2025
Working Paper
HBS Working Paper Series

Global Evidence on Gender Gaps and Generative AI

By: Nicholas G. Otis, Solène Delecourt, Katelynn Cranney and Rembrand Koning

Format:Electronic
| Language:English
| Pages:30

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 worldwide, 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 equalized, 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.

Keywords

AI and Machine Learning; Gender; Equality and Inequality; Technology Adoption; Behavior

Citation

Otis, Nicholas G., Solène Delecourt, Katelynn Cranney, and Rembrand Koning. "Global Evidence on Gender Gaps and Generative AI." Harvard Business School Working Paper, No. 25-023, October 2024. (Revised January 2025.)