{"type":"ScholarlyArticle","title":"Multiwinner Voting with Fairness Constraints","authors":[{"name":"L. Elisa Celis","slug":"l-elisa-celis","given":"L. Elisa","family":"Celis","affiliation":"EPFL , Switzerland"},{"name":"Lingxiao Huang","slug":"lingxiao-huang","given":"Lingxiao","family":"Huang","affiliation":"EPFL , Switzerland"},{"name":"Nisheeth K. Vishnoi","slug":"nisheeth-k-vishnoi","given":"Nisheeth K.","family":"Vishnoi","affiliation":"EPFL , Switzerland"}],"abstract":"Multiwinner voting rules are used to select a small representative subset of candidates or items from a larger set given the preferences of voters. However, if candidates have sensitive attributes such as gender or ethnicity  (when selecting a committee), or specified types such as political leaning (when selecting a subset of news items), an algorithm that chooses a subset by optimizing a multiwinner voting rule may be unbalanced in its selection -- it may under or over represent a particular gender or political orientation in the examples above. We introduce an algorithmic framework for multiwinner voting problems when there is an additional requirement that the selected subset should be ``fair'' with respect to a given set of attributes. Our framework provides the flexibility to (1) specify fairness with respect to multiple, non-disjoint attributes (e.g., ethnicity and gender) and (2) specify a score function. We study the computational complexity of this constrained multiwinner voting problem for monotone and submodular score functions and present several approximation algorithms and matching hardness of approximation results for various attribute group structure and types of score functions. We also present simulations that suggest that adding fairness constraints may not affect the scores significantly when compared to the unconstrained case.","doi":"10.24963/ijcai.2018/20","url":"https://ijcai.org/proceedings/2018/20","pdf":"https://ijcai.org/proceedings/2018/0020.pdf","markdown":"https://ijcai.org/proceedings/2018/20.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/20","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Agent-based and Multi-agent Systems","track":"Main track","number":20,"pages":{"first":144,"last":151},"keywords":["Agent-based and Multi-agent Systems: Computational Social Choice","Humans and AI: Ethical Issues in AI","Agent-based and Multi-agent Systems: Voting"],"videos":[],"references":[{"position":1,"raw":"[Alimonti and Kann, 1997] Paola Alimonti and Viggo Kann. 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