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.
