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 for HR tasks: 1) complexity of HR phenomena, 2) constraints imposed by small data sets, 3) accountability questions associated with fairness and other ethical and legal constraints, and 4) possible adverse employee reactions to management decisions via data-based algorithms. We propose practical responses to these challenges and converge on three overlapping principles - causal reasoning, randomization and experiments, and employee contribution—that could be both economically efficient and socially appropriate for using data science in the management of employees.