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Women remain underrepresented in artificial intelligence, especially in some research, software-development and leadership roles. The gap matters not because women share one viewpoint or automatically make AI fairer, but because access to the work also determines who can influence the systems and rules being built. UNESCO and World Economic Forum figures show both persistent disparities and signs of change—but they measure different populations, not one unified global workforce.
How large is the gender gap in AI?
There is no single, standardized global census of everyone who counts as an “AI professional.” UNESCO’s figures offer several views of participation, but each uses a different measure. In particular, a gender gap expressed as distance from parity is not the same thing as women’s share of a workforce.
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| Measure | Reported figure | What it describes |
|---|---|---|
| AI professionals | Women make up 30%, according to a UNESCO leadership article published December 11, 2024, and updated April 17, 2026. | An estimate reported by UNESCO, not a universal census based on one definition of an AI professional. |
| Science R&D and AI research positions | 21% gender gap in science R&D positions; 38% in AI research positions. | UNESCO’s 2024 Women for Ethical AI Outlook Study presents these as gaps from parity, not women’s workforce shares. |
| ICT and software-development professionals | 15% gender gap for ICT professionals; 44% for software-development professionals. | UNESCO’s 2024 Outlook Study compares these occupational indicators; definitions and source populations vary. |
| Leadership roles | 23% gender gap for director positions at STEM workplaces; 32% for C-suite positions at AI startups. | UNESCO’s 2024 Outlook Study reports figures from different contexts. They do not track the same people through a promotion pipeline. |
| AI inventors named on patents | About 37% of AI inventors named on patents filed in 2022–23 were women, according to UNESCO’s 2024 article. | This is a share of named inventors in that patent period—not a claim that women owned 37% of AI patents. |
Taken together, the indicators point to uneven participation across AI-related work and influence. They should not be combined into a single score: being an AI professional, conducting AI research, working in software development, holding an executive job and being named on a patent are distinct measures.
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Is participation changing?
Yes, but progress does not mean parity has been reached. The World Economic Forum’s 2024 report, using LinkedIn member data from 166 economies, says women’s share among AI talent grew over the previous four years while men remained substantially more represented.
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That trend is evidence of change within the population visible on LinkedIn, not a census of workers everywhere. Platform membership and profiles cover only part of the labor market, so the figures should be read as a view of that data source rather than a complete count of global AI talent.
Why does representation matter to AI?
People who enter technical roles, research teams and leadership can help set priorities: which problems get addressed, what risks are tested, how products are deployed and what rules govern them. If access to those roles is unequal, opportunities to shape those decisions may be unequal too. That is a reason to examine who participates—not proof that any group has a single perspective or that a more representative team will automatically produce fair results.
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What model testing can—and cannot—show
UNESCO’s 2024 summary of its study reports that, in stories generated by Llama 2 under the prompts it tested, women were described in domestic roles four times more often than men. The finding is a specific result from selected prompts and a tested model; it is not a measurement of all AI systems or every output. It illustrates why model behavior needs to be examined, but does not show that the gender composition of a particular development team caused that result.
Bias can arise through many parts of a system’s life: the data used, design choices, evaluation methods and the setting in which a product is used. Representation is one part of the question; it does not replace careful testing, accountability or governance.
Where can the barriers arise?
Participation is not only a question of who can study a technical subject. A fuller picture considers entry into AI work, whether people can remain and advance in those jobs, and who has influence over design, deployment and governance. The available indicators here show disparities across several roles, but do not establish the specific causes behind them or describe every workplace.
- Entry: Access to education and routes into technical work shapes who can apply for AI-related roles.
- Retention and advancement: Workforce representation alone does not show whether people have the support and opportunities to build careers or reach leadership.
- Influence: Participation in research, product decisions and policymaking affects who can contribute to choices about AI’s uses and safeguards.
These are useful questions for employers, educators and policymakers to investigate with better data. The statistics cited here do not establish the experience of any individual woman or explain why a particular person did or did not advance.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What responses do the sources recommend?
UNESCO’s 2024 Women for Ethical AI Outlook Study calls for more comprehensive, disaggregated data, targeted interventions and inclusive policies that support equitable participation in AI’s design, use and governance. Better breakdowns matter because broad totals can conceal differences between occupational fields and leadership levels.
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For support in science more broadly, UNESCO describes the Organization for Women in Science in the Developing World as offering research training, career development and networking opportunities to women scientists at different career stages. The description is of a science-support resource, not an AI-specific program.
What the numbers leave unresolved
Institutional statistics can identify disparities, but they cannot substitute for individual accounts or explain the full causes of unequal participation. Nor do the cited figures establish that representation alone prevents harmful outputs. A more complete account requires both careful measurement—using clearly defined roles and populations—and scrutiny of how AI systems are built, tested and governed.
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