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A recent study called Gender Shades looks at how accurately the facial recognition systems of three tech giants — IBM, Microsoft and the China-based company Face ++ — identify the gender of a face in an image.

At first glance, the MIT Media Lab researcher who did the study, Joy Buolamwini, said the overall accuracy rate was high even though all companies detected men's faces better than women's. But the error rate grew as she dug deeper.

"Lighter male faces were the easiest to guess the gender on, and darker female faces were the hardest to guess the gender on," Buolamwini said.

Joy Buolamwini.jpg
Joy Buolamwini of MIT holds a shield representing the Algorithmic Justice League, a collective aimed at addressing algorithmic bias.
Cristina Quinn/WGBH News

Buolamwini made her own data set of images to test out, which included the faces of women politicians from other nations. White guy? No problem. Black female Rwandan parliament member? Does not compute.

The accuracy rate of identifying light-skinned men’s faces was 99 percent across the board, but the three companies experienced higher error rates when identifying darker-skinned women. IBM's error rate was the highest, close to 35 percent. Face ++ clocked in at 34.5 percent, but had the lowest error rate of all three companies in identifying dark-skinned males. Microsoft's error rate in identifying dark-skinned females was 20.8 percent. 

"So when we're looking at these systems that are relying on data, we have to be honest about the kind of data that's being fed into it," she said.

If programmers are training artificial intelligence on a set of images primarily made up of white male faces, their systems will reflect that bias. Buolamwini calls this the “Coded Gaze.” Another part of the equation could be that most programmers are white men.

"I think that definitely contributes to it," Buolamwini said. "Because you might not even know to question your data or your benchmark if it's reflective of you in the first place. If you don't have a very diverse perspective, it can be easier to miss groups you're not as familiar with."

This can be problematic as facial recognition technology is increasingly relied on to decide everything from what you should pay for car insurance to when you’re likely to commit anothercrime. According to the Georgetown Center on Privacy and Technology, law enforcement has half of all American adults in their face recognition networks. But regulation and transparency are lacking, and our primary concern should be the potential civil liberties, says Phillip Atiba Goff, president of the Center for Policing Equity at John Jay College of Criminal Justice.

"The more accurate computers get, the more likely they are to be used to take away things like privacy and liberty," he said. "It's never been the case in the course of black American history that black freedom fighters have been fighting for more state surveillance. So that's my concern. If we don't make our values the priority, then we're going to end up with tools and systems that are out of line with our values. That's how we ended up incarcerating more than one out of every 100 people in the United States."

There’s no sign of state or federal legislation to impose standards on law enforcement. Massachusetts State Police use facial recognition software to scan the Registry of Motor Vehicles database of driver's license photos when searching for a suspect. What’s unknown is the software in use.

WGBH News reached out to state police multiple times for comment but never heard back. The Boston Police Department, on the other hand, said that it “does not use any technology that involves the use of facial recognition software.”

In a time where there's excitement about what artificial intelligence can do, Joy Buolamwini says there's a lot of blind faith in machine-learning algorithms.

"Yes, there are many things that it could do, but we have to be honest about how it's being implemented. At the end of the day, who's being harmed. Who's benefiting? If the technology is flawed, it shouldn't be used in the first place, and if this technology is going to be adopted, there have to be standards," she said. 

Buolamwini and a growing number of data scientists are offering to audit government, law enforcement and private systems for bias. Through the collective she founded, the Algorithmic Justice League, people can make these requests or report their experiences with bias. She wants to help people hold tech giants accountable.

"You have to be intentional about being inclusive because those in power reflect the current inequities that we have," Buolamwini said.

 As for those three companies whose technologies she tested, when she reached out to them with her findings in December, Buolamwini said they all responded differently

"I didn't hear anything back from Face ++. Microsoft responded in the New Year, saying 'This is something we care about. Thank you for bringing it to our attention.' Very corporate response," she said. 

IBM responded to her the same day and invited her to its headquarters in New York. The company wanted to fix this. They recreated her study and asked her to audit their system.

"They were around 65 percentage in terms of accuracy. They're now around 96 percent, self-reported. So we still have to go through and validate that," Buolamwini said. "But what this shows is change is a matter of priority." 

This post has been updated.