Good Counterfactuals and Where to Find Them: A Case-Based Technique for Generating Counterfactuals for Explainable AI (XAI)

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Title: Good Counterfactuals and Where to Find Them: A Case-Based Technique for Generating Counterfactuals for Explainable AI (XAI)
Authors: Smyth, BarryKeane, Mark T.
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Date: 25-May-2020
Online since: 2021-05-25T15:58:37Z
Abstract: Recently, a groundswell of research has identified the use of counter-factual explanations as a potentially significant solution to the Explainable AI (XAI) problem. It is argued that (i) technically, these counterfactual cases can be generated by permuting problem-features until a class-change is found, (ii) psychologically, they are much more causally informative than factual explanations, (iii) legally, they are GDPR-compliant. However, there are issues around the finding of “good” counterfactuals using current techniques (e.g.sparsity and plausibility). We show that many commonly-used datasets appear to have few “good” counterfactuals for explanation purposes. We propose a new case-based approach for generating counterfactuals, using novel ideas about the counterfactual potential and explanatory coverage of a case-base. The new technique reuses patterns of good counterfactuals, present in a case-base, to generate analogous counterfactuals that can explain new problems and their solutions. Several experiments show how this technique can improve the counterfactual potential and explanatory coverage of case-bases, that were previously found wanting.
Funding Details: Science Foundation Ireland
Funding Details: Insight Research Centre
Type of material: Conference Publication
Copyright (published version): 2020 Springer Nature
Keywords: Recommender systemsCBRExplanationXAICounterfactualsContrastive
DOI: 10.1007/978-3-030-58342-2_11
Language: en
Status of Item: Peer reviewed
Is part of: ICCBR 2020: Case-Based Reasoning Research and Development
Conference Details: The 28th International Conference on Case-Based Reasoning (ICCBR 2020), Salamanca, Spain, 8–12 June 2020 (held online due to COVID-19 pandemic)
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Appears in Collections:Computer Science Research Collection
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