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Transparency: Motivations and Challenges

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Explainable AI: Interpreting, Explaining and Visualizing Deep Learning

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11700))

Abstract

Transparency is often deemed critical to enable effective real-world deployment of intelligent systems. Yet the motivations for and benefits of different types of transparency can vary significantly depending on context, and objective measurement criteria are difficult to identify. We provide a brief survey, suggesting challenges and related concerns, particularly when agents have misaligned interests. We highlight and review settings where transparency may cause harm, discussing connections across privacy, multi-agent game theory, economics, fairness and trust.

Supported by The Alan Turing Institute, Darwin College and the Leverhulme Trust.

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Notes

  1. 1.

    Greater faithfulness of an explanation may challenge the ability of its audience to understand it well, perhaps requiring a greater investment of time and effort [54].

  2. 2.

    One view is that if rules are set up correctly, then transparency will not lead to ‘gaming’ since agents optimizing their own objectives subject to the rules will necessarily lead to a good outcome for all. However, it is often very challenging in practice to get the rules exactly right in this way – thus there may be a distinction between the ‘letter’ and the ‘spirit’ of the law. See Sect. 2.4 for a related example.

  3. 3.

    Consider \(c(x)= {\left\{ \begin{array}{ll} 10x &{} x \le 3\\ 30 &{} 3 \le x \le 3+\epsilon \\ 10(x-\epsilon ) &{} 3+\epsilon \le x. \end{array}\right. }\).

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Acknowledgements

This article is an extended version of [71]. The author thanks Frank Kelly for pointing out the Braess’ paradox example and related intuition; and thanks Vasco Carvalho, Stephen Cave, Jon Crowcroft, David Fohrman, Yarin Gal, Adria Gascon, Zoubin Ghahramani, Sanjeev Goyal, Krishna P. Gummadi, Dylan Hadfield-Menell, Bill Janeway, Frank Kelly, Aryeh Kontorovich, Neil Lawrence, Barney Pell and Mark Rowland for helpful discussions; and thanks the anonymous reviewers for helpful comments. The author acknowledges support from the David MacKay Newton research fellowship at Darwin College, The Alan Turing Institute under EPSRC grant EP/N510129/1 & TU/B/000074, and the Leverhulme Trust via the CFI.

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Authors and Affiliations

  1. University of Cambridge, Cambridge, UK

    Adrian Weller

  2. The Alan Turing Institute, London, UK

    Adrian Weller

  3. Leverhulme Centre for the Future of Intelligence, Cambridge, UK

    Adrian Weller

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Correspondence to Adrian Weller .

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  1. Fraunhofer Heinrich Hertz Institute, Berlin, Germany

    Wojciech Samek

  2. Technische Universität Berlin, Berlin, Germany

    Grégoire Montavon

  3. University of Oxford, Oxford, UK

    Andrea Vedaldi

  4. Technical University of Denmark, Kgs. Lyngby, Denmark

    Lars Kai Hansen

  5. Sekretariat MAR 4-1, Technical University of Berlin, Berlin, Berlin, Germany

    Klaus-Robert Müller

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Weller, A. (2019). Transparency: Motivations and Challenges. In: Samek, W., Montavon, G., Vedaldi, A., Hansen, L., Müller, KR. (eds) Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. Lecture Notes in Computer Science(), vol 11700. Springer, Cham. https://doi.org/10.1007/978-3-030-28954-6_2

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