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

Maaya Sakata, Kazuto Fukuchi
Proceedings of the 43rd International Conference on Machine Learning, PMLR 306:107267-107293, 2026.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v306-sakata26a, title = {Fair Classification with Efficient and Post-hoc Controllable Fairness-Accuracy Trade-off}, author = {Sakata, Maaya and Fukuchi, Kazuto}, booktitle = {Proceedings of the 43rd International Conference on Machine Learning}, pages = {107267--107293}, year = {2026}, editor = {Zhang, Tong and Dudik, Miroslav and Jaggi, Martin and Agarwal, Alekh and Li, Sharon and Schuurmans, Dale and Zhu, Jerry and Berkenkamp, Felix and Dong, Hanze and Bietti, Alberto}, volume = {306}, series = {Proceedings of Machine Learning Research}, month = {06--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v306/main/assets/sakata26a/sakata26a.pdf}, url = {https://proceedings.mlr.press/v306/sakata26a.html}, 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.} }
Endnote
%0 Conference Paper %T Fair Classification with Efficient and Post-hoc Controllable Fairness-Accuracy Trade-off %A Maaya Sakata %A Kazuto Fukuchi %B Proceedings of the 43rd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2026 %E Tong Zhang %E Miroslav Dudik %E Martin Jaggi %E Alekh Agarwal %E Sharon Li %E Dale Schuurmans %E Jerry Zhu %E Felix Berkenkamp %E Hanze Dong %E Alberto Bietti %F pmlr-v306-sakata26a %I PMLR %P 107267--107293 %U https://proceedings.mlr.press/v306/sakata26a.html %V 306 %X 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.
APA
Sakata, M. & Fukuchi, K.. (2026). Fair Classification with Efficient and Post-hoc Controllable Fairness-Accuracy Trade-off. Proceedings of the 43rd International Conference on Machine Learning, in Proceedings of Machine Learning Research 306:107267-107293 Available from https://proceedings.mlr.press/v306/sakata26a.html.

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