Optimal Group Fair Classifiers from Linear Post-Processing
<|reference_start|>Optimal Group Fair Classifiers from Linear Post-Processing: We propose a post-processing algorithm for fair classification that mitigates model bias under a unified family of group fairness criteria covering statistical parity, equal opportunity, and equalized odds, applicable to multi-class problems and both attribute-aware and attribute-blind settings. It achieves fairness by re-calibrating the output score of the given base model with a "fairness cost" -- a linear combination of the (predicted) group memberships. Our algorithm is based on a representation result showing that the optimal fair classifier can be expressed as a linear post-processing of the loss function and the group predictor, derived via using these as sufficient statistics to reformulate the fair classification problem as a linear program. The parameters of the post-processor are estimated by solving the empirical LP. Experiments on benchmark datasets show the efficiency and effectiveness of our algorithm at reducing disparity compared to existing algorithms, including in-processing, especially on larger problems.<|reference_end|>
@article{xian2024optimal,
    title={Optimal Group Fair Classifiers from Linear Post-Processing},
    author={Ruicheng Xian, Han Zhao},
    journal={arXiv preprint arXiv:2405.04025},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.04025},
    primaryClass={cs.LG cs.CY}
}