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Recently, it has been proposed that fruitful synergies may exist between DeepLearning (DL) and Case Based Reasoning (CBR); that there are insights to begained by applying CBR ideas to problems in DL (what could be called DeepCBR).In this paper, we report on a program of research that applies CBR solutions tothe problem of Explainable AI (XAI) in the DL. We describe a series oftwin-systems pairings of opaque DL models with transparent CBR models thatallow the latter to explain the former using factual, counterfactual andsemi-factual explanation strategies. This twinning shows that functionalabstractions of DL (e.g., feature weights, feature importance and decisionboundaries) can be used to drive these explanatory solutions. We also raise theprospect that this research also applies to the problem of Data Augmentation inDL, underscoring the fecundity of these DeepCBR ideas.

Twin Systems for DeepCBR: A Menagerie of Deep Learning and Case-Based Reasoning Pairings for Explanation and Data Augmentation