  Differentially private (DP) synthetic data is a promising approach to
maximizing the utility of data containing sensitive information. Due to the
suppression of underrepresented classes that is often required to achieve
privacy, however, it may be in conflict with fairness. We evaluate four DP
synthesizers and present empirical results indicating that three of these
models frequently degrade fairness outcomes on downstream binary classification
tasks. We draw a connection between fairness and the proportion of minority
groups present in the generated synthetic data, and find that training
synthesizers on data that are pre-processed via a multi-label undersampling
method can promote more fair outcomes without degrading accuracy.
