Disaggregated Interventions to Reduce Inequality

Lucius Bynum, Joshua Loftus, Julia Stoyanovich

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

    Abstract

    A significant body of research in the data sciences considers unfair discrimination against social categories such as race or gender that could occur or be amplified as a result of algorithmic decisions. Simultaneously, real-world disparities continue to exist, even before algorithmic decisions are made. In this work, we draw on insights from the social sciences brought into the realm of causal modeling and constrained optimization, and develop a novel algorithmic framework for tackling pre-existing real-world disparities. The purpose of our framework, which we call the "impact remediation framework,"is to measure real-world disparities and discover the optimal intervention policies that could help improve equity or access to opportunity for those who are underserved with respect to an outcome of interest. We develop a disaggregated approach to tackling pre-existing disparities that relaxes the typical set of assumptions required for the use of social categories in structural causal models. Our approach flexibly incorporates counterfactuals and is compatible with various ontological assumptions about the nature of social categories. We demonstrate impact remediation with a hypothetical case study and compare our disaggregated approach to an existing state-of-the-art approach, comparing its structure and resulting policy recommendations. In contrast to most work on optimal policy learning, we explore disparity reduction itself as an objective, explicitly focusing the power of algorithms on reducing inequality.

    Original languageEnglish (US)
    Title of host publicationProceedings of 2021 ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization, EAAMO 2021
    PublisherAssociation for Computing Machinery
    ISBN (Electronic)9781450385534
    DOIs
    StatePublished - Oct 5 2021
    Event2021 ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization, EAAMO 2021 - Virtual, Online, United States
    Duration: Oct 5 2021Oct 9 2021

    Publication series

    NameACM International Conference Proceeding Series

    Conference

    Conference2021 ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization, EAAMO 2021
    Country/TerritoryUnited States
    CityVirtual, Online
    Period10/5/2110/9/21

    Keywords

    • causal modeling
    • fairness
    • inequality
    • social categories

    ASJC Scopus subject areas

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

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