提出新方法实现公平性与准确率高效可控，无需重训练。
Fair Classification with Efficient and Post-hoc Controllable Fairness-Accuracy Trade-off
- 用梯度优化学习有效特征表示，提升后处理公平模型效率。
- 在真实数据集上达到与训练时方法相当的公平-准确权衡效果。
- 可后验调节公平性，适合需要灵活调整的部署场景。
后处理可调性指在训练后控制公平性与准确率之间的权衡，对实际部署至关重要。现有后处理方法虽具可调性但常导致显著准确率下降，而训练内方法虽高效却需每次调整都重新训练。为兼顾二者优势，本文提出一种新型公平分类算法，通过梯度优化方法学习有效特征表示，提升后处理公平分类器的权衡效率。实验结果表明，该方法在真实数据集上实现了与训练内方法相当甚至更优的权衡效率，且无需任何重训练。
原文摘要 · Abstract (English)
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.
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