Attribution in conversational context: Effect of mutual knowledge on explanation‐giving

By Ben R. Slugoski, M. Lalljee, R. Lamb, G. Ginsburg. 1993

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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, background): “t might incorrectly signal to the explainee that they might not understand the concept. There are also connections between linguistic theory and social contexts (Antaki 1989; Antaki and Leudar, 1992; Slugoski, Lalljee, Lamb, and Ginsburg, 1993). Graesser and colleagues have contributed substantially”
Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciences (2017, background): “Instead, people judge explanations based on so-called pragmatic inﬂuences of causes, which include criteria such as usefulness, relevance, etc. [Slugoski et al. , 1993].”
Explanation in Artificial Intelligence: Insights from the Social Sciences (2017, background): “[164] present evidence of Gricean maxims in explanation, and of support for the idea of explanation as conversation.”
Explaining Explanations in AI (2018, neutral): “They should further be relevant to the question asked by the recipient (epistemic relevance; (Miller, 2017), or what Slugoski et al .”
Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning (2020, background): “’s maxims of quality, quantity, relation, and manner [19] as core parts of a good conversation, and thus of explanation design [33, 44, 55].”
How People Explain Action (and Autonomous Intelligent Systems Should Too) (2017, background): “…what exact question the audience is interested in (McClure and Hilton 1998); they take into account what their audience member already knows (Slugoski et al. 1993); and they offer elements of explanations that build bridges be-tween presumed knowledge and novel information (Korman and Malle…”
Time to Give Up the Dogmas of Attribution (2011, background): “Finally, explanations that are designed to meet impression management goals will be constructed in part by considering what the audience knows, wants to know, and would approve of—another form of reasoning by knowledge structures (Slugoski et al., 1993; Turnbull & Slugoski, 1988).”