A young person applies for a training programme. A hiring algorithm screens their CV before a human ever sees it. A chatbot recommends what mental health resources they should trust. In each case, an AI system is quietly making a decision that shapes a young person’s opportunities — and in most cases, nobody explained how, or gave them a say.

This is the gap that fair and transparent AI practices are meant to close. For youth workers, understanding that gap isn’t an abstract technical concern. It’s becoming a core part of protecting and empowering the young people they work with every day.

What “fair” and “transparent” actually mean

The two words get used together so often that it’s worth separating them.

Fairness is about outcomes. An AI system is fair when it doesn’t systematically disadvantage people because of their background, gender, ethnicity, disability, or socioeconomic status. In practice, this is harder than it sounds: AI systems learn from historical data, and historical data reflects historical inequality. A recruitment tool trained on twenty years of hiring decisions will often reproduce the biases baked into those decisions, even if no one designed it to discriminate.

Transparency is about process. It means people can understand, at some meaningful level, how a system reached a decision that affects them — and that this information isn’t hidden behind a “proprietary algorithm” excuse. Transparency also covers consent: knowing when AI is being used at all. A young person filling out a form should know if a bot, not a person, will be the first to evaluate it.

Fairness without transparency is unverifiable — you’re asked to trust a black box. Transparency without fairness just gives you a clear view of a broken system. Youth work needs both.

Why this matters more for young people in vulnerable situations

Young people already facing disadvantage — due to migration status, disability, economic hardship, or exclusion from formal education — are disproportionately exposed to automated decision-making. They’re more likely to interact with algorithmic systems in welfare services, job platforms, content moderation, and school placement tools, and less likely to have the resources to challenge an unfair outcome or even recognise that AI was involved.

This is exactly the population non-formal education practitioners work with. An AI-illiterate youth sector isn’t equipped to spot when a system is failing a young person, let alone to teach that young person how to protect themselves.

What fair and transparent AI looks like in practice

A few concrete markers to check for, whether you’re evaluating a tool for your own organisation or helping a young person understand one they’ve encountered:

• Explainability — Can the organisation using the AI explain, in plain language, what factors influenced a decision?
• Human oversight — Is there a real person who can review, question, or override an automated decision?
• Disclosure — Are people told when they’re interacting with AI rather than a human?
• Bias testing — Has the system been tested for unequal outcomes across different groups, and are results published?
• Data minimisation — Does the system collect only what it actually needs, rather than harvesting data “just in case”?
• Accessible redress — Is there a straightforward way to challenge or appeal a decision the system made?

None of these require a technical background to ask about. They’re the same questions youth workers already know how to ask about any institution or process affecting the young people in their care — just pointed at a new kind of decision-maker.

Where this fits into Building the Future with AI

This is precisely the territory the Building the Future with AI (BFAI) project is mapping out. Through co-creation labs across Portugal, Italy, and Spain, youth workers and young people have been identifying where AI already shapes their daily lives — and where fairness and transparency break down in practice.

Those insights are feeding directly into the AI Toolkit for Youth Workers, a free, open-access resource being developed by RUA, lascò, and la cultura. The toolkit won’t just explain how AI works — it will give practitioners concrete activities and frameworks for teaching digital and AI literacy, including how to question a system’s fairness and demand transparency on behalf of the young people they support.

The takeaway

Fair and transparent AI practices aren’t a compliance checkbox for tech companies to worry about. They’re a literacy young people need, and a lens youth workers need to build into their everyday practice. The more automated decisions become, the more that literacy determines who gets a fair shot — and who doesn’t even know they didn’t.

This article is part of the Building the Future with AI (BFAI) project, co-funded by the Erasmus+ programme of the European Union. Views and opinions expressed are those of the author(s) only and do not necessarily reflect those of the European Union or the European Education and Culture Executive Agency (EACEA). Neither the European Union nor EACEA can be held responsible for them.