Explaining Decision-Tree predictions by addressing potential conflicts between predictions and plausible expectations

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We offer an approach to explain Decision Tree (DT) predictions by addressing potential conflicts between aspects of these predictions and plausible expectations licensed by background information. We define four types of conflicts, operationalize their identification, and specify explanatory schemas that address them. Our human evaluation focused on the effect of explanations on users’ understanding of a DT’s reasoning and their willingness to act on its predictions. The results show that (1) explanations that address potential conflicts are considered at least as good as baseline explanations that just follow a DT path; and (2) the conflict-based explanations are deemed especially valuable when users’ expectations disagree with the DT’s predictions.
Original languageEnglish
Title of host publicationThe 14th International Conference on Natural Language Generation
Subtitle of host publicationProceedings of the Conference 20-24
EditorsDavid M. Howcroft
Place of PublicationStroudsburg PA USA
PublisherAssociation for Computational Linguistics (ACL)
Number of pages14
ISBN (Electronic)9781954085510
Publication statusPublished - 2021
EventInternational Natural Language Generation Conference 2021 - Scotland, United Kingdom
Duration: 20 Sep 202124 Sep 2021
Conference number: 14th
https://aclanthology.org/volumes/2021.inlg-1/ (Proceedings)


ConferenceInternational Natural Language Generation Conference 2021
Abbreviated titleINLG 2021
Country/TerritoryUnited Kingdom
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