News  |    |  December 19, 2019

Federal study confirms racial bias of many facial-recognition systems, casts doubt on their expanding use

News article by Drew Harwell.
Published by The Washington Post.


Facial-recognition systems misidentified people of color more often than white people, a landmark federal study released Thursday shows, casting new doubts on a rapidly expanding investigative technique widely used by law enforcement across the United States.

Asian and African American people were up to 100 times more likely to be misidentified than white men, depending on the particular algorithm and type of search. Native Americans had the highest false-positive rate of all ethnicities, according to the study, which found that systems varied widely in their accuracy.

The faces of African American women were falsely identified more often in the kinds of searches used by police investigators where an image is compared to thousands or millions of others in hopes of identifying a suspect.

Algorithms developed in the United States also showed high error rates for “one-to-one” searches of Asians, African Americans, Native Americans and Pacific Islanders. Such searches are critical to functions including cellphone sign-ons and airport boarding schemes, and errors could make it easier for impostors to gain access to those systems.

Women were more likely to be falsely identified than men, and the elderly and children were more likely to be misidentified than those in other age groups, the study found. Middle-aged white men generally benefited from the highest accuracy rates.

The National Institute of Standards and Technology, the federal laboratory known as NIST that develops standards for new technology, found “empirical evidence” that most of the facial-recognition algorithms exhibit “demographic differentials” that can worsen their accuracy based on a person’s age, gender or race. [ . . . ]