剖析医疗AI中种族变量的使用争议与影响
Role and Use of Race in AI/ML Models Related to Health
- 从AI生命周期视角系统分析种族变量的使用问题
- 提出多维度评估框架供决策参考
- 适合政策制定者与医疗AI开发者阅读
医疗相关人工智能与机器学习（AI/ML）模型中种族变量的角色与使用引发越来越多关注与争议。尽管相关问题复杂且广泛，但目前仍缺乏全面、系统的指导框架来支持各利益相关方的审视与解决。本文从广泛、系统且跨领域的角度，对种族相关挑战进行整体性分析，围绕AI/ML生命周期展开，并以‘需考虑要点’的形式呈现，旨在支持探究与决策。
原文摘要 · Abstract (English)
The role and use of race within health-related artificial intelligence and machine learning (AI/ML) models has sparked increasing attention and controversy. Despite the complexity and breadth of related issues, a robust and holistic framework to guide stakeholders in their examination and resolution remains lacking. This perspective provides a broad-based, systematic, and cross-cutting landscape analysis of race-related challenges, structured around the AI/ML lifecycle and framed through "points to consider" to support inquiry and decision-making.
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