AI in human resource management the limits of empiricism

The rapid integration of artificial intelligence (AI) into Human Resource Management (HRM) is transforming how organizations recruit, manage, and evaluate their workforces. While proponents champion AI as a means to enhance efficiency, reduce bias, and align HR practices with strategic business goals, this paper argues that such optimism is misplaced. Drawing on a critical review of AI's application across four core HRM functions—recruitment, compensation, scheduling, and performance management—this paper identifies significant risks and limitations arising from the fundamental structure of AI systems. Central to the analysis is a three-parameter framework for assessing AI tools: their objective, the data they rely upon, and how they are programmed. The paper shows that across HR functions, AI systems frequently operationalize reductive or poorly aligned objectives, rely on low-quality or biased data, and are programmed in non-transparent ways that undermine their usefulness. These structural shortcomings not only undermine the effectiveness of AI systems but also introduce legal, ethical, and practical risks for firms and their workers.