Rank-Based Multi-task Learning for Fair Regression
This addresses fairness issues in regression models for applications with biased datasets, though it is incremental as it builds on existing rank-based methods.
The paper tackles fairness in multi-task regression models by using a rank-based independence test to measure dependency between target and protected variables, and develops an efficient non-convex optimization algorithm that outperforms state-of-the-art methods on fairness metrics in experiments.
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.