Multicalibrated Regression for Downstream Fairness
Ira Globus-Harris, Varun Gupta, Christopher Jung, Michael J. Kearns, Jamie Morgenstern, Aaron Roth · 2023
We show how to take a regression function 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 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 problems.