When Computational Fairness Is Not Enough: Explaining the Gap Between Algorithmic Fairness and Organizational Justice from a Business Ethics Perspective

Artificial intelligence (AI) is transforming organizational decision-making by improving efficiency, consistency, and scalability in human resource management. However, growing evidence suggests that algorithmically fair decisions do not necessarily lead to employees' perceptions of organizational justice. While existing research has extensively examined algorithmic fairness from computational and technical perspectives, limited attention has been paid to the ethical mechanisms that explain the gap between algorithmic fairness and organizational justice. Addressing this gap, this conceptual study develops a business ethics framework integrating organizational justice theory, ethical AI, algorithmic management, and organizational support theory. The proposed model argues that organizational justice is shaped not only by the fairness of algorithms but also by transparency, explainability, human oversight, accountability, ethical governance, and organizational trust. These mechanisms explain why technically fair AI systems may still generate perceptions of injustice when ethical and managerial processes are inadequate. The study extends organizational justice theory to the context of AI-enabled decision-making by distinguishing computational fairness from employees' justice perceptions. It contributes to the business ethics literature by providing a comprehensive conceptual framework for responsible AI governance and offers practical guidance for managers seeking to implement AI systems that are not only technically accurate but also ethically legitimate, trustworthy, and socially accepted within organizations.