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Recently, a groundswell of research has identified the use of counterfactualexplanations as a potentially significant solution to the Explainable AI (XAI)problem. It is argued that (a) technically, these counterfactual cases can begenerated by permuting problem-features until a class change is found, (b)psychologically, they are much more causally informative than factualexplanations, (c) legally, they are GDPR-compliant. However, there are issuesaround the finding of good counterfactuals using current techniques (e.g.sparsity and plausibility). We show that many commonly-used datasets appear tohave few good counterfactuals for explanation purposes. So, we propose a newcase based approach for generating counterfactuals using novel ideas about thecounterfactual potential and explanatory coverage of a case-base. The newtechnique reuses patterns of good counterfactuals, present in a case-base, togenerate analogous counterfactuals that can explain new problems and theirsolutions. Several experiments show how this technique can improve thecounterfactual potential and explanatory coverage of case-bases that werepreviously found wanting.

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