AI Technology – The New Gaydar detecting sexual orientation

The paper, due to be published in the Journal of Personality and Social Psychology, details a rather ordinary supervised-learning approach to addressing the possibility of identifying people as gay or straight from their faces alone. (Note: the paper is still in draft form.) Apparently, “Gay faces tended to be gender atypical,” the researchers said. “Gay men had narrower jaws and longer noses, while lesbians had larger jaws.”

The implications of automatically determining a person’s sexuality from a handful of photos are enormous, first and most importantly because it puts LGBTQ people at risk worldwide in places where they remain an oppressed minority. Before discussing the research please note that all indications done with good intentions. Most relevant are perhaps their remarks as to why the paper was released at all:

“We were really disturbed by these results and spent much time considering whether they should be made public at all. We did not create a privacy-invading tool, but rather showed that basic and widely used methods pose serious privacy threats. The ability to control when and to whom to reveal one’s sexual orientation is crucial not only for one’s well-being, but also for one’s safety.”

The paper suggests that the computer vision system is picking up on equally subtle patterns, both artificial and natural, but primarily the latter; the researchers were careful to focus on features that can’t be altered easily. It’s supportive of the hypothesis that sexuality is biologically determined, possibly by different fetal hormone exposure, which could then cause slight physiological differences in addition to affecting whom one is attracted to. The patterns observed by the system are similar to those predicted by the prenatal hormone theory.

This begins to verge on a larger social and philosophical debate regarding love, sexuality, determinism and the reduction of emotions and personalities to mere biological traits.

Seeing what we can’t (or won’t)

The researchers said the resulting software appeared to be able to distinguish between gay and heterosexual men and women. In one test, when the algorithm was presented with two photos, where one picture was definitely of a gay man and the other heterosexual. It was able to determine which was which 81% of the time. With women, the figure was 71%. But the software did not perform as well in other situations, A test in which it was given photos of 70 gay men and 930 heterosexual men the computer was asked to pick 100 men “most likely to be gay” it missed 23 of them.

Using a database of facial imagery (from a dating site that makes its data public), the researchers collected 35,326 images of 14,776 people, with (self-identified) gay and straight men and women all equally represented. Their facial features were extracted and quantified: everything from nose and eyebrow shape to facial hair and expression.

A deep learning network crunched through all these features, finding which tended to be associated with individuals of a given sexual orientation.

The researchers didn’t “seed” this with any preconceived notions of how gay or straight people look; the system merely correlated certain features with sexuality and identified patterns.

These patterns can be searched for in facial imagery to let the computer guess at the subject’s sexuality — and it turns out that the AI system is significantly better than humans at this task.

The Results

The 91% accuracy rate only applies when one of the two men whose images are shown is known to be gay. When presented with multiple pictures of a pair of faces, one gay and one straight, the algorithm could determine which was which 91 percent of the time with men and 83 percent of the time with women. People provided the same images were correct 61 and 54 percent of the time, respectively — not much better than flipping a coin.

The variation between the four groups is described in a second paper; apart from obvious behavioral differences like one group grooming or doing make-up one way, the general trend was toward “feminine” features in gay men and “masculine” features in lesbians. The potential for abuse of a system like this is huge, and some may rightly disagree with the researchers’ decision to create and document it.

It turns out people are just plain bad at this kind of task — we see too much of ourselves in people, and we act and perceive instinctively. No one doubts that machines can pick up on things humans don’t have the capacity to perceive, from minute chemical traces of bombs or drugs to subtle patterns in MRI scans that indicate cancer precursors.

Dangers and limitations

The implications of automatically determining a person’s sexuality from a handful of photos are enormous, first and most importantly because it puts LGBTQ people at risk worldwide in places where they remain an oppressed minority. But it’s also arguable that it’s better to have the possibility out in the open in order to either implement countermeasures or otherwise allow people to prepare themselves for it. After all, it’s also possible to determine sexuality and many other private traits by analyzing Facebook or Twitter feeds.

The difference here is that you can obfuscate or control the information you put online; you can’t control who sees your face. It’s a surveillance society we live in, and increasingly a surveillant one, as well.

The cat is out of the bag, in other words. And it’s probably better that it’s being shown by researchers sympathetic to the people most affected than by the FBI or some other party doing it for their own agenda. The implications of the technology are, as always, mixed, but it’s interesting how this kind of research helps define and guide humanity.

The big story is that we’re not going to have any privacy very soon. I’m all for privacy-protecting laws and technologies, but people keep forgetting that they’re just doomed to fail. “Even the best laws and technologies aimed at protecting privacy are, in our view, doomed to fail,” write the researchers. “The digital environment is very difficult to police; data can be easily moved across borders, stolen, or recorded without users’ consent.” Certainly, this is only one of many systematised attempts to derive secret information such as sexuality, emotional state or medical conditions.

At the end of the day, we shouldn’t judge a person by their appearance, nor guess at something as private as someones sexual orientation from something as simple as a snapshot or two.


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