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Fair machine learning has become a significant research topic with broadsocietal impact. However, most fair learning methods require direct access topersonal demographic data, which is increasingly restricted to use forprotecting user privacy (e.g. by the EU General Data Protection Regulation). Inthis paper, we propose a distributed fair learning framework for protecting theprivacy of demographic data. We assume this data is privately held by a thirdparty, which can communicate with the data center (responsible for modeldevelopment) without revealing the demographic information. We propose aprincipled approach to design fair learning methods under this framework,exemplify four methods and show they consistently outperform their existingcounterparts in both fairness and accuracy across three real-world data sets.We theoretically analyze the framework, and prove it can learn models with highfairness or high accuracy, with their trade-offs balanced by a thresholdvariable.

A Distributed Fair Machine Learning Framework with Private Demographic Data Protection