Confident Interpretations of Black Box Classifiers

Deep Learning models provide state of the art classification results, but are not human-interpretable. We propose a novel method to interpret the classification results of a black box model a posteriori. We emulate the complex classifier by surrogate decision trees. Each tree mimics the behavior of the complex classifier by overestimating one of the classes. This yields a global, interpretable approximation of the black box classifier. Our method provides interpretations that are at the same time general (applying to many data points), confident (generalizing well to other data points), faithful to the original model (making the same predictions), and simple (easy to understand). Our experiments show that our method beats competing methods in these desiderata, and our user study shows that users prefer this type of interpretations over others.