Identifying Drivers of Predictive Aleatoric Uncertainty
Keywords: uncertainty, explainability, trustworthy ML, probabilistic methods, transfer learning
TL;DR: We propose a straightforward approach for explaining predictive aleatoric uncertainties in regression and demonstrate its effectiveness compared to more complex methods, validated through synthetic and real-world datasets.
Abstract: Explainability and uncertainty quantification are two pillars of trustable artificial intelligence. However, the reasoning behind uncertainty estimates is generally left unexplained. Identifying the drivers of uncertainty complements explanations of point predictions in recognizing model limitations and enhances trust in decisions and their communication. So far, explanations of uncertainties have been rarely studied. The few exceptions rely on Bayesian neural networks or technically intricate approaches, such as auxiliary generative models, thereby hindering their broad adoption. We propose a straightforward approach to explain predictive aleatoric uncertainties. We estimate uncertainty in regression as predictive variance by adapting a neural network with a Gaussian output distribution. Subsequently, we apply out-of-the-box explainers to the model's variance output. This approach can explain uncertainty influences more reliably than more complex published approaches, which we demonstrate in a synthetic setting with a known data-generating process. We further adapt multiple metrics from conventional XAI research to uncertainty explanations. We quantify our findings with a nuanced benchmark analysis that includes real-world datasets. Finally, we apply our approach to an age regression model and discover reasonable drivers of uncertainty. Overall, the proposed straightforward method explains uncertainty estimates with little modifications to the model architecture and decisively outperforms more intricate methods.
Primary Area: interpretability and explainable AI
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Submission Number: 3044
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