{"type":"ScholarlyArticle","title":"Learning to Design Fair and Private Voting Rules (Extended Abstract)","authors":[{"name":"Farhad Mohsin","slug":"farhad-mohsin","given":"Farhad","family":"Mohsin","affiliation":"Rensselaer Polytechnic Institute"},{"name":"Ao Liu","slug":"ao-liu","given":"Ao","family":"Liu","affiliation":"Rensselaer Polytechnic Institute"},{"name":"Pin-Yu Chen","slug":"pin-yu-chen","given":"Pin-Yu","family":"Chen","affiliation":"IBM Research"},{"name":"Francesca Rossi","slug":"francesca-rossi","given":"Francesca","family":"Rossi","affiliation":"IBM Research"},{"name":"Lirong Xia","slug":"lirong-xia","given":"Lirong","family":"Xia","affiliation":"Rensselaer Polytechnic Institute"}],"abstract":"Voting is used widely to aggregate preferences to make a collective decision. In this paper, we focus on evaluating and designing voting rules that support both the privacy of the voting agents and a notion of fairness over such agents. First, we introduce a novel notion of group fairness and adopt the existing notion of local differential privacy. We then evaluate the level of group fairness in several existing voting rules, as well as the trade-offs between fairness and privacy, showing that it is not possible to always obtain maximal economic efficiency with high fairness. \r\nThen, we present both a machine learning and a constrained optimization approach to design new voting rules that are fair while maintaining a high level of economic efficiency. Finally, we empirically examine the effect of adding noise to create local differentially private voting rules and discuss the three-way trade-off between economic efficiency, fairness, and privacy.","doi":"10.24963/ijcai.2023/785","url":"https://ijcai.org/proceedings/2023/785","pdf":"https://ijcai.org/proceedings/2023/0785.pdf","markdown":"https://ijcai.org/proceedings/2023/785.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/785","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Journal Track","track":"Journal Track","number":785,"pages":{"first":6931,"last":6936},"keywords":["AI Ethics, Trust, Fairness: ETF: Fairness and diversity","Multidisciplinary Topics and Applications: MDA: Security and privacy","Game Theory and Economic Paradigms: GTEP: Computational social choice"],"videos":[],"references":[{"position":1,"raw":"[Anil and Bao, 2021] Cem Anil and Xuchan Bao. 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