CoChat
Sign Up
Cited by 149Ethics and Social Impacts of AIInformation Systems Theories and ImplementationAI and HR Technologies

Between Subjectivity and Imposition: Power Dynamics in Data Annotation for Computer Vision

Open access
Miceli, Milagros, Schuessler, Martin, Yang, Tianling·2020·Proceedings of the ACM on Human-Computer Interaction
DOI 10.1145/3415186
Open this paper in CoChat — free accountOpen PDF

Abstract

The interpretation of data is fundamental to machine learning. This paper investigates practices of image data annotation as performed in industrial contexts. We define data annotation as a sense-making practice, where annotators assign meaning to data through the use of labels. Previous human-centered investigations have largely focused on annotators? subjectivity as a major cause of biased labels. We propose a wider view on this issue: guided by constructivist grounded theory, we conducted several weeks of fieldwork at two annotation companies. We analyzed which structures, power relations, and naturalized impositions shape the interpretation of data. Our results show that the work of annotators is profoundly informed by the interests, values, and priorities of other actors above their station. Arbitrary classifications are vertically imposed on annotators, and through them, on data. This imposition is largely naturalized. Assigning meaning to data is often presented as a technical matter. This paper shows it is, in fact, an exercise of power with multiple implications for individuals and society.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1145/3415186

Cites

11 of 11 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • 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 [71]

    Section 4.4.1 (page unavailable) says annotation guidelines and quality assurance standardize labels to meet clients’ product and revenue plans; Section 4.4.4 says market logics and profit priorities become inscribed in labels. The paper does not establish objectivity as a market-oriented value driving label precision, and it questions the supposed neutrality of data-driven systems. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

  • The distance between lead firms and workers is geographical and operational, with layers of intermediaries acting as the buffer.

    “The distance between lead firms and workers is geographical and operational, with layers of intermediaries acting as the buffer [93].”

    From 'The plan is just survival': Data Work in Kenya and the Regime of Entrapment (2026) · cites this paper as [93]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 63

