Uploaded April 2024 | Updated September 2026, 2 weeks ago
Allen School Colloquium
Title: Targeting humanitarian aid with machine learning and digital data
Speaker: Emily Aiken (Berkeley)
Date: Tuesday, April 2, 2024
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 In low- and middle-income countries, the targeting of aid programs is often limited by low-quality, out-of-date, or missing data on poverty and vulnerability.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 PhD fellowship. Prior to Berkeley, Emily received her bachelor's degree in computer science from Harvard.
This video is closed captioned.
Allen School Colloquium
Title: Targeting humanitarian aid with machine learning and digital data
Speaker: Emily Aiken (Berkeley)
Date: Tuesday, April 2, 2024
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 In low- and middle-income countries, the targeting of aid programs is often limited by low-quality, out-of-date, or missing data on poverty and vulnerability.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 PhD fellowship. Prior to Berkeley, Emily received her bachelor's degree in computer science from Harvard.
This video is closed captioned.
![[Audio Descriptions] I Am CSE Overview](https://i.ytimg.com/vi/kz0b8RCsfKw/mqdefault.jpg "[Audio Descriptions] I Am CSE Overview
In this mash-up of footage from our collection of I Am CSE videos, four Allen School student researchers describe their work in artificial intelligence, robotics, accessibility, and more—and explain why the Allen School is a great place to do leading-edge research.
This video is in the process of being closed captioned.
A version of this video without audio descriptions is available here: https://youtu.be/u2C9HMSU6Qo")
