M$^2$FGB: A Min-Max Gradient Boosting Framework for Subgroup Fairness
This work addresses fairness for marginalized groups in predictive models, but it is incremental as it extends existing gradient-boosting methods with fairness terms.
The paper tackles fairness in machine learning by developing a min-max gradient boosting framework that incorporates subgroup fairness objectives, showing theoretical convergence and empirical effectiveness in addressing binary and subgroup fairness.
In recent years, fairness in machine learning has emerged as a critical concern to ensure that developed and deployed predictive models do not have disadvantageous predictions for marginalized groups. It is essential to mitigate discrimination against individuals based on protected attributes such as gender and race. In this work, we consider applying subgroup justice concepts to gradient-boosting machines designed for supervised learning problems. Our approach expanded gradient-boosting methodologies to explore a broader range of objective functions, which combines conventional losses such as the ones from classification and regression and a min-max fairness term. We study relevant theoretical properties of the solution of the min-max optimization problem. The optimization process explored the primal-dual problems at each boosting round. This generic framework can be adapted to diverse fairness concepts. The proposed min-max primal-dual gradient boosting algorithm was theoretically shown to converge under mild conditions and empirically shown to be a powerful and flexible approach to address binary and subgroup fairness.