Explainable post-training bias mitigation with distribution-based fairness metrics

• URL: http://arxiv.org/abs/2504.01223v1
• Date: Tue, 01 Apr 2025 22:22:25 GMT
• Title: Explainable post-training bias mitigation with distribution-based fairness metrics
• Authors: Ryan Franks, Alexey Miroshnikov,
• Abstract summary: We develop a novel optimization framework with distribution-based fairness constraints for producing demographically blind, explainable models.<n>Our framework, which is based on gradient descent, can be applied to a wide range of model types.<n>We design a broad class of interpretable global bias metrics compatible with our method by building on previous work.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We develop a novel optimization framework with distribution-based fairness constraints for efficiently producing demographically blind, explainable models across a wide range of fairness levels. This is accomplished through post-processing, avoiding the need for retraining. Our framework, which is based on stochastic gradient descent, can be applied to a wide range of model types, with a particular emphasis on the post-processing of gradient-boosted decision trees. Additionally, we design a broad class of interpretable global bias metrics compatible with our method by building on previous work. We empirically test our methodology on a variety of datasets and compare it to other methods.

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