发表机构
University of Arizona（亚利桑那大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文评估健康信息学中多级公平性的现状，识别其实施与报告的差距，分析MINIMAR和TRIPOD等报告标准的作用，提出提高透明度、推广该技术及优先健康公平的建议以推进健康公平。
AI 中文摘要
机器学习在医疗保健领域的日益整合凸显了与公平性、透明度和健康公平相关的关键挑战。具体而言，结合多个偏差缓解步骤或技术的多级公平性技术，有望减少不同患者群体间的偏差，但该方法在健康公平结果方面的探索仍显不足。本文通过聚焦多级公平性对公平医疗结果的影响，并评估透明度和报告标准如何推动这些进展，来评估健康信息学中多级公平性的现状。通过对现有文献的考察，我们识别出多级公平性技术实施以及健康公平影响一致性报告方面的关键差距。此外，我们分析了包括MINIMAR和TRIPOD在内的报告标准在提高模型透明度、确保医疗保健中的机器学习模型解决健康差距方面的作用。这些标准为机器学习模型的报告提供了有价值的基准，但我们也发现了改进这些报告捕捉公平性和公平结果方式的关键机遇。本文最后提出了相关建议，重点在于提高报告的透明度、倡导更广泛采用多级公平性技术，并确保未来研究工作明确将健康公平置于优先地位。
英文摘要
The increasing integration of machine learning in healthcare has highlighted critical challenges related to fairness, transparency, and health equity. Specifically, the use of multi-level fairness techniques, which combine multiple bias mitigation steps or techniques, show promise for reducing biases across different patient demographics, yet this approach remains underexplored in terms of its health equity outcomes. In this paper, we assess the current landscape of multi-level fairness in health informatics by focusing on its impact on equitable healthcare outcomes and evaluating how transparency and reporting standards contribute to these advancements. Through an examination of the existing literature, we identify key gaps in both the implementation of multi-level fairness techniques and the consistent reporting of health equity impacts. Furthermore, we analyze the role of reporting standards, including MINIMAR and TRIPOD, in improving model transparency and ensuring that machine learning models in healthcare address health disparities. These standards offer valuable benchmarks for reporting on ML models, yet we identify key opportunities for enhancing how these reports capture fairness and equity outcomes. The paper concludes by providing recommendations that focus on improving transparency in reporting, advocating for the broader adoption of multi-level fairness techniques, and ensuring that health equity is explicitly prioritized in future research efforts.
Comments10 pages, 3 figures, Submitted to Health Informatics Knowledge Management Conference 2026
