Journal article
A typology of artificial intelligence data work
- Abstract:
- This article provides a new typology for understanding human labour integrated into the production of artificial intelligence systems through data preparation and model evaluation. We call these forms of labour ‘AI data work’ and show how they are an important and necessary element of the artificial intelligence production process. We draw on fieldwork with an artificial intelligence data business process outsourcing centre specialising in computer vision data, alongside a decade of fieldwork with microwork platforms, business process outsourcing, and artificial intelligence companies to help dispel confusion around the multiple concepts and frames that encompass artificial intelligence data work including ‘ghost work’, ‘microwork’, ‘crowdwork’ and ‘cloudwork’. We argue that these different frames of reference obscure important differences between how this labour is organised in different contexts. The article provides a conceptual division between the different types of artificial intelligence data work institutions and the different stages of what we call the artificial intelligence data pipeline. This article thus contributes to our understanding of how the practices of workers become a valuable commodity integrated into global artificial intelligence production networks.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 381.5KB, Terms of use)
-
- Publisher copy:
- 10.1177/20539517241232632
Authors
+ Economic and Social Research Council
More from this funder
- Funder identifier:
- https://ror.org/03n0ht308
- Grant:
- ES/S00081X/
- Programme:
- Global Challenges Research Fund
+ European Research Council
More from this funder
- Funder identifier:
- https://ror.org/0472cxd90
- Grant:
- FP/2007–2013
- Programme:
- European Union’s Seventh Framework Programme
- Publisher:
- SAGE Publications
- Journal:
- Big Data and Society More from this journal
- Volume:
- 11
- Issue:
- 1
- Publication date:
- 2024-03-18
- Acceptance date:
- 2024-01-21
- DOI:
- EISSN:
-
2053-9517
- ISSN:
-
2053-9517
- Language:
-
English
- Keywords:
- Pubs id:
-
1603349
- Local pid:
-
pubs:1603349
- Deposit date:
-
2024-01-21
Terms of use
- Copyright holder:
- Muldoon et al.
- Copyright date:
- 2024
- Rights statement:
- © The Author(s) 2024. This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
- Licence:
- CC Attribution (CC BY)
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