  Deploying machine learning (ML) models often requires both fairness and
privacy guarantees. Both of these objectives present unique trade-offs with the
utility (e.g., accuracy) of the model. However, the mutual interactions between
fairness, privacy, and utility are less well-understood. As a result, often
only one objective is optimized, while the others are tuned as
hyper-parameters. Because they implicitly prioritize certain objectives, such
designs bias the model in pernicious, undetectable ways. To address this, we
adopt impartiality as a principle: design of ML pipelines should not favor one
objective over another. We propose impartially-specified models, which provide
us with accurate Pareto frontiers that show the inherent trade-offs between the
objectives. Extending two canonical ML frameworks for privacy-preserving
learning, we provide two methods (FairDP-SGD and FairPATE) to train
impartially-specified models and recover the Pareto frontier. Through
theoretical privacy analysis and a comprehensive empirical study, we provide an
answer to the question of where fairness mitigation should be integrated within
a privacy-aware ML pipeline.
