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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsWomen are already shaping artificial intelligence as researchers, engineers, entrepreneurs, policy experts and decision-makers. They remain a minority of the people building and governing AI, however, and their representation falls further in senior technical and corporate authority.
The most useful answer to “Who is leading AI?” is therefore not a single list of names. It is a look at who controls research agendas, model development, intellectual property, deployment and rules—and where women are being excluded from those decisions.
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What the available numbers actually measure
There is no single global percentage for women in AI. Different sources count different populations: professional profiles, national workforces, STEM occupations or publication authorship. Treating them as interchangeable produces a misleading picture.
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| Source and date | Population or measure | Finding | What it shows |
|---|---|---|---|
| Stanford HAI, 2025 AI Index Report, using LinkedIn data from 2024 | AI professionals represented on LinkedIn | 30.5% women; 69.5% men | Women are a minority of the identifiable AI professional talent pool. |
| UNESCO science, technology and innovation overview | AI professionals under that dataset’s definition | 22% women | A separate professional estimate that cannot be merged directly with the LinkedIn figure. |
| UNESCO and University of Delhi, 2026, six-country South Asia analysis | Overall workforce and STEM workforce | Women were 41.2% of the overall workforce and 28.2% of the STEM workforce | The technical pipeline is narrower than women’s participation in the wider workforce. |
| UNESCO and University of Delhi, 2026, same South Asia analysis | AI publication authorship | 71.76% of publications included at least one woman author, but women held 26% of corresponding or primary-author positions | Presence in a research team is much more common than lead authorship and formal intellectual authority. |
The 30.5% and 22% figures should therefore be read as estimates tied to their definitions, coverage and dates—not as a universal headcount of every woman working with AI.
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Where women are leading across the AI pipeline
Research and scientific direction
Women are contributing as scientists and researchers, including in the South Asian publication record. The authorship pattern matters: a paper that includes a woman author demonstrates participation, while corresponding or primary authorship more often signals responsibility for the research question, resources and final intellectual decisions. The gap between 71.76% with at least one woman author and 26% woman-led positions shows why counting participation alone can overstate authority.
Data work, model development and engineering
UNESCO’s South Asia analysis finds women concentrated in data preparation and downstream service functions relative to model development. Those roles are essential to an AI system’s quality and safety, but they generally provide less control over architecture, training priorities, computing budgets and intellectual-property ownership. A workforce can therefore contain many women while the highest-leverage technical choices remain concentrated elsewhere.
Entrepreneurship, deployment and governance
UNESCO describes women not merely as beneficiaries of AI but as entrepreneurs, decision-makers and experts influencing how systems are used. Leadership at these stages includes founding or directing companies, setting deployment policy, deciding which risks are acceptable, procuring systems and writing rules for public or workplace use. These positions determine whether women’s experience is translated into product requirements and safeguards.
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Professional entry is only one point in the pipeline. Leadership requires access to management tracks, capital, sponsorship, high-visibility projects and authority over budgets and teams. UNESCO’s 2024 outlook, reproducing World Economic Forum statistics, records sizeable gender gaps at successive levels:
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| Leadership level | Reported gender gap | Sector or setting |
|---|---|---|
| Director positions | 23.3% | STEM workplaces |
| Senior leadership | 30% | Technology, information and media |
| C-suite positions | 31.9% | Technology, information and media |
These figures describe leadership gaps, not the share of all employees. They indicate that the climb from technical or professional work into formal authority is a distinct barrier. The South Asian authorship result points to the same pattern in research: women may be present, yet less likely to hold the role that sets direction and receives recognition.
Tim Curtis, Director and Representative of UNESCO’s Regional Office for South Asia, summarized the structural problem: “Women and girls in South Asia are being left out of the AI story — not because they lack ability, but because the systems around them have not been designed with them in mind.”
Why this matters for the systems AI produces
Who has authority affects what problems receive investment, which data are collected, how performance is tested and whose harms are treated as urgent. UNESCO warns that AI can encode or amplify gender bias and can contribute to technology-facilitated gender-based violence. Broader participation does not automatically remove those risks, but excluding women from model development, product decisions and governance removes perspectives needed to detect them.
What would close the gap?
UNESCO’s response agenda combines interventions across the pipeline rather than relying on a single hiring target:
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- Strengthen STEM education. Build sustained routes into mathematics, computing, engineering and applied AI, with support that continues from school through advanced study and employment.
- Back women researchers and entrepreneurs. Funding, laboratory access, networks and procurement opportunities help women move from contributing to projects to directing them and owning resulting intellectual property.
- Require institutional gender action plans. Employers, universities and public bodies can track recruitment, promotion, pay, retention, authorship and leadership separately so that participation gains are not mistaken for decision-making power.
- Reduce bias in AI systems. Gender-responsive design, representative evaluation data, documented testing and channels for reporting harm address both algorithmic bias and technology-facilitated abuse.
Women for Ethical AI
UNESCO’s Women for Ethical AI initiative provides a named professional network for women leaders and experts. Its work connects peers, increases the visibility of women’s contributions, advances gender-responsive solutions and offers mentoring intended to support future generations of AI leaders. Participation and partnership arrangements can change, so prospective members should consult UNESCO’s current program information for the applicable route.
How to tell whether an AI leadership claim is substantive
When evaluating a company, laboratory, public program or conference, look beyond headcount. Ask:
- Does a woman hold authority over research priorities, model development or deployment decisions?
- Is she a corresponding or primary author, principal investigator, founder, executive or policy owner rather than only a contributor?
- Does she control resources such as budgets, computing, hiring or intellectual property?
- Are women represented in both technical and governance roles, including safety and evaluation?
- What institutional support—education, mentoring, funding or a formal gender action plan—made advancement possible?
Those questions connect visibility to power. The evidence from professional counts, workforce composition, authorship and executive representation points in the same direction: women are shaping AI, but they are still less likely to occupy the positions that determine its technical trajectory and social consequences.
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