Google and GiveDirectly Use AI to Provide Cash Assistance to Victims of Hurricane Ian

ODSC - Open Data Science
2 min readOct 27, 2022

Google and nonprofit GiveDirectly have teamed up and are using the power of artificial intelligence to target the poorest victims of Hurricanes Ian and Fiona with emergency cash payments. This is a new approach to disaster relief that sees governments and NGOs working separately or in tandem to provide aid in a more general sense through donations that focus on food and clothing for the overall affected population.

The method employed by Google and GiveDirectly sees the duo utilizing aerial images of storm damage and cross-referencing this information with poverty data in the state of Florida and the territory of Puerto Rico. In all, the goal is to reach those who have the fewest resources to manage a disaster quicker than traditional methods.

According to the GiveDirectly page. It states, “Together with our partners at Google, we’re using aerial storm damage imagery and poverty data to identify the highest-need communities… We currently have enough funding from our partners to deliver cash to several thousand families in the hardest-hit regions of Puerto Rico and Florida.”

The cash payment directed at families will see each receiving $700.00 with the purpose of covering the most common post-disaster items needed. This includes food, first aid supplies, and toiletries. Using artificial intelligence to aid in emergency response is a growing topic within the community. During ODSC’s Europe conference, Aoife Cahill, PhD of Dataminr spoke at length about using AI-powered technology to increase the effectiveness of aid response time by getting key information to the right hands.

For Google, artificial intelligence can prove to be a game changer in aiding those in most need who are affected by disasters. Head of Crisis Response and Humanitarian Aid at Google, Alex Diaz explains, “After most major natural disasters on record, inequality skyrockets…Those that are most affected by disasters tend to be the most marginalized communities… Imagine a system that integrates census data [and] poverty data, and overlays that with disaster data after a disaster has hit.”

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