IDEAS home Printed from https://ideas.repec.org/p/osf/osfxxx/h6a7c_v1.html
   My bibliography  Save this paper

Global Evidence on Gender Gaps and Generative AI

Author

Listed:
  • Otis, Nicholas G.
  • Cranney, Katelyn
  • Delecourt, Solene
  • Koning, Rembrand

    (Harvard Business School)

Abstract

Generative AI has the potential to transform productivity and reduce inequality, but only if adopted broadly. In this paper, we show that recently identified gender gaps in generative AI use are nearly universal. Synthesizing data from 18 studies covering more than 140,000 individuals across the world, combined with estimates of the gender share of the hundreds of millions of users of popular generative AI platforms, we demonstrate that the gender gap in generative AI usage holds across nearly all regions, sectors, and occupations. Using newly collected data, we also document that this gap remains even when access to try this new technology is improved, highlighting the need for further research into the gap’s underlying causes. If this global disparity persists, it risks creating a self-reinforcing cycle: women’s underrepresentation in generative AI usage would lead to systems trained on data that inadequately sample women’s preferences and needs, ultimately widening existing gender disparities in technology adoption and economic opportunity.

Suggested Citation

  • Otis, Nicholas G. & Cranney, Katelyn & Delecourt, Solene & Koning, Rembrand, 2024. "Global Evidence on Gender Gaps and Generative AI," OSF Preprints h6a7c_v1, Center for Open Science.
  • Handle: RePEc:osf:osfxxx:h6a7c_v1
    DOI: 10.31219/osf.io/h6a7c_v1
    as

    Download full text from publisher

    File URL: https://osf.io/download/6709bba1834fc0279ca5e186/
    Download Restriction: no

    File URL: https://libkey.io/10.31219/osf.io/h6a7c_v1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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:osf:osfxxx:h6a7c_v1. 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: OSF (email available below). General contact details of provider: https://osf.io/preprints/ .

    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.