Stanford’s Facial Recognition Algorithm Can Determine Sexual Orientation With Up To 91% Accuracy

By Vin Armani

New Stanford study shows that a facial recognition algorithm can correctly determine whether people are straight or gay with up to 91% accuracy when using multiple images. Vin Armani covers the implications and fallout from the findings.

The study’s description states:

We show that faces contain much more information about sexual orientation than can be perceived and 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 74% of cases for women. Human judges achieved much lower accuracy: 61% for men and 54% for women. The accuracy of the algorithm increased to 91% and 83%, respectively, given five facial images per person.

Watch the full broadcast here

Vin Armani is the host of The Vin Armani Show on Activist Post, author of Self Ownership, agorist entrepreneur, and co-founder of Counter Markets. Follow Vin on Twitter and subscribe on YouTube. Get the weekly podcast on iTunes or Stitcher. Vin is available for interviews at email – Vin (at) VinArmani.com.


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