现有不确定性估计方法存在根本缺陷，易误判模型信心。
Position: Epistemic uncertainty estimation methods are fundamentally incomplete
- 揭示了现有方法因未考虑偏差，导致对数据不确定性高估、模型不确定性低估。
- 发现现有方法仅捕捉部分模型不确定性来源，结果不完整且难以解释。
- 提醒高风险决策者必须理解这些局限，否则可能误用模型输出。
准确识别与解耦预测不确定性对于可信赖的监督学习至关重要。我们指出，广泛使用的二阶方法在解耦随机性（aleatoric）与认知性（epistemic）不确定性时存在根本性不完整性。首先，未被考虑的偏差会污染不确定性估计：过度高估数据相关的随机性不确定性，同时低估模型相关的认知性不确定性，导致错误的量化结果。其次，现有方法仅捕捉认知性不确定性中由方差驱动部分的局部贡献；不同方法关注不同的方差来源，因此估计结果不完整且难以解释。综上，当前的认知性不确定性估计只能在终端用户充分理解其局限性并得到人工智能开发者承认的前提下，用于安全关键或高风险决策场景。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。