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Fri Dec 08 08:00 AM -- 06:30 PM (PST) @ S7
Machine Learning for the Developing World
William Herlands · Maria De-Arteaga

Six billion people live in developing world countries. The unique development challenges faced by these regions have long been studied by researchers ranging from sociology to statistics and ecology to economics. With the emergence of mature machine learning methods in the past decades, researchers from many fields - including core machine learning - are increasingly turning to machine learning to study and address challenges in the developing world. This workshop is about delving into the intersection of machine learning and development research.

Machine learning present tremendous potential to development research and practice. Supervised methods can provide expert telemedicine decision support in regions with few resources; deep learning techniques can analyze satellite imagery to create novel economic indicators; NLP algorithms can preserve and translate obscure languages, some of which are only spoken. Yet, there are notable challenges with machine learning in the developing world. Data cleanliness, computational capacity, power availability, and internet accessibility are more limited than in developed countries. Additionally, the specific applications differ from what many machine learning researchers normally encounter. The confluence of machine learning's immense potential with the practical challenges posed by developing world settings has inspired a growing body of research at the intersection of machine learning and the developing world.

This one-day workshop is focused on machine learning for the developing world, with an emphasis on developing novel methods and technical applications that address core concerns of developing regions. We will consider a wide range of development areas including health, education, institutional integrity, violence mitigation, economics, societal analysis, and environment. From the machine learning perspective we are open to all methodologies with an emphasis on novel techniques inspired by particular use cases in the developing world.

Invited speakers will address particular areas of interest, while poster sessions and a guided panel discussion will encourage interaction between attendees. We wish to review the current approaches to machine learning in the developing world, and inspire new approaches and paradigms that can lay the groundwork for substantial innovation.

Introductory remarks (Introduction)
Skyler Speakman (IBM Research Africa): Three Population Covariate Shift for Mobile Phone-based Credit Scoring (Invited speaker)
Contributed talk: Unique Entity Estimation with Application to the Syrian Conflict (Contributed talk)
Contributed talk: Field Test Evaluation of Predictive Models for Wildlife Poaching Activity in Uganda (Contributed talk)
Contributed talk: Household poverty classification in data-scarce environments: a machine learning approach (Contributed talk)
Ernest Mwebaze (UN Global Pulse): ML4D: what works and how it works - case studies from the developing world (Invited speaker)
Emma Brunskill (Stanford) (Invited speaker)
P. Anandan (Wadhwani Institute of AI) (Presentation)
Posters (Poster session)
Lunch (Break)
Jen Ziemke (International Crisis Mappers) (Invited speaker)
Caitlin Augustin (DataKind): Data for Social Good (Invited speaker)
Coffee break (Break)
Stefano Ermon (Stanford): Measuring Progress Towards Sustainable Development Goals with Machine Learning (Invited speaker)
Panel discussion (Panel)