Women and AI: A Large-Scale, Validated Mapping of Women’s Under-Represented and Under-Recognized Contributions to AI Research

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Participation in AI research shapes which type of AI is built, whose societal needs it addresses, and how it is used in society. Despite increasing attention to gender diversity in AI, we lacksystematic evidence on the research topics, methods used, and collaboration structures associated with female AI researchers. This paper addresses this significant gap by developing the first large-scale, validated mapping of women’s contributions to both generative and non-generative AI research across data science and the main social science disciplines (economics, political science, and sociology). Using a sample of over 500,000 papers published between 2023 and 2026, we document three key findings. First, women are under-represented among academic AI authors across all four disciplines and recent years, and least represented in data science. Second, conditional on participating, women’s AI research is thematically distinctive: it is more applied, social and purpose-driven, concentrated in health, education and fairness, and, in data science, oriented towards using rather than building AI. Third, this distinctive work is heard less: conditional on working on the same AI topic women and men are cited equally, but the topics women disproportionately work on are themselves cited less, so women’s ideas receive systematically fewer citations. This voice gap is therefore a topic-composition penalty rather than a same-topic penalty, and it reduces efficiency by giving a distinctive, society-oriented research agenda too little voice in how AI develops.