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The Threat of Algocracy: Reality, Resistance and Accommodation
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“Another point is that increased used of data, and the translation of all aspects of human action into data is that such quantification by necessity involves multiple stages of human interpretation, challenging the objective veneer of big data-based decision making [21,34,36].”
From A shallow defence of a technocracy of artificial intelligence: Examining the political harms of algorithmic governance in the domain of government (2020) · cites this paper as [36]
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“Whether we like it or not, algorithms are increasingly being used to nudge, bias, guide, provoke, control, manipulate and constrain human behaviour.”
From Algorithmic governance: Developing a research agenda through the power of collective intelligence (2017) · cites this paper as Danaher, 2016
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“In public-law terms, the concern is not only whether AI improves efficiency but also whether algorithmic assistance remains compatible with accountable justification, discretionary control, and rule-of-law legitimacy (Danaher, 2016;Binns, 2018;Busuioc, 2021).”
From From Human Oversight to Cognitive Sovereignty: A Process-Based Governance Standard for AI-Assisted Legal Reasoning (2026) · cites this paper as Danaher, 2016
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“In this sense, Cognitive Sovereignty responds to the threat of algocracy: the risk that governance becomes dependent on decision systems whose operation is not meaningfully contestable by the persons subject to them (Danaher, 2016).”
From From Human Oversight to Cognitive Sovereignty: A Process-Based Governance Standard for AI-Assisted Legal Reasoning (2026) · cites this paper as Danaher, 2016
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“This may lead to ‘algocracy’, in which ‘the legitimacy of public decision-making processes’ is thwarted by ‘the opacity of certain algocratic governance systems’ (Danaher, 2016).”
From Algorithmic Accountability and Public Reason (2018) · cites this paper as Danaher, 2016
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“were similar to concepts used in the pre-existing literature on the problems of algorithmic governance (e.g. Danaher, 2016; Zarsky, 2016)”
From Algorithmic governance: Developing a research agenda through the power of collective intelligence (2017) · cites this paper as Danaher, 2016
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“For example, as mentioned in [28], 'the human may want to guard against certain kinds of discrimination, and their notion of fairness may be too abstract to be completely encoded into the system'”
From Beyond explainability: justifiability and contestability of algorithmic decision systems (2022) · cites this paper as [28]
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Metadata
- Authors
- Danaher, J.
- Affiliations
- Ollscoil na Gaillimhe – University of Galway (IE)
- Corresponding author
- Danaher, John
- ORCID
- Danaher, John
- Venue
- Philosophy & Technology
- Published
- 2016-01-09
- Volume
- 29
- Issue
- 3
- Pages
- 245-268
- ISSN
- 2210-5433 (ISSN-L)
- 2210-5441
- OpenAlex
- W2242985385
- Cited by
- 519
- References
- 72
- Citations by year
- 2026: 85 · 2025: 57 · 2024: 59 · 2023: 64 · 2022: 74 · 2021: 58 · 2020: 40 · 2019: 36 · 2018: 27 · 2017: 13 · 2016: 6
- FWCI
- 66.79
- Type
- journal article
- Language
- en
- Open access
- green
- philarchive.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Philosophy & Technology (publishedVersion)
- PhilPapers (PhilPapers Foundation) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Political Science and International Relations › Political Theory and Democracy
- Social Sciences › Political Science and International Relations › E-Government and Public Services
- Keywords
- algocracy, algorithmic governance, algorithmic decision-making, political legitimacy, epistocracy, public decision-making, human participation, social decision-making
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 ›
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