On the Fairness Impacts of Private Ensembles Models
It addresses fairness issues in privacy-preserving machine learning, which is crucial for ensuring equitable outcomes in sensitive applications, though it is incremental as it builds on existing PATE methods.
This paper investigates whether the Private Aggregation of Teacher Ensembles (PATE) framework, used for creating differentially private models, can cause unfairness, and finds that it leads to accuracy disparities among different groups of individuals.
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