By Daniel Björkegren and Joshua Blumenstock
As artificial intelligence (AI) reshapes developing economies, it raises familiar risks of disruption, misinformation, and surveillance—but also promises many potential benefits. Recent examples illustrate how AI-based technologies can target aid and credit better and improve access to tailored teaching and medical advice. But balancing these risks and opportunities means more than just plug and play of existing technology—it calls for local innovation and adaptation.
Most recent advances in artificial intelligence originated in wealthy nations—developed in those countries for local users, using local data. Over the past several years, we have conducted research with partners in low-income nations, working on AI applications for those countries, users, and data. In such settings, AI-based solutions will work only if they fit the local social and institutional context.
In Togo, where the government used machine learning technology to target cash aid during the COVID-19 pandemic, we found that adapting AI to local conditions was the key to successful outcomes. The government repurposed technology originally designed to target online advertising to the task of identifying the country’s poorest residents. Using AI, the system processed data from satellites and mobile phone companies to identify signatures of poverty—such as villages that appeared underdeveloped in aerial imagery and mobile subscribers with low balances on their phones. Targeting based on these signatures helped ensure that cash transfers reached people with the greatest need (Aiken and others 2022)...
Joshua Blumenstock is an associate professor of information and co-director of the Center for Effective Global Action (CEGA) at UC Berkeley.