为临终决策AI代理设计伦理公平框架，强调价值与关系的尊重。
Algorithmic Fairness in AI Surrogates for End-of-Life Decision-Making
- 基于现实临终场景映射公平性概念，构建伦理框架。
- 公平不仅是结果均等，更需体现患者价值观与文化背景。
- 适合医疗AI伦理、算法公平研究者参考。
人工智能代理系统旨在个体丧失决策能力时推断其偏好。然而，此类系统中的公平性问题尚未得到充分探讨。传统算法公平框架难以应对涉及人际关系、存在性抉择及文化多样性的复杂情境。本文通过将主要公平性概念映射到真实的临终决策场景，考察不同道德传统下的公平性表现。作者认为，该领域的公平性不仅限于结果均等，更应包含道德代表性、对患者价值观、人际关系及世界观的忠实反映。
原文摘要 · Abstract (English)
Artificial intelligence surrogates are systems designed to infer preferences when individuals lose decision-making capacity. Fairness in such systems is a domain that has been insufficiently explored. Traditional algorithmic fairness frameworks are insufficient for contexts where decisions are relational, existential, and culturally diverse. This paper explores an ethical framework for algorithmic fairness in AI surrogates by mapping major fairness notions onto potential real-world end-of-life scenarios. It then examines fairness across moral traditions. The authors argue that fairness in this domain extends beyond parity of outcomes to encompass moral representation, fidelity to the patient's values, relationships, and worldview.
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