Responsible AI. Empowering People.

Originally published on the Masakhane African Languages Hub, this article responds to The Economist’s March 18, 2026 piece on how leading AI models underperform in non‑English languages. It highlights the serious equity and safety implications of weaker AI performance in African languages across sectors such as healthcare, agriculture, and public services. The article showcases Masakhane’s African‑led efforts to address this gap through community‑driven data creation, collaborative research, and gender‑responsive innovation—arguing that reliable AI in African languages is essential to legitimate and inclusive AI adoption in Africa.

Africa’s healthcare challenges demand locally built AI solutions grounded in context. From data and infrastructure to policy and education, supporting local innovators is key to creating trusted, scalable systems. By investing in homegrown talent and enabling environments, the continent can reduce reliance on foreign technologies and build resilient, people-centered health systems for the future.

AI tools hold immense promise, but real-world outcomes often depend on how they interact with people and contexts. Research shows that factors like access, comprehension, trust, and opportunity what experts call “inflection points” can determine whether AI interventions succeed or fail. Case studies from Africa and beyond reveal that even high-performing models only deliver impact when these social and institutional conditions align