The Science of the Predicted Human Talk Series: Professor Joshua Blumenstock

Title

Targeting Social Assistance with Machine Learning

Abstract

Targeting is a central challenge in the design of anti-poverty programs: given available data, how does one rapidly identify the individuals and families with the greatest need? Here we show that machine learning, applied to non-traditional data from satellites and mobile phones, can improve the targeting of anti-poverty programs. Our analysis is based on data from three field-based projects — in Togo, Afghanistan, and Kenya — that illustrate the promise, as well as some of the potential pitfalls, of this new approach to targeting. Collectively, the results highlight the potential for new data sources to improve humanitarian response efforts, particularly in crisis settings when traditional data are missing or out of date.

About Joshua Blumenstock

Joshua Blumenstock is a Chancellor’s Associate Professor at the U.C. Berkeley School of Information and the Goldman School of Public Policy. He is the Co-director of the Global Policy Lab and the Center for Effective Global Action. Blumenstock is known for his research at the intersection of machine learning and empirical economics, which focuses on how novel data can better address the needs of poor and marginalized people around the world. He has a Ph.D. in Information Science and a M.A. in Economics from U.C. Berkeley, and Bachelor’s degrees in Computer Science and Physics from Wesleyan University. He is a recipient of awards including the NSF CAREER award, the Intel Faculty Early Career Honor, and the U.C. Berkeley Chancellor’s Award for Public Service.

The Predicted Human

Being human in 2023 implies being the target of a vast number of predictive infrastructures. In healthcare, algorithms predict not only potential pharmacological cures to disease but also their possible future incidence of those diseases. In governance, citizens are exposed to algorithms that predict – not only their day-to-day behaviors to craft better policy – but also to algorithms that attempt to predict, shape and manipulate their political attitudes and behaviors. In education, children’s emotional and intellectual development is increasingly the product of at-home and at-school interventions shaped around personalized algorithms. And humans worldwide are increasingly subject to advertising and marketing algorithms whose goal is to target them with specific products and ideas they will find palatable. Algorithms are everywhere – as are their intended as well as unintended consequences. The series is an activity of the Networks & Graphs collaboratory, and is arranged with generous support by the Villum Foundation and the Pioneer Center for Artificial Intelligence.