Explaining Black Box Models Through Twin Systems

This paper presents the early stages of my PhD research aiming at advancing the field of eXplainable AI (XAI) investigating the twin-systems, where an uninterpretable black-box model is twinned with a white-box one, usually less accurate but more inspectable, to provide explanations to the classification results. We focus in particular on the twinning occurring between an Artificial Neural Network (ANN) and a Case-Based Reasoning (CBR) system, so-called ANN-CBR twins, to explain the predictions in a post-hoc manner taking account of (i) a feature-weighting method for mirroring the ANN results in the CBR, (ii) a set of evaluation metrics that correlate the ANN to other white/grey models supporting explanations for users, (iii) a taxonomy of methods for generating explanations from the twinning for the neural network's predictions.