Optimizing and Tuning Fairness in Machine Learning: An Augmented Lagrangian Method with a Performance Budget
Abstract
Fairness in Machine Learning has become a concern, particularly if models are deployed in high-stakes decision-making. Most existing approaches aim to enforce fairness during training, but they face significant challenges for the scalability and the effectiveness of fairness enforcement. To address these limitations, we propose a method for training fair classifiers under multiple group and intersectional fairness constraints with high predictive performance. We combine an Augmented Lagrangian learning procedure with a tunable performance budget , which regulates the trade-off between fairness and utility. Experiments demonstrate that our method mitigates bias while scaling efficiently with increasing problem complexity. By adjusting the performance budget, we provide a flexible mechanism to balance fairness enforcement and predictive performance, offering a solution for real-world applications.
Cite
Text
Fontana et al. "Optimizing and Tuning Fairness in Machine Learning: An Augmented Lagrangian Method with a Performance Budget." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2025. doi:10.1007/978-3-032-05962-8_13Markdown
[Fontana et al. "Optimizing and Tuning Fairness in Machine Learning: An Augmented Lagrangian Method with a Performance Budget." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2025.](https://mlanthology.org/ecmlpkdd/2025/fontana2025ecmlpkdd-optimizing/) doi:10.1007/978-3-032-05962-8_13BibTeX
@inproceedings{fontana2025ecmlpkdd-optimizing,
title = {{Optimizing and Tuning Fairness in Machine Learning: An Augmented Lagrangian Method with a Performance Budget}},
author = {Fontana, Michele and Naretto, Francesca and Monreale, Anna},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2025},
pages = {213-230},
doi = {10.1007/978-3-032-05962-8_13},
url = {https://mlanthology.org/ecmlpkdd/2025/fontana2025ecmlpkdd-optimizing/}
}