The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
The Society of Algorithms
Open accessAbstract
The pairing of massive data sets with processes—or algorithms—written in computer code to sort through, organize, extract, or mine them has made inroads in almost every major social institution. This article proposes a reading of the scholarly literature concerned with the social implications of this transformation. First, we discuss the rise of a new occupational class, which we call the coding elite. This group has consolidated power through their technical control over the digital means of production and by extracting labor from a newly marginalized or unpaid workforce, the cybertariat. Second, we show that the implementation of techniques of mathematical optimization across domains as varied as education, medicine, credit and finance, and criminal justice has intensified the dominance of actuarial logics of decision-making, potentially transforming pathways to social reproduction and mobility but also generating a pushback by those so governed. Third, we explore how the same pervasive algorithmic intermediation in digital communication is transforming the way people interact, associate, and think. We conclude by cautioning against the wildest promises of artificial intelligence but acknowledging the increasingly tight coupling between algorithmic processes, social structures, and subjectivities.
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Cites
Dissecting racial bias in an algorithm used to manage the health of populations
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019) · Science · doi:10.1126/science.aax2342 · Cited by 7,137
Sorting Things Out: Classification and Its Consequences
Bowker, G. C., & Star, S. L. (1999) · doi:10.7551/mitpress/6352.001.0001 · Cited by 6,188
The Black Box Society: The Secret Algorithms That Control Money and Information
Pasquale, F. (2015) · doi:10.4159/harvard.9780674736061 · Cited by 3,796
Predicting the future - big data, machine learning, and clinical medicine
Obermeyer, Z., & Emanuel, E. J. (2016) · New England Journal of Medicine · doi:10.1056/nejmp1606181 · Cited by 3,652
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 3×
“And because they are embodied in mathematical formulas, they are “promoted and perceived as more objective or progressive than the discriminatory systems of a previous era” (Benjamin 2019, p. 5) and may thus be harder to see and to challenge (Burrell 2016).”
BackgroundSelf-citationAlgorithms at Work: The New Contested Terrain of Control
Kellogg, K. C., Valentine, M. A., & Christin, A. (2020) · Academy of Management Annals · doi:10.5465/annals.2018.0174 · Cited by 2,732
Algorithms of Oppression: How Search Engines Reinforce Racism
Noble, S. U. (2020) · doi:10.2307/j.ctt1pwt9w5 · Cited by 2,709
Haggerty, K. D., & Ericson, R. V. (2000) · The British Journal of Sociology · doi:10.1080/00071310020015280 · Cited by 2,244
Privacy in Context: Technology, Policy, and the Integrity of Social Life
Nissenbaum, H. (2009) · doi:10.1515/9780804772891 · Cited by 2,129
The World’s Technological Capacity to Store, Communicate, and Compute Information
Hilbert, M., & López, P. (2011) · Science · doi:10.1126/science.1200970 · Cited by 1,643
Cited by
Harms from Increasingly Agentic Algorithmic Systems
Chan, A., Salganik, R., Markelius, A., Pang, C., Rajkumar, N., Krasheninnikov, D., Langosco, L., He, Z., Duan, Y., Carroll, M., et al. (2023) · 2023 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3593013.3594033 · Cited by 149
Rethinking Fairness: An Interdisciplinary Survey of Critiques of Hegemonic ML Fairness Approaches
Weinberg, L. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.13196 · Cited by 127
Chan, A., Ezell, C., Kaufmann, M., Wei, K., Hammond, L., Bradley, H., Bluemke, E., Rajkumar, N., Krueger, D., Kolt, N., et al. (2024) · The 2024 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3630106.3658948 · Cited by 84
Trust, trustworthiness and AI governance
Lahusen, C., Maggetti, M., & Slavkovik, M. (2024) · Scientific Reports · doi:10.1038/s41598-024-71761-0 · Cited by 68
Beyond the author: Artificial intelligence, creative writing and intellectual emancipation
Tsao, J., & Nogues, C. (2024) · Poetics · doi:10.1016/j.poetic.2024.101865 · Cited by 67
Metadata
- Authors
- Burrell, J., & Fourcade, M.
- Venue
- Annual Review of Sociology
- Publisher
- Annual Reviews
- Published
- 2021-07-31
- Volume
- 47
- Issue
- 1
- Pages
- 213-237
- ISSN
- 0360-0572 (ISSN-L)
- 1545-2115
- OpenAlex
- W3167983954
- Cited by
- 430
- References
- 35
- FWCI
- 14.00
- Type
- journal article
- Language
- en
- Open access
- green
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
