  We show how to take a regression function $\hat{f}$ that is appropriately
``multicalibrated'' and efficiently post-process it into an approximately error
minimizing classifier satisfying a large variety of fairness constraints. The
post-processing requires no labeled data, and only a modest amount of unlabeled
data and computation. The computational and sample complexity requirements of
computing $\hat f$ are comparable to the requirements for solving a single fair
learning task optimally, but it can in fact be used to solve many different
downstream fairness-constrained learning problems efficiently. Our
post-processing method easily handles intersecting groups, generalizing prior
work on post-processing regression functions to satisfy fairness constraints
that only applied to disjoint groups. Our work extends recent work showing that
multicalibrated regression functions are ``omnipredictors'' (i.e. can be
post-processed to optimally solve unconstrained ERM problems) to constrained
optimization.
