This is not the Texture you are looking for! Introducing Novel Counterfactual Explanations for Non-Experts using Generative Adversarial Learning

Systems used here must provide comprehensible and transparent information about their decisions. Especially for patients, who are mostly no healthcare experts, comprehensible information is extremely important to understand diagnoses and treatment options (e.g., [2, 3]). To support more transparent Artificial Intelligence (AI) applications, approaches for Explainable Artificial Intelligence (XAI) are an ongoing topic of high interest [4]. Especially in the field of computer vision, a common strategy to achieve this kind of transparency is the creation of saliency maps that highlight areas in the input that were important for the decision of the AI system. The problem with those explanation strategies is, that they require the user of the XAI system to perform an additional thought process: Having the information what areas were important for a certain decision inevitably leads to the question why these areas were of importance. Especially in scenarios where relevant differences in the input data are originating from textural information rather than spatial information of certain objects, it becomes clear that the raw information about where important areas are is not always sufficient. One XAI approach that goes another way to avoid the aforementioned problems, are Counterfactual Explanations. Counterfactual explanations try to help to understand why the actual decision was made instead of another one, by creating a slightly modified version of the input which results in another decision of the AI [5, 6]. Creating such a slightly modified input that changes the model's prediction is by no means a trivial task.