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

翻译：私有教师集成聚合（PATE）是一种机器学习框架，通过结合多个“教师”模型和“学生”模型来创建具有隐私保护能力的模型。学生模型基于教师投票结果学习输出预测，且最终模型满足差分隐私要求。PATE已被证明在半监督场景或需要优先保护数据标签时能有效构建私有模型。本文探究使用PATE是否会导致不公平性，并证明其可能引发不同群体间的准确率差异。论文进一步分析了导致这些不成比例影响的算法与数据特性、相关因素为何对不同群体造成差异化影响，并提出了缓解这些效应的建议。