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Persuasive Contrastive Explanations for Bayesian Networks

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Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12897))

  • The original version of this chapter was revised: The errors in Algorithm 1 (page 235) were corrected to include intentionally blank lines in the algorithm. The correction to this chapter is available at https://doi.org/10.1007/978-3-030-86772-0_49

Abstract

Explanation in Artificial Intelligence is often focused on providing reasons for why a model under consideration and its outcome are correct. Recently, research in explainable machine learning has initiated a shift in focus on including so-called counterfactual explanations. In this paper we propose to combine both types of explanation in the context of explaining Bayesian networks. To this end we introduce persuasive contrastive explanations that aim to provide an answer to the question Why outcome t instead of \(t^\prime ?\) posed by a user. In addition, we propose an algorithm for computing persuasive contrastive explanations. Both our definition of persuasive contrastive explanation and the proposed algorithm can be employed beyond the current scope of Bayesian networks.

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Change history

  • 19 September 2021

    In the originally published article, the pseudocode in Algorithm 1 does not include the lines that were left intentionally blank. This has been corrected to include 2 lines (line 9 and 14), which are intentionally left blank in the algorithm for the addition of new code to these lines.

Notes

  1. 1.

    In case \(\top {(T\!\mid \!\mathbf {e})}\) is not unique, we assume all modes are output to the user and that \(t^\prime \) is not among these.

  2. 2.

    All mentioned networks are available from https://www.bnlearn.com/bnrepository/.

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Acknowledgements

This research was partially funded by the Hybrid Intelligence Center, a 10-year programme funded by the Dutch Ministry of Education, Culture and Science through the Netherlands Organisation for Scientific Research, https://hybrid-intelligence-centre.nl. We would like to thank the anonymous reviewers for their useful and inspiring comments.

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Authors and Affiliations

  1. Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands

    Tara Koopman & Silja Renooij

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  1. Tara Koopman
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  2. Silja Renooij
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Correspondence to Silja Renooij .

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Editors and Affiliations

  1. Institute of Information Theory and Automation, Prague, Czech Republic

    Jiřina Vejnarová

  2. Insight Centre for Data Analytics, School of Computer Science and IT University College Cork, Cork, Ireland

    Nic Wilson

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Koopman, T., Renooij, S. (2021). Persuasive Contrastive Explanations for Bayesian Networks. In: Vejnarová, J., Wilson, N. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2021. Lecture Notes in Computer Science(), vol 12897. Springer, Cham. https://doi.org/10.1007/978-3-030-86772-0_17

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  • DOI: https://doi.org/10.1007/978-3-030-86772-0_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86771-3

  • Online ISBN: 978-3-030-86772-0

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