Balanced ranking with diversity constraints

Ke Yang, Vasilis Gkatzelis, Julia Stoyanovich

    Research output: Chapter in Book/Report/Conference proceedingConference contribution


    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 overall 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.

    Original languageEnglish (US)
    Title of host publicationProceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019
    EditorsSarit Kraus
    PublisherInternational Joint Conferences on Artificial Intelligence
    Number of pages8
    ISBN (Electronic)9780999241141
    StatePublished - 2019
    Event28th International Joint Conference on Artificial Intelligence, IJCAI 2019 - Macao, China
    Duration: Aug 10 2019Aug 16 2019

    Publication series

    NameIJCAI International Joint Conference on Artificial Intelligence
    ISSN (Print)1045-0823


    Conference28th International Joint Conference on Artificial Intelligence, IJCAI 2019

    ASJC Scopus subject areas

    • Artificial Intelligence


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