Contrastive explanations to classification systems using sparse dictionaries

Providing algorithmic explanations for the decisions of machine learning systems to end users, data protection officers, and other stakeholders in the design, production, commercialisation and use of machine learning systems pipeline is an important and challenging research problem. Much work in this area focuses on image classification, where the required explanations can be given in terms of images, therefore making explanations relatively easy to communicate to end-users. For a classification problem, a contrastive explanation tries to understand why the classifier has not answered a particular class, say B, instead of the returned class A. Sparse dictionaries have been recently used to identify local image properties as main ingredients for a system producing humanly understandable explanations for the decisions of a classifier developed based on machine learning methods. In this paper, we show how the system mentioned above can be extended to produce contrastive explanations.