Obtaining Dyadic Fairness by Optimal Transport [article]

Moyi Yang, Junjie Sheng, Xiangfeng Wang, Wenyan Liu, Bo Jin, Jun Wang, Hongyuan Zha
2022 arXiv   pre-print
Fairness has been taken as a critical metric on machine learning models. Many works studying how to obtain fairness for different tasks emerge. This paper considers obtaining fairness for link prediction tasks, which can be measured by dyadic fairness. We aim to propose a pre-processing methodology to obtain dyadic fairness through data repairing and optimal transport. To obtain dyadic fairness with satisfying flexibility and unambiguity requirements, we transform the dyadic repairing to the
more » ... ditional distribution alignment problem based on optimal transport and obtain theoretical results on the connection between the proposed alignment and dyadic fairness. The optimal transport-based dyadic fairness algorithm is proposed for graph link prediction. Our proposed algorithm shows superior results on obtaining fairness compared with the other pre-processing methods on two benchmark graph datasets.
arXiv:2202.04520v1 fatcat:dl2ofav46jfq3mj4xpfenhayzu