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Recent advances in interpretable Machine Learning (iML) and eXplainable AI(XAI) construct explanations based on the importance of features inclassification tasks. However, in a high-dimensional feature space thisapproach may become unfeasible without restraining the set of importantfeatures. We propose to utilize the human tendency to ask questions like "Whythis output (the fact) instead of that output (the foil)?" to reduce the numberof features to those that play a main role in the asked contrast. Our proposedmethod utilizes locally trained one-versus-all decision trees to identify thedisjoint set of rules that causes the tree to classify data points as the foiland not as the fact. In this study we illustrate this approach on threebenchmark classification tasks.

Contrastive Explanations with Local Foil Trees