How Case Based Reasoning Explained Neural Networks: An XAI Survey of
  Post-Hoc Explanation-by-Example in ANN-CBR Twins
  This paper surveys an approach to the XAI problem, using post-hoc explanation
by example, that hinges on twinning Artificial Neural Networks (ANNs) with
Case-Based Reasoning (CBR) systems, so-called ANN-CBR twins. A systematic
survey of 1100+ papers was carried out to identify the fragmented literature on
this topic and to trace it influence through to more recent work involving Deep
Neural Networks (DNNs). The paper argues that this twin-system approach,
especially using ANN-CBR twins, presents one possible coherent, generic
solution to the XAI problem (and, indeed, XCBR problem). The paper concludes by
road-mapping some future directions for this XAI solution involving (i) further
tests of feature-weighting techniques, (iii) explorations of how explanatory
cases might best be deployed (e.g., in counterfactuals, near-miss cases, a
fortori cases), and (iii) the raising of the unwelcome and, much ignored, issue
of human user evaluation.
