多智能体医疗系统需可争议设计，才能真正可信可靠。
Position: Multi-Agent Algorithmic Care Systems Demand Contestability for Trustworthy AI
- 引入可争议性设计，支持人类全程挑战系统决策
- 构建角色化干预机制，保障临床责任不被算法取代
- 适合关注医疗AI伦理与人机协同的研究者和开发者
多智能体系统（MAS）在医疗领域日益用于通过专业化智能体协作支持复杂决策。由于这些系统作为集体决策者，对信任、问责和人工监管提出挑战。现有可信AI方法主要依赖可解释性，但在多智能体场景下不足，因无法支持护理伙伴对系统输出提出质疑或纠正。为此，本文提出可争议性AI（CAI），强调系统在整个决策生命周期中提供透明度、结构化干预机会及审查、修正或覆盖机制，以支持有效的人类挑战。本立场论文主张，可争议性是可信多智能体算法护理系统的基本设计要求。文章指出现有MAS与可解释AI（XAI）研究的关键局限，并提出一个融合结构化论证与角色化争议的人机协同框架，旨在维护人类自主性、临床责任与高风险照护场景中的信任。
原文摘要 · Abstract (English)
Multi-agent systems (MAS) are increasingly used in healthcare to support complex decision-making through collaboration among specialized agents. Because these systems act as collective decision-makers, they raise challenges for trust, accountability, and human oversight. Existing approaches to trustworthy AI largely rely on explainability, but explainability alone is insufficient in multi-agent settings, as it does not enable care partners to challenge or correct system outputs. To address this limitation, Contestable AI (CAI) characterizes systems that support effective human challenge throughout the decision-making lifecycle by providing transparency, structured opportunities for intervention, and mechanisms for review, correction, or override. This position paper argues that contestability is a necessary design requirement for trustworthy multi-agent algorithmic care systems. We identify key limitations in current MAS and Explainable AI (XAI) research and present a human-in-the-loop framework that integrates structured argumentation and role-based contestation to preserve human agency, clinical responsibility, and trust in high-stakes care contexts.
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