The trainer, the verifier, the imitator: Three ways in which human platform workers support artificial intelligence
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This paper sheds light on the role of digital platform labour in the development of today’s artificial intelligence, predicated on data-intensive machine learning algorithms. Focus is on the specific ways in which outsourcing of data tasks to myriad ‘micro-workers’, recruited and managed through specialized platforms, powers virtual assistants, self-driving vehicles and connected objects. Using qualitative data from multiple sources, we show that micro-work performs a variety of functions, between three poles that we label, respectively, ‘artificial intelligence preparation’, ‘artificial intelligence verification’ and ‘artificial intelligence impersonation’. Because of the wide scope of application of micro-work, it is a structural component of contemporary artificial intelligence production processes – not an ephemeral form of support that may vanish once the technology reaches maturity stage. Through the lens of micro-work, we prefigure the policy implications of a future in which data technologies do not replace human workforce but imply its marginalization and precariousness.
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A lot of human labour is needed to fill the cracks of AI.
“As sociologists of technological innovation highlight, a lot of human labour is needed to fill the cracks of AI (Gray and Suri 2019;Tubaro and Casilli 2019;Tubaro et al. 2020).”
From Artificial intelligence and work: a critical review of recent research from the social sciences (2024) · cites this paper as Tubaro et al. 2020
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Flandrin, P., Hellemans, C., Linden, J. V. D., & Leemput, C. V. D. (2021) · doi:10.1145/3486812.3486838 · Cited by 14 · see citing works
Alahmadi, D. H., Babour, A., Saeedi, K., & Visvizi, A. (2020) · Applied Sciences · doi:10.3390/app10186216 · Cited by 10 · see citing works
Nurturing Capabilities: Unpacking the Gap in Human-Centered Evaluations of AI-Based Systems
Khullar, A., Nalin, N., Prasad, A., Mampilli, A. J., & Kumar, N. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713278 · Cited by 9 · see citing works
Hernández, A. D., Perini, A., Hadjiloizou, S., Borda, A., Mahomed, S., & Leslie, D. (2025) · AI and Ethics · doi:10.1007/s43681-025-00902-6 · Cited by 11 · see citing works
Akinwalere, S. N., & Chang, K. (2026) · Journal of Knowledge Management · doi:10.1108/jkm-03-2025-0362 · Cited by 10 · see citing works
Providing feedback without verification: A hidden risk of trust in generative AI
Hu, P., Cao, C., & Li, D. (2026) · Technology in Society · doi:10.1016/j.techsoc.2026.103370 · Cited by 11 · see citing works
La modération abusive sur Twitter
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Research Ethics in the Age of Digital Platforms
Molina, J. L., Tubaro, P., Casilli, A. A., & Ortega, A. S. (2023) · Science and Engineering Ethics · doi:10.1007/s11948-023-00437-1 · Cited by 9 · see citing works
Self-citationCare, collaboration, and service in academic data work: biocuration as ‘academia otherwise’
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Zook, M., & Grote, M. H. (2024) · Cambridge Journal of Regions Economy and Society · doi:10.1093/cjres/rsae039 · Cited by 8 · see citing works
Operationalizing AI governance: data annotation, La Qi and manual alignment in China
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Ten reasons why—the case for more and better AI regulation
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Good for tech: Disability expertise and labor in China's artificial intelligence sector
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Wisdom for the Crowd: Discoursive Power in Annotation Instructions for Computer VisionPreprint
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The Making of Performative Accuracy in AI Training: Precision Labor and Its Consequences
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The politics of artificial intelligence supply chains
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Dialogues Towards Sociologies of Generative AI
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AI and the labour market: opening the black box
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- Authors
- Tubaro, P., Casilli, A. A., & Coville, M.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Télécom Paris (FR)
- Université de Poitiers (FR)
- Corresponding author
- Tubaro, Paola
- Venue
- Big Data & Society
- Publisher
- SAGE Publications
- Published
- 2020-01
- Volume
- 7
- Issue
- 1
- Pages
- 205395172091977
- ISSN
- 2053-9517 (ISSN-L)
- OpenAlex
- W3018104257
- Cited by
- 262
- References
- 26
- Citations by year
- 2026: 44 · 2025: 61 · 2024: 48 · 2023: 38 · 2022: 45 · 2021: 19 · 2020: 7
- FWCI
- 36.46
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Big Data & Society (publishedVersion, cc-by-nc-nd)
- RePEc: Research Papers in Economics (submittedVersion)
- HAL (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- France Stratégie
- MSH Paris Saclay (17-MA07)
- Force Ouvrière - Institut de recherches économiques et sociales
- Topics
- Social Sciences › Sociology and Political Science › Digital Economy and Work Transformation
- Social Sciences › Business, Management and Accounting › Industrial relations › Legal and Labor Studies
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- AI development, data annotation, machine learning training data, precarious employment, AI verification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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