The escalating integration of machine learning in high-stakes fields such as healthcare raises substantial concerns about model fairness. We propose an interpretable framework - Fairness-Aware Interpretable Modeling (FAIM), to improve model fairness without compromising performance, featuring an interactive interface to identify a "fairer" model from a set of high-performing models and promoting the integration of data-driven evidence and clinical expertise to enhance contextualized fairness. We demonstrated FAIM's value in reducing sex and race biases by predicting hospital admission with two real-world databases, MIMIC-IV-ED and SGH-ED. We show that for both datasets, FAIM models not only exhibited satisfactory discriminatory performance but also significantly mitigated biases as measured by well-established fairness metrics, outperforming commonly used bias-mitigation methods. Our approach demonstrates the feasibility of improving fairness without sacrificing performance and provides an a modeling mode that invites domain experts to engage, fostering a multidisciplinary effort toward tailored AI fairness.

我们提出了一个可解释的框架 - 公平感知可解释建模（FAIM），以提高模型的公平性，同时保持性能，通过交互界面从一组高性能模型中识别出一个“更公平”的模型，并推动数据驱动证据和临床经验的整合，以增强情境公平性。我们通过使用两个真实世界数据库（MIMIC-IV-ED和SGH-ED）在预测入院时减少性别和种族偏见方面展示了FAIM的价值。我们展示了针对这两个数据集，FAIM模型不仅表现出令人满意的区分能力，而且通过广泛使用的公平度量指标显著减轻了偏见，胜过常用的偏见减轻方法。我们的方法展示了在不牺牲性能的情况下提高公平性的可行性，并提供一种邀请领域专家参与的建模模式，促进定制人工智能公平性的多学科工作。

用于信任机器学习的公平可解释建模（FAIM）在医疗保健领域