COVID-19’s spread and lockdowns in low-income countries are leaving hundreds of millions of poor and vulnerable people without work or income. The United Nations World Food Programme has warned of devastating famines – 265 million people in low- and middle-income countries are projected to suffer from acute hunger by the end of the year. Big data and artificial intelligence can help.
Joshua Blumenstock
Associate Professor, School of Information
Director, Data-Intensive Data Lab
Co-Director, Center for Effective Global Action
University of California, Berkeley
My work focuses on using novel data and methods to better understand the economic lives of the poor. Most active projects are based in developing and conflict-affected countries.
SELECTED PUBLICATIONS
While real-world decisions involve many competing objectives, algorithmic decisions are often evaluated with a single objective function. We study algorithmic policies which explicitly trade off between a private objective (such as profit) and a public objective (such as social welfare). We analyze a natural class of policies which trace an empirical Pareto frontier based on learned scores, and focus on how such decisions can be made in noisy or data-limited regimes. Our theoretical results characterize the optimal strategies in this class, bound the Pareto errors due to inaccuracies in the scores, and show an equivalence between optimal strategies and a rich class of fairness-constrained profit-maximizing policies.