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.

翻译：机器学习在医疗等高风险管理领域的广泛应用引发了关于模型公平性的重大关切。我们提出了一种可解释框架——公平感知可解释建模（FAIM），该框架在不牺牲性能的前提下提升模型公平性，通过交互式界面从一组高性能模型中识别出“更公平”的模型，并促进数据驱动证据与临床专业知识的融合，以增强情境化公平性。我们利用两个真实世界数据库（MIMIC-IV-ED和SGH-ED）进行入院预测，验证了FAIM在减少性别和种族偏见方面的价值。研究结果表明，对于两个数据集，FAIM模型不仅表现出令人满意的判别性能，而且通过成熟的公平性度量指标衡量，其显著缓解了偏见，效果优于常用的偏见缓解方法。我们的方法证明了在不牺牲性能的前提下提升公平性的可行性，并提供了一种邀请领域专家参与的建模模式，从而推动通过多学科协作实现定制化人工智能公平性。