OxonFair: A Flexible Toolkit for Algorithmic Fairness

Eoin D. Delaney, Zihao Fu, Sandra Wachter, Brent Mittelstadt, Chris Russell

Keywords: Fairness Toolkit, Algorithmic Fairness, Trustworthy AI

TL;DR: We present a new toolkit for enforcing and measuring fairness with a focus on deep learning models.

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 extendable and much more expressive than existing toolkits. It supports 9/9 and 10/10 of the decision-based group metrics of two popular review papers. (iv) We jointly optimize a performance objective. This not only minimizes degradation while enforcing fairness, but can improve over the performance of inadequately tuned unfair baselines. OxonFair is compatible with standard ML toolkits including sklearn, Autogluon, and PyTorch and is available online.

Submission Number: 76