  We consider the problem of improving fairness when one lacks access to a
dataset labeled with protected groups, making it difficult to take advantage of
strategies that can improve fairness but require protected group labels, either
at training or runtime. To address this, we investigate improving fairness
metrics for proxy groups, and test whether doing so results in improved
fairness for the true sensitive groups. Results on benchmark and real-world
datasets demonstrate that such a proxy fairness strategy can work well in
practice. However, we caution that the effectiveness likely depends on the
choice of fairness metric, as well as how aligned the proxy groups are with the
true protected groups in terms of the constrained model parameters.
