How AI Can Accelerate Africa’s Scientific Breakthroughs?
Times
Thu, 4 Dec 25
15:00 - 16:00
Part of The Big AI Debate: Africa’s Outlook Series
Africa faces a unique set of scientific and developmental challenges such as endemic and emerging diseases and the growing impacts of climate change on food security. Traditional scientific approaches, while foundational, are often resource-intensive and time-consuming. Artificial Intelligence offers an opportunity to change this trajectory, enabling rapid pattern detection, prediction, and optimization that can accelerate scientific discovery, particularly in resource-constrained environments.
This second webinar, part of The Big AI Debate: Africa’s Outlook series will explore how existing science labs and research networks across Africa can effectively integrate and leverage AI to address some of the continent’s most pressing scientific challenges.
The discussion will highlight practical applications such as AI’s role in drug discovery for African-prevalent diseases like malaria, tuberculosis, and neglected tropical diseases as well as innovations in diagnostics and environmental modeling.
The conversation will underscore why African researchers are uniquely positioned to lead in this space. As global competition intensifies in frontier AI research, there is a growing risk that innovation could be shaped predominantly by Global North priorities. African scientists bring essential local expertise, diverse data contexts, and community-grounded perspectives that can ensure AI-driven science remains inclusive, ethical, and relevant to Africa’s development realities.
The session will also tackle critical challenges accompanying AI adoption, such as data poverty, algorithmic bias, ethical governance, and infrastructure gaps, while charting pathways for responsible and equitable AI integration across African research ecosystems.
What to Expect
Speaker Line-up
Webinar Details
📅 Date: 4 December 2025
🕒 Time: 3:00 PM – 4:00 PM SAST
🔗 Register here: https://us06web.zoom.us/meeting/register/g1jAc-ZfRUS-cdpUCM3cfg