  Making predictions that are fair with regard to protected group membership
(race, gender, age, etc.) has become an important requirement for
classification algorithms. Existing techniques derive a fair model from sampled
labeled data relying on the assumption that training and testing data are
identically and independently drawn (iid) from the same distribution. In
practice, distribution shift can and does occur between training and testing
datasets as the characteristics of individuals interacting with the machine
learning system change. We investigate fairness under covariate shift, a
relaxation of the iid assumption in which the inputs or covariates change while
the conditional label distribution remains the same. We seek fair decisions
under these assumptions on target data with unknown labels. We propose an
approach that obtains the predictor that is robust to the worst-case in terms
of target performance while satisfying target fairness requirements and
matching statistical properties of the source data. We demonstrate the benefits
of our approach on benchmark prediction tasks.
