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The social power of algorithms
Open accessAbstract
This article explores the questions associated with what might be thought of as the social power of algorithms. The article, which introduces a special issue on the same topic, begins by reflecting on how we might approach algorithms from a social scientific perspective. The article is then split into two sections. The first deals with the issues that might be associated with an analysis of the power of the algorithms themselves. This section outlines a series of issues associated with the functionality of the algorithms and how these functions are powerfully deployed within social world. The second section then focuses upon the notion of the algorithm. In this section, the article argues that we need to look beyond the algorithms themselves, as a technical and material presence, to explore how the notion or concept of the algorithm is also an important feature of their potential power. In this section, it is suggested that we look at the way that notions of the algorithm are evoked as a part of broader rationalities and ways of seeing the world. Exploring the notion of the algorithm may enable us to see how algorithms also play a part in social ordering processes, both in terms of how the algorithm is used to promote certain visions of calculative objectivity and also in relation to the wider governmentalities that this concept might be used to open up.
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“When professionals cannot analyze the reasoning behind AI decisions, they miss out on the learning process (Beer 2017), lacking opportunities to reflect on, deepen, or update their expertise (Beane 2019).”
From To Engage or Not to Engage with AI for Critical Judgments: How Professionals Deal with Opacity When Using AI for Medical Diagnosis (2022) · cites this paper as Beer 2017
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“Beer (2017) noted that power is “operationalized through the algorithm, in that the algorithmic output cements, maintains or produces certain truths” (p.8) and users' understanding of the world is changed through their algorithmic interaction.”
From Expanding on the Frames: Making a Case for Algorithmic Literacy (2023) · cites this paper as Beer, 2017
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“While machine algorithms shape human actions, humans shape these algorithms through their actions, which create a recursive relationship (Beer, 2017).”
From Artificial Intelligence and Management: The Automation–Augmentation Paradox (2021) · cites this paper as Beer, 2017
In the citing paper
“The collective impact of algorithms has spurred discussions on the autonomy afforded to end users.(Ananny and Crawford 2018;Beer 2017;Taddeo and Floridi 2018b;Möller et al. 2018;Malhotra et al. 2018;Shin and Park 2019;Hauer 2019;Bauer and Dubljević 2020).”
From The ethics of algorithms: key problems and solutions (2022) · cites this paper as Beer 2017
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Metadata
- Authors
- Beer, D.
- Affiliations
- University of York (GB)
- Corresponding author
- Beer, David
- ORCID
- Beer, David
- Venue
- Information, Communication & Society
- Publisher
- Informa UK Limited
- Published
- 2016-08-08
- Volume
- 20
- Issue
- 1
- Pages
- 1-13
- ISSN
- 1369-118X (ISSN-L)
- 1468-4462
- OpenAlex
- W2507975203
- Cited by
- 945
- References
- 45
- Citations by year
- 2026: 196 · 2025: 91 · 2024: 101 · 2023: 157 · 2022: 99 · 2021: 108 · 2020: 64 · 2019: 69 · 2018: 45 · 2017: 15
- FWCI
- 104.91
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Information Communication & Society (publishedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Cybernetics and Technology in Society
- Social Sciences › Business, Management and Accounting › Management Information Systems › Information Systems Theories and Implementation
- Keywords
- algorithms, social power, social ordering, governmentality, rationality, algorithmic governance
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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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