IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2310.04585.html

Interventions Against Machine-Assisted Statistical Discrimination

Author

Listed:
  • John Y. Zhu

Abstract

I study statistical discrimination driven by verifiable beliefs, such as those generated by machine learning, rather than by humans. When beliefs are verifiable, interventions against statistical discrimination can move beyond simple belief-free designs, like affirmative action and blinding, to more sophisticated belief-contingent ones. I analyze a belief-contingent intervention, common identity, and show that it can be more effective at combating statistical discrimination than popular alternatives -- particularly when the training dataset exhibits the kinds of statistical biases that often plague machine-assisted decision problems.

Suggested Citation

  • John Y. Zhu, 2023. "Interventions Against Machine-Assisted Statistical Discrimination," Papers 2310.04585, arXiv.org, revised Jul 2026.
    • Handle: RePEc:arx:papers:2310.04585
    as

    Download full text from publisher

    File URL: https://arxiv.org/pdf/2310.04585
    File Function: Latest version
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2310.04585. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: https://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.