Fair Classification with Efficient and Post-hoc Controllable Fairness-Accuracy Trade-off
For practitioners deploying fair ML models, this method provides a practical solution that combines the flexibility of post-hoc control with the efficiency of in-processing trade-offs, reducing computational costs.
The paper proposes a fair classification algorithm that learns effective feature representations to achieve post-hoc controllability of the fairness-accuracy trade-off without retraining, matching or surpassing in-processing methods in trade-off efficiency on real-world datasets.
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.