Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey

Ferdinando Fioretto, Cuong Tran, Pascal Van Hentenryck, Keyu Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper surveys recent work in the intersection of differential privacy (DP) and fairness. It reviews the conditions under which privacy and fairness may have aligned or contrasting goals, analyzes how and why DP may exacerbate bias and unfairness in decision problems and learning tasks, and describes available mitigation measures for the fairness issues arising in DP systems. The survey provides a unified understanding of the main challenges and potential risks arising when deploying privacy-preserving machine-learning or decisions-making tasks under a fairness lens.

Original languageEnglish (US)
Title of host publicationProceedings of the 31st International Joint Conference on Artificial Intelligence, IJCAI 2022
EditorsLuc De Raedt, Luc De Raedt
PublisherInternational Joint Conferences on Artificial Intelligence
Pages5470-5477
Number of pages8
ISBN (Electronic)9781956792003
StatePublished - 2022
Event31st International Joint Conference on Artificial Intelligence, IJCAI 2022 - Vienna, Austria
Duration: Jul 23 2022Jul 29 2022

Publication series

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

Conference

Conference31st International Joint Conference on Artificial Intelligence, IJCAI 2022
Country/TerritoryAustria
CityVienna
Period7/23/227/29/22

ASJC Scopus subject areas

  • Artificial Intelligence

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