Machine Learning for Development: Challenges, Opportunities, and a Roadmap

Researchers from across the social and computer sciences are increasingly using machine learning to study and address global development challenges, and an exciting new field of "Machine Learning for the Developing World", or "Machine Learning for Development" (ML4D) is beginning to emerge. In recent work (De Arteaga et al., ACM TMIS, 2018), we synthesize the prominent literature in the field and attempt to answer the key questions, "What is ML4D, and where is the field headed?". Based on the literature, we identify a set of best practices for ensuring that ML4D projects are relevant to the advancement of development objectives. Given the strong alignment between development needs and ML approaches, we lay out a roadmap detailing three technical stages where ML4D can play an essential role and meaningfully contribute to global development. Perhaps the most important aspect of ML4D is that development challenges are treated as research questions, not as roadblocks: we believe that the ML4D field can flourish in the coming years by using the unique challenges of the developing world as opportunities to inspire novel and impactful research across multiple machine learning disciplines. This talk is based on joint work with Maria de Arteaga, William Herlands, and Artur Dubrawski.