"Not just color as we know it but it could be differences in the brightness or saturation of the images. What it tells us is that there might be information in the images predictive of sexual orientation that we didn't expect, such as brighter images for one of the groups, or more saturated colors in one group. " the fact that the blurred images are reasonable predictors doesn't tell us that AI can't be good predictors. “While demonstrating that dating profile images carry rich information about sexual orientation, these results leave open the question of how much is determined by facial morphology and how much by differences in grooming, presentation, and lifestyle,” he admitted. Leuner’s results, however, don’t support that idea at all. It would mean that biological factors such as a person’s facial structure would indicate whether someone was gay or not. Wang and Kosinski said their research was proof for the “prenatal hormone theory,” an idea that connects a person’s sexuality to the hormones they were exposed to when they were a fetus inside their mother’s womb. It would appear the neural networks really are picking up on superficial signs rather than analyzing facial structure. In fact, it was accurate about 63 per cent for males and 72 per cent for females, pretty much on par with the non-blurred VGG-Face and facial morphology model.
#Am i gay or straight picture test software
In a third experiment, Leuner completely blurred out the faces so the algorithms couldn’t analyze each person’s facial structure at all.Īnd guess what? The software was still able predict sexual orientation. So, does this mean that AI really can tell if someone is gay or straight from their face? No, not really. The models are still able to predict sexual orientation even while controlling for the presence or absence of facial hair and eyewear,” he stated in his report. “The study shows that the head pose is not correlated with sexual orientation.
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#Am i gay or straight picture test code
When Leuner corrected for these factors in his test, by including photos of the same people wearing glasses and not wearing glasses or having more or less facial hair, his neural network code was still fairly accurate – better than a coin flip – at labeling people’s sexuality. The neural networks were picking on our own fashion and superficial biases, rather than scrutinizing the shape of our cheeks, noses, eyes, and so on. Straight men were more likely to wear glasses than gay men.
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Notably, straight women were more likely to wear eye shadow than gay women in Wang and Kosinski’s dataset. He believed neural networks were latching onto things like whether a person was wearing certain makeup or a particular fashion of glasses to determine sexual orientation, rather than using their actual facial structure. SlammedĪ Google engineer, Blaise Aguera y Arcas, blasted the original study early last year, and pointed out various reasons why software should struggle or fail to classify human sexuality correctly. So, Leuner's AI performed better than humans, and better than a fifty-fifty coin flip, but wasn't as good as the Stanford pair's software. Humans got it right 61 per cent of the time for men, and 54 per cent for women, in a comparison study.
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Not amazing, but not completely wrong.įor reference, the Wang and Kosinski study achieved 81 to 85 per cent accuracy for males, and 70 to 71 per cent for women, using their datasets. A facial morphology classifier, another machine learning model that inspects facial features in photographs, was 62 per cent accurate for males and 72 per cent accurate for females.
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He found that VGG-Face, a convolutional neural network pre-trained on one million photographs of 2,622 celebrities, when using his own dating-site-sourced dataset, was accurate at predicting the sexuality of males with 68 per cent accuracy – better than a coin flip – and females with 77 per cent accuracy.