How People Explain Action (and Autonomous Intelligent Systems Should Too)

By Maartje de Graaf, Bertram F. Malle. 2017

185 citations recorded by the source corpus.

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Citations in the corpus

Explanation in Artificial Intelligence: Insights from the Social Sciences (2017, background): “For example, de Graaf and Malle [34] argues that because people assign human-like traits to artificial agents, people will expect”
Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning (2019, background): “Humans use counterfactuals to reason about the enabling conditions of particular outcomes, as well as to identify situations where the outcome would have occurred even in the absence of some action or condition (de Graaf & Malle, 2017; Byrne, 2019).”
Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions (2023, background): “One class of challenge in XAI lies in providing explanations adapted explicitly to the humans receiving them.”
A Study of Automatic Metrics for the Evaluation of Natural Language Explanations (2021, background): “Previous studies have identiﬁed a high degree of inconsistency in human judgements of natural language (Novikova et al., 2018; Dethlefs et al., 2014); each participant can have a different perception of the interpretation of these metrics, even if a deﬁnition of these metrics is provided.”
Transparency in artificial intelligence (2020, background): “However, research on explainable AI typically does not cite or build on explanatory frameworks based in social science (Miller, Howe, & Sonenberg, 2017; see also de Graaf & Malle, 2017).”
Evaluating XAI: A comparison of rule-based and example-based explanations (2021, background): “Humans expect others to comprehensibly explain decisions that have an impact on them [1].”
A Grounded Interaction Protocol for Explainable Artificial Intelligence (2019, background): “Indeed recently, de Graaf and Malle [11] still find the need to emphasize the importance of understanding how humans respond to Autonomous Intelligent Systems (AIS).”
Explainable Artificial Intelligence: a Systematic Review (2020, background): “Additionally, as pointed out in [105, 106], distinct behaviours, distinct problems and distinct types of users require distinct explanations.”