JD Schramm is the MBA Class of 1978 Lecturer in Organizational Behavior at Stanford's Graduate School of Business. Computers are more accurate than humans at visually detecting sexual orientation, according to an article in this month's Journal of Personality and Social Psychology. That's not really news to me -- I've always been bad with "gaydar." I was once on a third date with a guy before I knew he was gay. Okay, I did not realize our first two coffees were actually dates in his mind, and the friends who introduced us failed to tell me it was a romantic setup.

"Our findings expose a threat to the privacy and safety of gay men and women," wrote Michal Kosinski in a paper set to be published by the Journal of Personality and Social Psychology--only he's the one now finding himself in danger. The New York Times takes a look at the quagmire Kosinski finds himself in following his decision to try--and, in some fashion, succeed--at building what many are referring to as "AI gaydar." The Stanford Graduate School of Business professor tells the Times he decided to attempt to use facial recognition analysis to determine whether someone is gay to flag how such analysis could reveal the very things we want to keep private. The Times delves into the research--first highlighted by the Economist in early September--and the many bones its many critics have to pick with it. Kosinski and co-author Yilun Wang pulled 35,000 photos of white Americans from online dating sites (those looking for same-sex partners were classified as gay) and ran them through a "widely used" facial analysis program that turns the location, size, and shape of one's facial characteristics into numbers.

Every face does not tell a story; it tells thousands of them. Over evolutionary time, the human brain has become an exceptional reader of the human face--computerlike, we like to think. A viewer instinctively knows the difference between a real smile and a fake one. In July, a Canadian study reported that college students can reliably tell if people are richer or poorer than average simply by looking at their expressionless faces. Scotland Yard employs a team of "super-recognizers" who can, from a pixelated photo, identify a suspect they may have seen briefly years earlier or come across in a mug shot.

A new computer algorithm, developed by researchers at Stanford University, can correctly determine someone's sexuality with up to 91% accuracy just by analysing a photo of their face. It's the robotic equivalent of a "gaydar" and it's sure to stir up a minefield of discussion, but first, a few caveats. The study used 35,326 photos from a US dating site and the artificial intelligence only distinguished sexuality between two photos (always one straight, one homosexual) of people of the same gender – the study did not include transgender or bisexual people. The photos used were portraits of faces, so the AI's judgments are made based on a "faceprint" determined from things like eyebrows, cheeks, hairline, neckline and nose, rather than clothing or hairstyle. Among men the system was accurate 81% of the time, and with women it was 71% accurate, but when given repeated photos of the same man (and so, more data) the accuracy increased to 91%.

Artificial intelligence can accurately guess whether people are gay or straight based on photos of their faces, according to new research suggesting that machines can have significantly better "gaydar" than humans. The study from Stanford University – which found that a computer algorithm could correctly distinguish between gay and straight men 81% of the time, and 74% for women – has raised questions about the biological origins of sexual orientation, the ethics of facial-detection technology and the potential for this kind of software to violate people's privacy or be abused for anti-LGBT purposes. The machine intelligence tested in the research, which was published in the Journal of Personality and Social Psychology and first reported in the Economist, was based on a sample of more than 35,000 facial images that men and women publicly posted on a US dating website. The researchers, Michal Kosinski and Yilun Wang, extracted features from the images using "deep neural networks", meaning a sophisticated mathematical system that learns to analyze visuals based on a large dataset. The research found that gay men and women tended to have "gender-atypical" features, expressions and "grooming styles", essentially meaning gay men appeared more feminine and vice versa.

Artificial intelligence can accurately predict whether people are gay or straight based on photos of their faces, according to new research suggesting that machines can have significantly better "gaydar" than humans. The study from Stanford University – which found that a computer algorithm could correctly distinguish between gay and straight men 81% of the time, and 74% for women – has raised questions about the biological origins of sexual orientation, the ethics of facial-detection technology and the potential for this kind of software to violate people's privacy or be abused for anti-LGBT purposes. The machine intelligence tested in the research, which was published in the Journal of Personality and Social Psychology and first reported in the Economist, was based on a sample of more than 35,000 facial images that men and women publicly posted on a US dating website. The researchers, Michal Kosinski and Yilun Wang, extracted features from the images using "deep neural networks", meaning a sophisticated mathematical system that learns to analyze visuals based on a large dataset. The research found that gay men and women tended to have "gender-atypical" features, expressions and "grooming styles", essentially meaning gay men appeared more feminine and visa versa.