Evaluating everyday explanations

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Reframing Human-AI Collaboration for Generating Free-Text Explanations (2021, methodology): “…truthfulness be ascertained or is more information needed?) and factuality, which aligns with “compatibility with receiver’s existing be-liefs” and that the best explanation is the “most explanation from the receiver/user’s perspective (Lombrozo, 2007; Zemla et al., 2017; Sulik et al., 2021).”
Human-grounded Evaluations of Explanation Methods for Text Classification (2019, result): “This is consistent with the findings by Zemla et al. (2017). Conversely, the DTs method performed in the opposite way.”
A Study of Automatic Metrics for the Evaluation of Natural Language Explanations (2021, background): “Furthermore, the importance of evaluating explanations has been emphasised by researchers within the social cognitive sciences (Leake, 2014; Zemla et al., 2017; Doshi-Velez and Kim, 2017).”
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI (2022, background): “Note that this property addresses external coherence, and is different from internal coherence to indicate that parts in an explanation fit together [292].”
Explanations in the wild. (2021, unknown): “We followed Zemla et al. (2017) in treating explanation length (here, by counting new content word types) as indicating level of detail, and thus one way to measure the relative complexity or simplicity of an explanation.”