Stanford engineers' 'Law, Order & Algorithms' data project aims to identify bias in the criminal justice system

If future scholars of American history remember 2015 for one defining issue, it may well be the rising public uproar over ugly and often fatal encounters between police and black citizens.

The police shooting of Michael Brown in Ferguson, Mo., along with videos of police killings in New York City, Cleveland and Chicago, ignited the Black Lives Matter movement. Equally graphic videos from Texas – of a police officer roughing up teenage girls at a pool party or of the officer who threatened to use a Taser on Sandra Bland after pulling her over for failing to signal a lane change – intensified charges that police unfairly target African Americans and other minorities.

As gripping as such incidents are, they still amount to individual anecdotes that can steer a narrative. To provide an unbiased, data-driven analysis of such issues, researchers at Stanford University’s School of Engineering have launched what they call the Project on Law, Order & Algorithms.

The project is led by computational social scientist Sharad Goel, an assistant professor of management science and engineering. He also teaches a course at Stanford Engineering that explores the intersection of data science and public policy issues revolving around policing.

Among other activities, Goel’s team is building a vast open database of 100 million traffic stops from cities and towns around the nation. The researchers have already gathered data on about 50 million stops from 11 states, recording basic facts about the stop – time, date and location – plus any available demographic data that do not reveal an individual’s identity. These demographics might include race, sex and age of the person.

Based on its work thus far, the Knight Foundation recently awarded the team a $310,000 grant to at least double the size of the database, compiling data from as many as 40 states, going back five to 10 years.