  1. Luis Araujo and Martin Spring. 2006. Services, Products, and the Institutional Structure of Production. Industrial Marketing Management 35, 7 (Oct. 2006), 797-805. https://doi.org/10.1016/j.indmarman.2006.05.013 · DOI
  2. Paul Baker and Amanda Potts. 2013. 'Why Do White People Have Thin Lips?' Google and the Perpetuation of Stereotypes via Auto-Complete Search Forms. Critical Discourse Studies 10, 2 (May 2013), 187-204. https://doi.org/10. 1080/17405904.2012.744320 · DOI · Paper page
  3. Chelsea Barabas, Colin Doyle, JB Rubinovitz, and Karthik Dinakar. 2020. Studying up: Reorienting the Study of Algorithmic Fairness around Issues of Power. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* '20). Association for Computing Machinery, Barcelona, Spain, 167-176. https://doi.org/10.1145/ 3351095.3372859 · DOI
  4. Jeanette Blomberg and Chuck Darrah. 2015. Toward an Anthropology of Services. The Design Journal 18, 2 (2015), 171-192. https://doi.org/10.2752/175630615X14212498964196 · DOI
  5. Pierre Bourdieu. 1977. Outline of a Theory of Practice. Cambridge University Press, Cambridge. https://doi.org/10. 1017/CBO9780511812507 · DOI · Paper page
  6. Pierre Bourdieu. 1985. The Social Space and the Genesis of Groups. Theory and Society 14, 6 (1985), 723-744. https://doi.org/10.1007/BF00174048 · DOI
  7. Pierre Bourdieu. 1989. Social Space and Symbolic Power. Sociological Theory 7, 1 (1989), 14-25. https://doi.org/10. 2307/202060 · DOI
  8. Pierre Bourdieu. 1990. The logic of practice (reprinted ed.). Polity Press, Cambridge.
  9. Pierre Bourdieu. 1992. Language and Symbolic Power (new ed.). Blackwell Publishers, Cambridge.
  10. Pierre Bourdieu. 2000. Pascalian Meditations. Stanford University Press, Stanford, Calif.
  11. Geoffrey C. Bowker. 2000. Biodiversity Datadiversity. Social Studies of Science 30, 5 (Oct. 2000), 643-683. https: //doi.org/10.1177/030631200030005001 · DOI
  12. Geoffrey C. Bowker and Susan Leigh Star. 1999. Sorting Things out: Classification and Its Consequences. MIT Press, Cambridge, Mass.
  13. danah boyd and Kate Crawford. 2012. Critical Questions for Big Data: Provocations for a Cultural, Technological, and Scholarly Phenomenon. Information, Communication & Society 15, 5 (June 2012), 662-679. https://doi.org/10.1080/ 1369118X.2012.678878 · DOI · Paper page
  14. C. E. Brodley and M. A. Friedl. 1999. Identifying Mislabeled Training Data. Journal of Artificial Intelligence Research 11 (Aug. 1999), 131-167. https://doi.org/10.1613/jair.606 · DOI · Paper page
  15. Joy Buolamwini and Timnit Gebru. 2018. Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency, Vol. 81. PMLR, 77-91.
  16. Ryan Burns. 2019. New Frontiers of Philanthro-capitalism: Digital Technologies and Humanitarianism. Antipode 51, 4 (April 2019), 1101-1122. https://doi.org/10.1111/anti.12534 · DOI
  17. Kathy Charmaz. 2006. Constructing Grounded Theory: A Practical Guide through Qualitative Analysis. Sage Publications, London ; Thousand Oaks, Calif.
  18. Angèle Christin. 2016. From Daguerreotypes to Algorithms: Machines, Expertise, and Three Forms of Objectivity. SIGCAS Computers and Society 46, 1 (2016), 27-32. https://doi.org/10.1145/2908216.2908220 · DOI
  19. Danielle Keats Citron and Frank Pasquale. 2014. The Scored Society: Due Process for Automated Predictions. Washington Law Review 89, 1 (2014).
  20. Juliet M. Corbin and Anselm L. Strauss. 2015. Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory (fourth edition ed.). SAGE, Los Angeles.
  21. Ciaran Cronin. 1996. Bourdieu and Foucault on Power and Modernity. Philosophy & Social Criticism 22, 6 (Nov. 1996), 55-85. https://doi.org/10.1177/019145379602200603 · DOI · Paper page
  22. Catherine D'Ignazio and Lauren F. Klein. 2020. Data Feminism. The MIT Press, Cambridge, Massachusetts.
  23. Ravit Dotan and Smitha Milli. 2020. Value-Laden Disciplinary Shifts in Machine Learning. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* '20). Association for Computing Machinery, Barcelona, Spain, 294. https://doi.org/10.1145/3351095.3373157 · DOI
  24. Emile Durkheim and Marcel Mauss. 1963. Primitive Classification. University of Chicago Press.
  25. M. C. Elish and danah boyd. 2018. Situating Methods in the Magic of Big Data and AI. Communication Monographs 85, 1 (Jan. 2018), 57-80. https://doi.org/10.1080/03637751.2017.1375130 · DOI
  26. Virginia Eubanks. 2018. Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin's Press, New York.
  27. Melanie Feinberg. 2017. A Design Perspective on Data. In CHI '17: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (CHI '17). Association for Computing Machinery, Denver, Colorado, USA, 2952-2963. https://doi.org/10.1145/3025453.3025837 · DOI
  28. Tim Finin, Will Murnane, Anand Karandikar, Nicholas Keller, Justin Martineau, and Mark Dredze. 2010. Annotating Named Entities in Twitter Data with Crowdsourcing. In Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk (CSLDAMT '10). Association for Computational Linguistics, Los Angeles, California, 80-88. https://doi.org/10.5555/1866696.1866709 · DOI
