Artificial intelligence (AI) is emerging as an influential force throughout the world, affecting millions of people from awarding scholarships and distributing social subsidies to citizen identification, and touching even on the humanitarian response for victims of violence. However, there is a flipside to it. The Regional Human Development Report 2025 points out that such rapid deployment is taking place in societies riddled with deeply entrenched inequalities. Algorithms trained on biased datasets may reinforce inequalities, and what starts as a technical problem of gender bias may soon become a serious development problem.
When datasets either lack adequate representation from sub-populations that are often deprived of basic needs—such as poor, indigenous, migrant, or rural women—or women from any of those backgrounds are excluded, the algorithms trained on those datasets will create unfair conditions, exacerbating distrust in institutions. But technology does not have to exacerbate divisions—if done properly, AI can be a powerful tool to protect those previously marginalized and now have access to enable decision-making. The challenge now lies with systems designed in equity, transparency, and accountability.
From technical flaw to development challenge
AI works by finding patterns in vast amounts of data. But when that data reflects unequal realities, it reproduces discrimination. For example, automated models used to allocate social benefits in several LAC countries can unintentionally exclude women if their life experiences aren’t adequately represented. Similarly, facial recognition systems have been shown to misidentify women—especially women of color—at higher rates, sometimes leading to unjust detentions and eroding public trust.
Bias also appears in economic systems. Hiring algorithms can favor male-dominated job histories while credit models penalize women who take nontraditional career paths. This limits women’s opportunities for advancement while also undermining economic productivity and innovation.
Countries must not only invest in research and data to better represent women, but also ensure independent audits of high-impact systems, and systematic accountability triggers are established. Moreover, this investment should even extend to the design of some virtual assistants, many of which have acceptable female monitors. Design decisions impact and further entrench existing biases.
The good news is women across the region are stepping into leading roles in AI—designing fairer systems, creating gendered models, developing tools to identify bias. Women’s empowerment as designers, regulators and users, not only makes AI fairer, it makes AI smarter.
Ultimately, fair AI is not only about justice for women—it’s a matter of building trust, inclusion, and shared prosperity for all.
