医疗AI前端设计忽视用户知情权，导致患者被算法监控却无法参与决策。
The Imbalanced User-AI Relationships as an Ethical Failure of Front-End Design in Healthcare AI
- 提出'不对称可读性'概念，揭示患者被数据追踪却无权理解或质疑AI判断。
- 案例显示默认推荐、输入限制等设计削弱医生判断力与患者自主权。
- 倡导'互惠设计'，适合医疗AI开发者与伦理审查者参考。
医疗AI的伦理讨论长期聚焦于后端问题，如偏见、公平性和可解释性，而用户实际接触AI输出的前端界面却未受充分关注。本文将不对称的用户-AI关系识别为一类前端伦理失败：患者通过数据推断被高度可见，却无法理解、质疑或影响自身在系统中的呈现方式。基于‘不对称可读性’概念及一个基于聊天的远程医疗案例，我们展示设计选择（如默认推荐、受限输入、抑制不确定性）如何即便在技术准确的情况下，仍损害用户自主性、临床判断与人类监督。本文提出以‘互惠’为导向的设计原则，并提供实现更平衡、参与式人机关系的干预措施。
原文摘要 · Abstract (English)
Ethical discourse on AI in healthcare has focused predominantly on back-end concerns such as bias, fairness and explainability, while the front-end interface, where patients and clinicians actually encounter AI outputs, remains under explored. This paper identifies imbalanced user-AI relationships as a distinct class of front-end ethical failure: patients are rendered highly visible to AI systems through data inference, yet cannot understand, question or influence how they are represented. Through the concept of asymmetric legibility and a chat-based telemedicine case, we show how design choices e.g., default recommendations, restricted inputs and suppressed uncertainty, undermine agency, clinician judgment and human oversight even where systems are technically accurate. We propose reciprocity as a design orientation and offer interventions for more balanced, participatory user-AI relationships in healthcare.
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