Building Open and Reproducible AI Practices for LMICs (and Beyond)

AI has become an integral part of modern research, offering tremendous opportunities, but also raising important questions for the Open Science community.

With the emergence of the Open Source AI Definition (OSAID) and its emphasis on the four freedoms, the “freedom to study” stands out as a cornerstone for achieving true reproducibility. You can read the OSAID definition here: https://opensource.org/ai/open-source-ai-definition.

This talk will explore how researchers can design, implement, and sustain reproducible AI practices within their work, especially in Low and Middle Income Countries (LMICs), where infrastructure and culture around reproducibility are still developing. Drawing from practical examples and community experiences, I’ll outline actionable steps for embedding openness and reproducibility in AI workflows. These approaches are adaptable across contexts and can help build a more transparent, collaborative, and trustworthy global AI ecosystem.

My perspective is shaped by my work as an Open Source Manager and Project Coordinator with Data Science Without Borders, and as a contributor to The Turing Way, where I advocate for open, inclusive, and reproducible research practices in data science and AI.