IDEAS home Printed from https://ideas.repec.org/p/osf/osfxxx/h6a7c.html

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 used broadly. In this paper, we show that recently identified gender gaps in AI use are nearly universal. Synthesizing evidence from 16 studies that surveyed 100,000 individuals across 26 countries, along with new data on the gender of AI platform users, we show that the AI gender gap is present in nearly all regions, sectors, and occupations. Using data from two studies that offered participants the chance to use AI tools, we then show that even when the opportunity for men and women to access AI is equalized, women are still less likely to use AI. Our findings underscore the critical need for targeted interventions that go beyond access to address the structural and behavioral barriers that have resulted in a global gender gap in AI use.

Suggested Citation

  • Otis, Nicholas G. & Cranney, Katelyn & Delecourt, Solene & Koning, Rembrand, 2024. "Global Evidence on Gender Gaps and Generative AI," OSF Preprints h6a7c, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:h6a7c
    DOI: 10.31219/osf.io/h6a7c
    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?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
    ---><---

    References listed on IDEAS

    as
    1. Anja Lambrecht & Catherine Tucker, 2019. "Algorithmic Bias? An Empirical Study of Apparent Gender-Based Discrimination in the Display of STEM Career Ads," Management Science, INFORMS, vol. 65(7), pages 2966-2981, July.
    2. Otis, Nicholas G. & Clarke, Rowan Philip & Delecourt, Solene & Holtz, David & Koning, Rembrand, 2023. "The Uneven Impact of Generative AI on Entrepreneurial Performance," OSF Preprints hdjpk, Center for Open Science.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zara Contractor & Germ'an Reyes, 2025. "Generative AI in Higher Education: Evidence from an Elite College," Papers 2508.00717, arXiv.org.
    2. Avanceña, Mary June G. & Bandong, Rosauro Jr., S. & Carbaquil, Kyla Marie T. & Genesis G. Camarista, 2025. "“AI In Mathematics Education: Level of Awareness and Perceptions on the Usefulness of AI-Driven Learning Tools Among Grade 12 STEM Studentsâ€," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 12(8), pages 1846-1859, August.
    3. Contractor, Zara & Reyes, Germán, 2025. "Generative AI in Higher Education: Evidence from an Elite College," IZA Discussion Papers 18055, Institute of Labor Economics (IZA).
    4. Melanie Arntz & Myriam Baum & Eduard Brüll & Ralf Dorau & Matthias Hartwig & Britta Matthes & Sophie-Charlotte Meyer & Oliver Schlenker & Anita Tisch & Sascha Wischniewski, 2025. "Low Barriers, High Stakes: Formal and Informal Diffusion of AI in the Workplace," ifo Working Paper Series 422, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    5. Zara Contractor & Germán Reyes, 2025. "Generative AI in Higher Education: Evidence from an Elite College," CEDLAS, Working Papers 0359, CEDLAS, Universidad Nacional de La Plata.
    6. Voraprapa Nakavachara, 2025. "AI and Worker Well-Being: Differential Impacts Across Generational Cohorts and Genders," Papers 2511.11021, arXiv.org, revised Feb 2026.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Klockmann, Victor & von Schenk, Alicia & Villeval, Marie Claire, 2022. "Artificial intelligence, ethics, and intergenerational responsibility," Journal of Economic Behavior & Organization, Elsevier, vol. 203(C), pages 284-317.
    2. Sievert, Martin & Vogel, Dominik & Döring, Matthias, 2024. "Gendered Language in Job Advertisements Relates to Gender Sorting in Public Labor Markets: A Multi-Source Analysis," SocArXiv u6z5e, Center for Open Science.
