Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons

Shahaf Bassan, Ron Eliav, Shlomit Gur

Keywords: XAI, explainability, explainable AI, self-explaining neural networks, Formal XAI, sufficient reasons, abductive explanations, interpretability, feature selection

TL;DR: A method to generate concise and faithful sufficient explanations for predictions using self-explaining neural networks

Abstract:

Minimal sufficient reasons represent a prevalent form of explanation - the smallest subset of input features which, when held constant at their corresponding values, ensure that the prediction remains unchanged. Previous post-hoc methods attempt to obtain such explanations but face two main limitations: (1) Obtaining these subsets poses a computational challenge, leading most scalable methods to converge towards suboptimal, less meaningful subsets; (2) These methods heavily rely on sampling out-of-distribution input assignments, potentially resulting in counterintuitive behaviors. To tackle these limitations, we propose in this work a self-supervised training approach, which we term sufficient subset training (SST). Using SST, we train models to generate concise sufficient reasons for their predictions as an integral part of their output. Our results indicate that our framework produces succinct and faithful subsets substantially more efficiently than competing post-hoc methods while maintaining comparable predictive performance.

Primary Area: interpretability and explainable AI

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Submission Number: 3019