Humans aren't always great at identifying a person's gender based on visual cues, and a new study suggests that computers may be even worse at it than humans. A group of researchers from University of Colorado Boulder tested the four biggest commercially available facial recognition software providers for potential race and gender biases. The researchers found the systems misclassified trans men up to 38 percent of the time and had no options for nonbinary people, meaning they were misclassified 100 percent of the time by default. 'These systems don't know any other language but male or female, so for many gender identities it is not possible for them to be correct,' researcher Jed Brubaker told CU Boulder Today. The facial recognition software was much more accurate when evaluating cisgender, accurately identifying cisgender women 98.3 percent of the time and cisgender men 97.6 percent of the time.