不同决策流程与解释方式影响人机协作信任，需分开评估主观信任与实际依赖行为。
Not All Trust is the Same: Effects of Decision Workflow and Explanations in Human-AI Decision Making
- 对比1步与2步决策流程，测试解释对信任的影响
- 无证据显示2步流程能减少过度依赖AI
- 专家与新手对解释的反应不同，适合不同人群使用
人机协同决策中的核心挑战是建立合理且精准的信任。应避免过度信任（接受错误建议）和信任不足（拒绝正确指导）。以往研究在决策流程设计上存在差异：用户是否先提交初步判断（2步设置）或直接看到AI建议（1步设置），以及信任测量方式不同——通过自述报告或行为指标（如采纳率、切换率）来衡量依赖程度。本研究考察了决策流程类型、解释提供与否、用户领域知识及此前AI使用经验三者的影响与交互作用。结果显示，2步流程并未降低过度依赖；决策流程对自我报告信任无直接影响，但与领域知识和解释存在交叉效应，表明解释效果不随流程通用。最终确认，主观信任与行为依赖是两个独立维度，应在研究中分别评估。
原文摘要 · Abstract (English)
A central challenge in AI-assisted decision making is achieving warranted, well-calibrated trust. Both overtrust (accepting incorrect AI recommendations) and undertrust (rejecting correct advice) should be prevented. Prior studies differ in the design of the decision workflow - whether users see the AI suggestion immediately (1-step setup) or have to submit a first decision beforehand (2-step setup) -, and in how trust is measured - through self-reports or as behavioral trust, that is, reliance. We examined the effects and interactions of (a) the type of decision workflow, (b) the presence of explanations, and (c) users' domain knowledge and prior AI experience. We compared reported trust, reliance (agreement rate and switch rate), and overreliance. Results showed no evidence that a 2-step setup reduces overreliance. The decision workflow also did not directly affect self-reported trust, but there was a crossover interaction effect with domain knowledge and explanations, suggesting that the effects of explanations alone may not generalize across workflow setups. Finally, our findings confirm that reported trust and reliance behavior are distinct constructs that should be evaluated separately in AI-assisted decision making.
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