From The Conversation
How AI helped deliver cash aid to many of the poorest people in Togo
Emily Aiken and Joshua Blumenstock
The big idea
Governments and humanitarian groups can use machine learning algorithms and mobile phone data to get aid to those who need it most during a humanitarian crisis, we found in newly published research.
The simple idea behind this approach is that wealthy people use phones differently from poor people. Their phone calls and text messages follow different patterns, and they use different data plans, for example. Machine learning algorithms – which are fancy tools for pattern recognition – can be trained to recognize those differences and infer whether a given mobile subscriber is wealthy or poor.
As the COVID-19 pandemic spread in early 2020, our research team helped Togo’s Ministry of Digital Economy and GiveDirectly, a nonprofit that sends cash to people living in poverty, turn this insight into a new type of aid program...
Emily Aiken is a doctoral student at the UC Berkeley School of Information. Joshua Blumenstock is an associate professor of information and co-director of the Center for Effective Global Action (CEGA) at UC Berkeley.
