CONCEPTUALIZING PRECISION LABOR IN ARTIFICIAL INTELLIGENCE TRAINING
Open accessAbstract
Accuracy and precision are among the central values in the ML communities and tech industry. What does it take to achieve a high level of technical accuracy? What are the harms resulting from technology companies' obsession with technical accuracy and precision, and who incurs the greatest burdens? This paper explores accuracy in the context of AI training in China. Drawing on 9-month multi-sited ethnographic fieldwork, we document workers’ everyday working practices and challenges and harms under the guise of achieving extreme levels of technical precision demanded by the clients and ML practitioners. We introduce the notion of precision labor, referring to the hidden work involved in erasing the messy, ambiguous, and uncertain aspects of technology production, all in the pursuit of presenting technology as objective, truthful, and high-quality. This notion provides a lens to understand the disproportionate impact of unnecessary and unrecognized labor on digital labor communities within AI production and the emerging harms on them, such as financial precarity and machine subordination. It joins existing work on the prevailing values in ML communities, questions the legitimacy and sustainability of the pursuit of performative accuracy, and calls for enhanced reflexivity and timely intervention.
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Studies of industry-scale data collection revealed that the pursuit of label precision is often driven by market-oriented values such as objectivity, standardization, and corporate interests.
“studies of industryscale data collection revealed that the pursuit of label precision is often driven by market-oriented values such as objectivity, standardization, and corporate interests [49, 51, 71, 112, 113].”
From Disability-First AI Dataset Annotation: Co-designing Stuttered Speech Annotation Guidelines with People Who Stutter (2026) · cites this paper as [113]
The Introduction links precision with objectivity and describes technical precision demanded by technology-company clients. The Findings describe standardized accuracy targets set by clients, supporting the role of corporate requirements. The paper does not establish that objectivity itself is a market-oriented value driving label precision, and its fieldwork concerns AI training in China rather than industry-scale data collection generally. (LLM, conf=0.80)
Experimental — accuracy not yet measuredStudies of industry-scale data collection revealed that the pursuit of label precision is often driven by market-oriented values such as objectivity, standardization, and corporate interests.
“studies of industryscale data collection revealed that the pursuit of label precision is often driven by market-oriented values such as objectivity, standardization, and corporate interests [49, 51, 71, 112, 113].”
From Disability-First AI Dataset Annotation: Co-designing Stuttered Speech Annotation Guidelines with People Who Stutter (2026) · cites this paper as [112]
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Research works printed but not linked: 17
- Baym, N. K. (2015). Connect With Your Audience! The Relational Labor of Connection. The Communication Review, 18(1), 14-22. https://doi.org/10.1080/10714421.2015.996401 · DOI
- Charmaz, K. (2006). Constructing Grounded Theory: A Practical Guide through Qualitative Analysis. SAGE.
- Crawford, K. (2021). The Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press.
- Daniels, A. K. (1987). Invisible Work. Social Problems, 34(5), 403-415. https://doi.org/10.2307/800538 · DOI
- Ekbia, H., & Nardi, B. (2014). Heteromation and its (dis)contents: The invisible division of labor between humans and machines. First Monday. https://doi.org/10.5210/fm.v19i6.5331 · DOI · Paper page
- Grill, G. (2022). Constructing Certainty in Machine Learning: On the performativity of testing and its hold on the future. https://doi.org/10.31219/osf.io/zekqv · DOI
- Hochschild, A. R. (2022). The Managed Heart. In Working in America (5th ed.). Routledge.
- MacKenzie, D. (1993). Inventing Accuracy: A Historical Sociology of Nuclear Missile Guidance. MIT Press.
- Miceli, M., & Posada, J. (2022). The Data-Production Dispositif (arXiv:2205.11963). arXiv. http://arxiv.org/abs/2205.11963 · arXiv
- Miceli, M., Schuessler, M., & Yang, T. (2020). Between Subjectivity and Imposition: Power Dynamics in Data Annotation for Computer Vision. Proceedings of the ACM on Human-Computer Interaction, 4(CSCW2), 115:1-115:25. https://doi.org/10.1145/3415186 · DOI · Paper page
- Muller, M., Lange, I., Wang, D., Piorkowski, D., Tsay, J., Liao, Q. V., Dugan, C., & Erickson, T. (2019). How Data Science Workers Work with Data: Discovery, Capture, Curation, Design, Creation. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 1-15. https://doi.org/10.1145/3290605.3300356 · DOI
- Pinch, T. J., & Bijker, W. E. (1984). The Social Construction of Facts and Artefacts: Or How the Sociology of Science and the Sociology of Technology might Benefit Each Other. Social Studies of Science, 14(3), 399-441. https://doi.org/10.1177/030631284014003004 · DOI · Paper page
- Suchman, L. (2020). Algorithmic warfare and the reinvention of accuracy. Critical Studies on Security, 8(2), 175-187. https://doi.org/10.1080/21624887.2020.1760587 · DOI
- Tsing, A. L. (2012). On Nonscalability: The Living World Is Not Amenable to Precision- Nested Scales. Common Knowledge, 18(3), 505-524. https://doi.org/10.1215/0961754X-1630424 · DOI
- Wise, M. N. (1997). The Values of Precision. Princeton University Press.
- Wu, D. (2023). Good for tech: Disability expertise and labor in China's artificial intelligence sector. First Monday. https://doi.org/10.5210/fm.v28i1.12887 · DOI
- Zhang, A. X., Muller, M., & Wang, D. (2020). How do Data Science Workers Collaborate? Roles, Workflows, and Tools. Proceedings of the ACM on Human- Computer Interaction, 4(CSCW1), 22:1-22:23. https://doi.org/10.1145/3392826 · DOI
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Tang, X., Li, J., & Wu, S. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790405 · Cited by 4 · see citing works
“For example, studies of industry-scale data collection revealed that the pursuit of label precision is often driven by market-oriented values such as objectivity, standardization, and corporate interests [49, 51, 71, 112, 113].”
- Authors
- Zhang, B. Z., Yang, T., Haimson, O., & Thomas, M.
- Affiliations
- Weizenbaum Institute (DE)
- University of Michigan (US)
- Venue
- AoIR Selected Papers of Internet Research
- Publisher
- University of Illinois Libraries
- Published
- 2025-01-02
- ISSN
- 2162-3317 (ISSN-L)
- OpenAlex
- W4407195621
- Cited by
- 0
- References
- 0
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- AoIR Selected Papers of Internet Research (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Digital Economy and Work Transformation
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Business, Management and Accounting › Industrial relations › Legal and Labor Studies
- Keywords
- AI training, machine learning, technical accuracy, hidden work, digital labor, economic precarity, reflexivity, China
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