IDEAS home Printed from https://ideas.repec.org/h/elg/eechap/21373_4.html
   My bibliography  Save this book chapter

Mitigating bias in AI-powered HRM

In: Research Handbook on Human Resource Management and Disruptive Technologies

Author

Listed:
  • Melika Soleimani
  • James Arrowsmith
  • Ali Intezari
  • David J. Pauleen

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.

Suggested Citation

  • Melika Soleimani & James Arrowsmith & Ali Intezari & David J. Pauleen, 2024. "Mitigating bias in AI-powered HRM," Chapters, in: Tanya Bondarouk & Jeroen Meijerink (ed.), Research Handbook on Human Resource Management and Disruptive Technologies, chapter 4, pages 39-50, Edward Elgar Publishing.
    • Handle: RePEc:elg:eechap:21373_4
    as

    Download full text from publisher

    File URL: https://www.elgaronline.com/doi/10.4337/9781802209242.00012
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:elg:eechap:21373_4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Darrel McCalla (email available below). General contact details of provider: http://www.e-elgar.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.