Conversation – Cina Lawson, Nezar Patria, Raafat Hindi (Moderator Debjani Ghosh)
Summary
The three ministers compared AI impact metrics across the Global South. Lawson highlighted Togo’s AI‑driven cash‑transfer prioritisation during COVID‑19 (satellite‑derived poverty mapping + telecom metadata). Patria discussed Indonesia’s AI‑enabled TB detection app for remote clinics, emphasizing AI‑augmented diagnostics on limited hardware. Hindi showcased Egypt’s AI tools for early breast‑cancer detection and AI‑assisted high‑school tutoring, noting government‑backed AI pilots.
All agreed that infrastructure (connectivity, compute) and multilingual models are the main bottlenecks. They called for regional AI commons to share best practices, data, and models.
Key Takeaways
- AI for social safety nets: Satellite imaging + telecom data can target assistance efficiently.
- Healthcare AI in remote settings: Low‑cost AI diagnostic tools can extend specialist expertise.
- Education AI: AI tutors in local languages improve learning outcomes.
- Infrastructure gaps: Reliable connectivity and compute remain critical barriers.
- Regional AI commons: Shared repositories of models, data, and policy frameworks are needed.