将概念不确定性分解为可解释的两类，提升决策可靠性。
Credal Concept Bottleneck Models for Epistemic-Aleatoric Uncertainty Decomposition
- 用概率区间表示概念，通过多头分歧度量认知不确定性
- 认知不确定性与预测错误正相关，偶然不确定性匹配标注者分歧
- 适合需要可信决策的医疗、金融等高风险场景
概念瓶颈模型（CBM）通过人类可理解的概念进行预测，但通常输出点估计的概念概率，混淆了可减少的认知不确定性（模型不完善）与不可减少的偶然不确定性（输入模糊）。这使得概念级不确定性难以解释且难用于实际决策。本文提出CREDENCE框架，通过构造性方式分解概念不确定性：每个概念以概率区间（可信集）表示，认知不确定性由多样概念头之间的分歧度量，偶然不确定性则通过专门训练的模糊性输出来估计，该输出在有标注数据时与标注者分歧保持一致。由此产生的信号支持具体行动：自动化低不确定性案例，针对高认知不确定性案例优先收集数据，将高偶然不确定性案例转交人工审核，当两者均高时选择不预测。在多个任务中验证，认知不确定性与预测误差呈正相关，偶然不确定性紧密追踪标注者分歧，提供了超越误差关联的指导意义。代码已开源。
原文摘要 · Abstract (English)
Concept Bottleneck Models (CBMs) predict through human-interpretable concepts, but they typically output point concept probabilities that conflate epistemic uncertainty (reducible model underspecification) with aleatoric uncertainty (irreducible input ambiguity). This makes concept-level uncertainty hard to interpret and, more importantly, hard to act upon. We introduce CREDENCE (Credal Ensemble Concept Estimation), a CBM framework that decomposes concept uncertainty by construction. CREDENCE represents each concept as a credal prediction (a probability interval), derives epistemic uncertainty from disagreement across diverse concept heads, and estimates aleatoric uncertainty via a dedicated ambiguity output trained to match annotator disagreement when available. The resulting signals support prescriptive decisions: automate low-uncertainty cases, prioritize data collection for high-epistemic cases, route high-aleatoric cases to human review, and abstain when both are high. Across several tasks, we show that epistemic uncertainty is positively associated with prediction errors, whereas aleatoric uncertainty closely tracks annotator disagreement, providing guidance beyond error correlation. Our implementation is available at the following link: https://github.com/Tankiit/Credal_Sets/tree/ensemble-credal-cbm
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