A typology of artificial intelligence data work

Bibliographic Data

ID5260682
AuthorsJanine Muldoon (0000-0003-3307-1318, corresponding author), Callum Cant (0000-0002-7729-900X), Boxi Wu (0009-0007-0641-6065, University of Oxford), Mark Graham (0000-0003-2521-5190)
Year2024
Volume11
Issue1
Publication date2024-03-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueBig Data & Society (JOURNAL)
Journal identifiersISSN: 2053-9517 • E-ISSN: 2053-9517
PublisherSAGE Publications Inc (PUBLISHER)
DOI10.1177/20539517241232632
OpenAlexW4392932113
LanguageEN
Citations received22
References cited31

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

Business · Data science · Division of labour · Human intelligence · Knowledge management · Outsourcing · Political science · Sociology · Typology · Computer Science · Digital Economy and Work Transformation · Engineering · Ethics and Social Impacts of AI · Law · Sharing Economy and Platforms · Artificial Intelligence · Marketing