Identifying and disentangling sources of predictive uncertainty is essential for trustworthy supervised learning. We argue that widely used second-order methods that disentangle aleatoric and epistemic uncertainty are fundamentally incomplete. First, we show that unaccounted bias contaminates uncertainty estimates by overestimating aleatoric (data-related) uncertainty and underestimating the epistemic (model-related) counterpart, leading to incorrect uncertainty quantification. Second, we demonstrate that existing methods capture only partial contributions to the variance-driven part of epistemic uncertainty; different approaches account for different variance sources, yielding estimates that are incomplete and difficult to interpret. Together, these results highlight that current epistemic uncertainty estimates can only be used in safety-critical and high-stakes decision-making when limitations are fully understood by end users and acknowledged by AI developers.

翻译：识别并分离预测不确定性的来源对于可信监督学习至关重要。本文认为，广泛使用的区分偶然不确定性与认知不确定性的二阶方法本质上是非完备的。首先，我们证明未考虑的偏差会污染不确定性估计：高估偶然（数据相关）不确定性并低估认知（模型相关）不确定性，导致错误的不确定性量化。其次，我们证明现有方法仅捕捉了认知不确定性中方差驱动部分的局部贡献；不同方法对应不同的方差来源，产生难以解释的非完备估计。综上，这些结果表明：当前认知不确定性估计仅当最终用户完全理解其局限且AI开发者明确承认时，方可用于安全关键和高风险决策场景。