AI is already rewriting reality for billions of people. It is getting women wrong.

Generative AI is now among the most widely used technologies in day-to-day marketing and communications work, in the United Kingdom (UK) alone, 88 per cent of advertising and media agencies are already using it in some form. Discriminatory algorithms could therefore further perpetuate gender inequality and discrimination. As AI tools become embedded in content generation and media buying at scale, decisions about who gets seen, how they are portrayed, and whose stories get told are being made at speed, and largely without human scrutiny or gender perspective.

Large Language Models (LLMs) have been found to consistently associate women with "home," "family," and "children," and men with "business," "executive," "salary," and "career." When tasked with completing sentences that start with a person’s gender, about 20 per cent of responses from LLMs exhibited sexist and misogynistic attitudes, including portrayals of women as sex objects and property of their husbands. These are the predictable output of AI systems trained on decades of unequal representation of women and men. AI bias is not only a system design problem, but also a policy problem. Of 138 countries assessed, only 24 referenced gender in a national AI strategy, and just 18 included substantive gender-responsive provisions, risking inequality being “baked in” to future systems.

According to UN Women data, women and girls globally already have less access to digital spaces – and when they do, they are far more likely to experience online violence. Almost one in four surveyed women human rights defenders, activists and journalists had experienced AI-assisted online violence and 12 per cent report having experienced the non-consensual sharing of personal images, including intimate or sexual content. Six per cent say they have been targeted through “deepfakes” or manipulated images/video, while more than one in four have received unsolicited sexual advances through digital messaging. AI is compounding this. Deepfakes are among the most visible examples of AI-enabled abuse that disproportionately targets women and girls. As AI-generated content becomes the norm, the tools for harassment, manipulation, and image-based abuse are scaling alongside it.

Gen AI is expected to drive job growth in tech-intensive sectors, yet women remain underrepresented in Science, Technology, Engineering and Mathematics (STEM) and AI, making up only 30 per cent of the AI workforce globally. The people designing these systems are not representative of the billions of people the systems are expected to serve – and that glaring gap is compounding the problem.

Women outside the AI sector are nearly twice as likely as men to hold jobs at high risk of automation. AI disparity does not manifest in gender inequality alone – harms are multiplied across race, disability, socioeconomic status, and geography. The communities already most underrepresented in media and labour markets face the greatest risk of being left further behind.

In a first-ever global study, the Unstereotype Alliance, an industry-led initiative convened by UN Women, proved that inclusive advertising has a positive impact on business profit, sales and brand value. Brands that create inclusive advertising, free of gender stereotypes, enjoy +3.46 per cent short-term sales and +16.26 per cent long-term sales uplift. They are 62 per cent more likely to be a consumer's first choice, have 54 per cent higher pricing power, and experience 15 per cent higher customer loyalty. As AI becomes central to how campaigns are planned and produced, the brands that embed inclusion into those processes stand to gain – and those that do not, face significant reputational and commercial risk. The Unstereotype Alliance playbook launched in June 2026 gives marketers a way to catch bias before it ships, every time they use generative AI.

UN Women calls for gender equality and the rights and experiences of women and girls to be embedded at every stage of AI life cycle from development, deployment, and governance. When designed with safety and used with intention, AI can help detect stereotypes, broaden representation, and improve accessibility at scale. The choice of whether it does lies with the people making decisions – in governments, in companies, in experts researching and developing AI – and it depends on whether we incorporate the voice, expertise, and lived experience of women and girls from diverse contexts, civil society organizations who work with them and know their issues deeply.