
Uncertainty Propagation in XAI: A Comparison of Analytical and Empirical Estimators
Teodor Chiaburu, Felix Bießmann, Frank Haußer
Accepted at WCXAI 2025, Istanbul
This paper introduces a framework to measure uncertainty in XAI explanations, which stems from input data and model parameter variations. We use analytical and empirical methods to estimate how this uncertainty affects explanations and evaluate their robustness across different datasets. Our study identifies XAI methods that don't reliably handle uncertainty, emphasizing the need for uncertainty-aware explanations in critical applications and revealing limitations in current XAI techniques. All the code and experiments can be found in our repository. Download paper here.