Explaining Explanation, Part 2: Empirical Foundations
By Robert R. Hoffman, Shane T. Mueller, Gary Klein. 2017
52 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Cognitive Neuroscience, History and Philosophy of Science
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- D. Gunning
- David William Aha
- Niloofar Zarei
- Ben Shneiderman
- Eric D. Ragan
- Sina Mohseni
- Shane T. Mueller
- Gary Klein
In Cognitive Neuroscience
- David William Aha
- D. Gunning
- Gary Klein
- Robert R. Hoffman
- William J. Clancey
- Ben Shneiderman
- Eric D. Ragan
- Brian Y. Lim
In History and Philosophy of Science
- David William Aha
- D. Gunning
- Ben Shneiderman
- Gary Klein
- Robert R. Hoffman
- William J. Clancey
- Brian Y. Lim
- Ashraf Abdul
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, background): “…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, 2017; Miller et al., 2017;…”
- 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).”
- Transitioning to Human Interaction with AI Systems: New Challenges and Opportunities for HCI Professionals to Enable Human-Centered AI (2021, unknown): “For example, inductive reasoning, causal reasoning, self-explanation, comparative explanation, counterfactual reasoning (Broniatowski, 2021; Hoffman et al., 2017).”
- 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].”
- A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems (2018, background): “s 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]. They lastly focus on deep neural networks (DNN) to examine their theoretical and empi”
- Designing Theory-Driven User-Centric Explainable AI (2019, background): “[40, 41, 42] and Klein [52] summarized several theoretical foundations of how people formulate”