Targeting humanitarian aid with machine learning and digital data
Emily Aiken (UC Berkeley)
Colloquium
Tuesday, April 2, 2024, 3:30 pm
Abstract
The vast majority of humanitarian aid and social protection programs globally are targeted, providing assistance to individuals or communities identified to be poorest or most in need. In low- and middle-income countries, the targeting of aid programs is often limited by low-quality, out-of-date, or missing data on poverty and vulnerability. Novel "big" digital data sources, such as those captured by satellites, mobile phones, and financial services providers -- when combined with advances in machine learning -- can improve the accuracy of aid program targeting. In this talk, I will cover empirical results on the accuracy of these new data-driven and algorithmic approaches to aid allocation in Togo and Bangladesh, and will discuss emergent implications for fairness, privacy, transparency, and community dynamics.
Bio
Emily Aiken is a PhD candidate at the UC Berkeley School of Information, where she studies the application of novel algorithms and digital data sources for social protection programs. Her work has been published in venues including Nature and Science Advances, and she is a recipient of a Microsoft Research Ph.D. fellowship. Prior to Berkeley, Emily received her bachelor's degree in computer science from Harvard.(/p>