A General Approach to Compute the Relevance of Middle-Level Input Features

This work proposes a novel general framework, in the context of eXplainable Artificial Intelligence (XAI), to construct explanations for the behaviour of Machine Learning (ML) models in terms of middle-level features which represent perceptually salient input parts. One can isolate two different ways to provide explanations in the context of XAI: low and middle-level explanations. Middle-level explanations have been introduced for alleviating some deficiencies of low-level explanations such as, in the context of image classification, the fact that human users are left with a significant interpretive burden: starting from low-level explanations, one has to identify properties of the overall input that are perceptually salient for the human visual system. However, a general approach to correctly evaluate the elements of middle-level explanations with respect ML model responses has never been proposed in the literature. We experimentally evaluate the proposed approach to explain the decisions made by an Imagenet pre-trained VGG16 model on STL-10 images and by a customised model trained on the JAFFE dataset, using two different computational definitions of middle-level features and compare it with two different XAI middle-level methods. The results show that our approach can be used successfully in different computational definitions of middle-level explanations.