Can Machine Learning and Mobile Phone Data Improve the Targeting of Humanitarian Assistance? Targeting is a central challenge in the administration of anti-poverty programs: given available data, how does one rapidly identify the individuals and families with the greatest need? Here we show that non-traditional “big” data from satellites and mobile phone networks can improve the targeting of anti-poverty programs. Our analysis compares outcomes – including exclusion errors, total social welfare, and measures of fairness – under different targeting regimes. Relative to other feasible targeting options, the machine learning approach reduces errors of exclusion by 4-21%. These results highlight the potential for new data sources to contribute to humanitarian response efforts, particularly in crisis settings when traditional data are missing or out of date. Joshua Blumenstock is a Chancellor’s Associate Professor at the U.C. Berkeley School of Information and the Goldman School of Public Policy. He is the Director of the Data-Intensive Development Lab and the co-Director of the Center for Effective Global Action. Blumenstock does research at the intersection of machine learning and empirical economics, and focuses on using novel data and methods to understand the causes and consequences of global poverty, and to improve the lives of disadvantaged people around the world. He 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 awards including the NSF CAREER award, the Intel Faculty Early Career Honor, and the U.C. Berkeley Chancellor's Award for Public Service. His work has appeared in general interest journals including Science, Nature, and Proceedings of the National Academy of Sciences,as well as top economics journals (e.g., the American Economic Review) and computer science conferences (e.g., ICML, KDD, AAAI, WWW, CHI).