Mitigating bias in AI-powered HRM

Melika Soleimani, James Arrowsmith, Ali Intezari and David J. Pauleen

Chapter 4 in Research Handbook on Human Resource Management and Disruptive Technologies, 2024, pp 39-50 from Edward Elgar Publishing

Abstract: Artificial intelligence (AI) can provide organizations with valuable insights to improve management decision-making, including in human resource management (HRM). Its use makes decisions faster, more consistent and autonomous, but ethical issues persist. A major concern around AI-augmented HRM is the prospect of reinforcing rather than eliminating bias in decisions that impact on existing and potential employees. Hence, understanding the types of biases, their effects and bias mitigation techniques is crucial for organisations and individuals alike. This chapter explores the risk of bias becoming encoded in datasets and algorithms and the role of HRM and AI developers in addressing this. We first discuss three dominant categories of AI bias: systematic, statistical and computational and human. Then mitigation techniques and their challenges are discussed. Finally the chapter concludes by providing recommendations for actions to mitigate biases while developing AI for HRM.

Keywords: Business and Management; Innovations and Technology (search for similar items in EconPapers)
Date: 2024
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