Good Counterfactuals and Where to Find Them: A Case-Based Technique for Generating Counterfactuals for Explainable AI (XAI)
|Title:||Good Counterfactuals and Where to Find Them: A Case-Based Technique for Generating Counterfactuals for Explainable AI (XAI)||Authors:||Smyth, Barry; Keane, Mark T.||Permanent link:||http://hdl.handle.net/10197/12197||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 systems; CBR; Explanation; XAI; Counterfactuals; Contrastive||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)||This item is made available under a Creative Commons License:||https://creativecommons.org/licenses/by-nc-nd/3.0/ie/|
|Appears in Collections:||Computer Science Research Collection|
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