A Fairness Analysis on Private Aggregation of Teacher Ensembles

TL;DR

This paper investigates whether the PATE framework for private machine learning introduces bias or unfairness, analyzing causes and proposing mitigation strategies to ensure equitable outcomes across groups.

Contribution

It provides the first comprehensive analysis of bias in PATE, identifying key factors and offering guidelines to reduce fairness disparities in private ensemble learning.

Findings

PATE can cause accuracy disparities among different groups.

Certain algorithmic and data properties influence bias in PATE.

Mitigation guidelines can reduce unfairness in private ensemble models.

Abstract

The Private Aggregation of Teacher Ensembles (PATE) is an important private machine learning framework. It combines multiple learning models used as teachers for a student model that learns to predict an output chosen by noisy voting among the teachers. The resulting model satisfies differential privacy and has been shown effective in learning high-quality private models in semisupervised settings or when one wishes to protect the data labels. This paper asks whether this privacy-preserving framework introduces or exacerbates bias and unfairness and shows that PATE can introduce accuracy disparity among individuals and groups of individuals. The paper analyzes which algorithmic and data properties are responsible for the disproportionate impacts, why these aspects are affecting different groups disproportionately, and proposes guidelines to mitigate these effects. The proposed approach is evaluated on several datasets and settings.