Contrastive explanation: a structural-model approach

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