Good Counterfactuals and Where to Find Them: A Case Based Technique for Generating Counterfactuals for Explainable AI (XAI)
Recently, a groundswell of research has identified the use of counterfactual explanations as a potentially significant solution to the Explainable AI (XAI) problem. It is argued that (a) technically, these counterfactual cases can be generated by permuting problem features until a class change is found, (b) psychologically, they are much more causally informative than factual explanations, (c) 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. So, 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.
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