发表机构
Robert Koch Institute; Freie Universitat, Berlin（罗伯特·科赫研究所; 柏林自由大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出不确定性-行动绑定框架与ActionCue可视化方法，明确AI不确定性条件到人工监督响应的组合逻辑，通过多领域案例验证其有效性。
AI 中文摘要
人工智能系统常披露不确定性，但很少明确该不确定性应触发何种响应。多数不确定性可视化将不确定性编码在模型输出中，让用户自行判断最合适的行动方案；而设计空间的另一部分——决策过程本身的不确定性，包括多种不确定性条件如何组合为监督响应——则相对未被充分探索。我们通过两项关联贡献解决这一空白：其一，提出不确定性-行动绑定框架，该框架在带有上下文安全修正项的优先级策略下，将多种不确定性条件组合为单一监督响应，该响应涉及AI支持的决策是否及如何推进，而非实质性领域决策本身；其二，提出ActionCue，一种过程透明度可视化方法，将上述组合过程明确呈现。我们通过与仅置信度、数据级不确定性显示的三方对比，结合医疗、信贷评估及灾害预测的工作案例，演示了该方法。总体而言，该框架明确了不确定性条件如何被解析为监督响应，而可视化使该解析可被检查，而非隐含状态。
英文摘要
Artificial intelligence systems often disclose uncertainty, yet they rarely make clear what response that uncertainty should trigger. Most uncertainty visualizations encode uncertainty in model outputs, leaving users to discern the most appropriate course of action. A second region of the design space--uncertainty in the decision process itself, including how multiple uncertainty conditions compose into an oversight response-- remains comparatively underexplored. We address this gap with two coupled contributions. First, we introduce an uncertainty-to-action binding framework that composes multiple uncertainty conditions into a single oversight response under a precedence policy with a contextual safety modifier. That response concerns whether and how an AI-supported decision may proceed, not the substantive domain decision itself. Second, we present ActionCue, a process-transparency visualization that renders that composition explicit. We demonstrate the approach through a three-way comparison with confidence-only and data-level uncertainty displays, using worked cases from healthcare, credit assessment, and disaster forecasting. Together, the framework specifies how uncertainty conditions are resolved into an oversight response, and the visualization makes that resolution inspectable rather than implicit.
Comments5 pages, 2 figures, IEEEVis 2026 UncertaintyVis workshop
