AI gets faces wrong – study
– Facial recognition systems can produce wildly inaccurate results, especially for non-whites, according to a US government study that is likely to raise fresh doubts on deployment of the artificial intelligence (AI) technology.
The study of dozens of facial recognition algorithms showed “false positives” for Asian and African American as much as 100 times higher than for whites.
The National Institute of Standards and Technology, a government research centre, also found two algorithms assigned the wrong gender to black females 35% of the time.
The study comes amid widespread deployment of facial recognition for law enforcement, airports, border security, banking, retailing, schools and for personal technology such as unlocking smartphones.
Some activists and researchers have claimed the potential for errors is too great and that mistakes could result in the jailing of innocent people, and that the technology could be used to create databases that may be hacked.
The study found both “false positives”, in which an individual is mistakenly identified, and “false negatives”, where the algorithm fails to match a face to a specific person in a database.
“A false negative might be merely an inconvenience – you can’t get into your phone, but the issue can usually be remediated by a second attempt,” said lead researcher Patrick Grother.
“But a false positive in a oneto-many search puts an incorrect match on a list of candidates that warrant further scrutiny.”
The study found US face recognition systems had higher error rates for Asians, African Americans and Native American groups, with the American Indian demographic showing the highest rates of false positives.
However, some algorithms developed in Asian countries produced similar accuracy rates for matching between Asian and Caucasian faces – which the researchers said suggests these disparities can be corrected.
“These results are an encouraging sign,” Grother said.
Nonetheless, Jay Stanley of the American Civil Liberties Union, which has criticised the deployment of face recognition, said the new study shows the technology is not ready for wide deployment.