Researchers from Stanford’s School of Engineering have launched a database project funded by the Knight Foundation that aims to address confrontations between police and minority communities in the Unites States. According to a Stanford News report, the project will collect information from traffic stops to paint a broader understanding of these issues in unbiased, data-driven analysis.
The Project on Law, Order, and Algorithms is spearheaded by Sharad Goel, assistant professor of management science and engineering. Project team members come from various fields such as journalism, mathematics, artificial intelligence and computer science. Goel and his colleagues are building an open database of 100 million traffic stops across America that record basic facts about each stop, including demographic data.
The team intends to develop a statistical method that determines if a police force is discriminating against people with regards to race, ethnicity, age or gender. They will examine the context of the occurrences, ultimately producing data that will help law enforcement agencies design fairer and more effective practices.
The initiative aims to expand the database capabilities through their traffic stop analysis and to create a software toolkit that others can use to collect data from municipal governments. Goel’s team wants other researchers, journalists, community groups and police departments to learn and utilize this method; they hope to facilitate the same level of data mining that Goel and his team can do.
Although this approach is not flawless, research has shown that this data-driven method has been effective.
“This can be a win-win situation. Everybody wants to reduce crime in a way that is supportive of the community,” Goel said. “We’d like to help law enforcement agencies make better decisions — decisions that are more equitable, efficient and transparent.”
Stanford undergraduates who are interested in exploring data mining more generally can do so in Goel’s MS&E class, Law, Order, and Algorithms, that focuses on the collaboration of data science and public policy.