AI systems are only as good as the data they learn from, but what happens when that data overwhelmingly comes from Western nations? This talk explores the systemic bias in AI models caused by uneven global data representation, and how it impacts developing nations in areas like healthcare, finance, and governance. We’ll examine real-world consequences of this imbalance, discuss why diversity in datasets matters for security and fairness, and propose strategies for building inclusive AI that serves the entire world, not just the Global North.