About 900 million people globally—one-third in Africa and another one-third in India—live in extreme poverty. Operating on the assumption that impoverished communities are trapped in poverty, major global donors have deployed a stream of development programs to break these traps. Despite the scale of programs, scholars have little knowledge about the distribution of global poverty historically and geographically. To address these knowledge gaps, scholars must first tackle a data challenge: the lack of historical and geographical poverty data. The AI Global Development Lab is innovating global-poverty research by combining deep-learning, satellite technologies, and knowledge on human development to overcome the data challenge. The Lab is recreating historical and geographical human-development trajectories from satellite images from 1984 to 2022. These new data will measure poverty at unprecedented temporal and spatial granularity. Among other things, these data will enable the Lab (and other scholars) to start examining—with a high precision—the causal effects of foreign aid on poor communities’ chances of breaking poverty. This talk will discuss key scientific challenges and early findings.
The AI Global Development Lab is primarily based at the Institute for Analytical Sociology (IAS), Linköping University, and the Division of Data Science and Artificial Intelligence (DSAI) of the Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden. The Lab is led by Adel Daoud (Primary Investigator), Associate Professor in analytical sociology, IAS, and Affiliated Associate Professor in Data Science and Artificial Intelligence for the Social Sciences, DSAI. Professor Devdatt Dubhashi and Assistant Professor Fredrik Johansson, at DSAI, are co-PIs. Other key partners are based at the University of Gothenburg and Harvard University.
The vision of the Lab is to “combine AI and earth observation to estimate sustainable and human development globally.” The Swedish Research Council funds this Lab through a Research Environment Program and a Consolidator Grant. Chalmers AI Research Centre (CHAIR) is supporting the Lab in partnership with IMCG. Google, in partnership with the Group on Earth Observations, provides mentorship and in-kind technical support for the Lab.