Deep neural networks are more accurate than humans at detecting sexual orientation from facial images.
▻https://psycnet.apa.org/doiLanding?doi=10.1037%2Fpspa0000098
We show that faces contain much more information about sexual orientation than can be perceived or interpreted by the human brain. We used deep neural networks to extract features from 35,326 facial images. These features were entered into a logistic regression aimed at classifying sexual orientation. Given a single facial image, a classifier could correctly distinguish between gay and heterosexual men in 81% of cases, and in 71% of cases for women. Human judges achieved much lower accuracy (...)