AI Research in Africa Must Scale Up — Talks at Hackathons, Institutes & Open Data

While projections show large market growth for AI in Africa, recent research reveals that progress in AI research, infrastructure, and open data is uneven—and far from sufficient to meet the demand. Studies highlight that startups, academic institutions, and innovation hubs must accelerate efforts in these areas.

State of AI Research & Data Gaps

An academic paper titled “The State of Computer Vision Research in Africa” found that despite thousands of publications over the past decade, Africa accounts for only ~0.06% of top-tier computer vision publications globally. Barriers include limited access to large datasets, insufficient compute resources, and scant collaboration networks.

Another initiative, th Amplify Initiative, is working to build localized multilingual datasets with domain experts from Ghana, Kenya, Malawi, Nigeria, and Uganda. This kind of open data effort is key to making AI more contextually relevant for African users.

Role of Hackathons, Institutes & Open Data

Hackathons like MCP, workshops, and AI-education institutes are essential in igniting innovation, helping researchers build skills, prototype context-aware tools, and share open datasets. Combined with support from government policies and private partnerships, these platforms help fill gaps in skills, ethical guidelines, and data accessibility.

Challenges & What Needs Scaling

To truly scale, the following are critical:

• More multi-institution collaborations and funding for foundational AI research (not just applied).

• Access to compute infrastructure, GPU resources, data storage, and open, well-annotated datasets.

• Better policy frameworks that support open data and cross-border research.

• Inclusion of underrepresented languages and regions in datasets and research initiatives.

Bottom Line

Africa’s path to becoming a global AI player depends not only on market size forecasts but also on the depth and relevance of its research ecosystem. Scaling up research, investing in infrastructure, and making data accessible are as important as funding and policy.