A Flexible Fairness Framework with Surrogate Loss Reweighting for Addressing Sociodemographic Disparities

This paper presents a new algorithmic fairness framework called boldsymbolα-boldsymbolβ Fair Machine Learning (boldsymbolα-boldsymbolβ FML), designed to optimize fairness levels across sociodemographic attributes. Our framework employs a new family of surrogate loss functions, paired with loss reweighting techniques, allowing precise control over fairness-accuracy trade-offs through tunable hyperparameters boldsymbolα and boldsymbolβ. To efficiently solve the learning objective, we propose Parallel Stochastic Gradient Descent with Surrogate Loss (P-SGD-S) and establish convergence guarantees for both convex and nonconvex loss functions. Experimental results demonstrate that our framework improves overall accuracy while reducing fairness violations, offering a smooth trade-off between standard empirical risk minimization and strict minimax fairness. Results across multiple datasets confirm its adaptability, ensuring fairness improvements without excessive performance degradation.