Fairness in network representation by latent structural heterogeneity in observational data

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    Abstract

    While recent advances in machine learning put many focuses on fairness of algorithmic decision making, topics about fairness of representation, especially fairness of network representation, are still underexplored. Network representation learning learns a function mapping nodes to low-dimensional vectors. Structural properties, e.g. communities and roles, are preserved in the latent embedding space. In this paper, we argue that latent structural heterogeneity in the observational data could bias the classical network representation model. The unknown heterogeneous distribution across subgroups raises new challenges for fairness in machine learning. Pre-defined groups with sensitive attributes cannot properly tackle the potential unfairness of network representation. We propose a method which can automatically discover subgroups which are unfairly treated by the network representation model. The fairness measure we propose can evaluate complex targets with multi-degree interactions. We conduct randomly controlled experiments on synthetic datasets and verify our methods on real-world datasets. Both quantitative and quantitative results show that our method is effective to recover the fairness of network representations. Our research draws insight on how structural heterogeneity across subgroups restricted by attributes would affect the fairness of network representation learning.
    Original languageEnglish
    Title of host publicationProceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020)
    Place of PublicationPalo Alto
    PublisherAAI Press
    Pages3809-3816
    Number of pages8
    ISBN (Electronic)9781577358350
    ISBN (Print)978-1-57735-835-0
    DOIs
    Publication statusPublished - 3 Apr 2020
    Event34th AAAI conference on Artificial Intelligence (AAAI2020) - Hilton new York Midtown, New York, United States
    Duration: 7 Feb 202012 Feb 2020
    Conference number: 34
    https://aaai.org/Conferences/AAAI-20/
    https://aaai.org/Conferences/AAAI-20/aaai20call/

    Publication series

    NameProceedings of the AAAI Conference on Artificial Intelligence
    Number4
    Volume34

    Conference

    Conference34th AAAI conference on Artificial Intelligence (AAAI2020)
    Abbreviated titleAAAI2020
    Country/TerritoryUnited States
    CityNew York
    Period7/02/2012/02/20
    Internet address

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