A Rate-Distortion Framework for Explaining Black-box Model Decisions

Powerful machine learning models such as deep neural networks are inherently opaque, which has motivated numerous explanation methods that the research community developed over the last decade [1, 24, 26, 20, 15, 16, 7, 2]. The meaning and validity of an explanation depends on the underlying principle of the explanation framework. Therefore, a trustworthy explanation framework must align intuition with mathematical rigor while maintaining maximal flexibility and applicability. We believe the Rate-Distortion Explanation (RDE) framework, first proposed by [16], then extended by [9], as well as the similar framework in [2], meets the desired qualities. In this chapter, we aim to present the RDE framework in a revised and holistic manner. Our generalized RDE framework can be applied to any model (not just classification tasks), supports in-distribution interpretability (by leveraging in-painting GANs), and admits interpretation queries (by considering suitable input signal representations).