{"id":20548,"date":"2025-12-01T16:23:08","date_gmt":"2025-12-01T15:23:08","guid":{"rendered":"https:\/\/donne4.it\/?p=20548"},"modified":"2025-12-01T16:23:13","modified_gmt":"2025-12-01T15:23:13","slug":"building-ai-for-the-world","status":"publish","type":"post","link":"https:\/\/donne4.it\/en\/building-ai-for-the-world\/","title":{"rendered":"Building AI for the World"},"content":{"rendered":"\n

In today\u2019s world, technology grows faster than any of us can follow, and artificial intelligence is suddenly part of everyone\u2019s life. Thanks to huge improvements in computers and the explosion of big data, large language models have become so advanced that it\u2019s basically impossible to avoid them. Everyone, even my grandma, has interacted with an LLM at least once and she doesn\u2019t even know what \u201cLLM\u201d stands for.<\/p>\n\n\n\n

But with great power comes great responsibility. Yes, data is a kind of universal language. But it also carries big risks: unfairness, lack of inclusiveness, and a lot of hidden biases.<\/p>\n\n\n\n

I\u2019m Maryam, an Iranian AI specialist working in an international consulting firm in Milan. My daily job is about mixing technology with business and helping companies understand how to use AI wisely. I moved to Italy around four years ago to study computer science, and somehow destiny decided I should stay here. So here I am, still hanging around, still learning, still building.<\/p>\n\n\n\n

When I entered Italy, everything was new. I came with my own culture, studied in English, bought groceries in Italian, and lived with international students from all over the world on campus. Every single thing was different, and I had to adapt. And that\u2019s exactly why I started noticing how culture affects the way we think, even the way we think about AI.<\/p>\n\n\n\n

Because of my job, I always work with different AI models. And let\u2019s be honest, most of them are built in Western countries, trained on Western data, and shaped by Western perspectives. So of course they struggle with anything outside that bubble.<\/p>\n\n\n\n

Ask them something about a smaller ethnic group in Africa? Maybe they were never trained on it. Talk to them in a non-English accent through voice assistants? Good luck! they pronounce Italian with a weird American twist. These small things made me realize a bigger truth, AI often doesn\u2019t understand cultures that didn\u2019t build it.<\/p>\n\n\n\n

And the bias doesn\u2019t stop at culture, it hits gender too, in ways that are subtle but incredibly powerful. For example, ask an AI model to describe a \u201csoftware developer,\u201d and mostly it imagines a man. Ask it for an image of a nurse, and suddenly it\u2019s always a woman. So, a girl who dreams of becoming an engineer might notice that an AI model doesn\u2019t automatically picture someone like her. And a boy who wants to be a nurse might feel like he\u2019s choosing something unusual, because the AI keeps showing it as a \u201cfemale job.\u201d<\/p>\n\n\n\n

For most of history, women weren\u2019t even allowed to study, work freely, or choose their own path. Many professions were legally or socially restricted to men. So of course, the historical data shows \u201csoftware developer = man\u201d, because only men were permitted to do those jobs in the past. And when you look at roles like nursing, they appear more \u201cfemale\u201d in old data because during wars men were fighting and women were the ones caring for the wounded. <\/p>\n\n\n\n

Again, not because of talent or ability, but because society decided who was allowed to do what. In some periods of history, even a curious, educated woman could be punished, silenced, or called a \u201cwitch\u201d simply for thinking differently. That\u2019s the kind of world this old data comes from. So, when AI models repeat those patterns, they\u2019re not reflecting our present, they\u2019re repeating a past where choice wasn\u2019t equal. <\/strong><\/p>\n\n\n\n

Without even realizing it, these systems can shape imagination, confidence, and even identity, especially for younger generations who use AI every day. Kids widely use AI now too, it\u2019s everywhere, so what AI \u201cthinks\u201d actually matters. We need to educate people on how to use AI responsibly, but we also need to improve what we feed into these models. It turns stereotypes into \u201cfacts.\u201d And the problem isn\u2019t that the model is evil, the problem is that it has learned from years and years of human data that already carries these biases.<\/p>\n\n\n\n

This is why updating the data matters. This is why changing mindset matters. And this is why we need more women, from every background and culture, involved in building AI from the start, inside the rooms where these systems are built. Not afterwards, not as \u201ccorrectors,\u201d but from the beginning.<\/p>\n\n\n\n

We need more unbiased data, and new data that actually includes everyone, every gender, every culture, every story.<\/strong> Because AI can only reflect the world it sees, and right now it sees only a small part of it. To balance these old biases, we need people who bring different histories, different languages, different ways of thinking. People who can look at a dataset and say, \u201cWait, something is missing here.\u201d People who understand what it feels like to not be represented.<\/p>\n\n\n\n

And it\u2019s not only about who builds the technology, but also about who uses it. AI learns from us; The more people use it, the more perspectives it absorbs. So, we need to make AI accessible to everyone, not just a privileged group. We need kids, adults, immigrants, seniors, women, minorities, all voices, interacting with it. That\u2019s how we push it toward fairness.<\/p>\n\n\n\n

But access alone is not enough. People need to be educated on how to use AI, how to question it, and how to guide it. Because the better humans understand how to ask, the better AI can learn how to answer. Healthy usage from diverse groups can help correct old patterns and teach the system new ones.<\/p>\n\n\n\n

This isn\u2019t just a technical challenge; it\u2019s a human one. AI becomes fair when the people building it and the people using it are diverse enough to notice what others might overlook. The next generation of AI has to be built by all of us. Immigrant women, cultural minorities, people from places the old data never wrote about, we all have something essential to add. Our voices are not \u201cnice to have.\u201d They\u2019re the missing pieces. And if we show up, use the tools, and shape them with our own hands, we can create AI that finally sees the whole world, not just the part that wrote the history books.<\/p>\n\n\n\n

That\u2019s the future I want to help build.<\/p>\n\n\n\n

Our story matters.
Bring it.
Be visible.
Shape the systems.<\/p>\n\n\n\n


Maryam Asgari, AI Specialist & Ambassador Donne 4.0<\/em><\/p>\n\n\n\n

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