揭示医疗算法公平性研究与实践的脱节，提出行动框架
From Awareness to Action: Understanding and Overcoming the Research-Practice Gap in Algorithmic Fairness for Public Health
- 通过访谈、调查和映射分析，识别公平性实施障碍
- 发现公平性定义混乱，培训不足，评估使用率低
- 适合政策制定者与医疗AI开发者参考
算法公平性对负责任的医疗机器学习研究至关重要，但其实际应用仍有限。为探究这一认知-行动差距，我们开展了一项包含专家访谈、在线调查和系统映射的混合方法研究。访谈结果指导了问卷设计，调查揭示了公平性定义碎片化、培训与指导缺乏、依赖外部资源、正式评估、缓解或监控使用率极低等问题。这些发现被映射到三个研究-实践差距理论视角：知识-实践差距、知识到行动循环、知行差距，各自提供互补见解。在此基础上，我们提出「Fairness-to-Action」框架，整合方法、组织与系统维度，识别公平性知识转化受阻的关键环节。分析表明，公平性尚未充分制度化，转化机制多由外部推动，系统级优先级仍以准确率为先。这些发现指出了推动安全、公平、伦理的医疗机器学习实践的关键切入点。
原文摘要 · Abstract (English)
Algorithmic fairness is essential for responsible ML-driven public health research, yet its practical implementation remains limited. To investigate this awareness-action gap, we conducted a sequential mixed-methods study comprising expert interviews, an online survey, and systematic mapping. The expert interviews informed the design of the survey, which in turn revealed fragmented definitions of fairness, limited training and guidance, reliance on external sources, and rare use of formal assessment, mitigation, or monitoring. These findings were subsequently mapped onto three established research-practice gap lenses: the Knowledge-Practice Gap, the Knowledge-to-Action Cycle, and the Knowing-Doing Gap, each offering complementary perspectives. Building on this synthesis, we introduce the Fairness-to-Action framework, which integrates methodological, organizational, and systemic dimensions to identify where translation of algorithmic fairness knowledge stalls. Our analysis shows that fairness remains weakly institutionalized, translation mechanisms are externally driven, and system-level priorities continue to emphasize accuracy over fairness. These insights suggest critical leverage points for advancing safe, fair, and ethical ML-driven public health research practice.
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