Algorithms as fetish: Faith and possibility in algorithmic work
Open accessAbstract
Algorithms are powerful because we invest in them the power to do things. With such promise, they can transform the ordinary, say snapshots along a robotic vacuum cleaner’s route, into something much more, such as a clean home. Echoing David Graeber’s revision of fetishism, we argue that this easy slip from technical capabilities to broader claims betrays not the “magic” of algorithms but rather the dynamics of their exchange. Fetishes are not indicators of false thinking, but social contracts in material form. They mediate emerging distributions of power often too nascent, too slippery or too disconcerting to directly acknowledge. Drawing primarily on 2016 ethnographic research with computer vision professionals, we show how faith in what algorithms can do shapes the social encounters and exchanges of their production. By analyzing algorithms through the lens of fetishism, we can see the social and economic investment in some people’s labor over others. We also see everyday opportunities for social creativity and change. We conclude that what is problematic about algorithms is not their fetishization but instead their stabilization into full-fledged gods and demons – the more deserving objects of critique.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Algorithm-based decisions emerge as from a black box or magic box.
“Algorithm-based decisions emerge as from a “black box” (Pasquale 2015) or “magic box” (Thomas et al. 2018).”
From Artificial intelligence and work: a critical review of recent research from the social sciences (2024) · cites this paper as Thomas et al. 2018
Cites
Situated Learning: Legitimate Peripheral Participation.
Bloch, M. E. F., Lave, J., & Wenger, É. (1994) · Man · doi:10.2307/2804509 · Cited by 39,323 · see citing works
Lave, J., & Wenger, E. (1991) · Cambridge University Press eBooks · doi:10.1017/cbo9780511815355 · Cited by 33,219 · see citing works
Plans and Situated Actions: The Problem of Human-Machine Communication
Kantowitz, B. H., & Suchman, L. A. (1990) · The American Journal of Psychology · doi:10.2307/1423221 · Cited by 5,306 · see citing works
The Black Box Society: The Secret Algorithms That Control Money and Information
Pasquale, F. (2015) · doi:10.4159/harvard.9780674736061 · Cited by 3,796 · see citing works
Goodwin, C. (1994) · American Anthropologist · doi:10.1525/aa.1994.96.3.02a00100 · Cited by 3,345 · see citing works
How the machine “thinks”: Understanding opacity in machine learning algorithms
Burrell, J. (2016) · Big Data & Society · doi:10.1177/2053951715622512 · Cited by 3,196 · cited 2× · see citing works
“Jenna Burrell (2016) highlights the particular opacity of deep learning algorithms given unpredictable input data and a gap between how people value these data versus how the code handles them.”
BackgroundBarocas, S., & Selbst, A. D. (2014) · doi:10.2139/ssrn.2477899 · Cited by 3,048 · see citing works
Plans and situated actions: The problem of human-machine communication
Agre, P. E. (1990) · Artificial Intelligence · doi:10.1016/0004-3702(90)90078-e · Cited by 1,684 · see citing works
The Black Box Society: The Secret Algorithms That Control Money and Information
(2016) · Contemporary Sociology: A Journal of Reviews · doi:10.1177/0094306116641409c · Cited by 1,334 · see citing works
Gillespie, T. (2014) · Media Technologies · doi:10.7551/mitpress/9042.003.0013 · Cited by 1,016 · see citing works
Algorithms and their others: Algorithmic culture in context
Dourish, P. (2016) · Big Data & Society · doi:10.1177/2053951716665128 · Cited by 481 · see citing works
Data for life: wearable technology and the design of self-care
Schüll, N. D. (2016) · BioSocieties · doi:10.1057/biosoc.2015.47 · Cited by 445 · see citing works
Pietz, W. (1985) · Res Anthropology and Aesthetics · doi:10.1086/resv9n1ms20166719 · Cited by 396 · see citing works
The Crowd is a Collaborative Network
