  Despite achieving promising fairness-error trade-offs, in-processing
mitigation techniques for group fairness cannot be employed in numerous
practical applications with limited computation resources or no access to the
training pipeline of the prediction model. In these situations, post-processing
is a viable alternative. However, current methods are tailored to specific
problem settings and fairness definitions and hence, are not as broadly
applicable as in-processing. In this work, we propose a framework that turns
any regularized in-processing method into a post-processing approach. This
procedure prescribes a way to obtain post-processing techniques for a much
broader range of problem settings than the prior post-processing literature. We
show theoretically and through extensive experiments that our framework
preserves the good fairness-error trade-offs achieved with in-processing and
can improve over the effectiveness of prior post-processing methods. Finally,
we demonstrate several advantages of a modular mitigation strategy that
disentangles the training of the prediction model from the fairness mitigation,
including better performance on tasks with partial group labels.
