Building Responsible AI Systems for Human Resource Practices: Challenges, Opportunities, and the Way Forward
Keywords:
Responsible Artificial Intelligence, Human Resource Management, Sustainable HR Practices, HR AnalyticsAbstract
The rapid integration of Artificial Intelligence (AI) into Human Resource (HR) practices has transformed key organizational functions such as recruitment, performance management, training, and employee engagement. AI-driven systems present important ethical, legal, and managerial issues in addition to their many benefits in terms of efficiency, data-driven decision-making, and cost optimization. The ethical use of AI in HR procedures is severely hampered by problems with algorithmic bias, data privacy, and a lack of accountability, transparency, and fairness.
This paper investigates the idea of Responsible AI in the context of Human Resource Management (HRM) by looking at the main obstacles that businesses must overcome, the chances AI offers to improve HR efficacy, and the tactical routes for moral and long-term application. The paper offers a structured framework for developing ethical AI systems that comply with legal requirements, corporate ideals, and ethical principles. It does this by drawing on existing literature and modern organizational practices.
By providing practical insights for HR professionals, legislators, and organizational leaders to guarantee that AI adoption in HR fosters trust, diversity, and long-term organizational sustainability, the study adds to the expanding conversation on ethical AI.
References
Bodie, M. T., Cherry, M. A., McCormick, J. B., & Tang, K. (2020). Algorithmic fairness in human resource management. Journal of Business Ethics, 162(4), 707–723.
Bose, I., & Mahapatra, R. (2021). Data governance and employee privacy in AI-driven HRM. Information Systems Frontiers, 23(3), 601–617.
Bujold, A., Roberge-Maltais, I., & Léger, P.-M. (2023).Responsible AI in human resource management: Empirical review. AI & Ethics, 3(2), 245–268.
Chen, Z. et al. (2023).Ethics and discrimination in AI-enabled recruitment (Nature Humanities & Social Sciences Communications).
Glikson, E., & Woolley, A. (2020). Human trust in AI — determinants and implications (management studies review). (review of empirical trust literature).
Jarrahi, M. H. (2018).Artificial intelligence and the future of work: Human-AI collaboration in HRM. Journal of Business Research, 89, 46–60.
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1, 389–399.
Margherita, A. (2022).AI in workforce planning: Opportunities and risks. Journal of Management Analytics, 9(3), 215–237.
Meijerink, J., Bondarouk, T., & Lepak, D. (2021). AI-driven HR analytics and decision-making: Evidence and challenges. Human Resource Management Review, 31(4), 100799.
Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. (2019/2020). *Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices.
Siau, K., & Wang, W. (2020). Building responsible AI in human resource management. Journal of Database Management, 31(3), 1–19.
Stone, D. L., Deadrick, D. L., Lukaszewski, K.,& Johnson, R. (2018).Artificial intelligence in HR: Challenges and a research agenda. Human Resource Management Review, 28(2), 93–103.
Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial intelligence in human resources management: Challenges and a research agenda. Academy of Management Perspectives, 33(2), 161–177.
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