Trained on Half the World

Deployed to All of It.

“AI isn’t objective – it’s optimized.”
That’s what I wrote down after finishing the second module of my Generative AI specialization. And the more I looked into it, the more that line haunted me.

Because it’s true.

The Data That Trains the Machines

Large Language Models – like ChatGPT, Gemini, Claude – are trained on a staggering amount of internet data. But look closer at the sources.

You’ll find:

• Western media outlets
• English-language books
• U.S. academic journals
• Social platforms shaped by U.S. policy – or increasingly, by mob sentiment

What you won’t find are:

• Indigenous oral traditions
• Non-Western medical knowledge
• Texts from underrepresented cultures
• Or diverse political perspectives not indexed by Google

This Isn’t Just a Data Problem – It’s a Deployment Risk

AI models don’t stay in research labs. They’re already deployed into:

• Search engines
• Policy tools
• Disaster response systems
• Even your neighbor’s social media feed

If your model sees only one part of the world, how can it serve all of it?

When Bias is Invisible, It’s Most Dangerous

The threat isn’t intentional malice – it’s structural blindness.
If the model doesn’t know something exists, it can’t warn you, help you, or flag it as relevant.

That’s a problem for:

• Policymakers
• Cybersecurity planners
• Frontline responders
• And anyone relying on AI as a second brain

So What Do We Do About It?

1. Acknowledge the bias.
   Stop pretending the model is “objective.”
2. Demand transparency.
   Ask what data sources were used and who curated them.
3. Push for global voices in AI.
   Translate, source, and amplify texts and traditions left out of training corpora.

This post was drafted with the assistance of generative AI tools and grounded in professional experience in both field operations and AI training.