Rank-Based Multi-task Learning for Fair Regression

TL;DR

This paper introduces a novel multi-task regression fairness learning method using rank-based independence testing and non-convex optimization, demonstrating superior performance on synthetic and real datasets.

Contribution

It proposes a new fairness-aware multi-task regression framework based on rank-based dependency measures and non-convex optimization, with an efficient training algorithm.

Findings

Outperforms state-of-the-art fairness methods on multiple metrics

Effective in both synthetic and real-world datasets

Uses rank-based independence testing for fairness measurement

Abstract

In this work, we develop a novel fairness learning approach for multi-task regression models based on a biased training dataset, using a popular rank-based non-parametric independence test, i.e., Mann Whitney U statistic, for measuring the dependency between target variable and protected variables. To solve this learning problem efficiently, we first reformulate the problem as a new non-convex optimization problem, in which a non-convex constraint is defined based on group-wise ranking functions of individual objects. We then develop an efficient model-training algorithm based on the framework of non-convex alternating direction method of multipliers (NC-ADMM), in which one of the main challenges is to implement an efficient projection oracle to the preceding non-convex set defined based on ranking functions. Through the extensive experiments on both synthetic and real-world datasets, we validated the out-performance of our new approach against several state-of-the-art competitive methods on several popular metrics relevant to fairness learning.