论文标题
通过最佳运输获得二元公平
Obtaining Dyadic Fairness by Optimal Transport
论文作者
论文摘要
公平性已被视为机器学习模型中的关键指标，这被认为是值得信赖的机器学习的重要组成部分。在本文中，我们专注于通过二元公平衡量的流行链接预测任务获得公平性。提出了一种新颖的预处理方法，以通过基于最佳运输理论的数据修复来建立二元公平。由于图形链路预测的二元二元公平性与条件分布对准问题之间建立了良好的理论联系，二元修复方案可以等效地转化为条件分布对准问题。此外，通过有效解决对齐问题，满足灵活性和明确的要求，可以获得一种称为Dyadicot的最佳基于运输的二元公平算法，称为Dyadicot。与两个基准图数据集中的其他公平方法相比，提出的二元算法在获得公平性方面显示出优异的结果。
Fairness has been taken as a critical metric in machine learning models, which is considered as an important component of trustworthy machine learning. In this paper, we focus on obtaining fairness for popular link prediction tasks, which are measured by dyadic fairness. A novel pre-processing methodology is proposed to establish dyadic fairness through data repairing based on optimal transport theory. With the well-established theoretical connection between the dyadic fairness for graph link prediction and a conditional distribution alignment problem, the dyadic repairing scheme can be equivalently transformed into a conditional distribution alignment problem. Furthermore, an optimal transport-based dyadic fairness algorithm called DyadicOT is obtained by efficiently solving the alignment problem, satisfying flexibility and unambiguity requirements. The proposed DyadicOT algorithm shows superior results in obtaining fairness compared to other fairness methods on two benchmark graph datasets.
