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        <title>Explaining Decision-Tree Predictions by Addressing Potential Conflicts between Predictions and Plausible Expectations</title>
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        <namePart type="given">Sameen</namePart>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Explaining Decision-Tree Predictions by Addressing Potential Conflicts between Predictions and Plausible Expectations
%A Maruf, Sameen
%A Zukerman, Ingrid
%A Reiter, Ehud
%A Haffari, Gholamreza
%Y Belz, Anya
%Y Fan, Angela
%Y Reiter, Ehud
%Y Sripada, Yaji
%S Proceedings of the 14th International Conference on Natural Language Generation
%D 2021
%8 August
%I Association for Computational Linguistics
%C Aberdeen, Scotland, UK
%F maruf-etal-2021-explaining
%X 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.
%R 10.18653/v1/2021.inlg-1.12
%U https://aclanthology.org/2021.inlg-1.12
%U https://doi.org/10.18653/v1/2021.inlg-1.12
%P 114-127

Markdown (Informal)

[Explaining Decision-Tree Predictions by Addressing Potential Conflicts between Predictions and Plausible Expectations](https://aclanthology.org/2021.inlg-1.12) (Maruf et al., INLG 2021)

• Explaining Decision-Tree Predictions by Addressing Potential Conflicts between Predictions and Plausible Expectations (Maruf et al., INLG 2021)

ACL

• Sameen Maruf, Ingrid Zukerman, Ehud Reiter, and Gholamreza Haffari. 2021. Explaining Decision-Tree Predictions by Addressing Potential Conflicts between Predictions and Plausible Expectations. In Proceedings of the 14th International Conference on Natural Language Generation, pages 114–127, Aberdeen, Scotland, UK. Association for Computational Linguistics.