GetFair: Generalized Fairness Tuning of Classification Models
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We present GetFair, a novel framework for tuning fairness of classification models. The fair classification problem deals with training models for a given classification task where data points have sensitive attributes. The goal of fair classification models is to not only generate accurate classification results but also to prevent discrimination against subpopulations (i.e., individuals with a specific value for the sensitive attribute). Existing methods for enhancing fairness of classification models, however, are often specifically designed for a particular fairness metric or a classifier model. They may also not be suitable for scenarios with incomplete training data or where optimizing for multiple fairness metrics is important. GetFair represents a general solution to this problem. The GetFair approach works in the following way: First, a given classifier is trained on training data without any fairness objective. This is followed by a reinforcement learning inspired tuning procedure which updates the parameters of the learned model on a given fairness objective. This disentangles classifier training from fairness tuning, making our framework more general and allowing for the adoption of any parameterized classifier model. Because fairness metrics are designed as reward functions during tuning, GetFair generalizes across any fairness metric. We demonstrate the generalizability of GetFair via evaluation over a benchmark suite of datasets, classification models, and fairness metrics. In addition, GetFair can also be deployed in settings where the training data is incomplete or the classifier needs to be tuned on multiple fairness metrics. GetFair not only contributes a flexible method to the repertoire of tools available to improve the fairness of classification models, it also seamlessly adapts to settings where existing fair classification methods may not be suitable or applicable.
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