We propose Black Box Explanations through Transparent Approximations (BETA), a novel model agnostic framework for explaining the behavior of any black-box classi er by simultaneously optimizing for delity to the original model and interpretability of the explanation. To this end, we develop a novel objective function which allows us to learn (with optimality guarantees), a small number of compact decision sets each of which explains the behavior of the black box model in unambiguous, well-de ned regions of feature space. Furthermore, our framework also is capable of accepting user input when generating these approximations, thus allowing users to interactively explore how the black-box model behaves in di erent subspaces that are of interest to the user. To the best of our knowledge, this is the rst approach which can produce global explanations of the behavior of any given black box model through joint optimization of unambiguity, delity, and interpretability, while also allowing users to explore model behavior based on their preferences. Experimental evaluation with realworld datasets and user studies demonstrates that our approach can generate highly compact, easy-to-understand, yet accurate approximations of various kinds of predictive models compared to state-of-the-art baselines.
REF use the input data and the model predictions to learn decision sets that are optimized to jointly maximize the interpretability of the explanations and the extent to which the original model is explained.
Interpretable&Explorable Approximations of Black Box Models
ArXiv
ArXiv
Computer Science