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.

翻译：部署机器学习模型通常需要同时保证公平性和隐私性。这两个目标都与模型的效用（如准确性）存在独特的权衡。然而，公平性、隐私性和效用之间的相互影响尚不明确。因此，实践中通常只优化其中一个目标，而将其他目标作为超参数进行调节。由于这种设计隐式地优先考虑了某些目标，会导致模型以有害且难以察觉的方式产生偏差。为解决这一问题，我们采用"公正性"原则：机器学习流程的设计不应偏袒任何特定目标。我们提出公正性指定模型，该模型能够提供精确的帕累托前沿，揭示各目标间的固有权衡。通过扩展两种经典的隐私保护学习框架，我们提出两种方法（FairDP-SGD和FairPATE）来训练公正性指定模型并恢复帕累托前沿。结合理论隐私分析与全面的实证研究，我们回答了"在隐私感知的机器学习流程中应在何处整合公平性缓解措施"这一关键问题。