Unifying Model Explainability and Robustness via Machine-Checkable Concepts

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Other Links: arxiv.org

Abstract:

As deep neural networks (DNNs) get adopted in an ever-increasing number of applications, explainability has emerged as a crucial desideratum for these models. In many real-world tasks, one of the principal reasons for requiring explainability is to in turn assess prediction robustness, where predictions (i.e., class labels) that do not ...More

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