Developing AI tools that preserve fairness is of critical importance, specifically in high-stakes applications such as those in healthcare. However, health AI models' overall prediction performance is often prioritized over the possible biases such models could have. In this study, we show one possible approach to mitigate bias concerns by having healthcare institutions collaborate through a federated learning paradigm (FL; which is a popular choice in healthcare settings). While FL methods with an emphasis on fairness have been previously proposed, their underlying model and local implementation techniques, as well as their possible applications to the healthcare domain remain widely underinvestigated. Therefore, we propose a comprehensive FL approach with adversarial debiasing and a fair aggregation method, suitable to various fairness metrics, in the healthcare domain where electronic health records are used. Not only our approach explicitly mitigates bias as part of the optimization process, but an FL-based paradigm would also implicitly help with addressing data imbalance and increasing the data size, offering a practical solution for healthcare applications. We empirically demonstrate our method's superior performance on multiple experiments simulating large-scale real-world scenarios and compare it to several baselines. Our method has achieved promising fairness performance with the lowest impact on overall discrimination performance (accuracy).

翻译：开发保持公平性的AI工具至关重要，尤其是在医疗等高风险应用中。然而，健康AI模型的整体预测性能往往被优先考虑，而忽视了此类模型可能存在的偏见。在本研究中，我们展示了一种通过医疗机构采用联邦学习范式（FL；医疗场景中的常用选择）进行协作来缓解偏见问题的可能方法。虽然此前已有强调公平性的FL方法被提出，但其底层模型、局部实现技术以及在医疗领域的可能应用仍缺乏深入探究。因此，我们针对使用电子健康记录的医疗领域，提出了一种包含对抗性去偏与公平聚合方法的综合FL框架，适用于多种公平性指标。我们的方法不仅通过优化过程显式缓解偏见，基于FL的范式还能隐式帮助解决数据不平衡问题并扩大数据规模，为医疗应用提供实用解决方案。我们通过多项模拟大规模真实场景的实验，实证证明了该方法相较于多个基线的优越性能。该方法在保持整体判别性能（准确性）影响最低的同时，实现了有前景的公平性表现。