From Google searches to Fitbit tracking and everything in between, our lives are inundated with data. At the same time, computers are getting smarter about how to handle it. Machine learning (the science of programming computers to better perform tasks as they gain experience) holds tremendous possibilities to better inform public policy research. Currently, machine learning is used to predict global health epidemics like Ebola and prevent childhood lead poisoning.
As the world’s population grows and cities become more connected and data driven, how can we harness data for evidence-based public policy research? What are the limitations and challenges? How might machine learning change the way we approach public policy research?
On July 14th, our expert panel from the field explored these questions and more.
- Dan Chenok, executive director, IBM Center for the Business of Government
- Constantine E. Kontokosta, deputy director for academics and assistant professor of urban informatics, Center for Urban Science and Progress, New York University
- Stephanie Shipp, deputy director and research professor, Social and Decision Laboratory, Virginia Bioinformatics Institute, Virginia Tech
- Moderator: Robert Santos, chief methodologist and director of the statistical methods group, Urban Institute
Washington , DC , 20037