Rapid strides in machine learning have transformed the efficiency of public and private sector organizations alike; however, those working on poverty reduction have struggled to harness these cutting-edge technologies to improve outcomes for the poor. There are also risks in the use of artificial intelligence (AI) in social programs and policies, including algorithmic bias and privacy concerns.
To address these challenges, CEGA and the World Bank’s Development Impact Evaluation initiative (DIME) are holding a one-day workshop at the Google Community Space in San Francisco to explore the role of machine learning in economic research and public policy. We will examine how AI and related technologies are being applied to a broad array of challenges in global development—from predicting migration patterns and crop yields, to detecting corruption, estimating poverty, and learning about consumers at the base of the economic pyramid.
Speakers include Susan Athey (Stanford), Josh Blumenstock (UC Berkeley), Marshall Burke (Stanford), Timnit Gebru (Stanford), Sol Hsiang (UC Berkeley), Aprajit Mahajan (UC Berkeley), David McKenzie (World Bank Group), Cyrus Samii (NYU), and more…
The event will examine how AI and related technologies are being applied to a broad array of challenges in global development—from predicting migration patterns and crop yields, to detecting corruption, estimating poverty, and learning about consumers at the base of the economic pyramid.
To get your ticket, visit us on Eventbrite.
For more information, visit measuredev.org.