Case-based selection of explanation methods for neural network image classifiers

Deep learning is especially remarkable in terms of image classification. However, the outcomes of models are not explainable to users due to their complex nature, having an impact on the users' trust in the provided classifications. To solve this problem, several explanation techniques have been proposed, but they greatly depend on the nature of the images being classified and the users' perception of the explanations. In this work, we present Case-Based Reasoning as a learning-based solution to the problem of selecting the best explanation method for the image classifications obtained by models. We propose the elicitation of a case base that reflects the human perception of the quality of the explanations and how to reuse this knowledge to select the best explanation approach for a given image classification.