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The notion of twin systems is proposed to address the eXplainable AI (XAI)problem, where an uninterpretable black-box system is mapped to a white-box'twin' that is more interpretable. In this short paper, we overview very recentwork that advances a generic solution to the XAI problem, the so called twinsystem approach. The most popular twinning in the literature is that between anArtificial Neural Networks (ANN ) as a black box and Case Based Reasoning (CBR)system as a white-box, where the latter acts as an interpretable proxy for theformer. We outline how recent work reviving this idea has applied it to deeplearning methods. Furthermore, we detail the many fruitful directions in whichthis work may be taken; such as, determining the most (i) accuratefeature-weighting methods to be used, (ii) appropriate deployments forexplanatory cases, (iii) useful cases of explanatory value to users.

The Twin-System Approach as One Generic Solution for XAI: An Overview of ANN-CBR Twins for Explaining Deep Learning