CEO of Facial Recognition Company Kairos Argues that the Technology's Bias and Capacity For Abuse Make It Too Dangerous For Use By Law Enforcement (techcrunch.com)

Brian Brackeen, chief executive officer of the facial recognition software developer Kairos, writes in an op-ed: Recent news of Amazon's engagement with law enforcement to provide facial recognition surveillance (branded "Rekognition"), along with the almost unbelievable news of China's use of the technology, means that the technology industry needs to address the darker, more offensive side of some of its more spectacular advancements. Facial recognition technologies, used in the identification of suspects, negatively affects people of color. To deny this fact would be a lie. And clearly, facial recognition-powered government surveillance is an extraordinary invasion of the privacy of all citizens -- and a slippery slope to losing control of our identities altogether.

There's really no "nice" way to acknowledge these things. I've been pretty clear about the potential dangers associated with current racial biases in face recognition, and open in my opposition to the use of the technology in law enforcement. [...] To be truly effective, the algorithms powering facial recognition software require a massive amount of information. The more images of people of color it sees, the more likely it is to properly identify them. The problem is, existing software has not been exposed to enough images of people of color to be confidently relied upon to identify them.

Comments

Re:What?

[Immerman]

>Not sure I know enough about how that works to understand why less data would mean more false positives.
More training data means it needs to learn to recognize more subtle distinctions to be able to correctly identify an image. Without that subtly it will tend to overlook the differences and misidentify images.

It's actually very similar to the "X all look alike to me" effect. Let's take an extreme example: Imagine you live somewhere where pretty much everyone is white. You've only ever seen a handful of black people in your life, and Fred is the only black guy you personally know. Cool guy - you like him, grab beers after work, etc. And since we identify people by recognizing the differences between them and everyone else, "dark skin", "wide nose", "full lips", etc. are some of the big features you use to identify Fred. And why not? Nobody else you encounter has those features, so they really stand out to identify him from everyone else you see.

Then one day you're walking down the hall and see a black guy coming your way - similar build to Fred, with the same dark skin, wide nose, full lips, etc. And so you identify him as Fred, ask him how his project is going, and if he wants to grab a beer after work. And a totally confused Steve tries to figure out why the hell some complete stranger is acting like an old friend. Then Fred walks up, and seeing them stand side by side you start noticing the differences you didn't see initially - Fred has way more wrinkles around his eyes, Steve's cheeks are considerably rounder, etc. And, with a bit of practice you get good at telling them apart. Then you go to a conference where almost everyone is black - and once again you keep losing track of Fred, because there's a sea of faces around you, all bearing features superficially similar to Fred's, and you've really only learned to identify the small subset of obvious differences between Fred and Steve. You'll get better at it eventually, but in the meantime you just haven't yet recognized enough of the normal range of variance to make a clear distinction even between not-all-that-similar-looking people that share the same obvious features.