Optimal Group Fair Classifiers from Linear Post-Processing

• URL: http://arxiv.org/abs/2405.04025v1
• Date: Tue, 7 May 2024 05:58:44 GMT
• Title: Optimal Group Fair Classifiers from Linear Post-Processing
• Authors: Ruicheng Xian, Han Zhao,
• Abstract summary: We propose a post-processing algorithm for fair classification that mitigates model bias under a unified family of group fairness criteria. 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.
• Score: 10.615965454674901
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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