On the Fairness Impacts of Private Ensembles Models

Research output: Chapter in Book/Entry/PoemConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023
EditorsEdith Elkind
PublisherInternational Joint Conferences on Artificial Intelligence
Pages510-518
Number of pages9
ISBN (Electronic)9781956792034
StatePublished - 2023
Externally publishedYes
Event32nd International Joint Conference on Artificial Intelligence, IJCAI 2023 - Macao, China
Duration: Aug 19 2023Aug 25 2023

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
Volume2023-August
ISSN (Print)1045-0823

Conference

Conference32nd International Joint Conference on Artificial Intelligence, IJCAI 2023
Country/TerritoryChina
CityMacao
Period8/19/238/25/23

ASJC Scopus subject areas

  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'On the Fairness Impacts of Private Ensembles Models'. Together they form a unique fingerprint.

Cite this