Machine Learning Explainability from an Information-theoretic PerspectiveDownload PDF

Published: 21 Nov 2022, Last Modified: 05 May 2023InfoCog @ NeurIPS 2022 PosterReaders: Everyone
Abstract: The primary challenge for practitioners with multiple \textit{post-hoc gradient-based} interpretability methods is to benchmark them and select the best. Using information theory, we represent finding the optimal explainer as a rate-distortion optimization problem. Therefore : \begin{itemize} \item We propose an information-theoretic test \verb|InfoExplain| to resolve the benchmarking ambiguity in a model agnostic manner without additional user data (apart from the input features, model, and explanations). \item We show that \verb|InfoExplain| is extendable to utilise human interpretable concepts, deliver performance guarantees, and filter out erroneous explanations. \end{itemize} The adjoining experiments, code can be found at \url{}
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