Last year, a House Judiciary subcommittee heard a harrowing, but increasingly common, story of injustice. Robert Williams, a Black man, was arrested in 2020 on suspicion of stealing watches from a store in Detroit. But even though he hadn’t been in that store in several years, police took him away in a squad car in front of his two young daughters. He was held in custody for more than 30 hours for a crime he didn’t commit.
Law enforcement identifying the wrong suspect isn’t new. What is new is how police make these kinds of mistakes. In Williams’ case, the Detroit Police Department used Michigan State Police’s facial recognition program to identify a suspect from a grainy surveillance image. The technology used Michigan’s database of driver’s license photos to land on Williams as a possible match — a high-tech mistake with grave human consequences. It is essential that a federal law is created to help prevent these kinds of mistakes by law enforcement.
The powerful surveillance tools that were used against Williams are up to 100 times more likely to misidentify Asian and Black people compared with white men, according to a 2019 National Institute of Standards and Technology study. False positive rates are also elevated in South Asian, Central American and Native American people. Facial recognition technology (FRT), in addition to its algorithmic biases, can — and has — been used by law enforcement to identify peaceful protesters, investigate minor offenses and arrest people with no evidence of guilt other than a single FRT match. As a result, there is an ever-growing list of people, particularly those of color, who have been victims of this flawed, unregulated surveillance system.
