AI Triumphs Again, Predicts Sexual Orientation by Facial Features
More proof that most homosexuality is a genetic or gene-expression defect, and the defects involved have an effect on facial appearance to some extent — which closely corresponds with the commonsense observations of people for centuries.
A NEW STUDY from Stanford University states as follows:
Deep neural networks are more accurate than humans at detecting sexual orientation from facial images.
Description: 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. Facial features employed by the classifier included both fixed (e.g., nose shape) and transient facial features (e.g., grooming style). Consistent with the prenatal hormone theory of sexual orientation, gay men and women tended to have gender-atypical facial morphology, expression, and grooming styles. Prediction models aimed at gender alone allowed for detecting gay males with 57% accuracy and gay females with 58% accuracy.
Those findings advance our understanding of the origins of sexual orientation and the limits of human perception. Additionally, given that companies and governments are increasingly using computer vision algorithms to detect people’s intimate traits, our findings expose a threat to the privacy and safety of gay men and women.
The Guardian reports on the study:
The paper suggested that the findings provide “strong support” for the theory that sexual orientation stems from exposure to certain hormones before birth, meaning people are born gay and being queer is not a choice. The machine’s lower success rate for women also could support the notion that female sexual orientation is more fluid. . . .
In the Stanford study, the authors also noted that artificial intelligence could be used to explore links between facial features and a range of other phenomena, such as political views, psychological conditions or personality.
This type of research further raises concerns about the potential for scenarios like the science-fiction movie Minority Report, in which people can be arrested based solely on the prediction that they will commit a crime.
“AI can tell you anything about anyone with enough data,” said Brian Brackeen, CEO of Kairos, a face recognition company. “The question is as a society, do we want to know?”
Brackeen, who said the Stanford data on sexual orientation was “startlingly correct”, said there needs to be an increased focus on privacy and tools to prevent the misuse of machine learning as it becomes more widespread and advanced.
We here at National Vanguard have developed our own software which predicts sexual orientation — with 95 per cent. accuracy — based on photographs of any person, and the photographs don’t even have to show the individual’s face. Our software achieves its amazing accuracy simply by predicting that everyone in every photograph is heterosexual.
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Source: Stanford University, The Guardian, and National Vanguard correspondents