Program Highlights »
Fri Dec 8th 08:00 AM -- 06:30 PM @ S7
Machine Learning for the Developing World
Maria De-Arteaga · William Herlands

Workshop Home Page

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.

08:45 AM Introductory remarks (Introduction)
Artur Dubrawski
09:00 AM Skyler Speakman (IBM Research Africa): Three Population Covariate Shift for Mobile Phone-based Credit Scoring (Invited speaker)
Skyler D. Speakman
09:30 AM Contributed talk: Unique Entity Estimation with Application to the Syrian Conflict (Contributed talk)
Beidi Chen
09:40 AM Contributed talk: Field Test Evaluation of Predictive Models for Wildlife Poaching Activity in Uganda (Contributed talk)
Shahrzad Gholami
09:50 AM Contributed talk: Household poverty classification in data-scarce environments: a machine learning approach (Contributed talk)
Varun Kshirsagar
10:00 AM Ernest Mwebaze (UN Global Pulse): ML4D: what works and how it works - case studies from the developing world (Invited speaker)
Ernest Mwebaze
11:00 AM Emma Brunskill (Stanford) (Invited speaker)
Emma Brunskill
11:30 AM P. Anandan (Wadhwani Institute of AI) (Presentation)
11:40 AM Posters (Poster session)
Biswarup Bhattacharya, Darius Lam, Sandeep Vidyapu, Shreya Shankar, Therese Anders, Bryan Wilder, Muhammad R Khan, Yunpeng Li, Nazmus Saquib, Varun Kshirsagar, Anthony Perez, Pengfei Zhang, Shahrzad Gholami, Rediet Abebe
12:30 PM Lunch (Break)
02:00 PM Jen Ziemke (International Crisis Mappers) (Invited speaker)
Jen Ziemke
02:30 PM Caitlin Augustin (DataKind): Data for Social Good (Invited speaker)
Caitlin Augustin
03:00 PM Coffee break (Break)
03:30 PM Stefano Ermon (Stanford): Measuring Progress Towards Sustainable Development Goals with Machine Learning (Invited speaker)
Stefano Ermon
04:00 PM Panel discussion (Panel)