审视医疗AI中技术去偏策略的现实局限，提出五大实施瓶颈
Revisiting Technical Bias Mitigation Strategies
- 从定义、选择、时机、人群到场景，系统分析去偏策略落地困境
- 实证研究揭示现有技术方案在医疗场景中存在兼容性差、效果不一等问题
- 倡导价值敏感设计，让多方利益相关者参与构建公平AI
AI领域对偏见缓解与公平性的努力主要依赖技术手段。尽管已有大量关于AI偏见的综述，本文聚焦于技术解决方案在医疗环境中的实际局限，从五个关键维度进行结构化分析：谁定义偏见与公平；在数十种不一致且不兼容的缓解策略中如何选择与优先；在AI开发周期的哪个阶段最有效；适用于哪些人群；以及解决方案的设计背景。本文通过聚焦医疗与生物医学应用的实证研究，展示每一项限制。此外，讨论了源于技术设计的价值敏感设计框架，以促进利益相关者参与，确保其价值观融入去偏与公平性解决方案。最后，指出需进一步研究的领域，并提供应对上述局限的实际建议。
原文摘要 · Abstract (English)
Efforts to mitigate bias and enhance fairness in the artificial intelligence (AI) community have predominantly focused on technical solutions. While numerous reviews have addressed bias in AI, this review uniquely focuses on the practical limitations of technical solutions in healthcare settings, providing a structured analysis across five key dimensions affecting their real-world implementation: who defines bias and fairness; which mitigation strategy to use and prioritize among dozens that are inconsistent and incompatible; when in the AI development stages the solutions are most effective; for which populations; and the context in which the solutions are designed. We illustrate each limitation with empirical studies focusing on healthcare and biomedical applications. Moreover, we discuss how value-sensitive AI, a framework derived from technology design, can engage stakeholders and ensure that their values are embodied in bias and fairness mitigation solutions. Finally, we discuss areas that require further investigation and provide practical recommendations to address the limitations covered in the study.
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