Machine Learning Approaches to Targeting Emergency Humanitarian Assistance
- Starts11:00 am on Friday, April 30, 2021
- Ends12:00 pm on Friday, April 30, 2021
Speaker: Josh Blumenstock, Associate Professor in the School of Information, Director of the Data-Intensive Development Lab, and Co-Director of the Center for Effective Global Action at University of California, Berkeley This event is part of our Distinguished Speaker Series: Machine Learning for Model-Rich Problems. Abstract: As COVID-19 spreads in low and middle-income countries, economic disruptions have left hundreds of millions without work or income. To offset the pandemic’s most devastating effects, national policymakers and humanitarian organizations are scrambling to provide emergency assistance to those who need it most. But determining “those who need it most” is difficult in many developing countries, where official government registries are often incomplete and out of date. This talk describes ongoing work that uses recent advances in machine learning, applied to rich data from satellites and mobile phone networks, to target and deliver cash aid to individuals and families living in extreme poverty. These methods now form the basis for emergency COVID-19 response programs in Nigeria and Togo, and highlight the role that machine learning might play in the future of humanitarian response.
- Zoom Webinar. The Zoom login information can be found on our Eventbrite page, below, after registering.