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.

机器学习中的公平性问题引起了广泛关注，然而现有研究主要关注模型的分配公平性，而忽视了程序公平性。本文首先定义了机器学习模型的程序公平性，然后给出了个体和群体程序公平性的形式化定义，提出了一种用于评估机器学习模型群体程序公平性的新指标$GPF_{FAE}$，该指标利用特征归因解释的人工智能技术捕捉了模型的决策过程。我们在合成数据集和八个真实数据集上验证了$GPF_{FAE}$的有效性。实验结果揭示了模型程序公平性和分配公平性之间的关系。基于我们的分析，我们提出了一种识别导致模型程序不公平的特征的方法，以及两种改善程序公平性的方法。我们的实验证明，我们可以准确地识别导致模型程序不公平的特征，并且我们提出的两种方法在轻微影响模型性能的同时，可以显著改善程序公平性和分配公平性。

机器学习中的程序公正