Interpretable & Explorable Approximations of Black Box Models

TL;DR

BETA is a flexible, model-agnostic framework that creates interpretable, compact approximations of black-box classifiers, enabling both global explanations and user-driven exploration of model behavior.

Contribution

It introduces a novel joint optimization method for fidelity and interpretability, allowing interactive, global explanations of black-box models.

Findings

Produces highly compact, accurate approximations

Outperforms state-of-the-art baselines in interpretability and fidelity

Enables user interaction for exploring model behavior

Abstract

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.