Abstract

In response to the economic crisis caused by Covid-19, the Togolese government implemented a new national policy to provide the country’s poorest households with subsistence cash transfers to help them survive the next few months. PI Blumenstock has supported the Togolese government by developing machine-learning algorithms that help identify the poorest households with the greatest need for these cash transfers. While the technical system holds great promise, it also raises important societal and ethical concerns. For instance, how might the use of predictive algorithms in the provision of social welfare impact physical safety, economic justice, and privacy or leave out critical populations? Even if the data are carefully protected, whose privacy might be at risk and in what ways? Toward mitigating large-scale unintended harms, this research entails developing and testing a new tool in the Togolese context—Social Impact Scenarios—that (1) engages impacted communities to identify situated societal and ethical concerns and (2) employs narrative storytelling to make those concerns, tensions, and tradeoffs visible, and at times actionable, to technologists and policymakers throughout the technical design and data-driven policymaking process. While Social Impact Scenarios will be developed in the Togolese context, our intention is for Social Impact Scenarios to be used more broadly as a tool to support data-driven policies that account for situated societal and ethical concerns.

Principal Investigators

Joshua E. Blumenstock

Associate Professor, University of California, Berkeley

Bio

Joshua Blumenstock is an associate professor at the UC Berkeley School of Information, the director of the Data-Intensive Development Lab, and the faculty codirector of the Center for Effective Global Action. His research lies at the intersection of machine learning and empirical economics, and focuses on using novel data and methods to better understand the causes and consequences of global poverty. Joshua has a PhD in information science and an MA in economics from UC Berkeley, and bachelor’s degrees in computer science and physics from Wesleyan University. He is a recipient of the National Science Foundation’s CAREER award, the Intel Faculty Early Career Honor, a Gates Millennium Grand Challenge award, a Google Faculty Research Award, and the UC Berkeley Chancellor's Award for Public Service. His work has appeared in a variety of publications, including Science and Nature, as well as top economics journals (e.g., the American Economic Review) and computer science conferences (e.g., ICML, KDD, AAAI, WWW, CHI).

Zoe Kahn

PhD Student, University of California, Berkeley

Bio

Zoe Kahn is a PhD student in the Information School at UC Berkeley, where her research investigates how technologies impact people and society, with a focus on algorithmic decision-making and responsible innovation. Zoe's research surfaces rich and actionable insights for building more just and equitable technical systems. She brings an interdisciplinary background to her work that blends sociology, technology, law and policy. Zoe is a fellow at the UC Berkeley Center for Technology, Society, and Policy, Center for Long-Term Cyber Security, and Algorithmic Fairness and Opacity Group. She received her BA summa cum laude in sociology from New York University.