Obtaining Dyadic Fairness by Optimal Transport
This work addresses fairness in link prediction for graph data, which is an incremental improvement in the domain of trustworthy machine learning.
The paper tackled the problem of achieving dyadic fairness in link prediction tasks by proposing a pre-processing methodology based on optimal transport theory, resulting in the DyadicOT algorithm that showed superior fairness results on two benchmark graph datasets.
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.