OxonFair: A Flexible Toolkit for Algorithmic Fairness
Abstract
We present OxonFair, a new open source toolkit for enforcing fairness in binary classification. Compared to existing toolkits: (i) We support NLP and Computer Vision classification as well as standard tabular problems. (ii) We support enforcing fairness on validation data, making us robust to a wide range of overfitting challenges. (iii) Our approach can optimize any measure based on True Positives, False Positive, False Negatives, and True Negatives. This makes it easily extensible and much more expressive than existing toolkits. It supports all 9 and all 10 of the decision-based group metrics of two popular review articles. (iv) We jointly optimize a performance objective alongside fairness constraints. This minimizes degradation while enforcing fairness, and even improves the performance of inadequately tuned unfair baselines. OxonFair is compatible with standard ML toolkits, including sklearn, Autogluon, and PyTorch and is available at https://github.com/oxfordinternetinstitute/oxonfair
Cite
@article{arxiv.2407.13710,
title = {OxonFair: A Flexible Toolkit for Algorithmic Fairness},
author = {Eoin Delaney and Zihao Fu and Sandra Wachter and Brent Mittelstadt and Chris Russell},
journal= {arXiv preprint arXiv:2407.13710},
year = {2024}
}
Comments
Accepted at NeurIPS 2024