Fairness in machine learning (ML) has received much attention. However, existing studies have mainly focused on the distributive fairness of ML models. The other dimension of fairness, i.e., procedural fairness, has been neglected. In this paper, we first define the procedural fairness of ML models, and then give formal definitions of individual and group procedural fairness. We propose a novel metric to evaluate the group procedural fairness of ML models, called $GPF_{FAE}$, which utilizes a widely used explainable artificial intelligence technique, namely feature attribution explanation (FAE), to capture the decision process of the ML models. We validate the effectiveness of $GPF_{FAE}$ on a synthetic dataset and eight real-world datasets. Our experiments reveal the relationship between procedural and distributive fairness of the ML model. Based on our analysis, we propose a method for identifying the features that lead to the procedural unfairness of the model and propose two methods to improve procedural fairness after identifying unfair features. Our experimental results demonstrate that we can accurately identify the features that lead to procedural unfairness in the ML model, and both of our proposed methods can significantly improve procedural fairness with a slight impact on model performance, while also improving distributive fairness.

翻译：机器学习（ML）中的公平性已受到广泛关注。然而，现有研究主要聚焦于ML模型的分配公平性，另一维度——程序公平性——却被忽视。本文首先定义ML模型的程序公平性，进而给出个体程序公平性与群体程序公平性的形式化定义。我们提出一种评估ML模型群体程序公平性的新指标——$GPF_{FAE}$，该指标利用可解释人工智能中广泛使用的特征归因解释（FAE）技术来捕获ML模型的决策过程。我们在一个合成数据集和八个真实数据集上验证了$GPF_{FAE}$的有效性。实验揭示了ML模型程序公平性与分配公平性之间的关系。基于分析，我们提出了一种识别导致模型程序不公平特征的方法，并在识别出不公平特征后提出两种改善程序公平性的方法。实验结果表明，我们能够准确识别导致ML模型程序不公平的特征，且两种方法均能在对模型性能影响较小的前提下显著提升程序公平性，同时改善分配公平性。