Searching for Fairer Machine Learning Ensembles
Published in AutoML, 2023
Recommended citation: Feffer, M., Hirzel, M., Hoffman, S. C., Kate, K., Ram, P., & Shinnar, A. (2023). Searching for Fairer Machine Learning Ensembles. AutoML (2023) https://openreview.net/forum?id=7Nbd1Ru1M_t
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 extended the open-source library Lale to enable the modular composition of 8 mitigators, 4 ensembles, and their corresponding hyperparameters, and we empirically explored the space of configurations on 13 datasets.
Recommended citation: Feffer, M., Hirzel, M., Hoffman, S. C., Kate, K., Ram, P., & Shinnar, A. (2023). Searching for Fairer Machine Learning Ensembles. AutoML (2023)