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

Understand

*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.