This talk describes ongoing work that leverages recent advances in ML, applied to rich data from satellites and mobile phone networks, to target and deliver emergency social assist
Abstract: As Covid-19 spreads in low and middle-income countries, economic disruptions have left hundreds of millions without work or income, precipitating the first rise in global extreme poverty in over 20 years. To offset the pandemic’s most devastating effects, national policymakers and humanitarian organizations are scrambling to provide emergency humanitarian aid to those who need it most. But determining “those who need it most” is difficult in many poor and conflict-affected countries, where official government registries are often incomplete and out of date.
This talk describes ongoing work that leverages recent advances in machine learning, applied to rich data from satellites and mobile phone networks, to target and deliver emergency social assistance. The algorithms we have developed now form the basis for Covid-19 response programs in Togo and Nigeria, which are providing subsistence cash transfers to hundreds of thousands of poor families. More broadly, this work illustrates the important role that machine learning can play in the future of humanitarian response. It also highlights several open technical challenges – as well as important social and ethical considerations – that arise at the intersection of machine learning and applied economics.
Bio: Joshua Blumenstock is an Associate Professor at the U.C. Berkeley School of Information, the Director of the Data-Intensive Development Lab, and the faculty co-Director of the Center for Effective Global Action. His research lies at the intersection of machine learning and empirical economics, and focuses on developing new computational approaches to better understand the causes and consequences of global poverty. Joshua has a Ph.D. in Information Science and a M.A. in Economics from U.C. Berkeley, and Bachelor’s degrees in Computer Science and Physics from Wesleyan University. He is a recipient of an NSF CAREER award, the Intel Faculty Early Career Honor, a Gates Millennium Grand Challenge award, a Google Faculty Research Award, and the U.C. Berkeley Chancellor's Award for Public Service. His work has appeared in a variety of publications including Science and Nature, as well as top economics journals (e.g., the American Economic Review) and computer science conferences (e.g., ICML, KDD, AAAI, WWW, CHI).
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