Early lessons in ML4d from the field

In this talk, I will share lessons from our efforts on the ground in creating AI-for-social-good solutions, spanning cellphone-image-based anthropometry for babies, AI-enabled active case-finding in tuberculosis, and early pest detection in cotton farming. The promise of AI as a powerful aid for achieving global-development goals is bolstered by five current forces: large frontline workforces enabling service delivery and data collection, growing smartphone penetration providing compute, connectivity, imaging, localization, and interfaces, large tech-enabled development programs having established data pipelines, infrastructure, and processes, rural populations increasingly adopting technology, and strong policy and institutional support for AI in development. We recommend that AI-for-social-good efforts utilize these forces by piggybacking on large tech-enabled development programs to achieve scaled impact. I will provide examples for such programs, point to opportunity areas, list criteria for AI-for-social-good innovators to assess likelihood of scaled impact, discuss risks and mitigation strategies, and suggest frontier areas for AI-for-social-good research.