The DOI matches its Crossref record; 61 of the 72 references it cites were found in a registry or an index.
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
The cited source says what the claim attributes to it.
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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
In the citing paper
“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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Cites
Polanyi, M. (1997) · Elsevier eBooks · doi:10.1016/b978-0-7506-9718-7.50010-x · Cited by 12,410
Clark, A., & Chalmers, D. (1998) · Analysis · doi:10.1093/analys/58.1.7 · Cited by 6,333 · cited 1×
“Within the philosophy of mind, there is a school of thought that endorses the ‘extended mind thesis’ (Clark & Chalmers 1998; Clark 2010).”
BackgroundThe second machine age: work, progress, and prosperity in a time of brilliant technologies
(2015) · Choice Reviews Online · doi:10.5860/choice.184834 · Cited by 4,409
Big data: a revolution that will transform how we live, work, and think
(2013) · Choice Reviews Online · doi:10.5860/choice.50-6804 · Cited by 3,657
(1999) · Medical Entomology and Zoology · Cited by 3,259
Clinical versus statistical prediction: A theoretical analysis and a review of the evidence.
Meehl, P. E. (1954) · University of Minnesota Press eBooks · doi:10.1037/11281-000 · Cited by 2,227
Supersizing the mind: embodiment, action, and cognitive extension
(2009) · Choice Reviews Online · doi:10.5860/choice.47-0788 · Cited by 2,205
Supersizing the Mind: Embodiment, Action, and Cognitive Extension
Clark, A. (2008) · doi:10.1093/acprof:oso/9780195333213.001.0001 · Cited by 2,201
Superintelligence: paths, dangers, strategies
Bostrom, N. (2015) · Choice Reviews Online · doi:10.5860/choice.187536 · Cited by 1,998
The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies
Dong, X., & McIntyre, S. H. (2014) · Quantitative Finance · doi:10.1080/14697688.2014.946440 · Cited by 1,759
The Data Revolution: Big Data, Open Data, Data Infrastructures & Their Consequences
Kitchin, R. (2014) · doi:10.4135/9781473909472 · Cited by 1,469 · cited 8×
“With the ongoing data revolution and the transition towards the so-called Internet of Things, this trend can only be set to grow (Kitchin 2014a; Kellermeit & Obodovski 2013; Rifkin 2014).”
BackgroundMethodInfluentialGrove, W. M., & Meehl, P. E. (1996) · Psychology, Public Policy, and Law · doi:10.1037/1076-8971.2.2.293 · Cited by 1,457
Code/Space: Software and Everyday Life
Kitchin, R., & Dodge, M. (2011) · doi:10.7551/mitpress/9780262042482.001.0001 · Cited by 964
(2014) · Choice Reviews Online · doi:10.5860/choice.186403 · Cited by 910
Superintelligence: Paths, Dangers, Strategies
Mulgan, T. (2015) · The Philosophical Quarterly · doi:10.1093/pq/pqv034 · Cited by 878
Kurzweil, R. (2014) · Palgrave Macmillan UK eBooks · doi:10.1057/9781137349088_26 · Cited by 866
Mann, S., Nolan, J., & Wellman, B. (2002) · Surveillance & Society · doi:10.24908/ss.v1i3.3344 · Cited by 846
The ethics of big data: current and foreseeable issues in biomedical contexts
Mittelstadt, B. D., & Floridi, L. (2016) · Science and Engineering Ethics · doi:10.1007/s11948-015-9652-2 · Cited by 714
(2014) · CERN Document Server (European Organization for Nuclear Research) · Cited by 608
Epistemic Democracy: Generalizing the Condorcet Jury Theorem
List, C., & Goodin, R. E. (2001) · Journal of Political Philosophy · doi:10.1111/1467-9760.00128 · Cited by 607
Code/space: software and everyday life
(2011) · Choice Reviews Online · doi:10.5860/choice.49-2108 · Cited by 573
(2014) · Digital Commons at University of Maryland Carey Law (University of Maryland Francis King Carey School of Law) · Cited by 524
Marx, G. T. (2005) · Encyclopedia of Social Theory · doi:10.4135/9781412952552.n303 · Cited by 456
(2007) · Digital Commons at University of Maryland Carey Law (University of Maryland Francis King Carey School of Law) · Cited by 277
Global Labor: Algocratic Modes of Organization
Aneesh, A. (2009) · Sociological Theory · doi:10.1111/j.1467-9558.2009.01352.x · Cited by 262
Predictive analytics: the power to predict who will click, buy, lie, or die
(2013) · Choice Reviews Online · doi:10.5860/choice.51-0326 · Cited by 257
Deliberative Democracy and its Discontents
Martí, J. L., & Besson, S. (2017) · Routledge eBooks · doi:10.4324/9781315258249 · Cited by 257
(2012) · Cited by 253
Average is over: powering America beyond the age of the great stagnation
(2014) · Choice Reviews Online · doi:10.5860/choice.51-4549 · Cited by 231
Estlund, D. (2009) · Princeton University Press eBooks · doi:10.1515/9781400831548 · Cited by 229
Interpretable classification models for recidivism prediction
Zeng, J., Ustun, B., & Rudin, C. (2017) · Journal of the Royal Statistical Society Series A: Statistics in Society · doi:10.1111/rssa.12227 · Cited by 220
Truth, Error, and Criminal Law: An Essay in Legal Epistemology
Laudan, L. (2006) · Cited by 179
Andrejevic, M. (2014) · International journal of communication · Cited by 160
Gaus, G. F. (2010) · Cambridge University Press eBooks · doi:10.1017/cbo9780511780844 · Cited by 159
