Last Autumn, University of Virginia computer-science professor Vicente Ordóñez noticed a pattern in some of the guesses made by image-recognition software he was building. “It would see a picture of a kitchen and more often than not associate it with women, not men,” he says.

That got Ordóñez wondering whether he and other researchers were unconsciously injecting biases into their software. So he teamed up with colleagues to test two large collections of labeled photos used to “train” image-recognition software.

Their results are illuminating. Two prominent research-image collections—including one supported by Microsoft and Facebook—display a predictable gender bias in their depiction of activities such as cooking and sports. Images of shopping and washing are linked to women, for example, while coaching and shooting are tied to men. Machine-learning software trained on the datasets didn’t just mirror those biases, it amplified them. If a photo set generally associated women with cooking, software trained by studying those photos and their labels created an even stronger association.

Mark Yatskar, a researcher at the Allen Institute for Artificial Intelligence, says that phenomenon could also amplify other biases in data, for example related to race. “This could work to not only reinforce existing social biases but actually make them worse,” says Yatskar, who worked with Ordóñez and others on the project while at the University of Washington.