Causal intersectionality and fair ranking

Ke Yang, Joshua R. Loftus, Julia Stoyanovich

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


    In this paper we propose a causal modeling approach to intersectional fairness, and a flexible, task-specific method for computing intersectionally fair rankings. Rankings are used in many contexts, ranging from Web search to college admissions, but causal inference for fair rankings has received limited attention. Additionally, the growing literature on causal fairness has directed little attention to intersectionality. By bringing these issues together in a formal causal framework we make the application of intersectionality in algorithmic fairness explicit, connected to important real world effects and domain knowledge, and transparent about technical limitations. We experimentally evaluate our approach on real and synthetic datasets, exploring its behavior under different structural assumptions.

    Original languageEnglish (US)
    Title of host publication2nd Symposium on Foundations of Responsible Computing, FORC 2021
    EditorsKatrina Ligett, Swati Gupta
    PublisherSchloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
    ISBN (Electronic)9783959771870
    StatePublished - Jun 1 2021
    Event2nd Symposium on Foundations of Responsible Computing, FORC 2021 - Virtual, Online
    Duration: Jun 9 2021Jun 11 2021

    Publication series

    NameLeibniz International Proceedings in Informatics, LIPIcs
    ISSN (Print)1868-8969


    Conference2nd Symposium on Foundations of Responsible Computing, FORC 2021
    CityVirtual, Online


    • Causality
    • Fairness
    • Intersectionality
    • Ranking

    ASJC Scopus subject areas

    • Software


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