  29. Marion Fourcade and Kieran Healy. 2013. Classification Situations: Life-Chances in the Neoliberal Era. Accounting, Organizations and Society 38, 8 (Nov. 2013), 559-572. https://doi.org/10.1016/j.aos.2013.11.002 · DOI · Paper page
  30. Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumeé III, and Kate Crawford. 2018. Datasheets for Datasets. arXiv:1803.09010 (March 2018). arXiv:1803.09010 · arXiv
  31. R. Stuart Geiger, Kevin Yu, Yanlai Yang, Mindy Dai, Jie Qiu, Rebekah Tang, and Jenny Huang. 2020. Garbage in, Garbage out? Do Machine Learning Application Papers in Social Computing Report Where Human-Labeled Training Data Comes From?. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* '20). Association for Computing Machinery, Barcelona, Spain, 325-336. https://doi.org/10.1145/3351095.3372862 · DOI
  32. Bhavya Ghai, Q. Vera Liao, Yunfeng Zhang, and Klaus Mueller. 2020. Measuring Social Biases of Crowd Workers Using Counterfactual Queries. In Workshop on Fair & Responsible AI at ACM CHI Conference on Human Factors in Computing Systems. Honolulu, HI, USA.
  33. Tarleton Gillespie and Tarleton Gillespie. 2014. The Relevance of Algorithms. In Media Technologies: Essays on Communication, Materiality, and Society, Pablo J. Boczkowski and Kirsten A. Foot (Eds.). The MIT Press, 167-194. https://doi.org/10.7551/mitpress/9780262525374.003.0009 · DOI · Paper page
  34. Lisa Gitelman (Ed.). 2013. "Raw Data" Is an Oxymoron. The MIT Press, Cambridge, Massachusetts ; London, England.
  35. Foad Hamidi, Morgan Klaus Scheuerman, and Stacy M. Branham. 2018. Gender Recognition or Gender Reductionism? The Social Implications of Embedded Gender Recognition Systems.. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI '18). Association for Computing Machinery, New York, 1-13. https: //doi.org/10.1145/3173574.3173582 · DOI
  36. Sarah Holland, Ahmed Hosny, Sarah Newman, Joshua Joseph, and Kasia Chmielinski. 2018. The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards. arXiv:1805.03677 (2018). · arXiv
  37. Christoph Hube, Besnik Fetahu, and Ujwal Gadiraju. 2019. Understanding and Mitigating Worker Biases in the Crowdsourced Collection of Subjective Judgments. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI '19). Association for Computing Machinery, New York, NY, USA, 1-12. https://doi.org/10. 1145/3290605.3300637 · DOI · Paper page
  38. Gunay Kazimzade and Milagros Miceli. 2020. Biased Priorities, Biased Outcomes: Three Recommendations for Ethics- Oriented Data Annotation Practices. In Proceedings of the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society. (AIES '20). Association for Computing Machinery, New York, NY, USA, 1-7. https://doi.org/10.1145/3375627. 3375809 · DOI · Paper page
  39. Lucy Kimbell and Jeanette Blomberg. 2017. The Object of Service Design. In Designing for Service: Key Issues and New Directions. Bloomsbury Publishing, 81-94.
  40. Rob Kitchin. 2017. Thinking Critically about and Researching Algorithms. Information, Communication & Society 20, 1 (Jan. 2017), 14-29. https://doi.org/10.1080/1369118X.2016.1154087 · DOI
  41. Gary Klein, Jennifer K. Phillips, Erica L. Rall, and Deborah A. Peluso. 2007. A Data-Frame Theory of Sensemaking. In Expertise out of Context: Proceedings of the Sixth International Conference on Naturalistic Decision Making. Lawrence Erlbaum Associates Publishers, Mahwah, NJ, US, 113-155.
  42. Ulrike Klinger and Jakob Svensson. 2018. The End of Media Logics? On Algorithms and Agency. New Media & Society 20, 12 (Dec. 2018), 4653-4670. https://doi.org/10.1177/1461444818779750 · DOI
  43. Natalia M Libakova and Ekaterina A Sertakova. 2015. The Method of Expert Interview as an Effective Research Procedure of Studying the Indigenous Peoples of the North. Journal of Siberian Federal University. Humanities & Social Sciences 8, 1 (2015), 114-129. https://doi.org/10.17516/1997-1370-2015-8-1-114-129 · DOI
  44. Michael A. Madaio, Luke Stark, Jennifer Wortman Vaughan, and Hanna Wallach. 2020. Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI '20). Association for Computing Machinery, Honolulu, HI, USA, 1-14. https://doi.org/10.1145/3313831.3376445 · DOI · Paper page
  45. Astrid Mager. 2012. Algorithmic Ideology: How Capitalist Society Shapes Search Engines. Information, Communication & Society 15, 5 (June 2012), 769-787. https://doi.org/10.1080/1369118X.2012.676056 · DOI
  46. Steffen Mau. 2019. The Metric Society: On the Quantification of the Social. Polity, Cambridge ; Medford, MA.
  47. Michael Muller. 2014. Curiosity, Creativity, and Surprise as Analytic Tools: Grounded Theory Method. In Ways of Knowing in HCI, Judith S. Olson and Wendy A. Kellogg (Eds.). Springer, New York, NY, 25-48. https://doi.org/10.1007/ 978-1-4939-0378-8_2 · DOI
