Across various sectors such as healthcare, criminal justice, national security, finance, and technology, large-scale machine learning (ML) and artificial intelligence (AI) systems are being deployed to make critical data-driven decisions. Many have asked if we can and should trust these ML systems to be making these decisions. Two critical components are prerequisites for trust in ML systems: interpretability, or the ability to understand why the ML system makes the decisions it does, and fairness, which ensures that ML systems do not exhibit bias against certain individuals or groups. Both interpretability and fairness are important and have separately received abundant attention in the ML literature, but so far, there have been very few methods developed to directly interpret models with regard to their fairness. In this paper, we focus on arguably the most popular type of ML interpretation: feature importance scores. Inspired by the use of decision trees in knowledge distillation, we propose to leverage trees as interpretable surrogates for complex black-box ML models. Specifically, we develop a novel fair feature importance score for trees that can be used to interpret how each feature contributes to fairness or bias in trees, tree-based ensembles, or tree-based surrogates of any complex ML system. Like the popular mean decrease in impurity for trees, our Fair Feature Importance Score is defined based on the mean decrease (or increase) in group bias. Through simulations as well as real examples on benchmark fairness datasets, we demonstrate that our Fair Feature Importance Score offers valid interpretations for both tree-based ensembles and tree-based surrogates of other ML systems.

翻译：在医疗保健、刑事司法、国家安全、金融和技术等多个领域，大规模机器学习（ML）和人工智能（AI）系统正被部署用于做出关键的数据驱动决策。许多人质疑我们能否且应否信任这些ML系统来做出此类决策。对ML系统的信任需以两个关键组件为前提：可解释性，即理解ML系统为何做出特定决策的能力；以及公平性，它确保ML系统不会对特定个体或群体表现出偏见。可解释性与公平性均至关重要，并在ML文献中分别获得了广泛关注，但迄今极少有方法能直接解释模型的公平性。本文聚焦于最流行的ML解释类型之一：特征重要性评分。受决策树在知识蒸馏中应用的启发，我们提出利用树作为复杂黑盒ML模型的可解释代理。具体而言，我们为树开发了一种新型公平特征重要性评分，可用于解释每个特征如何对树、树基集成模型或任意复杂ML系统的树基代理中的公平性或偏见产生贡献。与树中常用的平均杂质减少指标类似，我们的公平特征重要性评分基于群体偏见的平均减少（或增加）来定义。通过仿真实验以及基于基准公平性数据集的真实案例，我们证明了该公平特征重要性评分能为树基集成模型及其他ML系统的树基代理提供有效的解释。