I’ve just returned from Deep Learning Indaba, a conference that seeks to foster collaboration, knowledge sharing, and networking within the African AI and machine learning community, and make AI and machine learning education and research more accessible and inclusive in Africa.
Our participation in the Data Science for Health in Africa Workshop gave us the platform to share some of our techniques, learnings, and insights from designing and developing AI for our context.
We got the chance to explain how PROMPTS worked, our learning journey around building responsive Natural Language Processing (NLP) models in low resource settings, and how we have trained these models to accurately understand nuanced maternal healthcare questions from mothers.
It was interesting to discuss shared challenges and opportunities around scalability, data compliance, and ethical considerations with the other participants, and share our commitment and principles for responsible, person-centered AI.
The workshops and hands-on sessions gave us a chance to hone skills in deep learning and AI, and deepen our understanding of ethical AI, bias mitigation, and responsible AI development – as a great springboard for future AI / ML development and an opportunity to deepen the local capacity we’re building in AI.