Obtaining Dyadic Fairness by Optimal Transport

TL;DR

This paper introduces DyadicOT, a novel optimal transport-based pre-processing method that improves fairness in link prediction tasks by aligning conditional distributions, demonstrating superior results on benchmark datasets.

Contribution

It proposes a new data repairing approach based on optimal transport theory to achieve dyadic fairness in graph link prediction, linking fairness with distribution alignment.

Findings

DyadicOT outperforms existing fairness methods on benchmark datasets.

The method effectively achieves dyadic fairness through optimal transport.

Theoretical connection established between dyadic fairness and distribution alignment.

Abstract

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.