Explaining Explanation, Part 1: Theoretical Foundations

By Robert R. Hoffman, Gary Klein. 2017

85 citations recorded by the source corpus.

Subfields: Artificial Intelligence, History and Philosophy of Science, Management Science and Operations Research

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Citations in the corpus

Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, neutral): “…1996; Doshi-Velez and Kim, 2017; Doyle, Tsymbal, and Cunningham, 2003; Ellis, 1989; Fox, Long, and Magazzeni, 2017; Goguen, Weiner, and Linde, 1983; Hoffman and Klein, 2017; Hoffman, Mueller, and Klein, 2017; Kass and Finin, 1988; Lacave and Díez, 2002; Miller, 2017; Miller, Howe, and Sonenberg,…”
DARPA's Explainable Artificial Intelligence (XAI) Program (2019, methodology): “This methodology was described in a series of essays (Hoffman and Klein 2017; Hoffman et al. 2017; Klein 2018; Hoffman et al. 2018).”
Bias in data‐driven artificial intelligence systems—An introductory survey (2020, background): “Although attempts to tackle interpretable ML have existed for some time (Hoffman & Klein, 2017), there has been an exceptional growth of research literature in the last years with emerging keywords such as explainable AI (Adadi & Berrada, 2018) and black box explanation (Guidotti et al., 2019).”
A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems (2018, background): “Hoffman, Mueller, and Klein reviewed the key concepts of explanations for intelligent systems in a series of essays to identify how people formulate and accept explanations, ways to generate self-explanations, and identified purposes and patterns for causal reasoning [83, 84, 102].”
How the Experts Do It: Assessing and Explaining Agent Behaviors in Real-Time Strategy Games (2017, background): “Hoffman & Klein [9] researched ﬁve stages of explanation, looking at how explanations are formed from observation of an event, generating one or more possible explanations, judging the plausibility of said explanations, and either resolving or extending the explanation.”
Explanations in Autonomous Driving: A Survey (2021, background): “, in [53]) draw on philosophy, cognitive psychology, social science,”
Expanding Explainability: Towards Social Transparency in AI systems (2021, background): “Explanations play a central role in sense-making, decision-making, coordination, and many other aspects of our personal and social lives [41].”
Designing Theory-Driven User-Centric Explainable AI (2019, background): “[40, 41, 42] and Klein [52] summarized several theoretical foundations of how people formulate”
Bridging the Gap Between Ethics and Practice (2020, background): “This controversial GDPR requirement is vague and difficult to satisfy in general, but international research efforts at developing Explainable AI (XAI) have blossomed [9, 47, 48, 106, 116, 75, 71].”