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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
