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Contrastive explanation: a structural-model approach

Published online by Cambridge University Press:  20 October 2021

Tim Miller Open the ORCID record for Tim Miller [Opens in a new window]
Tim Miller*
Affiliation:
School of Computing and Information Systems, University of Melbourne, Melbourne, Australia E-mail: tmiller@unimelb.edu.au
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Abstract

This paper presents a model of contrastive explanation using structural casual models. The topic of causal explanation in artificial intelligence has gathered interest in recent years as researchers and practitioners aim to increase trust and understanding of intelligent decision-making. While different sub-fields of artificial intelligence have looked into this problem with a sub-field-specific view, there are few models that aim to capture explanation more generally. One general model is based on structural causal models. It defines an explanation as a fact that, if found to be true, would constitute an actual cause of a specific event. However, research in philosophy and social sciences shows that explanations are contrastive: that is, when people ask for an explanation of an event—the fact—they (sometimes implicitly) are asking for an explanation relative to some contrast case; that is, ‘Why P rather than Q?’. In this paper, we extend the structural causal model approach to define two complementary notions of contrastive explanation, and demonstrate them on two classical problems in artificial intelligence: classification and planning. We believe that this model can help researchers in subfields of artificial intelligence to better understand contrastive explanation.

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Type
Research Article
Information
The Knowledge Engineering Review , Volume 36 , 2021 , e14
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© The Author(s), 2021. Published by Cambridge University Press

