Hurricane Ian Destroyed Their Homes. Algorithms Sent Them Money
By Chris Stokel-Walker
When Hurricane Ian churned over Florida in late September, it left a trail of destruction from high winds and flooding. But a week after the storm passed, some people in three of the worst-hit counties saw an unexpected beacon of hope.
Nearly 3,500 residents of Collier, Charlotte, and Lee Counties received a push notification on their smartphones offering $700 cash assistance, no questions asked. A Google algorithm deployed in partnership with nonprofit GiveDirectly had estimated from satellite images that those people lived in badly damaged neighborhoods and needed some help...
This is the first time GiveDirectly has used this technology in the US, but it previously tested a similar idea in Togo in the months after the pandemic crippled the world's economy. There, households were offered aid based on signs of poverty detected by image algorithms from researchers at UC Berkeley, and clues from cell phone bills.
In 2020, Professor Joshua Blumenstock and a team developed machine learning algorithms that seek signs of poverty in satellite photos and cellphone data. GiveDirectly then helped deliver financial aid to those hit hardest economically by the COVID-19 pandemic.
Blumenstock is a Chancellor’s Associate Professor in the School of Information and Public Policy Director at the UC Berkeley Global Policy Lab.