Model Multiplicity: Opportunities, Concerns, and Solutions

Emily Black, Manish Raghavan, Solon Barocas

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

    Abstract

    Recent scholarship has brought attention to the fact that there often exist multiple models for a given prediction task with equal accuracy that differ in their individual-level predictions or aggregate properties. This phenomenon - which we call model multiplicity - can introduce a good deal of flexibility into the model selection process, creating a range of exciting opportunities. By demonstrating that there are many different ways of making equally accurate predictions, multiplicity gives model developers the freedom to prioritize other values in their model selection process without having to abandon their commitment to maximizing accuracy. However, multiplicity also brings to light a concerning truth: model selection on the basis of accuracy alone - the default procedure in many deployment scenarios - fails to consider what might be meaningful differences between equally accurate models with respect to other criteria such as fairness, robustness, and interpretability. Unless these criteria are taken into account explicitly, developers might end up making unnecessary trade-offs or could even mask intentional discrimination.

    Original languageEnglish (US)
    Title of host publicationProceedings of 2022 5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022
    PublisherAssociation for Computing Machinery
    Pages850-863
    Number of pages14
    ISBN (Electronic)9781450393522
    DOIs
    StatePublished - Jun 21 2022
    Event5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022 - Virtual, Online, Korea, Republic of
    Duration: Jun 21 2022Jun 24 2022

    Publication series

    NameACM International Conference Proceeding Series

    Conference

    Conference5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022
    Country/TerritoryKorea, Republic of
    CityVirtual, Online
    Period6/21/226/24/22

    Keywords

    • arbitrariness
    • discrimination
    • fairness
    • Model multiplicity
    • predictive multiplicity
    • procedural multiplicity
    • recourse

    ASJC Scopus subject areas

    • Software
    • Human-Computer Interaction
    • Computer Vision and Pattern Recognition
    • Computer Networks and Communications

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