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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: [email protected]
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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.

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

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