Robust and Stable Black Box Explanations

Jul 12, 2020

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As machine learning black boxes are increasingly being deployed in real-world applications, there has been a growing interest in developing post hoc explanations that summarize the behaviors of these black box models. However, existing algorithms for generating such explanations have been shown to lack robustness with respect to shifts in the underlying data distribution. In this paper, we propose a novel framework for generating robust explanations of black box models based on adversarial training. In particular, our framework optimizes a minimax objective that aims to construct the highest fidelity explanation with respect to the worst-case over a set of distribution shifts. We instantiate this algorithm for explanations in the form of linear models and decision sets by devising the required optimization procedures. To the best of our knowledge, this work makes the first attempt at generating post hoc explanations that are robust to a general class of distribution shifts that are of practical interest. Experimental evaluation with real-world and synthetic datasets demonstrates that our approach substantially improves the robustness of explanations without sacrificing their fidelity on the original data distribution.

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About ICML 2020

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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