    3. De Bruyn, Arnaud & Viswanathan, Vijay & Beh, Yean Shan & Brock, Jürgen Kai-Uwe & von Wangenheim, Florian, 2020. "Artificial Intelligence and Marketing: Pitfalls and Opportunities," Journal of Interactive Marketing, Elsevier, vol. 51(C), pages 91-105.
    4. Alina Köchling & Marius Claus Wehner, 2020. "Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development," Business Research, Springer;German Academic Association for Business Research, vol. 13(3), pages 795-848, November.
    5. Fu, Xiaolan & Ghauri, Pervez & Lu, Jianan, 2025. "Digital technology and inclusive development during global crisis: Evidence from a randomised experiment in Bangladesh," Research Policy, Elsevier, vol. 54(3).
    6. Nir Chemaya & Daniel Martin, 2024. "Perceptions and detection of AI use in manuscript preparation for academic journals," PLOS ONE, Public Library of Science, vol. 19(7), pages 1-16, July.
    7. Wencheng Lu, 2024. "Inevitable challenges of autonomy: ethical concerns in personalized algorithmic decision-making," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 11(1), pages 1-9, December.
    8. Walkowiak, Emmanuelle, 2021. "Neurodiversity of the workforce and digital transformation: The case of inclusion of autistic workers at the workplace," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
    9. Neil K. R. Sehgal & Dan Svirsky, 2024. "Race Discrimination in Internet Advertising: Evidence From a Field Experiment," Papers 2412.14307, arXiv.org.
    10. Ji Wu & Zhiqiang (Eric) Zheng & J. Leon Zhao, 2021. "FairPlay: Detecting and Deterring Online Customer Misbehavior," Information Systems Research, INFORMS, vol. 32(4), pages 1323-1346, December.
    11. Chenfeng Yan & Quan Chen & Xinyue Zhou & Xin Dai & Zhilin Yang, 2024. "When the Automated fire Backfires: The Adoption of Algorithm-based HR Decision-making Could Induce Consumer’s Unfavorable Ethicality Inferences of the Company," Journal of Business Ethics, Springer, vol. 190(4), pages 841-859, April.
    12. Donati,Dante & Rao,Nandan & Orozco Olvera, Victor Hugo & Munoz Boudet, Ana Maria, 2024. "Can Facebook Ads Prevent Malaria ? Two Field Experiments in India," Policy Research Working Paper Series 10967, The World Bank.
    13. Anderson, Brian S., 2022. "What executives get wrong about statistics: Moving from statistical significance to effect sizes and practical impact," Business Horizons, Elsevier, vol. 65(3), pages 379-388.
    14. Singh, Ashutosh & Sajeesh, S. & Bhardwaj, Pradeep, 2024. "Whitelisting versus advertising-recovery: Strategies to overcome advertising blocking by consumers," European Journal of Operational Research, Elsevier, vol. 318(1), pages 217-229.
    15. Lara Mendes Bacalhau & Miguel Cachulo Pereira & Joana Neves, 2025. "A bibliometric analysis of AI bias in marketing: field evolution and future research agenda," Journal of Marketing Analytics, Palgrave Macmillan, vol. 13(2), pages 308-327, June.
    16. Xiyang Hu & Yan Huang & Beibei Li & Tian Lu, 2022. "Uncovering the Source of Machine Bias," Papers 2201.03092, arXiv.org.
    17. Bouacida, Elias & Foucart, Renaud & Jalloul, Maya, 2025. "When expert advice fails to reduce the productivity gap: Experimental evidence from chess players," Journal of Economic Behavior & Organization, Elsevier, vol. 236(C).
    18. Alison Watts, 2021. "Fairness and Efficiency in Online Advertising Mechanisms," Games, MDPI, vol. 12(2), pages 1-11, April.
    19. Adair Morse & Karen Pence, 2021. "Technological Innovation and Discrimination in Household Finance," Springer Books, in: Raghavendra Rau & Robert Wardrop & Luigi Zingales (ed.), The Palgrave Handbook of Technological Finance, pages 783-808, Springer.
    20. Eleanor W. Dillon & Sonia Jaffe & Nicole Immorlica & Christopher T. Stanton, 2025. "Shifting Work Patterns with Generative AI," NBER Working Papers 33795, National Bureau of Economic Research, Inc.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.