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：识别并区分预测不确定性的来源对于可信监督学习至关重要。本文论证了广泛使用的二阶方法——旨在区分偶然不确定性与认知不确定性的方法——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，具体表现为高估偶然（数据相关）不确定性并低估认知（模型相关）不确定性，从而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部分贡献；不同方法考虑了不同的方差来源，产生的估计结果既不完整又难以解释。综上所述，这些结果表明，当前的认知不确定性估计只有在最终用户完全理解其局限性且AI开发者明确承认这些局限的前提下，才能用于安全关键和高风险决策场景。