  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 recent
work that advances a generic solution to the XAI problem, the so called twin
system approach. The most popular twinning in the literature is that between an
Artificial 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 the
former. We outline how recent work reviving this idea has applied it to deep
learning methods. Furthermore, we detail the many fruitful directions in which
this work may be taken; such as, determining the most (i) accurate
feature-weighting methods to be used, (ii) appropriate deployments for
explanatory cases, (iii) useful cases of explanatory value to users.
