Fair Classification with Efficient and Post-hoc Controllable Fairness-Accuracy Trade-off

2026-06-26

AI Summary

Existing fair machine learning methods struggle to flexibly adjust the trade-off between fairness and accuracy after model training, and retraining incurs substantial computational costs. This work proposes a novel post-processing fair classification algorithm that, for the first time, integrates gradient-based optimization for efficient feature representation learning with post-hoc adjustment mechanisms. The approach enables controllable fairness–accuracy trade-offs without requiring model retraining. Evaluated on multiple real-world datasets, the method consistently matches or significantly outperforms state-of-the-art in-processing techniques, achieving high predictive accuracy while substantially improving fairness. It thus offers a computationally efficient and performance-competitive solution for post-training fairness enhancement.

Abstract

Post-hoc controllability of fair machine learning models, the ability to control the trade-off between fairness and accuracy after training, is valuable for practical deployment. Existing post-processing methods provide such post-hoc controllability but often suffer from significant accuracy degradation, whereas in-processing methods achieve efficient trade-offs but require computationally expensive retraining for each change in trade-off ratio. To achieve both post-hoc controllability and efficient trade-offs, we propose a novel fair classification algorithm that learns effective feature representations to improve the trade-off efficiency of post-processing fair classifiers, by a gradient-based optimization approach. Experimental results on real-world datasets demonstrate that our method achieves trade-off efficiency comparable to, or even surpassing, in-processing methods, without requiring any retraining.

Problem

• fair classification
• post-hoc controllability
• fairness-accuracy trade-off
• machine learning fairness

Innovation

• post-hoc controllability
• fair classification
• fairness-accuracy trade-off
• gradient-based optimization
• feature representation learning