Using Artificial Intelligence and digital data to reach the poorest during emergencies

Cash-based transfers represent a powerful weapon through which the governments of low- and middle-income countries can fight poverty and increase the resilience of their citizens. A key question for these programmes is how to prioritize the people that are most in need of assistance. However, this can be difficult in developing countries, where official government registries are often incomplete and out of date. The problem is particularly acute in settings where collecting information on potential beneficiaries is more challenging like in remote rural areas or during an emergency such as the COVID-19 pandemic.

New methods based on non-traditional data sources have expanded the opportunities for identifying the poor in information-scarce settings. But are these new methods a feasible and effective option for targeting social protection programmes at scale? In this webinar, Professor Joshua Blumenstock will present ongoing work that uses recent advances in machine learning, applied to data from satellites and mobile phone networks, to target and deliver cash transfers to individuals and families living in extreme poverty. These new methods have been used to support the governments of Togo and Nigeria in expanding innovative emergency programmes during the pandemic, including to rural areas. Prof. Blumenstock will provide an overview of the new methods, comparing their performance in terms of reaching the poorest to more traditional targeting options. He will also reflect on the practical, social, and ethical challenges involved in the application of these novel methods to social protection programmes at scale.

SPEAKER:

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 using novel data and methods to better understand the causes and consequences of global poverty.  Prof. Blumenstock 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 the National Science Foundation’s 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).

Webcast