{"type":"ScholarlyArticle","title":"Balanced Ranking with Diversity Constraints","authors":[{"name":"Ke Yang","slug":"ke-yang","given":"Ke","family":"Yang","affiliation":"New York University , Department of Computer Science and Engineering"},{"name":"Vasilis Gkatzelis","slug":"vasilis-gkatzelis","given":"Vasilis","family":"Gkatzelis","affiliation":"Drexel University , Department of Computer Science"},{"name":"Julia Stoyanovich","slug":"julia-stoyanovich","given":"Julia","family":"Stoyanovich","affiliation":"New York University , Department of Computer Science and Engineering"}],"abstract":"Many set selection and ranking algorithms have recently been enhanced with diversity constraints that aim to explicitly increase representation of historically disadvantaged populations, or to improve the over-all representativeness of the selected set. An unintended consequence of these constraints, however, is reduced in-group fairness: the selected candidates from a given group may not be the best ones, and this unfairness may not be well-balanced across groups. In this paper we study this phenomenon using datasets that comprise multiple sensitive attributes. We then introduce additional constraints, aimed at balancing the in-group fairness across groups, and formalize the induced optimization problems as integer linear programs. Using these programs, we conduct an experimental evaluation with real datasets, and quantify the feasible trade-offs between balance and overall performance in the presence of diversity constraints.","doi":"10.24963/ijcai.2019/836","url":"https://ijcai.org/proceedings/2019/836","pdf":"https://ijcai.org/proceedings/2019/0836.pdf","markdown":"https://ijcai.org/proceedings/2019/836.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/836","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"AI for Improving Human Well-being","track":"AI for Improving Human Well-being","number":836,"pages":{"first":6035,"last":6042},"keywords":["Special Track on AI for Improving Human-Well Being: AI ethics (Special Track on AI and Human Wellbeing)"],"videos":[],"references":[{"position":1,"raw":"[Agrawal et al., 2009] Rakesh Agrawal, Sreenivas Golla- pudi, Alan Halverson, and Samuel Ieong. 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