Interpretable & Explorable Approximations of Black Box Models
This addresses the need for interpretable and explorable explanations of black-box models, which is crucial for users in domains like healthcare or finance, though it is an incremental improvement over existing methods.
The paper tackles the problem of explaining black-box classifiers by proposing BETA, a framework that optimizes for fidelity and interpretability to produce compact decision sets, achieving highly compact and accurate approximations compared to state-of-the-art baselines in real-world evaluations.
We propose Black Box Explanations through Transparent Approximations (BETA), a novel model agnostic framework for explaining the behavior of any black-box classifier by simultaneously optimizing for fidelity 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-defined 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 different subspaces that are of interest to the user. To the best of our knowledge, this is the first approach which can produce global explanations of the behavior of any given black box model through joint optimization of unambiguity, fidelity, and interpretability, while also allowing users to explore model behavior based on their preferences. Experimental evaluation with real-world 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.