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The Private Aggregation of Teacher Ensembles (PATE) is a machine learningframework that enables the creation of private models through the combinationof multiple "teacher" models and a "student" model. The student model learns topredict an output based on the voting of the teachers, and the resulting modelsatisfies differential privacy. PATE has been shown to be effective in creatingprivate models in semi-supervised settings or when protecting data labels is apriority. This paper explores whether the use of PATE can result in unfairness,and demonstrates that it can lead to accuracy disparities among groups ofindividuals. The paper also analyzes the algorithmic and data properties thatcontribute to these disproportionate impacts, why these aspects are affectingdifferent groups disproportionately, and offers recommendations for mitigatingthese effects

On the Fairness Impacts of Private Ensembles Models