{"type":"ScholarlyArticle","title":"Individual Fairness under Group Fairness Constraints in Bipartite Matching - One Framework to Approximate Them All","authors":[{"name":"Atasi Panda","slug":"atasi-panda","given":"Atasi","family":"Panda","affiliation":"Indian Institute of Science"},{"name":"Anand Louis","slug":"anand-louis","given":"Anand","family":"Louis","affiliation":"Indian Institute of Science"},{"name":"Prajakta Nimbhorkar","slug":"prajakta-nimbhorkar","given":"Prajakta","family":"Nimbhorkar","affiliation":"Chennai Mathematical Institute"}],"abstract":"We study the probabilistic assignment of items to platforms that satisfies both group and individual fairness constraints. Each item belongs to specific groups and has a preference ordering over platforms. Each platform enforces group fairness by limiting the number of items per group that can be assigned to it. There could be multiple optimal solutions that satisfy the group fairness constraints, but this alone ignores item preferences. Our approach explores a `best of both worlds fairness' solution to get a randomized matching, which is ex-ante individually fair and ex-post group-fair. Thus, we seek a `probabilistic individually fair' distribution over `group-fair' matchings where each item has a `high' probability of matching to one of its top choices. This distribution is also ex-ante group-fair. Users can customize fairness constraints to suit their requirements. Our first result is a polynomial-time algorithm that computes a distribution over `group-fair' matchings such that the individual fairness constraints are approximately satisfied and the expected size of a matching is close to OPT. We empirically test this on real-world datasets. We present two additional polynomial-time bi-criteria approximation algorithms that users can choose from to balance group fairness and individual fairness trade-offs. \r\nFor disjoint groups, we provide an exact polynomial-time algorithm adaptable to additional lower `group fairness' bounds. Extending our model, we encompass `maxmin group fairness,' amplifying underrepresented groups, and `mindom group fairness,' reducing the representation of dominant groups.'","doi":"10.24963/ijcai.2024/20","url":"https://ijcai.org/proceedings/2024/20","pdf":"https://ijcai.org/proceedings/2024/0020.pdf","markdown":"https://ijcai.org/proceedings/2024/20.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/20","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Agent-based and Multi-agent Systems","track":"Main Track","number":20,"pages":{"first":175,"last":183},"keywords":["Agent-based and Multi-agent Systems: MAS: Resource allocation","AI Ethics, Trust, Fairness: ETF: Fairness and diversity"],"videos":[],"references":[{"position":1,"raw":"[Abdulkadiroglu and Sönmez, 2003] Atila Abdulkadiroglu and T. 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