用户验证不靠信任，而是日常认知管理，能提升使用满意度。
Verification Without Distrust: Reframing User-Side Oversight as Routine Epistemic Governance in Everyday Human-Chatbot Interaction
- 将用户验证视为独立于信任的常规认知治理行为。
- 验证、修正、审批等操作与满意度强相关，但与信任无关。
- 适合关注人机交互体验与对话系统设计的研究者。
关于人机交互的研究长期认为，对AI输出的验证是依赖信任的行为，更精准的信任应减少验证。我们通过一项包含153名频繁使用聊天机器人的混合方法调查，检验了这一假设。结果与主流预测相反：信任与验证之间无显著关联，且在多种敏感性分析下均保持稳健。另外三种用户行为——改写、修正和自动化操作前的审批——被广泛采纳，并与更高满意度正相关。数据揭示了评价型监督（与信任解耦，满意度关联弱）与干预型监督（与信任弱相关，满意度关联强）的本质区别。中到大的满意度-控制感差距表明，任务成功并不带来自主感。定性研究发现用户存在工具性心智模型、特定故障模式下的怀疑，以及对认知基础设施的需求。本文将用户监督重构为与信任兼容的日常认知治理，并提出四种可支撑监督的对话AI设计方向。
原文摘要 · Abstract (English)
Research on human-AI interaction has long framed verification of system outputs as a trust-contingent behavior that better-calibrated trust should reduce. We test this assumption in everyday human-chatbot interaction through a mixed-methods survey of 153 frequent chatbot users. Contrary to the canonical prediction, we find no detectable association between trust and verification, with the result robust across sensitivity analyses. Three further user-side practices - refinement, correction, and approval before automated actions - are widely endorsed and positively associated with satisfaction. The data reveal a substantive distinction between evaluative oversight (trust-decoupled, weakly tied to satisfaction) and interventionist oversight (weakly trust-correlated, strongly tied to satisfaction). A medium-to-large satisfaction-control gap shows that effective task outcomes do not produce a felt sense of agency. Qualitative findings identify instrumental mental models, failure-mode-specific doubt, and demand for epistemic infrastructure. We reframe user-side oversight as routine epistemic governance compatible with trust, and derive four design directions for scaffolded oversight in conversational AI.
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