OxonFair: A Flexible Toolkit for Algorithmic Fairness
This toolkit addresses fairness enforcement for researchers and practitioners in machine learning, offering a more expressive and robust solution compared to existing tools, though it is incremental in extending support and optimization methods.
The authors tackled the problem of algorithmic fairness in binary classification by introducing OxonFair, a flexible toolkit that supports NLP, computer vision, and tabular data, enforces fairness on validation data, and jointly optimizes performance and fairness constraints, resulting in minimized degradation and improved performance for inadequately tuned baselines.
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