  The Private Aggregation of Teacher Ensembles (PATE) is a machine learning
framework that enables the creation of private models through the combination
of multiple "teacher" models and a "student" model. The student model learns to
predict an output based on the voting of the teachers, and the resulting model
satisfies differential privacy. PATE has been shown to be effective in creating
private models in semi-supervised settings or when protecting data labels is a
priority. This paper explores whether the use of PATE can result in unfairness,
and demonstrates that it can lead to accuracy disparities among groups of
individuals. The paper also analyzes the algorithmic and data properties that
contribute to these disproportionate impacts, why these aspects are affecting
different groups disproportionately, and offers recommendations for mitigating
these effects
