The Inherent Bias of Facial Recognition

Rose Eveleth in Motherboard:

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.

More here.