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

• URL: http://arxiv.org/abs/2503.16836v1
• Date: Fri, 21 Mar 2025 04:10:14 GMT
• Title: A Flexible Fairness Framework with Surrogate Loss Reweighting for Addressing Sociodemographic Disparities
• Authors: Wen Xu, Elham Dolatabadi,
• Abstract summary: This paper presents a new algorithmic framework called $boldsymbolalpha$boldbeta$ Fair Machine Learning ($symbolalphasymbolsymbolbetabeta$ FML)<n>Our framework employs a new surrogate loss minimization, paired with loss reweighting, allowing precise accuracy trade-offs through tunable attributes.
• Score: 2.057770398219001
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a new algorithmic fairness framework called $\boldsymbol{\alpha}$-$\boldsymbol{\beta}$ Fair Machine Learning ($\boldsymbol{\alpha}$-$\boldsymbol{\beta}$ 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 $\boldsymbol{\alpha}$ and $\boldsymbol{\beta}$. 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.

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