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Transparency in Algorithmic and Human Decision-Making: Is There a Double Standard?


We are sceptical of concerns over the opacity of algorithmic decision tools. While transparency and explainability are certainly important desiderata in algorithmic governance, we worry that automated decision-making is being held to an unrealistically high standard, possibly owing to an unrealistically high estimate of the degree of transparency attainable from human decision-makers. In this paper, we review evidence demonstrating that much human decision-making is fraught with transparency problems, show in what respects AI fares little worse or better and argue that at least some regulatory proposals for explainable AI could end up setting the bar higher than is necessary or indeed helpful. The demands of practical reason require the justification of action to be pitched at the level of practical reason. Decision tools that support or supplant practical reasoning should not be expected to aim higher than this. We cast this desideratum in terms of Daniel Dennett’s theory of the “intentional stance” and argue that since the justification of action for human purposes takes the form of intentional stance explanation, the justification of algorithmic decisions should take the same form. In practice, this means that the sorts of explanations for algorithmic decisions that are analogous to intentional stance explanations should be preferred over ones that aim at the architectural innards of a decision tool.

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  1. 1.

    Here, we have in mind certain professions that stand to lose out to automation, e.g. conveyancing, accountancy, and the like.

  2. 2.

    Traditional algorithms, like expert systems, could be inscrutable after the fact: even simple rules can generate complex and inscrutable emergent properties. But these effects were not baked in. We are grateful to an anonymous reviewer for pointing this out to us.

  3. 3.

    See, e.g. <>.

  4. 4.

    Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data and repealing Directive 95/46/EC (General Data Protection Regulation), OJ L 119, 27.3.2016, p. 1.

  5. 5.

    Strictly speaking, this “good practice” recommendation (Annex 1) pertains to Article 15, not Article 22, of the GDPR. Article 15(1)(h) requires the disclosure of “meaningful information about the logic involved” in certain kinds of fully automated decisions.

  6. 6.

    The merits of various pragmatic theories of truth are not especially relevant to us here. Another way we could put our point is that utility in the service of one aim is not utility in the service of another.

  7. 7.

    Actually, many private, purely personal decisions (regarding, e.g. what to study, which career to pursue, whether to rent or purchase) are also frequently made in consultation with friends, family, mentors, career advisers, and so on.

  8. 8.

    Our citing Damasio (1994) might seem odd, for we are suggesting that the effects of emotions may be reason-distorting, whereas for Damasio this is not the main point. Damasio sees emotions as an essential component of rational thought (and we agree). Nevertheless, he does see emotions as engendering biases in some cases. For instance, he says: “I will not deny that uncontrolled or misdirected emotion can be a major source of irrational behavior. Nor will I deny that seemingly normal reason can be disturbed by subtle biases rooted in emotion” (1994, pp. 52–53, our emphasis). (He goes on to say: “Nonetheless, (…) [r]eduction in emotion may constitute an equally important source of irrational behavior.” But the key point is that he does see emotions as a potential source of bias in some contexts.)

  9. 9.

    Copyright law is not the only culprit here. Other factors impeding access include privacy and income disparities.

  10. 10.

    House v. The King (1936) 55 C.L.R. 499 (High Court of Australia).

  11. 11.

    Devries v. Australian National Railways Commission (1993) 177 CLR 472 (High Court of Australia); Abalos v. Australian Postal Commission (1990) 171 CLR 167 (High Court of Australia); cf. Fox v. Percy (2003) 214 C.L.R. 118 (High Court of Australia).

  12. 12.

    See, e.g. Supreme Court Act, s. 101(2) (New South Wales).

  13. 13.

    This classification is not to be confused with the more traditional one found in the standards literature, e.g. Coglianese and Lazer (2003).

  14. 14.

    We are grateful to an anonymous reviewer for bringing these to our attention.


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The authors wish to thank the participants of two roundtables, one held in Oxford, November 23–24, 2017, in partnership with the Uehiro Centre for Practical Ethics, University of Oxford, and the other in Dunedin, December 11–12, at the University of Otago.


This research was supported by a New Zealand Law Foundation grant (2016/ILP/10).

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Correspondence to John Zerilli.

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AK works for Soul Machines Ltd under contract. JZ, JM, and CG have no other disclosures or relevant affiliations apart from the appointments above.

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Zerilli, J., Knott, A., Maclaurin, J. et al. Transparency in Algorithmic and Human Decision-Making: Is There a Double Standard?. Philos. Technol. 32, 661–683 (2019).

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  • Algorithmic decision-making
  • Transparency
  • Explainable AI
  • Intentional stance