AI决策支持系统需设计能引导人类推理的提示信息。
Intelligent Reasoning Cues: A Framework and Case Study of the Roles of AI Information in Complex Decisions
- 将AI信息拆解为独立推理提示，影响人类决策过程
- 8类提示在重症患者治疗中作用各异，可指导界面设计
- 适合医疗、金融等高风险决策场景的AI系统设计
基于人工智能的决策支持系统虽准确，却常无法有效辅助用户或提升决策质量。现有理论多聚焦于用户对AI建议的信任程度调节，却未说明不同系统设计如何影响背后的推理过程。本文提出将AI界面视为‘智能推理提示’的集合：即能够单独影响决策的离散信息片段。我们以重症监护中脓毒症患者治疗这一高风险临床决策为例，通过与六支团队的上下文访谈及25名医生的思考过程研究，发现八类推理提示具有不同的影响模式，可直接用于指导设计。结果表明，推理提示应优先覆盖高变异性与高自主性任务，具备随决策需求动态调整的能力，并为复杂病例提供互补且严谨的洞察。
原文摘要 · Abstract (English)
Artificial intelligence (AI)-based decision support systems can be highly accurate yet still fail to support users or improve decisions. Existing theories of AI-assisted decision-making focus on calibrating reliance on AI advice, leaving it unclear how different system designs might influence the reasoning processes underneath. We address this gap by reconsidering AI interfaces as collections of intelligent reasoning cues: discrete pieces of AI information that can individually influence decision-making. We then explore the roles of eight types of reasoning cues in a high-stakes clinical decision (treating patients with sepsis in intensive care). Through contextual inquiries with six teams and a think-aloud study with 25 physicians, we find that reasoning cues have distinct patterns of influence that can directly inform design. Our results also suggest that reasoning cues should prioritize tasks with high variability and discretion, adapt to ensure compatibility with evolving decision needs, and provide complementary, rigorous insights on complex cases.
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