Enhancing Fairness through Reweighting: A Path to Attain the Sufficiency Rule
<|reference_start|>Enhancing Fairness through Reweighting: A Path to Attain the Sufficiency Rule: We introduce an innovative approach to enhancing the empirical risk minimization (ERM) process in model training through a refined reweighting scheme of the training data to enhance fairness. This scheme aims to uphold the sufficiency rule in fairness by ensuring that optimal predictors maintain consistency across diverse sub-groups. We employ a bilevel formulation to address this challenge, wherein we explore sample reweighting strategies. Unlike conventional methods that hinge on model size, our formulation bases generalization complexity on the space of sample weights. We discretize the weights to improve training speed. Empirical validation of our method showcases its effectiveness and robustness, revealing a consistent improvement in the balance between prediction performance and fairness metrics across various experiments.<|reference_end|>
@article{zhao2024enhancing,
    title={Enhancing Fairness through Reweighting: A Path to Attain the Sufficiency
  Rule},
    author={Xuan Zhao and Klaus Broelemann and Salvatore Ruggieri and Gjergji
  Kasneci},
    journal={arXiv preprint arXiv:2408.14126},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.14126},
    primaryClass={cs.LG cs.CY}
}