系统梳理不确定性如何融入AI解释，提升可信度。
Concerning Uncertainty -- A Systematic Survey of Uncertainty-Aware XAI
- 归纳三类不确定性量化方法：贝叶斯、蒙特卡洛与校准
- 发现评估体系碎片化，缺乏对用户与解释稳定性的关注
- 适合关注AI可靠性与可解释性融合的研究者
本文系统综述了不确定性感知的可解释人工智能（UAXAI），分析不确定性在解释流程中的整合方式及评估方法。文献中涌现出三种主流不确定性量化方法：贝叶斯、蒙特卡洛与校准方法，并形成三类集成策略：评估可信度、约束模型或解释内容、显式传达不确定性。当前评估仍以模型为中心，缺乏对用户的影响考量，且对校准性、覆盖率、解释稳定性等可靠性属性报告不一致。近期研究倾向采用校准与分布无关技术，将解释器变异性视为核心问题。本文主张推动统一评估原则，连接不确定性传播、鲁棒性与人类决策，指出反事实分析与校准方法是实现可解释性与可靠性对齐的潜在路径。
原文摘要 · Abstract (English)
This paper surveys uncertainty-aware explainable artificial intelligence (UAXAI), examining how uncertainty is incorporated into explanatory pipelines and how such methods are evaluated. Across the literature, three recurring approaches to uncertainty quantification emerge (Bayesian, Monte Carlo, and Conformal methods), alongside distinct strategies for integrating uncertainty into explanations: assessing trustworthiness, constraining models or explanations, and explicitly communicating uncertainty. Evaluation practices remain fragmented and largely model centered, with limited attention to users and inconsistent reporting of reliability properties (e.g., calibration, coverage, explanation stability). Recent work leans towards calibration, distribution free techniques and recognizes explainer variability as a central concern. We argue that progress in UAXAI requires unified evaluation principles that link uncertainty propagation, robustness, and human decision-making, and highlight counterfactual and calibration approaches as promising avenues for aligning interpretability with reliability.
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