Explaining Black Box Models Through Twin Systems

Abstract

This paper presents the early stages of my PhD research aiming at advancing the field of eXplainable AI (XAI) investigating the twinsystems, 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 ANNCBR 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.