在准确性和可接受性冲突下设计医疗聊天机器人：一项针对印度城市居民的探索性、情境化研究
Designing Medical Chatbots where Accuracy and Acceptability are in Conflict: An Exploratory, Vignette-based Study in Urban India
AI总结：
研究探讨了医疗聊天机器人中准确性与可接受性冲突的问题，通过200名印度城市成年人的调查，发现用户倾向于拒绝与指南不一致的建议，并通过设计情境感知的提示来调整期望，以提升指南一致性的建议接受度。
摘要：
当医疗聊天机器人提供的建议与用户的实际护理经验冲突时，用户需要解释、协商和评估该建议的正当性。在印度，抗生素、止泻药和注射的过度使用已改变了患者对指南一致建议的期望。我们通过200名印度城市成年人的混合方法、情境化研究，探讨了用户对指南一致和规范偏离建议的偏好。研究发现，大多数用户拒绝此类建议，并基于其实际经验提出多种理由。通过设计和引入情境感知的提示，我们支持期望的一致性，使偏好向包含指南一致建议的转录文本倾斜。在此过程中，我们揭示了在南半球公平设计医疗聊天机器人中的关键矛盾。
英文摘要：
When medical chatbots provide advice that conflicts with users' lived care experiences, users are left to interpret, negotiate, and evaluate the legitimacy of that guidance. In India, the widespread overuse of antibiotics, antidiarrheals, and injections has shifted patient expectations away from the guideline-aligned advice that chatbots are trained to provide. We present a mixed-methods, vignette-based study with 200 urban Indian adults examining preferences for and against guideline-aligned, norm-divergent advice in chatbot transcripts. We find that a majority of users reject such advice, drawing on diverse rationales grounded in their lived expectations. Through the design and introduction of context-aware nudges, we support expectation alignment that shifts preferences towards transcripts containing guideline-aligned advice. In doing so, we surface key tensions in the equitable design of medical chatbots in the Global South.
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