{"type":"ScholarlyArticle","title":"Counterfactuals in Explainable Artificial Intelligence (XAI): Evidence from Human Reasoning","authors":[{"name":"Ruth M. J. Byrne","slug":"ruth-m-j-byrne","given":"Ruth M. J.","family":"Byrne","affiliation":"School of Psychology & Institute of Neuroscience , Trinity College Dublin , University of Dublin , Ireland"}],"abstract":"Counterfactuals about what could have happened are increasingly used in an array of Artificial Intelligence (AI) applications, and especially in explainable AI (XAI).  Counterfactuals can aid the provision of interpretable models to make the decisions of inscrutable systems intelligible to developers and users. However, not all counterfactuals are equally helpful in assisting human comprehension. Discoveries about the nature of the counterfactuals that humans create are a helpful guide to maximize the effectiveness of counterfactual use in AI.","doi":"10.24963/ijcai.2019/876","url":"https://ijcai.org/proceedings/2019/876","pdf":"https://ijcai.org/proceedings/2019/0876.pdf","markdown":"https://ijcai.org/proceedings/2019/876.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/876","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Survey track","track":"Survey track","number":876,"pages":{"first":6276,"last":6282},"keywords":["Humans and AI: Cognitive Modeling","Machine Learning: Explainable Machine Learning"],"videos":[],"references":[{"position":1,"raw":"[Beike et al., 2009] Denise R. 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