitas Lambung Mangkurat, Indonesia
  • Meiske Claudia Faculty of Economics and Business, Universitas Lambung Mangkurat, Indonesia
  • Rini Rahmawati Faculty of Economics and Business, Universitas Lambung Mangkurat, Indonesia
Keywords: AI-driven HRM, employee well-being, fairness audits, digital ethics, hybrid work, HRM 4.0, explainable AI

Abstract

This integrative review explores the intersection of artificial intelligence (AI) and human resource management (HRM), with a specific focus on ethical considerations, hybrid work adaptations, and employee well-being. Drawing on 50 peer-reviewed studies published between 2014 and 2024, the review synthesizes emerging themes related to how AI technologies are transforming recruitment, employee monitoring, and performance evaluation practices. The findings indicate that while AI enhances HR efficiency, it also raises significant ethical concerns, including algorithmic bias, digital fatigue, and privacy violations. Furthermore, the analysis underscores regional disparities in regulatory approaches, particularly between the Global North and emerging economies such as Indonesia. To address these challenges, a five-stage implementation framework is proposed, emphasizing fairness audits, explainable AI (XAI), and cross-functional governance. This review contributes to the evolving discourse on HRM 4.0 by embedding ethical AI governance within strategic workforce management. Practical insights are provided for organizations, policymakers, and academics seeking to align AI innovations with human-centered values.

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Published
2025-07-05
How to Cite
Sudarwati, Meiske Claudia, & Rini Rahmawati. (2025). Balancing Efficiency, Ethics, and Employee Well-Being: An Integrative Review of AI-Driven Human Resource Management. International Journal of Economics, Business and Innovation Research, 4(04), 972-999. https://doi.org/10.63922/ijebir.v4i04.2022