提出AI行动决策的三要素模型，让AI更懂何时该干预。
When Should an AI Act? A Human-Centered Model of Scene, Context, and Behavior for Agentic AI Design
- 用场景、上下文和行为因素重构AI决策逻辑
- 区分客观事实与用户主观意义，避免误判
- 给出5条设计原则，指导AI何时该出手
具身化AI日益主动介入，通过上下文数据推断用户情境，却常因缺乏判断何时、为何以及是否该行动的能力而失败。本文提出一个概念模型，将行为视为整合场景（可观测情境）、上下文（用户建构的意义）及人类行为因素（影响行为可能性的决定因素）的解释性结果。该模型基于人文、社会科学、人机交互与工程学的多学科视角，区分了可观察事实与对用户有意义的内容，说明相同场景可产生不同行为意义与结果。为将此视角转化为设计实践，我们提炼出五项代理设计原则：行为一致性、上下文敏感性、时间适宜性、动机校准和代理权保留，指导干预的深度、时机、强度与克制。该模型与原则共同为具身化AI系统的设计提供基础，使其在互动中具备情境敏感性与判断力。
原文摘要 · Abstract (English)
Agentic AI increasingly intervenes proactively by inferring users' situations from contextual data yet often fails for lack of principled judgment about when, why, and whether to act. We address this gap by proposing a conceptual model that reframes behavior as an interpretive outcome integrating Scene (observable situation), Context (user-constructed meaning), and Human Behavior Factors (determinants shaping behavioral likelihood). Grounded in multidisciplinary perspectives across the humanities, social sciences, HCI, and engineering, the model separates what is observable from what is meaningful to the user and explains how the same scene can yield different behavioral meanings and outcomes. To translate this lens into design action, we derive five agent design principles (behavioral alignment, contextual sensitivity, temporal appropriateness, motivational calibration, and agency preservation) that guide intervention depth, timing, intensity, and restraint. Together, the model and principles provide a foundation for designing agentic AI systems that act with contextual sensitivity and judgment in interactions.
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。