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AI could deepen gender inequality, but the evidence does not show that it has already widened the overall gender gap. Women are more concentrated in jobs exposed to generative AI, remain underrepresented in AI work, and can be harmed by biased systems. Exposure is not the same as job loss: the likely effects for many workers are changes to tasks and working conditions.
What does “AI widens the gender gap” mean?
It is a risk to examine, not a settled finding that AI has caused an economy-wide increase in gender inequality. The evidence points to several pathways: women’s concentration in some occupations exposed to generative AI (GenAI), unequal access to new AI-related roles and skills, and systems that may reproduce or amplify gender bias.
Those pathways should not be collapsed into a single claim. An occupation’s exposure to GenAI measures the potential for tasks to be affected; it does not say how many people have lost jobs. A biased output from a language model also does not, by itself, prove that a particular hiring or pay decision was discriminatory. The available sources establish risks and unequal exposure, not one causal estimate of AI’s overall effect on the gender gap.
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Are women’s jobs more exposed to generative AI?
In its 5 March 2026 summary of Gen AI, occupational segregation and gender equality in the world of work, the International Labour Organization (ILO) reports that female-dominated occupations are more exposed to GenAI than male-dominated occupations. Its estimates draw on harmonized data covering 84 countries.
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| ILO exposure measure | Female-dominated occupations | Male-dominated occupations |
|---|---|---|
| Occupations exposed to GenAI | 29% | 16% |
| Occupations in the highest exposure categories | 16% | 3% |
These are exposure estimates, not percentages of workers expected to lose their jobs. The ILO reports that women are more exposed than men in 88% of the countries analysed. It links the pattern in part to women’s concentration in clerical, administrative and business-support roles, where work can include routine, codifiable tasks.
Does higher exposure mean women will lose more jobs?
Not necessarily. The ILO says the more widespread effect of GenAI is likely to be on job quality rather than job quantity. A role may change as some tasks are automated or supported by AI, even if the role itself remains. Possible changes include different task mixes and skill requirements, as well as shifts in workload, monitoring and worker autonomy.
Implementation matters. AI could support productivity, working conditions and work–life balance, or it could intensify work and reduce control over how tasks are done. Exposure alone cannot tell which outcome will occur. It also does not establish how many jobs, if any, will be displaced in a particular occupation or country.
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Who gets access to AI’s new jobs and influence?
Women’s lower representation in AI and STEM occupations raises a different concern from exposure to automation: who can move into emerging roles, gain relevant skills and influence how systems are built and used? The ILO reports that women made up about 30% of the global AI workforce in 2022, four percentage points more than in 2016. It identifies engineering and software development as high-demand areas where women remain underrepresented.
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The ILO links that underrepresentation to reduced access to emerging jobs and skills development, and to fewer opportunities for women to shape system design and deployment. This matters because occupational segregation can affect both sides of the transition: which workers face changes in existing roles and who can access new ones.
Can AI systems discriminate against women?
They can produce biased outputs or contribute to differential treatment, but the form and evidence depend on the system and its use. In a 2024 summary of a study examining GPT-3.5, GPT-2 and Llama 2, UNESCO described gendered associations between women and domestic roles and men and business or career terms. For stories generated by Llama 2 in that study, women were described as working in domestic roles four times more often than men.
That is a study-specific result about named models and tested content, not a universal measure of current AI systems. UNESCO also reported more significant gender bias in the open-source models studied, while noting that openness can make collaboration on mitigation easier. The study discussed racial and sexuality-related stereotypes too, a reminder that gendered harms can intersect with race, ethnicity, disability or migration status.
In employment and other consequential settings, the concern extends beyond generated text. The OECD’s 2025 review says biased or unrepresentative data and model weights can lead to incorrect or discriminatory treatment in areas such as job search, job advertising, human-resources management and performance management. The ILO also identifies risks involving hiring, pay decisions, credit scoring and access to services. These sources identify possible harms; they do not quantify one common causal effect across those settings.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can AI also reduce inequality?
Yes, if systems and institutions are designed and implemented to widen access rather than reproduce existing barriers. The OECD’s 2025 review describes both risks and opportunities: intentional design and review across the AI lifecycle can help improve fairness. The ILO likewise says responsible implementation could support better working conditions, productivity and work–life balance.
Education is part of that opportunity. UNESCO’s 2024 gender report, Technology on her terms, says technology can help girls who might otherwise be excluded from education reach learning and valuable content. It also warns that unequal access to technology and digital skills persists, and raises concerns about negative norms and safe learning environments. It points to girls’ mathematics skills and pathways into STEM as important to a more gender-balanced future in technology.
What safeguards can make AI’s effects fairer?
The sources point to changes at several levels rather than a single technical fix. The ILO recommends embedding gender equality in AI design, deployment and governance; addressing occupational segregation; expanding women’s access to skills; and ensuring representation in AI roles. It also calls for dialogue among governments, employers and workers. The OECD emphasizes careful planning and review, with women and other underrepresented groups involved early and throughout the AI system lifecycle. These are policy directions, not interventions with quantified effects established by the sources cited here.
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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →- For employers: examine where AI is introduced, which tasks and workers are affected, and whether changes to workload, monitoring or autonomy are distributed unevenly.
- For organizations buying or building AI: review data, system behavior and consequential decisions across the lifecycle, rather than treating a model’s initial performance as proof of fairness.
- For education and training providers: address differences in technology access and digital skills, and support girls’ routes into mathematics and STEM.
- For workers and the public: ask whether a system is assisting a decision or making it, how its use is reviewed, and how affected people can raise concerns.
These checks help make risks visible; they cannot guarantee a fair outcome on their own. The central question is not only whether AI can perform a task, but who bears the costs of adopting it, who gains access to new opportunities, and who has a voice in the decisions.
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