Machine Learning for the Developing World

Researchers from across the social and computer sciences are increasingly using machine learning to study and address global development challenges. In this talk, I examine the burgeoning field of machine learning for the developing world (ML4D). First, a brief review of prominent literature is presented, followed by suggestions of best practices drawn from the literature for ensuring that ML4D projects are relevant to the advancement of development objectives. Finally, I discuss how developing world challenges can motivate the design of novel machine learning methodologies. This talk provides insights into systematic differences between ML4D and more traditional machine learning applications. It also discusses how technical complications of ML4D can be treated as novel research questions, how ML4D can motivate new research directions, and where machine learning can be most useful.

Maria De-Arteaga is a fourth year PhD student in the joint Machine Learning and Public Policy program at Carnegie Mellon University, where she is advised by Artur Dubrawski. She is passionate about creating novel machine learning algorithms that are motivated by existing societal challenges, improving fairness qualities of machine learning systems, and understanding how machine learning can better help us achieve global development goals. During her PhD, Maria has applied her work to counter-human trafficking and sexual violence initiatives, as well as various projects in the healthcare domain. In 2017, she co-organized the NIPS Workshop on Machine Learning for the Developing World.