APFEx: Adaptive Pareto Front Explorer for Intersectional Fairness
This addresses a critical gap in fair ML for applications requiring fairness across intersecting subgroups, though it builds incrementally on existing multi-objective optimization methods.
The paper tackles the problem of intersectional fairness in machine learning, where biases compound across multiple protected attributes like race and gender, by introducing APFEx, a framework that reduces fairness violations while maintaining competitive accuracy on real-world datasets.
Ensuring fairness in machine learning models is critical, especially when biases compound across intersecting protected attributes like race, gender, and age. While existing methods address fairness for single attributes, they fail to capture the nuanced, multiplicative biases faced by intersectional subgroups. We introduce Adaptive Pareto Front Explorer (APFEx), the first framework to explicitly model intersectional fairness as a joint optimization problem over the Cartesian product of sensitive attributes. APFEx combines three key innovations- (1) an adaptive multi-objective optimizer that dynamically switches between Pareto cone projection, gradient weighting, and exploration strategies to navigate fairness-accuracy trade-offs, (2) differentiable intersectional fairness metrics enabling gradient-based optimization of non-smooth subgroup disparities, and (3) theoretical guarantees of convergence to Pareto-optimal solutions. Experiments on four real-world datasets demonstrate APFEx's superiority, reducing fairness violations while maintaining competitive accuracy. Our work bridges a critical gap in fair ML, providing a scalable, model-agnostic solution for intersectional fairness.