Lessons learned from building language models in Africa: under strict data constraints in non-western environments.

The global proliferation of language models has predominantly followed a Western-centric approach that neglects cultural and environmental sustainability. This talk draws on firsthand experiences building language models in Africa under significant data constraints to challenge prevailing assumptions in AI development, while demonstrating how Big Tech's commoditization threatens both ecological and cultural sustainability.

Through case studies and practical examples, this presentation will show that the "bigger is better" paradigm is reaching diminishing returns while providing attendees with actionable frameworks for creating models that respect both environmental limitations and cultural contexts. Participants will gain practical insights into developing language AI that is more effective, economical, and sustainable, offering a roadmap for implementing human-centered AI solutions that better serve diverse global communities.