Explore our latest articles, tutorials, and insights
The NIST AI Risk Management Framework (AI RMF) is increasingly treated as a global blueprint for “trustworthy AI.” But what happens when a framework designed for resource-rich Western institutions is applied to the Global South?
Why the EU AI Act—designed for data-rich, institutionally mature European economies—breaks down when applied to the Global South.
How Aadhaar’s promise of digital inclusion turned into one of the largest algorithmic exclusion crises in the world.
As AI enters courts and welfare systems worldwide, the COMPAS debate reveals a critical lesson: fairness depends on context, and exporting models without reform risks scaling inequality.
America's AI Action Plan's redefinition of fairness, by removing Diversity, Equity, and Inclusivity (DEI), risks hard-coding inequities for the Global South, necessitating a proactive response to define its own culturally and contextually relevant AI fairness standards.
Why efficiency-focused 'Green AI' claims often hide deeper environmental, social, and governance costs—especially in the Global South.
How a design-phase failure in the Dutch childcare fraud algorithm created one of the worst AI governance disasters in Europe — and what the Global South must learn from it.
A deep dive into Michigan’s MiDAS unemployment fraud algorithm — and how design-phase failures, automation bias, and the removal of human oversight turned efficiency into injustice.