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References

Akula, A. R., Wang, S. & Zhu, S.-C. 2020. CoCoX: Generating conceptual and counterfactual explanations via fault-lines. In AAAI, 2594–2601.CrossRefGoogle Scholar
Angwin, J., Larson, J., Mattu, S. & Kirchner, L. 2016. Machine bias. ProPublica, May, 23.Google Scholar
Bromberger, S. 1966. Why–questions. In Mind and Cosmos: Essays in Contemporary Science and Philosophy, Colodny, R. G. (ed.). Pittsburgh University Press, 68–111.Google Scholar
Buchanan, B. & Shortliffe, E. 1984. Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project. Addison-Wesley.Google Scholar
Chandrasekaran, B., Tanner, M. C. & Josephson, J. R. 1989. Explaining control strategies in problem solving. IEEE Expert 4 (1), 9–15.CrossRefGoogle Scholar
Chin-Parker, S. & Cantelon, J. 2017. Contrastive constraints guide explanation-based category learning. Cognitive Science 41 (6), 1645–1655.CrossRefGoogle ScholarPubMed
Dhurandhar, A., Chen, P.-Y., Luss, R., Tu, C.-C., Ting, P., Shanmugam, K. & Das, P. 2018. Explanations based on the missing: Towards contrastive explanations with pertinent negatives. In Advances in Neural Information Processing Systems, 592–603.Google Scholar
Garfinkel, A. 1981. Forms of Explanation: Rethinking the Questions in Social Theory. Yale University Press.Google Scholar
Grice, H. P. 1975. Logic and conversation. In Syntax and Semantics 3: Speech Arts. Academic Press, 41–58.Google Scholar
Halpern, J. Y. 2015. A modification of the Halpern-Pearl definition of causality. In Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI 2015), 3022–3033.Google Scholar
Halpern, J. Y. & Pearl, J. 2005a. Causes and explanations: A structural-model approach. part i: Causes. The British Journal for the Philosophy of Science 56(4), 843–887.Google Scholar
Halpern, J. Y. & Pearl, J. 2005b. Causes and explanations: A structural-model approach. part ii: Explanations. The British Journal for the Philosophy of Science 56 (4), 889–911.CrossRefGoogle Scholar
Haynes, S. R., Cohen, M. A. & Ritter, F. E. 2009. Designs for explaining intelligent agents. International Journal of Human-Computer Studies 67 (1), 90–110.CrossRefGoogle Scholar
Hesslow, G. 1983. Explaining differences and weighting causes. Theoria 49 (2), 87–111.CrossRefGoogle Scholar
Hesslow, G. 1988. The problem of causal selection. Contemporary Science and Natural Explanation: Commonsense Conceptions of Causality, 11–32.Google Scholar
Hilton, D. J. 1990. Conversational processes and causal explanation. Psychological Bulletin 107 (1), 65–81.CrossRefGoogle Scholar
Kean, A. 1998. A characterization of contrastive explanations computation. In Pacific Rim International Conference on Artificial Intelligence. Springer, 599–610.Google Scholar
Krarup, B., Cashmore, M., Magazzeni, D. & Miller, T. 2019. Model-based contrastive explanations for explainable planning. In 2nd ICAPS Workshop on Explainable Planning (XAIP-2019). AAAI Press.Google Scholar
Lewis, D. 1986. Causal explanation. Philosophical Papers 2, 214–240.Google Scholar
Lim, B. Y. & Dey, A. K. 2009. Assessing demand for intelligibility in context-aware applications. In Proceedings of the 11th International Conference on Ubiquitous Computing. ACM, 195–204.CrossRefGoogle Scholar
Linegang, M. P., Stoner, H. A., Patterson, M. J., Seppelt, B. D., Hoffman, J. D., Crittendon, Z. B. & Lee, J. D. 2006. Human-automation collaboration in dynamic mission planning: A challenge requiring an ecological approach. Proceedings of the Human Factors and Ergonomics Society Annual Meeting 500 (23), 0 2482–2486.CrossRefGoogle Scholar
Lipton, P. 1990. Contrastive explanation. Royal Institute of Philosophy Supplement 27, 247–266.CrossRefGoogle Scholar
Madumal, P., Miller, T., Sonenberg, L. & Vetere, F. 2020. Explainable reinforcement learning through a causal lens. In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2493–2500.CrossRefGoogle Scholar
Mercado, J. E., Rupp, M. A., Chen, J. Y. C., Barnes, M. J., Barber, D. & Procci, K. 2016. Intelligent agent transparency in human–agent teaming for multi-UxV management. Human Factors 580 (3), 401–415.CrossRefGoogle Scholar
Miller, T. 2018. Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence. https://arxiv.org/abs/1706.07269.Google Scholar
Mothilal, R. K., Sharma, A. & Tan, C. 2020. Explaining machine learning classifiers through diverse counterfactual explanations. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 607–617.CrossRefGoogle Scholar
Ruben, D.-H. 1987. Explaining contrastive facts. Analysis 470 (1), 35–37.CrossRefGoogle Scholar
Slugoski, B. R., Lalljee, M., Lamb, R. & Ginsburg, G. P. 1993. Attribution in conversational context: Effect of mutual knowledge on explanation-giving. European Journal of Social Psychology 230 (3), 219–238.CrossRefGoogle Scholar
Sreedharan, S., Srivastava, S. & Kambhampati, S. 2018. Hierarchical expertise level modeling for user specific contrastive explanations. In IJCAI, 4829–4836.Google Scholar
Stubbs, K., Hinds, P. & Wettergreen, D. 2007. Autonomy and common ground in human-robot interaction: A field study. IEEE Intell. Syst. 22 (2), 42–50.CrossRefGoogle Scholar
Swartout, W. R. & Moore, J. D. 1993. Explanation in second generation expert systems. In Second Generation Expert Systems. Springer, 543–585.CrossRefGoogle Scholar
Temple, D. 1988. The contrast theory of why–questions. Philosophy of Science 55(1), 141–151.CrossRefGoogle Scholar
Van Bouwel, J. & Weber, E. 2002. Remote causes, bad explanations? Journal for the Theory of Social Behaviour 320 (4), 437–449.CrossRefGoogle Scholar
Van Fraassen, B. C. 1980. The Scientific Image. Oxford University Press.CrossRefGoogle Scholar
Waa, J., van Diggelen, J., Bosch, K. & Neerincx, M. 2018. Contrastive explanations for reinforcement learning in terms of expected consequences. In Proceedings of the Workshop on Explainable AI at IJCAI. Google Scholar
Wachter, S., Mittelstadt, B. & Russell, C. 2017. Counterfactual explanations without opening the black box: Automated decisions and the GDPR. Harvard Journal of Law & Technology 31, 841.Google Scholar
Wang, D., Yang, Q., Abdul, A. & Lim, B. Y. 2019. Designing theory-driven user-centric explainable ai. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 1–15.Google Scholar
Winikoff, M. 2017. Debugging agent programs with Why?: Questions. In Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, AAMAS 2017. IFAAMAS, 251–259.Google Scholar
Ylikoski, P. 2007. The idea of contrastive explanandum. In Rethinking Explanation. Springer, 27–42.CrossRefGoogle Scholar