Facial recognition systems are all over the place: Facebook, airports, shopping malls. And they're poised to become nearly ubiquitous as everything from a security measure to a way to recognize frequent shoppers. For some people that will make certain interactions even more seamless. But because many facial recognition systems struggle with non-white faces, for others, facial recognition is a simple reminder: once again, this tech is not made for you.There are plenty of anecdotes to start with here: We could talk about the time Google's image tagging algorithm labeled a pair of black friends "gorillas," or when Flickr's system made the same mistake and tagged a black man with "animal" and "ape." Or when Nikon's cameras designed to detect whether someone blinked continually told at least one Asian user that her eyes were closed. Or when HP's webcams easily tracked a white face, but couldn't see a black one.There are always technical explanations for these things. Computers are programmed to measure certain variables, and to trigger when enough of them are met. Algorithms are trained using a set of faces. If the computer has never seen anybody with thin eyes or darker skin, it doesn't know to see them. It hasn't been told how. More specifically: the people designing it haven't told it how.The fact that algorithms can contain latent biases is becoming clearer and clearer. And some people saw this coming.
"It's one of those things where if you understand the ways that systemic bias works and you understand the way that machine learning works and you ask yourself the question: 'could these be making biased decisions?', the answer is obviously yes," said Sorelle Friedler, a professor of computer science at Haverford College. But when I asked her how many people actually do understand both systemic bias and the way algorithms are built, she said that the number was "unfortunately small."When you ask people who make facial recognition systems if they worry about these problems, they generally say no. Moshe Greenshpan, the founder and CEO of Face-Six, a company that develops facial recognition systems for churches and stores, told me that it's unreasonable to expect these systems to be 100 percent accurate, and that he doesn't worry about what he called "little issues," like a system not being able to parse trans people."I don't think my engineers or other companies engineers have any hidden agenda to give more attention to one ethnicity," said Greenshpan. "It's just a matter of practical use cases."And he's right, mostly. By and large, no one at these companies is intentionally programing their systems to ignore black people or tease Asians. And folks who work on algorithmic bias, like Suresh Venkatasubramanian, a professor of computer science at the University of Utah, say that's generally what they're seeing too. "I don't think there's a conscious desire to ignore these issues," he said. "I think it's just that they don't think about it at all. No one really spends a lot of time thinking about privilege and status, if you are the defaults you just assume you just are."
"No one really spends a lot of time thinking about privilege and status, if you are the defaults you just assume you just are."
And at the border we've already seen how biometric failures can be extremely painful. Trans people traveling through TSA checkpoints have all sorts of humiliating stories of what happens when their scans don't "match" their stated identity. Shadi Petosky live-tweeted her detention at the Orlando International Airport in Florida, where she said that "TSA agent Bramlet told me to get back in the machine as a man or it was going to be a problem." Since then, several more stories of "traveling while trans" have emerged revealing what happens when a biometric scan doesn't line up with what the TSA agent is expecting. Last year the TSA said they would stop using the word "anomaly" to describe the genitalia of trans passengers.
TSA agent Bramlet told me to get back in the machine as a man or it was going to be a problem.
Shadi PetoskySeptember 21, 2015