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

Multicalibrated Regression for Downstream Fairness