Ellen Levish, MJLST Staffer
Recently, two Stanford researchers made a frightening claim; computers can use facial recognition algorithms to identify people as gay or straight.
One MJLST blog tackled facial recognition issues before back in 2012. Then, Rebecca Boxhorn posited that we shouldn’t worry too much, because “it is easy to overstate the danger” of emerging technology. In the wake of the “gaydar,” we should re-evaluate that position.
First, a little background. Facial recognition, like fingerprint recognition, relies on matching a subject to given standards. An algorithm measures points on a test-face, compares it to a standard face, and determines if the test is a close fit to the standard. The algorithm matches thousands of points on test pictures to reference points on standards. These test points include those you’d expect: nose width, eyebrow shape, intraocular distance. But the software also quantifies many “aspects of the face we don’t have words for.” In the case of the Stanford “gaydar,” researchers modified existing facial recognition software and used dating profile pictures as their standards. They fed in test pictures, also from dating profiles, and waited.
Recognizing patterns in these measurements, the Stanford study’s software determined if a test face was more like a standard “gay” or “straight” face. The model was accurate up to 91 percent of the time. That is higher than just chance, and far beyond human ability.
The Economist first broke the story on this study. As expected, it gained traction. Hyperbolic headlines littered tech blogs and magazines. And of course, when the dust settled, the “gaydar” scare wasn’t that straightforward. The “gaydar” algorithm was simple, the study was a draft posted online, and the results, though astounding, left a lot of room for both statistical and socio-political criticism. The researchers stated that their primary purpose in pursuing this inquiry was to “raise the alarm” about the dangers of facial recognition technology.
Facial recognition has become much more commonplace in recent years. Governments worldwide openly employ it for security purposes. Apple and Facebook both “recognize individuals in the videos you take” and the pictures you post online. Samsung allows smartphone users to unlock their device with a selfie. The Walt Disney Company, too, owns a huge database of facial recognition technology, which it uses (among other things) to determine how much you’ll laugh at movies. These current, commercial uses seem at worst benign and at best helpful. But the Stanford “gaydar” highlights the insidious, Orwellian nature of “function creep,” which policy makers need to keep an eye on.
Function creep “is the phenomenon by which a technology designed for a limited purpose may gain additional, unanticipated purposes or functions.” And it poses a major ethical problem for the use of facial recognition software. No doubt inspired developers will create new and enterprising means of analyzing people. No doubt most of these means will continue to be benign and commercial. But we must admit: classification based on appearance and/or affect is ripe for unintended consequences. The dystopian train of thought is easy to follow. It begs that we consider normative questions about facial recognition technology.
Who should be allowed to use facial recognition technologies? When are they allowed to use it? Under what conditions can users of facial technology store, share, and sell information?
The goal should be to keep facial recognition technology from doing harm. America has a disturbing dearth of regulation designed to protect citizens from ne’er-do-wells who have access to this technology. We should change that.
These normative questions can guide our future policy on the subject. At the very least, they should help us start thinking about cogent guidelines for the future use of facial recognition technology. The “gaydar” might not be cause for immediate alarm, but its implications are certainly worth a second thought. I’d recommend thinking on this sooner, rather than later.