Different AI systems do different things when they attempt to identify humans. Everyone has heard about face recognition (a k a facial recognition), which you might expect would return a name and other personal data about a person whose face is “seen” with a camera.
No, not always.
A system that analyzes human faces might simply try to return information about the person that you or I would tag in our minds when we see a stranger. The person’s gender, for example. That’s relatively easy to do most of the time for most humans — but it turns out to be tricky for machines.
Machines often get it wrong when trying to identify the gender of a trans person. But machines also misidentify the gender of people of color. In particular, they have a big problem recognizing Black women as women.
A short and good article about this ran in Time magazine in 2019, and the accompanying video is well worth watching. It shows various face recognition software systems at work.
Another serious problem concerns differentiating among people of Asian descent. When apartment buildings and other housing developments have installed face recognition as a security system — to open for residents and stay locked for others — the Asian residents can find themselves locked out of their own home. The doors can also open for Asian people who don’t live there.
You can find a lot of articles about this widespread and very serious problem with AI technology, including the deservedly famous mug shots test by the American Civil Liberties Union.
“While it is usually incorrect to make statements across algorithms, we found empirical evidence for the existence of demographic differentials in the majority of the face recognition algorithms we studied.”—Patrick Grother, NIST computer scientist
So how does this happen? How do companies with almost infinite resources deploy products that are so seriously — and even dangerously — flawed?
Yesterday I wrote a little about training data for object-detection AI. To identify any image, or any part of an image, an AI system is usually trained on an immense set of images. If you want to identify human faces, you feed the system hundreds of thousands, or even millions, of pictures of human faces. If you’re using supervised learning to train the system, the images are labeled: Man, woman. Black, white. Old, young. Convicted criminal. Sex offender. Psychopath.
Who is in the images? How are those images labeled?
This is part of how the whole thing goes sideways. There’s more to it, though. Before a system is marketed, or released to the public, its developers are going to test it. They’re going to test the hell out of it. This can be compared with when an AI is developed that plays a particular game, like Go, or chess. After the system has been trained, you test it. To test the system, you’re going to have it play, and see if it can win — consistently. So when developers create a face recognition system, and they’ve tested it extensively, and they say, great, now it’s ready for the public, it’s ready for commercial use — ask yourself how they missed these glaring flaws.
Ask yourself how they missed the fact that the system can’t differentiate between various Asian faces.
Ask yourself how they missed the fact that the system identifies Black women as men.
Fortunately, in just the past year these flaws have received so much attention that a number of large firms (Amazon, IBM, Microsoft) have pulled back on commercial deployments of face recognition technologies. Whether they will be able to build more trustworthy systems remains to be seen.
More about bias in face recognition systems:
- Meet the computer scientist and activist who got Big Tech to stand down (Fast Company, August 2020) — about AI researcher Joy Buolamwini
- NIST Study Evaluates Effects of Race, Age, Sex on Face Recognition Software (December 2019) — this article is the source for the pullout quote above
- Timnit Gebru: Machine learning, bias, and product design (March 2018) — although this interview is older, I like how Gebru speaks about design: “We need more people who think about design working in AI, because oftentimes what’s happening is little things. … think about Siri or Alexa or these personal assistants who are women — what does that do to society, just portraying that stereotype?”
- Meet the Googlers working to ensure tech is for everyone (May 2020) — Gebru is now an engineer at Google. Along with colleagues Tiffany Deng and Tulsee Doshi, she works on ensuring fairness in AI.
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