“Ethical Artificial Intelligence in University HRM: Implications for Faculty Mental Satisfaction, Commitment and Retention”

The increasing use of Artificial Intelligence (AI) in Human Resource Management (HRM) marks a transformative change in how organizations structure, execute, and rationalize people management practices. AI-driven HRM systems now play a pivotal role in recruitment, performance evaluation, workload distribution, and career progression, fundamentally altering the dynamics of employee–organization relationships. Although existing HRM research has primarily highlighted the efficiency and analytical strengths of AI, considerably less focus has been placed on examining its ethical consequences for employees’ psychological well-being and workplace experiences.

From a Human Resource Management (HRM) theory perspective, Artificial Intelligence (AI) disrupts foundational assumptions concerning fairness, managerial discretion, and the nature of relational exchanges between organizations and employees. While algorithmic decision-making has the potential to mitigate certain forms of human bias, it simultaneously introduces challenges such as increased opacity, depersonalization, and the risk of perceived injustice. These ethical and psychological concerns are especially pronounced in knowledge-intensive settings like higher education institutions, where professional autonomy, identity, and psychological safety are vital to employee well-being.

Although ethical AI and HRM have been examined independently, conceptual integration explaining how ethical AI practices influence employee psychological outcomes and retention remains underdeveloped. Addressing this gap, the present paper adopts a conceptual approach to develop a theory-driven framework linking ethical AI practices in HRM to mental satisfaction, organizational commitment, and retention intentions.

By proposing research propositions and a theoretically integrated framework, the paper advances HRM theory by positioning ethical AI as a human-centric and sustainable HRM practice rather than a purely technological tool. The paper concludes by outlining directions for future research and implications for responsible AI governance in HRM.