There is a substantial gap between the promise and reality of artificial intelligence in human resource (HR) management. This article identifies four challenges in using data science techniques for HR tasks: complexity of HR phenomena, constraints imposed by small data sets, accountability questions associated with fairness and other ethical and legal constraints, and possible adverse employee reactions to management decisions via data-based algorithms. It then proposes practical responses to these challenges based on three overlapping principles-causal reasoning, randomization and experiments, and employee contribution-that would be both economically efficient and socially appropriate for using data science in the management of employees.

Industries like restaurants and retail, which are populated by low-wage workers, have found it hard to increase employee productivity. New technology and methods are allowing them to increase their profits by shifting more work to their best workers.