Journal Press India^®

Author Details ( * ) denotes Corresponding author

We examine the gap between the promise and reality of artificial intelligence in human resource management and propose ways forward. We highlight four problems with using data science approaches to human resource tasks: 1) the complexity of HR phenomena, 2) the restrictions imposed by tiny data sets, 3) accountability problems related to fairness and other ethical and regulatory constraints, and 4) the possibility of unfavorable employee responses to management choices using data-based algorithms. We suggest practical solutions to these issues, focusing on three overlapping concepts-cause and effect, randomization and trials, and employee input-that might be both economically efficient and socially suitable for employing data science in employee management.

Keywords

AL; ML; HRM; Issues; Prospect.

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