Academic literature on the topic 'Explanation'

According to the Rules of Procedure of the German Bundestag (”GOBT”), every Member of Parliament is granted a five minutes’ verbal explanation of vote . It is granted for nearly every kind of vote in the House . The verbal explanation is often considered a privilege to MPs going against the position taken by their group . Yet, it is also used to confirm the party position and it is abused to continue already closed debates . In either case, they can be a grab bag for both parliament’s plenum and its president; the verbal explanation’s content is only revealed when the explanation is given . A quantitative and qualitative analysis of the explanations given in the Bundestag shows that explanations from dissenters contribute quantitatively, but not to a large extent . While members of the coalition more often declare to go against their parliamentary party group, members of the opposition tend to confirm the line of their respective party . When used to reveal personal implications in the decision‑making process, the verbal explanation is meaningful and widely accepted .

Processual and postprocessual archaeologists implicitly employ the same epistemological system to evaluate the worth of different explanations: inference to the best explanation. This is good since inference to the best explanation is the most effective epistemological approach to archaeological reasoning available. Underlying the logic of inference to the best explanation is the assumption that the explanation that accounts for the most evidence is also most likely to be true. This view of explanation often reflects the practice of archaeological reasoning better than either the hypothetico-deductive method or hermeneutics. This article explores the logic of inference to the best explanation and provides clear criteria to determine what makes one explanation better than another. Explanations that are empirically broad, general, modest, conservative, simple, testable, and address many perspectives are better than explanations that are not. This article also introduces a system of understanding explanation that emphasizes the role of contrastive pairings in the construction of specific explanations. This view of explanation allows for a better understanding of when, and when not, to engage in the testing of specific explanations.

This study aimed to observe the impact of eight explainable AI (XAI) explanation techniques on user trust and satisfaction in the context of XAI-enhanced learning analytics while comparing two groups of STEM college students based on their Bologna study level, using various established feature relevance techniques, certainty, and comparison explanations. Overall, the students reported the highest trust in local feature explanation in the form of a bar graph. Additionally, master’s students presented with global feature explanations also reported high trust in this form of explanation. The highest measured explanation satisfaction was observed with the local feature explanation technique in the group of bachelor’s and master’s students, with master’s students additionally expressing high satisfaction with the global feature importance explanation. A detailed overview shows that the two observed groups of students displayed consensus in favored explanation techniques when evaluating trust and explanation satisfaction. Certainty explanation techniques were perceived with lower trust and satisfaction than were local feature relevance explanation techniques. The correlation between itemized results was documented and measured with the Trust in Automation questionnaire and Explanation Satisfaction Scale questionnaire. Master’s-level students self-reported an overall higher understanding of the explanations and higher overall satisfaction with explanations and perceived the explanations as less harmful.

Explanations of psychological phenomena seem to generate more public interest when they contain neuroscientific information. Even irrelevant neuroscience information in an explanation of a psychological phenomenon may interfere with people's abilities to critically consider the underlying logic of this explanation. We tested this hypothesis by giving naïve adults, students in a neuroscience course, and neuroscience experts brief descriptions of psychological phenomena followed by one of four types of explanation, according to a 2 (good explanation vs. bad explanation) × 2 (without neuroscience vs. with neuroscience) design. Crucially, the neuroscience information was irrelevant to the logic of the explanation, as confirmed by the expert subjects. Subjects in all three groups judged good explanations as more satisfying than bad ones. But subjects in the two nonexpert groups additionally judged that explanations with logically irrelevant neuroscience information were more satisfying than explanations without. The neuroscience information had a particularly striking effect on nonexperts' judgments of bad explanations, masking otherwise salient problems in these explanations.