# On the (In)fidelity and Sensitivity for Explanations

arXiv: https://arxiv.org/abs/1901.09392 · HTML: https://mlsanity.com/paper/1901.09392/

## Key facts

- Cited by 310 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2019.

## Built on by (most cited first, 30 of 310)

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- [Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI](https://mlsanity.com/paper/2206.10847/) (2022)
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- [Vinoground: Scrutinizing LMMs over Dense Temporal Reasoning with Short Videos](https://mlsanity.com/paper/2410.02763/) (2024)
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## Abstract (arXiv)

We consider objective evaluation measures of saliency explanations for complex black-box machine learning models. We propose simple robust variants of two notions that have been considered in recent literature: (in)fidelity, and sensitivity. We analyze optimal explanations with respect to both these measures, and while the optimal explanation for sensitivity is a vacuous constant explanation, the optimal explanation for infidelity is a novel combination of two popular explanation methods. By varying the perturbation distribution that defines infidelity, we obtain novel explanations by optimizing infidelity, which we show to out-perform existing explanations in both quantitative and qualitative measurements. Another salient question given these measures is how to modify any given explanation to have better values with respect to these measures. We propose a simple modification based on lowering sensitivity, and moreover show that when done appropriately, we could simultaneously improve both sensitivity as well as fidelity.