# How Much Can I Trust You? -- Quantifying Uncertainties in Explaining Neural Networks

arXiv: https://arxiv.org/abs/2006.09000 · HTML: https://mlsanity.com/paper/2006.09000/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2020.

## Built on by (most cited first, 18 of 18)

- [Explainability of deep vision-based autonomous driving systems: Review and challenges](https://mlsanity.com/paper/2101.05307/) (2021)
- [This looks more like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation](https://mlsanity.com/paper/2108.12204/) (2021)
- [Toward Explainable AI for Regression Models](https://mlsanity.com/paper/2112.11407/) (2021)
- [NoiseGrad: Enhancing Explanations by Introducing Stochasticity to Model Weights](https://mlsanity.com/paper/2106.10185/) (2021)
- [RELAX: Representation Learning Explainability](https://mlsanity.com/paper/2112.10161/) (2021)
- [Explainable Artificial Intelligence for Bayesian Neural Networks: Towards trustworthy predictions of ocean dynamics](https://mlsanity.com/paper/2205.00202/) (2022)
- [Boundary-Aware Uncertainty for Feature Attribution Explainers](https://mlsanity.com/paper/2210.02419/) (2022)
- [Trustworthy AI Must Account for Interactions](https://mlsanity.com/paper/2504.07170/) (2025)
- [On the use of uncertainty in classifying Aedes Albopictus mosquitoes](https://mlsanity.com/paper/2110.15912/) (2021)
- [Investigating the Impact of Model Instability on Explanations and Uncertainty](https://mlsanity.com/paper/2402.13006/) (2024)
- [Resilience of Bayesian Layer-Wise Explanations under Adversarial Attacks](https://mlsanity.com/paper/2102.11010/) (2021)
- [Bayesian neural networks for the probabilistic forecasting of wind direction and speed using ocean data](https://mlsanity.com/paper/2206.08953/) (2022)
- [Uncertainty Quantification for Local Model Explanations Without Model Access](https://mlsanity.com/paper/2301.05761/) (2023)
- [Towards ML Methods for Biodiversity: A Novel Wild Bee Dataset and Evaluations of XAI Methods for ML-Assisted Rare Species Annotations](https://mlsanity.com/paper/2206.07497/) (2022)
- [Metric Tools for Sensitivity Analysis with Applications to Neural Networks](https://mlsanity.com/paper/2305.02368/) (2023)
- [Uncertainty-Guided Expert-AI Collaboration for Efficient Soil Horizon Annotation](https://mlsanity.com/paper/2509.24873/) (2025)
- [Post-Hoc Uncertainty-Aware Explanations for Deployed Power Quality Disturbance Classifiers via Laplace Approximation](https://mlsanity.com/paper/2604.13658/) (2026)
- [A Unified Framework for Uncertainty-Aware Explainable Artificial Intelligence: A Case Study in Power Quality Disturbance Classification](https://mlsanity.com/paper/2605.21114/) (2026)

## Abstract (arXiv)

Explainable AI (XAI) aims to provide interpretations for predictions made by learning machines, such as deep neural networks, in order to make the machines more transparent for the user and furthermore trustworthy also for applications in e.g. safety-critical areas. So far, however, no methods for quantifying uncertainties of explanations have been conceived, which is problematic in domains where a high confidence in explanations is a prerequisite. We therefore contribute by proposing a new framework that allows to convert any arbitrary explanation method for neural networks into an explanation method for Bayesian neural networks, with an in-built modeling of uncertainties. Within the Bayesian framework a network's weights follow a distribution that extends standard single explanation scores and heatmaps to distributions thereof, in this manner translating the intrinsic network model uncertainties into a quantification of explanation uncertainties. This allows us for the first time to carve out uncertainties associated with a model explanation and subsequently gauge the appropriate level of explanation confidence for a user (using percentiles). We demonstrate the effectiveness and usefulness of our approach extensively in various experiments, both qualitatively and quantitatively.