Gray, M. L., Suri, S., Ali, S. S., & Kulkarni, D. (2016) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2818048.2819942 · Cited by 239 · see citing works
Engeström, Y., & Miettinen, R. (1999) · Perspectives on Activity Theory · doi:10.1017/cbo9780511812774.002 · Cited by 228 · see citing works
This One Does Not Go Up to 11: The Quantified Self Movement as an Alternative Big Data Practice
Nafus, D., & Sherman, J. (2014) · Cited by 210 · see citing works
Self-citationOn "Sourcery," or Code as Fetish
Chun, W. H. K. (2008) · Configurations · doi:10.1353/con.0.0064 · Cited by 169 · see citing works
Fetishism as social creativity: or, Fetishes are gods in the process of construction
Graeber, D. (2005) · Anthropological Theory · doi:10.1177/1463499605059230 · Cited by 168 · see citing works
A path to understanding the effects of algorithm awareness
Hamilton, K., Karahalios, K. G., Sandvig, C., & Eslami, M. (2014) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/2559206.2578883 · Cited by 139 · see citing works
Passi, S., & Jackson, S. (2017) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2998181.2998331 · Cited by 113 · see citing works
Perspectives: Angles on African Art
Silverman, R. A., Baldwin, J., Bearden, R. H., et al. (1987) · African Arts · doi:10.2307/3336494 · Cited by 68 · see citing works
Computable Bodies : Instrumented life and the human somatic niche
(2015) · Bloomsbury Academic eBooks · doi:10.5040/9781474276245 · Cited by 65 · see citing works
Algorithmic Media Need Democratic Methods: Why Publics Matter
McKelvey, F. R. (2014) · Canadian Journal of Communication · doi:10.22230/cjc.2014v39n4a2746 · Cited by 58 · see citing works
Data & Civil Rights: Technology PrimerPreprint
Barocas, S., Rosenblat, A., <!>boyd, <., et al. (2014) · SSRN Electronic Journal · doi:10.2139/ssrn.2536579 · Cited by 9 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
The Challenges of Algorithm-Based HR Decision-Making for Personal Integrity
Leicht-Deobald, U., Busch, T., Schank, C., Weibel, A., Schafheitle, S., Wildhaber, I., & Kasper, G. (2019) · Journal of Business Ethics · doi:10.1007/s10551-019-04204-w · Cited by 347 · see citing works
Keding, C., & Meissner, P. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.120970 · Cited by 168 · see citing works
Artificial intelligence and work: a critical review of recent research from the social sciences
Deranty, J.-P., & Corbin, T. (2024) · AI & SOCIETY · doi:10.1007/s00146-022-01496-x · Cited by 118 · see citing works
“Algorithm-based decisions emerge as from a “black box” (Pasquale 2015) or “magic box” (Thomas et al. 2018).”
Municipal surveillance regulation and algorithmic accountability
Young, M., Katell, M., & Krafft, P. M. (2019) · Big Data & Society · doi:10.1177/2053951719868492 · Cited by 51 · see citing works
Bureaucracy as a Lens for Analyzing and Designing Algorithmic Systems
Pääkkönen, J., Nelimarkka, M., Haapoja, J., & Lampinen, A. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376780 · Cited by 35 · see citing works
- Authors
- Thomas, S. L., Nafus, D., & Sherman, J.
- Affiliations
- Intel (United States) (US)
- Corresponding author
- Thomas, Suzanne L
- ORCID
- Nafus, Dawn
- Venue
- Big Data & Society
- Publisher
- SAGE Publications
- Published
- 2018-01
- Volume
- 5
- Issue
- 1
- ISSN
- 2053-9517 (ISSN-L)
- OpenAlex
- W2784256807
- Cited by
- 72
- References
- 24
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 11 · 2023: 16 · 2022: 8 · 2021: 12 · 2020: 5 · 2019: 11 · 2018: 3
- FWCI
- 15.41
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Big Data & Society (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Cybernetics and Technology in Society
- Social Sciences › Sociology and Political Science › Digital Economy and Work Transformation
- Keywords
- fetishism, David Graeber, algorithmic labor, social contract, power relations, social creativity
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