Bias mitigators can improve algorithmic fairness in machine learning models, but their effect on fairness is often not stable across data splits. A popular approach to train more stable models is ensemble learning, but unfortunately, it is unclear how to combine ensembles with mitigators to best navigate trade-offs between fairness and predictive performance. To that end, we built an open-source library enabling the modular composition of 8 mitigators, 4 ensembles, and their corresponding hyperparameters, and we empirically explored the space of configurations on 13 datasets. We distilled our insights from this exploration in the form of a guidance diagram for practitioners that we demonstrate is robust and reproducible.
@article{arxiv.2210.05594,
title = {Navigating Ensemble Configurations for Algorithmic Fairness},
author = {Michael Feffer and Martin Hirzel and Samuel C. Hoffman and Kiran Kate and Parikshit Ram and Avraham Shinnar},
journal= {arXiv preprint arXiv:2210.05594},
year = {2022}
}
Comments
arXiv admin note: text overlap with arXiv:2202.00751