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Recently, eXplainable AI (XAI) research has focused on counterfactualexplanations as post-hoc justifications for AI-system decisions (e.g. acustomer refused a loan might be told: If you asked for a loan with a shorterterm, it would have been approved). Counterfactuals explain what changes to theinput-features of an AI system change the output-decision. However, there is asub-type of counterfactual, semi-factuals, that have received less attention inAI (though the Cognitive Sciences have studied them extensively). This papersurveys these literatures to summarise historical and recent breakthroughs inthis area. It defines key desiderata for semi-factual XAI and reports benchmarktests of historical algorithms (along with a novel, naieve method) to provide asolid basis for future algorithmic developments.

Even if Explanations: Prior Work, Desiderata & Benchmarks for Semi-Factual XAI