AI for Africa: Opportunity and Risk in the Same Technology
Published · September 6, 2026 The Paradox Africa’s AI economy is projected to contribute $2.9 trillion by 2030. Financial institutions, government agencies, healthcare systems, and agricultural enterprises are deploying AI at scale. The opportunity is real and structural. So is the problem. The same AI systems driving this growth systematically misrepresent, exclude, and harm African […]
AfroEval Scorecard: Your AI Deployment Readiness Checklist for African Contexts
Published · August 11, 2026 Your AI model scores 92% accuracy on standard Western benchmarks. Impressive. Until it hits production in Lagos, Nairobi, Ethiopia, or Accra—and suddenly that same model drops to 63% accuracy on African language-inflected English users. This is not a failure of your model. It is a failure of your evaluation framework. […]
When Institutions Deploy AI Without Knowing If It Works for Their Users
Published · July 1, 2026 The Benchmark Score Is Not the Deployment Score Most AI procurement decisions rest on a single question: did the model pass the benchmark? The answer is almost always yes. The model scored well on GLUE, MMLU, or a vendor-supplied accuracy report, and the procurement committee approved the deployment. What the […]
The Benchmark Problem: How Aggregate AI Accuracy Scores Hide the Gaps That Harm African Users
When an AI vendor hands you a benchmark score, they are handing you one number. That number is usually high enough to feel reassuring. It is almost never the number that matters. Our benchmarks show a 29-point accuracy gap — 92% for Standard American English users, 63% for Yoruba-inflected English users — on the same […]
