Spencer, D.A. orcid.org/0000-0002-7803-6105 (2024) AI, automation and the lightening of work. AI and Society. ISSN 0951-5666
Abstract
Artificial intelligence (AI) technology poses possible threats to existing jobs. These threats extend not just to the number of jobs available but also to their quality. In the future, so some predict, workers could face fewer and potentially worse jobs, at least if society does not embrace reforms that manage the coming AI revolution. This paper uses the example of Daron Acemoglu and Simon Johnson’s recent book—Power and Progress (2023)—to illustrate some of the dilemmas and options for managing the future of work under AI. Acemoglu and Johnson, while warning of the potential negative effects of an AI-driven automation, argue that AI can be used for positive ends. In particular, they argue for its uses in creating more ‘good jobs’. This outcome will depend on democratising AI technology. This paper is critical of the approach taken by Acemoglu and Johnson—specifically, it misses the possibility for using AI to lighten work (i.e., to reduce its duration and improve its quality). This paper stresses the potential benefits of automation as a mechanism for lightening work. Its key arguments aim to advance critical debates focused on creating a future in which AI works for people not just for profits.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2024. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | AI, Automation, Work, Work quality, Work time, Future of work |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Business (Leeds) > Economics Division (LUBS) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 29 May 2024 10:08 |
Last Modified: | 29 May 2024 10:08 |
Status: | Published online |
Publisher: | Springer |
Identification Number: | 10.1007/s00146-024-01959-3 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:212779 |