Facial-recognition technology is already being used for applications ranging from unlocking phones to identifying potential criminals. Despite advances, it has still come under fire for racial bias: many algorithms that successfully identify white faces still fail to properly do so for people of color. Recently the National Institute of Standards and Technology (NIST) published a report showing how 189 face-recognition algorithms, submitted by 99 developers across the globe, fared at identifying people from different demographics.
Along with other findings, NIST’s tests revealed that many of these algorithms were 10 to 100 times more likely to inaccurately identify a photograph of a black or East Asian face, compared with a white one. In searching a database to find a given face, most of them picked incorrect images among black women at significantly higher rates than they did among other demographics. This report is the third part of the latest assessment to come out of a NIST program called the Face Recognition Vendor Test (FRVT), which assesses the capabilities of different face-recognition algorithms.