{"type":"ScholarlyArticle","title":"Decision Making with Differential Privacy under a Fairness Lens","authors":[{"name":"Cuong Tran","slug":"cuong-tran","given":"Cuong","family":"Tran","affiliation":""},{"name":"Ferdinando Fioretto","slug":"ferdinando-fioretto","given":"Ferdinando","family":"Fioretto","affiliation":""},{"name":"Pascal Van Hentenryck","slug":"pascal-van-hentenryck","given":"Pascal","family":"Van Hentenryck","affiliation":""},{"name":"Zhiyan Yao","slug":"zhiyan-yao","given":"Zhiyan","family":"Yao","affiliation":""}],"abstract":"Many agencies release datasets and statistics about groups of individuals that are used as input to a number of critical decision processes. To conform with privacy and confidentiality requirements, these agencies are often required to release privacy-preserving versions of the data. This paper studies the release of differentially private datasets and analyzes their impact on some critical resource allocation tasks under a fairness perspective.  The paper shows that, when the decisions take as input differentially private data, the noise added to achieve privacy disproportionately impacts some groups over others. The paper analyzes the reasons for these disproportionate impacts and proposes guidelines to mitigate these effects. The proposed approaches are evaluated on critical decision problems that use differentially private census data.","doi":"10.24963/ijcai.2021/78","url":"https://ijcai.org/proceedings/2021/78","pdf":"https://ijcai.org/proceedings/2021/0078.pdf","markdown":"https://ijcai.org/proceedings/2021/78.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/78","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"AI Ethics, Trust, Fairness","track":"Main Track","number":78,"pages":{"first":560,"last":566},"keywords":["Machine Learning: Classification","Data Mining: Privacy Preserving Data Mining","Multidisciplinary Topics and Applications: Security and Privacy","AI Ethics, Trust, Fairness: Fairness"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}