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 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.