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The Threat of Algocracy: Reality, Resistance and Accommodation


One of the most noticeable trends in recent years has been the increasing reliance of public decision-making processes (bureaucratic, legislative and legal) on algorithms, i.e. computer-programmed step-by-step instructions for taking a given set of inputs and producing an output. The question raised by this article is whether the rise of such algorithmic governance creates problems for the moral or political legitimacy of our public decision-making processes. Ignoring common concerns with data protection and privacy, it is argued that algorithmic governance does pose a significant threat to the legitimacy of such processes. Modelling my argument on Estlund’s threat of epistocracy, I call this the ‘threat of algocracy’. The article clarifies the nature of this threat and addresses two possible solutions (named, respectively, ‘resistance’ and ‘accommodation’). It is argued that neither solution is likely to be successful, at least not without risking many other things we value about social decision-making. The result is a somewhat pessimistic conclusion in which we confront the possibility that we are creating decision-making processes that constrain and limit opportunities for human participation.

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  1. The possibility of an intelligent AI controlling the world is explored at length in Bostrom 2014.

  2. I add ‘computer programmed’ here since algorithms are, in effect, recipes or step-by-step instructions for deriving outputs from a set of inputs. As such, algorithms do not need to be implemented by some computer architecture, but I limit interest to computer-programmed variants because the threat of algocracy is acutely linked to the data revolution (Kitchin 2014a).

  3. Dormehl gives some striking illustrations of bureaucratic systems that are automated, e.g. the facial recognition algorithm system used to revoke driving licences in Massachusetts (Dormehl 2014, 157–58)

  4. There are also connections here with Lessig’s work (1999 and 2006) on code as a type of regulatory architecture. Lessig is concerned primarily with who owns and controls that architecture; I am concerned with ways in which that architecture facilitates a lack of transparency in public decision-making.

  5. Debates about other systems, e.g. automated cars and weapon systems, can raise other moral and political issues.

  6. For an overview, see the Stanford Law Review symposium issue on Privacy and Big Data. Available at: (visited 10/4/14)

  7. The Edward Snowden controversy being, perhaps, the most conspicuous example of this.

  8. For example, the European Directive on this is Directive 95/46/EC

  9. Case C-293/12 (joined with Case C-594/12 Digital Rights Ireland Ltd v. Minister for Communications, Marine and Natural Resources, and Ors 8th April 2014

  10. Ibid, para. 65

  11. There may also, of course, be a connection here with a more substantive conception of justice (Ceva 2012).

  12. I am not sure that there are any pure instrumentalists, but those who endorse an epistemic theory of democracy certainly emphasise this virtue (Estlund 2008; List & Goodin 2001)

  13. The oddness reflects arguments in the consequentialist/deontologist debate in ethics.

  14. A classic example would be if the sub-population satisfies the conditions for the Condorcet Jury Theorem or one of its extrapolations (e.g. List and Goodin, 2001).

  15. This is a reference to the work of Michael Polanyi (1966).

  16. Estlund offers alternative arguments for thinking that epistocracies are politically problematic. These have to do with reasonable rejection on the grounds of suspicion of the epistemic elite. I ignore those arguments here since they tie into his conflation of epistocracy with rule by a stable group of generally superior human agents.

  17. Morozov (2013)—see the subsection entitled ‘Even programmes that seem innocuous can undermine democracy’ for this quote.

  18. The society that worries Morozov is no imaginative dystopia. It is actively pursued by some: see Alex Pentland (2014)

  19. I take this illustration from the artist James Bridle who uses it in some of his talks. See for more.

  20. For the time being anyway. It is likely that, in the future, robot workers will take over such systems. Amazon already works with Kiva robots in some warehouses. See (visited 1/3/15) for a video illustration.

  21. For example, neural network models are widely recognized as having an interpretability problem. See, for example, the discussion in Miner et al. 2014, 249.

  22. It is also worth noting that ‘interpretability’, for many working in this field, seems to mean ‘interpretability by appropriately trained peers’. This would be insufficient for political purposes.

  23. I would like to thank an anonymous reviewer for encouraging further discussion of this issue.

  24. I am indebted to DI for pressing me on this point. This reduction would raise similar kinds of concerns to those animating Lessig in his classic works on the topic (1999 & 2006).

  25. A stark example of this is the Pavlok, a technology which uses basic principles of psychological conditioning to encourage behavioural change. See—note how the website promises to ‘break bad habits in five days’.

  26. Directive 95/46/EC, Art. 15.3

  27. David Brin, one of the chief proponents of sousveillance, has explicitly argued for this in response to Morozov’s worries about the threat to democracy posed by algocratic control (reference omitted for anonymity)

  28. Of course, there may be some processing whenever sousveillance technologies record digital and audio information, but that is not the kind of processing and sorting that would be made possible if humans had their own mining algorithms.

  29. See, generally,; Thompson (2013) also discusses the phenomenon. The story of Chris Dancy, a Denver-based IT executive who is known as the world’s ‘most connected man’, might also be instructive. Dancy wears up to ten data-collection devices on his person every day, in addition to other non-wearable devices. He claims that this has greatly improved his life. See for an interview with him (accessed 1/3/15).

  30. This is the vision of transhumanists like Ray Kurzweil who seek to saturate the cosmos with our intelligence, i.e. to make everything in the universe an extension of and input into our cognitive processes (Kurzweil 2006, 29).


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The author would like to thank audiences at Exeter and Maynooth Universities, and two anonymous referees for feedback on earlier drafts of this paper.

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Correspondence to John Danaher.

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The author declares no conflicts of interest. Research for this paper was not funded nor did it involve any work involving human or animal subjects.

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Danaher, J. The Threat of Algocracy: Reality, Resistance and Accommodation. Philos. Technol. 29, 245–268 (2016).

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  • Algocracy
  • Epistocracy
  • Big data
  • Data mining
  • Legitimacy
  • Human enhancement