Global Evidence on Gender Gaps and Generative AI

Nicholas G. Otis, Katelyn Cranney, Solene Delecourt and Rembrand Koning
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Rembrand Koning: Harvard Business School

No h6a7c_v1, OSF Preprints from Center for Open Science

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.

Date: 2024-10-14
New Economics Papers: this item is included in nep-ain
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:h6a7c_v1

DOI: 10.31219/osf.io/h6a7c_v1

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