Hurricane Ian Destroyed Their Homes. Algorithms Sent Them Money

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.

GiveDirectly is testing this new way of targeting emergency aid in collaboration with Google.org, the search and ad company’s charitable arm. The individuals offered money were users of a benefits app called Providers that manages food stamp payments. Targeting messages with help from AI software from Google allowed GiveDirectly to offer aid only to people who lived in areas devastated by Ian more quickly than manually sorting through the rolls of the app’s users.

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.

The Florida project was powered by a mapping tool called Delphi, developed by four Google machine-learning experts who worked with GiveDirectly over six months starting in late 2019. The software highlights communities in need after disasters such as hurricanes by overlaying live maps of storm damage with data on poverty from sources including the US Centers for Disease Control and Prevention. The storm damage data is provided by another Google tool, called Skai, that uses machine learning to analyze satellite imagery from before and after a disaster and estimate the severity of damage to buildings.

“You now have a map that says where is socio-economically vulnerable, and where has been damaged," says Alex Diaz, who leads Google.org's AI for Social Good team. “That can help on-the-ground support and speed up delivery of aid.”

The algorithms that power Skai’s damage assessments are trained by manually labeling satellite images of a couple of hundred buildings in a disaster-struck area that are known to have been damaged. The software can then, at speed, detect damaged buildings across the whole affected area. A research paper on the underlying technology presented at a 2020 academic workshop on AI for disaster response claimed the auto-generated damage assessments match those of human experts with between 85 and 98 percent accuracy.

In Florida this month, GiveDirectly sent its push notification offering $700 to any user of the Providers app with a registered address in neighborhoods of Collier, Charlotte, and Lee Counties where Google’s AI system deemed more than 50 percent of buildings had been damaged. So far, 900 people have taken up the offer, and half of those have been paid. If every recipient takes up GiveDirectly’s offer, the organization will pay out $2.4 million in direct financial aid.

Some may be skeptical of automated disaster response. But in the chaos after an event like a hurricane making landfall, the conventional, human response can be far from perfect. Diaz points to an analysis GiveDirectly conducted looking at their work after Hurricane Harvey, which hit Texas and Louisiana in 2017, before the project with Google. Two out of the three areas that were most damaged and economically depressed were initially overlooked. A data-driven approach is "much better than what we’ll have from boots on the ground and word of mouth,” Diaz says.