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

Fairness-Aware Interpretable Modeling (FAIM) for Trustworthy Machine  Learning in Healthcare

We present the design and implementation of a custom discrete optimization
technique for building rule lists over a categorical feature space. Our
algorithm produces rule lists with optimal training performance, according to
the regularized empirical risk, with a certificate of optimality. By leveraging
algorithmic bounds, efficient data structures, and computational reuse, we
achieve several orders of magnitude speedup in time and a massive reduction of
memory consumption. We demonstrate that our approach produces optimal rule
lists on practical problems in seconds. Our results indicate that it is
possible to construct optimal sparse rule lists that are approximately as
accurate as the COMPAS proprietary risk prediction tool on data from Broward
County, Florida, but that are completely interpretable. This framework is a
novel alternative to CART and other decision tree methods for interpretable
modeling.

本文提出了一种基于离散优化的过程，用于构建基于分类特征空间的规则列表，并且通过算法边界、高效数据结构和计算复用来获得数倍的时间加速和大幅度降低的内存消耗。其结果表明，通过本方法可以在几秒钟内构建出比 Broward County，Florida 的数据上的 COMPAS 专有风险预测工具准确性近似的解释性优化规则列表，这是一种新的解释性建模决策树方法的替代方案。