Artificial Intelligence in Human Resource Management: Challenges and a path forward

Abstract

We consider the gap between the promise and reality of artificial intelligence in human resource management and suggest how progress might be made. We identify four challenges in using data science techniques in HR practices: 1) complexity of HR phenomena, 2) constraints imposed by small data sets, 3) ethical questions associated with fairness and legal constraints, and 4) employee reaction to management via data-based algorithms. We propose practical responses to these challenges and converge on three overlapping principles - causal reasoning, randomization, and process formalization—that could be both economically efficient and socially appropriate for using data analytics in the management of employees.