'I was shocked it was so easy': meet the professor who says facial recognition can tell if you're gay | Technology | The Guardian
'I was shocked it was so easy': meet the professor who says facial recognition can tell if you're gay | Technology | The Guardian: Kosinski’s research dismisses physiognomy as “a mix of superstition and racism disguised as science” – but then argues it created a taboo around “studying or even discussing the links between facial features and character”. There is growing evidence, he insists, that links between faces and psychology exist, even if they are invisible to the human eye; now, with advances in machine learning, such links can be perceived. “We didn’t have algorithms 50 years ago that could spot patterns,” he says. “We only had human judges.”
In a paper published last year, Kosinski and a Stanford computer scientist, Yilun Wang, reported that a machine-learning system was able to distinguish between photos of gay and straight people with a high degree of accuracy. They used 35,326 photographs from dating websites and what Kosinski describes as “off-the-shelf” facial-recognition software.
Presented with two pictures – one of a gay person, the other straight – the algorithm was trained to distinguish the two in 81% of cases involving images of men and 74% of photographs of women. Human judges, by contrast, were able to identify the straight and gay people in 61% and 54% of cases, respectively. When the algorithm was shown five facial images per person in the pair, its accuracy increased to 91% for men, 83% for women. “I was just shocked to discover that it is so easy for an algorithm to distinguish between gay and straight people,” Kosinski tells me. “I didn’t see why that would be possible.”
In a paper published last year, Kosinski and a Stanford computer scientist, Yilun Wang, reported that a machine-learning system was able to distinguish between photos of gay and straight people with a high degree of accuracy. They used 35,326 photographs from dating websites and what Kosinski describes as “off-the-shelf” facial-recognition software.
Presented with two pictures – one of a gay person, the other straight – the algorithm was trained to distinguish the two in 81% of cases involving images of men and 74% of photographs of women. Human judges, by contrast, were able to identify the straight and gay people in 61% and 54% of cases, respectively. When the algorithm was shown five facial images per person in the pair, its accuracy increased to 91% for men, 83% for women. “I was just shocked to discover that it is so easy for an algorithm to distinguish between gay and straight people,” Kosinski tells me. “I didn’t see why that would be possible.”