Imbalanced User-AI Relationships as Ethical Failure in Healthcare Front-End Design

A recent study published on arXiv highlights the ethical shortcomings in healthcare AI front-end design, specifically regarding the imbalance in user-AI interactions. While discussions about ethics typically center on back-end concerns such as bias and fairness, the critical interface where patients and healthcare providers engage with AI remains largely overlooked. The research contends that although patients are made highly visible to AI through data inference, they lack the ability to comprehend, question, or shape their representation. By employing the idea of asymmetric legibility and examining a chat-based telemedicine scenario, it illustrates how design elements—like default suggestions, limited inputs, and minimized uncertainty—can erode agency and clinician judgment. The authors advocate for reciprocity in design and suggest strategies to foster more equitable, participatory user-AI dynamics.