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The quantification of aleatoric and epistemic uncertainty in terms ofconditional entropy and mutual information, respectively, has recently becomequite common in machine learning. While the properties of these measures, whichare rooted in information theory, seem appealing at first glance, we identifyvarious incoherencies that call their appropriateness into question. Inaddition to the measures themselves, we critically discuss the idea of anadditive decomposition of total uncertainty into its aleatoric and epistemicconstituents. Experiments across different computer vision tasks support ourtheoretical findings and raise concerns about current practice in uncertaintyquantification.

Quantifying Aleatoric and Epistemic Uncertainty in Machine Learning: Are Conditional Entropy and Mutual Information Appropriate Measures?