When AI Sees a Man, It Thinks ‘Official.’ A Woman? ‘Smile’

All 20 lawmakers are smiling in their official photos. Google’s top suggested labels noted a smile for only one of the men, but for seven of the women. The company’s AI vision service labeled all 10 of the men as “businessperson,” often also with “official” or “white collar worker.” Only five of the women senators received one or more of those terms. Women also received appearance-related tags, such as “skin,” “hairstyle,” and “neck,” that were not applied to men.

Amazon and Microsoft’s services appeared to show less obvious bias, although Amazon reported being more than 99 percent sure that two of the 10 women senators were either a “girl” or “kid.” It didn’t suggest any of the 10 men were minors. Microsoft’s service identified the gender of all the men, but only eight of the women, calling one a man and not tagging a gender for another.

Google switched off its AI vision service’s gender detection earlier this year, saying that gender cannot be inferred from a person’s appearance. Tracy Frey, managing director of responsible AI at Google’s cloud division, says the company continues to work on reducing bias and welcomes outside input. “We always strive to be better and continue to collaborate with outside stakeholders—like academic researchers—to further our work in this space,” she says. Amazon and Microsoft declined to comment; both companies’ services recognize gender only as binary.

The US-European study was inspired in part by what happened when the researchers fed Google’s vision service a striking, award-winning image from Texas showing a Honduran toddler in tears as a US Border Patrol officer detained her mother. Google’s AI suggested labels including “fun,” with a score of 77 percent, higher than the 52 percent score it assigned the label “child.” WIRED got the same suggestion after uploading the image to Google’s service Wednesday.

Schwemmer and his colleagues began playing with Google’s service in hopes it could help them measure patterns in how people use images to talk about politics online. What he subsequently helped uncover about gender bias in the image services has convinced him the technology isn’t ready to be used by researchers that way, and that companies using such services could suffer unsavory consequences. “You could get a completely false image of reality,” he says. A company that used a skewed AI service to organize a large photo collection might inadvertently end up obscuring women businesspeople, indexing them instead by their smiles.