Apr 5, 2022 · This work focuses on applying existing XAI techniques to deep neural networks to understand how features contribute to epistemic uncertainty.
This work focuses on applying existing XAI techniques to deep neural networks to understand how features contribute to epistemic uncertainty. Epistemic ...
Using Explainable AI to Measure Feature Contribution to Uncertainty ...
Sep 1, 2024 · The goal of uncertainty estimation is to quantify the data and model uncertainty in predictions made by an ML model. The classification with ...
Jan 1, 2024 · This study investigates the efficacy of Explainable Artificial Intelligence (XAI) methods, specifically Gradient-weighted Class Activation Mapping (Grad-CAM) ...
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Explainable AI (XAI) aims to present model behavior in a way that humans can easily under- stand. Some models are inherently more interpretable, such as ...
Mar 15, 2024 · Uncertainty is a key feature of any machine learning model and is particularly important in neural networks, which tend to be overconfident.
We present a method that allows to propagate uncertainty of data into the explanation model, providing more insight into the certainty of the decision making ...
Uncertainty-Aware eXplainable Artificial Intelligence (UAXAI) is proposed as a critical paradigm to address these challenges.
In order to foster trust in AI predictions, many approaches towards explainable AI (XAI) have been developed and evaluated.