AI could widen gender inequality, but the evidence does not show that it has already caused a single, measurable increase in the overall gender gap. The risk is real: women are more concentrated in occupations exposed to generative AI, remain underrepresented in AI jobs, and can face biased systems. But exposure is not the same as job loss. The International Labour Organization (ILO) says AI’s most widespread effects are more likely to change tasks, skills and working conditions than to eliminate jobs outright.
What does it mean to say AI widens the gender gap?
There is no single gender gap. The phrase can refer to differences in employment, pay, career opportunities, access to education or treatment by automated systems. AI could affect each of these through different pathways: by changing which jobs and tasks are in demand, by influencing workplace decisions, or by shaping who gains access to new technical roles.
As an Amazon Associate I earn from qualifying purchases.
The evidence points to credible ways these inequalities could worsen, not proof that generative AI has already caused an economy-wide widening. In particular, an occupation’s exposure to AI indicates that some of its tasks may be affected; it does not predict what share of its workers will lose their jobs.
Recommended Free Tools
Are women’s jobs more exposed to generative AI?
In its 2026 summary of research using harmonized data from 84 countries, the ILO found higher generative-AI exposure in female-dominated occupations than in male-dominated ones. It also found that women were more exposed than men in 88% of the countries analyzed. The comparison below describes occupational exposure, not realized job losses.
#1 Best Overall
| Occupational group | Share exposed to generative AI | Share in the highest exposure categories |
|---|---|---|
| Female-dominated occupations | 29% | 16% |
| Male-dominated occupations | 16% | 3% |
These figures are from the ILO’s 2026 analysis across 84 countries. “Exposed” means work tasks have potential to be affected by generative AI; it does not mean that a worker or occupation will disappear. The ILO links the disparity in part to women’s concentration in clerical, administrative and business-support roles, where tasks are often routine and codifiable.
Will AI take women’s jobs, or change the work?
The ILO’s assessment is that changes to job quality are likely to be more widespread than changes to job quantity. AI tools may alter the mix of tasks employees perform, the skills employers require, how closely work is monitored, employees’ workload and their autonomy. Whether those changes help or harm workers depends on how the technology is introduced and managed.
Rank #2
- Task change: Some routine tasks may be automated or assisted, while workers take on different responsibilities.
- Work intensification: Productivity tools can increase output expectations or monitoring if employers use them that way.
- Job displacement: Some roles or tasks may be reduced, but exposure figures alone do not establish how many jobs will go.
- Improved job quality: Responsible implementation could support productivity, working conditions and work–life balance.
These outcomes are not interchangeable. A tool that changes tasks without cutting jobs can still affect pay, autonomy or workload; a tool that reduces repetitive work could also benefit employees if the gains are shared. The ILO’s Janine Berg, a senior economist and co-author of its report, said the impact “is not predetermined.”
What’s actually slowing this PC down?
Pick the symptom - the matching free tool is one click away.
Who gets the new AI jobs and skills?
Unequal exposure is only one side of the labor-market picture. The ILO reports that women made up about 30% of the global AI workforce in 2022, only four percentage points more than in 2016. It identifies engineering and software development as examples of high-demand fields where women remain underrepresented.
Underrepresentation can limit women’s access to emerging jobs and skills development. It can also reduce their opportunities to shape how AI systems are designed and deployed. This creates a potential feedback loop: existing occupational segregation influences which workers encounter AI-driven change, while unequal access to new roles can affect who benefits from the technology and whose perspectives inform it.
The ILO identifies hiring, pay decisions, credit scoring and access to services as areas where AI-related disadvantages may arise. Its sources do not quantify one common causal effect across these different domains, so a specific impact on the overall gender pay gap cannot be inferred from the occupational-exposure figures.
Can AI systems discriminate against women?
Yes. If training data or model weights reflect historical stereotypes or fail to represent groups adequately, an AI system can produce biased content or contribute to unequal treatment. The OECD’s 2025 review describes this risk in areas including job search, job advertising, human-resources management and performance management. It also says that deliberate design and review throughout an AI system’s lifecycle can help improve fairness.
One illustration comes from a UNESCO summary published in 2024 of a study that tested GPT-3.5, GPT-2 and Llama 2. In the tested Llama 2-generated stories, women were described as working in domestic roles four times more often than men. UNESCO also reported gendered associations between women and domestic roles and men and business or career terms. These are findings about the specific models and study content, not a universal measure of every AI system or current model.
Best Value
UNESCO’s summary said that the open-source models in that study showed more significant gender bias, while also noting that openness can make collaboration on mitigation easier. Bias may intersect with race, ethnicity, disability or migration status; the ILO notes that multiple forms of discrimination can compound risks for women.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can AI improve access to education and opportunity?
Technology can also expand access. UNESCO’s 2024 Gender Report, Technology on her terms, says technology can provide a lifeline for girls otherwise excluded from education and improve access to valuable learning content. That potential depends on whether learners can access the technology, build digital skills and use it in a safe environment.
The same report highlights persistent gender divides in technology access and digital skills. It points to safe learning environments, girls’ mathematics skills and pathways into science, technology, engineering and mathematics (STEM) as important to a more gender-balanced future in technology. Access to a tool alone does not ensure equal learning or career outcomes.
What would make AI’s effects more equitable?
The OECD and ILO identify measures that address different points in the system: how AI is built, where it is used, who can develop and govern it, and how workers are affected. These are policy directions, not interventions whose effects have been quantified by the sources cited here.
- Review systems for bias: Examine training data, model behavior and workplace decisions for differential treatment, then monitor systems after deployment.
- Include affected groups: Involve women and other underrepresented groups early and throughout the AI lifecycle, from planning and design to deployment and review.
- Expand access to skills: Improve women’s access to digital and AI skills, as well as education and pathways into STEM and technical roles.
- Address occupational segregation: Consider who is concentrated in exposed roles and who can move into emerging jobs, rather than treating AI as a neutral change affecting all workers equally.
- Consult workers: The ILO calls for social dialogue among governments, employers and workers when AI is introduced and governed.
Whether AI widens or narrows gender gaps will depend not only on what systems can do, but also on who has access to them, how employers use them and whether their effects are scrutinized.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
