Amazon facial-identification software used by police falls short on tests for accuracy and bias, new research finds

Facial-recognition software developed by Amazon and marketed to local and federal law enforcement as a powerful crime-fighting tool struggles to pass basic tests of accuracy, such as correctly identifying a person's gender, new research released Thursday says. Researchers with M.I.T. Media Lab also said Amazon's Rekognition system performed more accurately when assessing lighter-skinned faces, raising concerns about how biased results could tarnish the artificial-intelligence technology's use by police and in public venues, including airports and schools. Amazon's system performed flawlessly in predicting the gender of lighter-skinned men, the researchers said, but misidentified the gender of darker-skinned women in roughly 30 percent of their tests. Rival facial-recognition systems from Microsoft and other companies performed better but were also error-prone, they said. The problem, AI researchers and engineers say, is that the vast sets of images the systems have been trained on skew heavily toward white men.