Charlwood, A orcid.org/0000-0002-5444-194X and Guenole, N (2022) Can HR adapt to the paradoxes of artificial intelligence? Human Resource Management Journal, 32 (4). pp. 729-742. ISSN 0954-5395
Abstract
Artificial intelligence (AI) is widely heralded as a new and revolutionary technology that will transform the world of work. While the impact of AI on human resource (HR) and people management is difficult to predict, the article considers potential scenarios for how AI will affect our field. We argue that although popular accounts of AI stress the risks of bias and unfairness, these problems are eminently solvable. However, the way that the AI industry is currently constituted and wider trends in the use of technology for organising work mean that there is a significant risk that AI use will degrade the quality of work. Viewing different scenarios through a paradox lens, we argue that both positive and negative visions of the future are likely to coexist. The HR profession has a degree of agency to shape the future if it chooses to use it; HR professionals need to develop the skills to ensure that ethics and fairness are at the centre of AI development for HR and people management.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 The Authors. This is an open access article under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0) (https://creativecommons.org/licenses/by/4.0/) |
Keywords: | artificial intelligence; human resource management |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Business (Leeds) > Work and Employment Relation Division (Leeds) |
Funding Information: | Funder Grant number ESRC (Economic and Social Research Council) ES/S012532/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 19 Jan 2022 12:26 |
Last Modified: | 25 Jun 2023 22:52 |
Status: | Published |
Publisher: | Wiley |
Identification Number: | 10.1111/1748-8583.12433 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:182081 |