Smarter than you think: how technology is changing our minds for the better
(2014) · Choice Reviews Online · doi:10.5860/choice.51-3207 · Cited by 156
Brynjolfsson, E., & McAfee, A. P. (2011) · Cited by 150
Aneesh, A. (2006) · Duke University Press eBooks · doi:10.1215/9780822387534 · Cited by 148
Big data a revolution that will transform how we live, work, and think
Mayer-Schönberger, V., & Cukier, K. N. (2013) · Cited by 140
Brin, D. (1998) · Cited by 128
Agar, N. (2013) · The MIT Press eBooks · doi:10.7551/mitpress/9780262026635.001.0001 · Cited by 110
Peter, F. (2008) · Episteme · doi:10.3366/e1742360008000221 · Cited by 75
50 Years of Successful Predictive Modeling Should Be Enough: Lessons for Philosophy of Science
Bishop, M. A., & Trout, J. D. (2002) · Philosophy of Science · doi:10.1086/341846 · Cited by 72
Interpretability in Machine Learning – Principles and Practice
Lisboa, P. J. G. (2013) · Lecture notes in computer science · doi:10.1007/978-3-319-03200-9_2 · Cited by 58
Computational Intelligence in Intelligent Data Analysis
Moewes, C., & Nürnberger, A. (2012) · Studies in computational intelligence · doi:10.1007/978-3-642-32378-2 · Cited by 49
Lessig, L. (2009) · Cited by 48
Safe and Interpretable Machine Learning: A Methodological Review
Otte, C. (2012) · Studies in computational intelligence · doi:10.1007/978-3-642-32378-2_8 · Cited by 44
Estlund on Epistocracy: A Critique
Lippert‐Rasmussen, K. (2012) · Res Publica · doi:10.1007/s11158-012-9179-1 · Cited by 44
Beyond legitimacy. Can proceduralism say anything relevant about justice?
Ceva, E. (2012) · Critical Review of International Social and Political Philosophy · doi:10.1080/13698230.2012.651018 · Cited by 43
Governmental Data Mining and its Alternatives
Zarsky, T. (2011) · bepress Legal Repository · Cited by 39
Making Truth Safe For Democracy
Estlund, D. (1993) · PhilPapers (PhilPapers Foundation) · Cited by 35
Thinking Critically About and Researching AlgorithmsPreprint
Kitchin, R. (2014) · SSRN Electronic Journal · doi:10.2139/ssrn.2515786 · Cited by 32
Practical Predictive Analytics and Decisioning Systems for Medicine
Winters-Miner, L. A. (2015) · Elsevier eBooks · doi:10.1016/c2012-0-02184-9 · Cited by 25
Dark pools : the rise of A.I. trading machines and the looming threat to Wall Street
Patterson, S. M. (2013) · Random House Business Books · Cited by 20
Automated prediction: perception, law, and policy
Zarsky, T. Z. (2012) · Communications of the ACM · doi:10.1145/2330667.2330678 · Cited by 19
Ali, M. A., & Mann, S. (2013) · doi:10.1109/istas.2013.6613126 · Cited by 19
Thinking critically about and researching algorithms. Programmable City Working Paper 5
Kitchin, R. (2014) · Maynooth University ePrints and eTheses Archive (Maynooth University) · Cited by 14
Schoon, M. (2015) · Global Governance A Review of Multilateralism and International Organizations · doi:10.1163/19426720-02102010 · Cited by 8
On the Need for Epistemic Enhancement: Democratic Legitimacy and the Enhancement Project
Danaher, J. (2013) · Law Innovation and Technology · doi:10.5235/17579961.5.1.85 · Cited by 5
Self-citationThe Irrelevance of Democracy to the Public Justification of Political Authority
Machin, D. J. (2009) · Res Publica · doi:10.1007/s11158-009-9084-4 · Cited by 5
We Are the Borg! Human Assimilation into Cellular Society
Lipschutz, R. D., & Hester, R. J. (2013) · Advances in human and social aspects of technology book series · doi:10.4018/978-1-4666-4582-0.ch016 · Cited by 1
Research works printed but not linked: 6
- Clark, A. (2010). Supersizing the mind. Oxford: OUP.
- Crawford, K., & Schultz, J. (2014). Big data and due process: towards a framework to redress predictive privacy harms. Boston College Law Review, 55, 93.
- Habermas, J. (1990). Discourse ethics: notes on a program of philosophical justification. In Moral Consciousness and Communicative Action. Trans. Christian Lenhart and Shierry Weber Nicholson. Cambridge, MA: MIT Press.
- Pentland, A. (2014). Social Physics. London: Penguin Press
- Seaver, N. (2013). Knowing algorithms. In Media in Transition 8, Cambridge MA
- Zarsky, T. (2013). Transparent prediction. University of Illinois Law Review, 4, 1504.
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Kellogg, K. C., Valentine, M. A., & Christin, A. (2020) · Academy of Management Annals · doi:10.5465/annals.2018.0174 · Cited by 2,732
Automation, Algorithms, and Beyond: Why Work Design Matters More Than Ever in a Digital World
Parker, S. K., & Grote, G. (2022) · Applied Psychology · doi:10.1111/apps.12241 · Cited by 960
A systematic review of algorithm aversion in augmented decision making
Burton, J. W., Stein, M., & Jensen, T. B. (2020) · Journal of Behavioral Decision Making · doi:10.1002/bdm.2155 · Cited by 814
Transparency and the black box problem: why we do not trust AI
von Eschenbach, W. J. (2021) · Philosophy & Technology · doi:10.1007/s13347-021-00477-0 · Cited by 786
Veale, M., & Binns, R. (2017) · Big Data & Society · doi:10.1177/2053951717743530 · Cited by 512
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
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