The Aleatoric-Epistemic Dichotomy of Uncertainty is Meaningful and Indispensable for Machine Learning

The quantification of uncertainty, in particular the distinction between aleatoric and epistemic uncertainty, is receiving growing interest in machine learning. However, both its conceptual meaningfulness and practical usefulness have recently been questioned. In this paper, we take the position that the dichotomy is conceptually meaningful and indispensable for (uncertainty-aware) machine learning. In particular, we argue that much of the recent criticism is flawed, either because it targets specific mathematical frameworks for the dichotomy, or because it is based on implicit assumptions that are not implied by the dichotomy itself. Moreover, we highlight that the dichotomy is indispensable for a broad class of decision-making problems, where optimal performance provably requires a distinction between reducible and irreducible uncertainty. Therefore, while existing methodology should certainly be improved further, there is no reason to question the distinction per se.