Even if Explanations: Prior Work, Desiderata & Benchmarks for Semi-Factual XAI
It addresses a gap in explainable AI for researchers and practitioners, but is incremental as it builds on existing cognitive science and XAI concepts.
The paper tackles the lack of attention to semi-factual explanations in XAI by surveying literature, defining desiderata, and benchmarking algorithms to establish a foundation for future work.
Recently, eXplainable AI (XAI) research has focused on counterfactual explanations as post-hoc justifications for AI-system decisions (e.g. a customer refused a loan might be told: If you asked for a loan with a shorter term, it would have been approved). Counterfactuals explain what changes to the input-features of an AI system change the output-decision. However, there is a sub-type of counterfactual, semi-factuals, that have received less attention in AI (though the Cognitive Sciences have studied them extensively). This paper surveys these literatures to summarise historical and recent breakthroughs in this area. It defines key desiderata for semi-factual XAI and reports benchmark tests of historical algorithms (along with a novel, naieve method) to provide a solid basis for future algorithmic developments.