Targeting humanitarian aid with machine learning and digital data | Emily Aiken

Abstract: The majority of humanitarian aid and social protection programs globally are targeted, providing assistance to individuals or communities identified to be poorest or most in need. In low- and middle-income countries, the targeting of aid programs is often limited by low-quality, out-of-date, or missing data on poverty and vulnerability. Novel “big” digital data sources, such as those captured by satellites, mobile phones, and financial services providers — when combined with advances in machine learning — can improve the accuracy of aid program targeting. In this talk, I will cover empirical results on the accuracy of these new data-driven and algorithmic approaches to aid allocation, and will discuss emergent implications for fairness, privacy, transparency, and community dynamics.