Law, Order & Algorithms

The Board of Trustees of Leland Stanford Junior University

Date Awarded
01/01/16
Amount
$310,000
Grant Period
01/01/16 to 07/01/17
Focus Area
Media Innovation
Community
San Jose
Initiative
Knight News Challenge

Project Links

Grantee Contact

  • Stanford, CA

Featured Media

Law, Order and Algorithms from Knight Foundation on Vimeo.

Increasing transparency and accountability in law enforcement by compiling, analyzing and releasing a data set of more than 100 million highway patrol stops throughout the country.

Project Team

Sharad Goel

Sam Corbett-Davies is a Ph.D. student at Stanford in the Department of Computer Science. He is a Fulbright Scholar from New Zealand who received his Bachelor of Engineering in Mechatronics from the University of Canterbury. Sam is interested in applying machine learning and statistics to questions of politics and policy.

Sharad Goel is an assistant professor at Stanford in the Department of Management Science & Engineering, and, by courtesy, Computer Science and Sociology. Sharad draws on a combination of methods from computer science and statistics to study contemporary issues in public policy, including police practices, voter laws, media bias, and online privacy.

Vignesh Ramachandran is the managing editor of Stanford’s Peninsula Press and is affiliated with the Stanford Computational Journalism Lab. He previously covered technology news at Mashable, and U.S. news and travel issues for NBC News Digital. He received an M.A. in journalism from Stanford.

Ravi Shroff is a research scientist at New York University’s Center for Urban Science and Progress. He applies statistical and machine learning methods to study urban settings, particularly in the context of criminal justice. Ravi has a Ph.D. in mathematics from UC San Diego, and an M.S. in applied urban science and informatics from NYU.

Camelia Simoiu is a Ph.D. student at Stanford in the Department of Management Science & Engineering, with a background in applied statistics and artificial intelligence. Camelia draws on computational methods from statistics, machine learning, and network science, in order to engineer and evaluate the effectiveness of complex social processes such as public policy and online platforms.

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