How can machine learning and data science help get cash to families in need as quickly as possible? Data.org’s recap of Han Sheng Chia’s talk at Good Tech Fest highlights CEGA’s joint project with GiveDirectly to improve MobileAid targeting in Togo:
“Instead of dialing 911 for emergency assistance, what if you could dial 999 for the cash you need to feed your family and buy supplies during a crisis? Imagine a world in which you, along with thousands of others, could rapidly and accurately be identified as someone needing assistance, and this assistance could be sent to you via text message almost immediately after you’d applied for it.
This service, called MobileAid, is now a reality in Togo. And it will soon be scaling internationally thanks to new funding from the data.org Inclusive Growth and Recovery Challenge supported by founding partners the Mastercard Center for Inclusive Growth and The Rockefeller Foundation.
MobileAid is just one example of data-driven global transformation among many being considered by more than 1,500 people from 52 countries at Good Tech Fest 2021. The conference attendees attended numerous workshops to engage with compelling case studies that illustrate what data and data science for social good can do.
GiveDirectly, one of the fastest growing NGOs focused on international issues, and its academic partner, the Center for Effective Global Action (CEGA) at UC Berkeley, have used data science and machine learning to identify and send cash with unprecedented speed to 95,000 people living in poverty in Togo during the COVID-19 pandemic. Governmental advisors in Togo have described the project as “foundational” in terms of setting up the country’s social protection system. It’s one of eight projects chosen to receive a combined $10 million in funding and technical assistance in the Challenge. The Challenge drew ideas and proposals from 108 countries around the world to tackle social problems with data and technology.”
Copyright 2022. All Rights Reserved
Design & Dev by Wonderland Collective