  48. Michael Muller, Shion Guha, Eric P.S. Baumer, David Mimno, and N. Sadat Shami. 2016. Machine Learning and Grounded Theory Method: Convergence, Divergence, and Combination. In Proceedings of the 19th International Conference on Supporting Group Work (GROUP '16). Association for Computing Machinery, Sanibel Island, Florida, USA, 3-8. https://doi.org/10.1145/2957276.2957280 · DOI
  49. Michael Muller, Ingrid Lange, Dakuo Wang, David Piorkowski, Jason Tsay, Q. Vera Liao, Casey Dugan, and Thomas Erickson. 2019. How Data Science Workers Work with Data: Discovery, Capture, Curation, Design, Creation. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI '19). Association for Computing Machinery, Glasgow, Scotland Uk, 1-15. https://doi.org/10.1145/3290605.3300356 · DOI
  50. Safiya Umoja Noble. 2018. Algorithms of Oppression: How Search Engines Reinforce Racism. NYU Press, New York.
  51. Juho Pääkkönen, Matti Nelimarkka, Jesse Haapoja, and Airi Lampinen. 2020. Bureaucracy as a Lens for Analyzing and Designing. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI '20). Association for Computing Machinery, Honolulu, HI, USA., 1-14. https://doi.org/10.1145/3313831.3376780 · DOI · Paper page
  52. Samir Passi and Steven Jackson. 2017. Data Vision: Learning to See Through Algorithmic Abstraction. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW '17). Association for Computing Machinery, Portland, Oregon, USA, 2436-2447. https://doi.org/10.1145/2998181.2998331 · DOI
  53. Kathleen H. Pine and Max Liboiron. 2015. The Politics of Measurement and Action. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15). Association for Computing Machinery, New York, NY, USA, 3147-3156. https://doi.org/10.1145/2702123.2702298 · DOI
  54. Joelle Pineau, Philippe Vincent-Lamarre, Koustuv Sinha, Vincent Larivière, Alina Beygelzimer, Florence d'Alché-Buc, Emily Fox, and Hugo Larochelle. 2020. Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program). arXiv:2003.12206 (April 2020). arXiv:2003.12206 · arXiv
  55. Alex Rosenblat, Tamara Kneese, and Danah Boyd. 2014. Networked Employment Discrimination. SSRN Electronic Journal (2014). https://doi.org/10.2139/ssrn.2543507 · DOI · Paper page
  56. Morgan Klaus Scheuerman, Kandrea Wade, Caitlin Lustig, and Jed R Brubaker. 2020. How We've Taught Algorithms to See Identity: Constructing Race and Gender in Image Databases for Facial Analysis. Proc. ACM Hum.-Comput. Interact. 4, CSCW1 (2020). https://doi.org/10.1145/3392866 · DOI · Paper page
  57. Nick Seaver. 2019. Knowing Algorithms. In digitalSTS: A Field Guide for Science & Technology Studies. Princeton University Press, PRINCETON; OXFORD, 412-422.
  58. Ismaïla Seck, Khouloud Dahmane, Pierre Duthon, and Gaëlle Loosli. 2018. Baselines and a Datasheet for the Cerema AWP Dataset. In Conférence d'Apprentissage CAp (Conférence d'Apprentissage Francophone 2018). Rouen, France. https://doi.org/10.13140/RG.2.2.36360.93448 · DOI
  59. Susan Leigh Star and Anselm Strauss. 1999. Layers of Silence, Arenas of Voice: The Ecology of Visible and Invisible Work. Computer Supported Cooperative Work 8, 1-2 (March 1999), 9-30. https://doi.org/10.1023/A:1008651105359 · DOI · Paper page
  60. Robert Thornberg. 2012. Informed Grounded Theory. Scandinavian Journal of Educational Research 56, 3 (June 2012), 243-259. https://doi.org/10.1080/00313831.2011.581686 · DOI · Paper page
  61. Fabian L. Wauthier and Michael I. Jordan. 2011. Bayesian Bias Mitigation for Crowdsourcing. In Proceedings of the 24th International Conference on Neural Information Processing Systems (NIPS'11).
  62. Eviatar Zerubavel. 1993. The Fine Line: Making Distinctions in Everyday Life. (2nd ed. ed.). University of Chicago Press.
  63. Honglei Zhuang and Joel Young. 2015. Leveraging In-Batch Annotation Bias for Crowdsourced Active Learning. In Proceedings of the Eighth ACM International Conference on Web Search and Data Mining (WSDM '15). Association for Computing Machinery, Shanghai, China, 243-252. https://doi.org/10.1145/2684822.2685301 · DOI

Cited by

Sorted by citations

Authors
Miceli, M., Schuessler, M., & Yang, T.
Affiliations
Weizenbaum Institute (DE)
ORCID
Venue
Proceedings of the ACM on Human-Computer Interaction
Publisher
Association for Computing Machinery (ACM)
Published
2020-10-14
Volume
4
Issue
CSCW2
Pages
1-25
ISSN
2573-0142 (ISSN-L)
DOI
10.1145/3415186
OpenAlex
W3093541323
Cited by
149
References
11
Citations by year
2026: 23 · 2025: 38 · 2024: 36 · 2023: 18 · 2022: 24 · 2021: 10
FWCI
17.69
Type
journal article
Language
en
Open access
Licence
Locations
Proceedings of the ACM on Human-Computer Interaction (publishedVersion, cc-by-nc-nd)
Funders
Bundesministerium für Bildung und Forschung (16DII113)
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Sociology and Political Science › Information Systems Theories and Implementation
  • Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
Keywords
sensemaking, classification bias, data interpretation

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 ›

Are you an author, or do you see a problem with this page? Request a review