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>> No.15502835 [View]
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15502835

>>15502822
If you're only "metaphorically right" you're 100% wrong.

>> No.15308447 [View]
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15308447

>>15307559
Please everyone shut the fuck up and actually read a single paper for once in your life: https://www.gsb.stanford.edu/faculty-research/publications/deep-neural-networks-are-more-accurate-humans-detecting-sexual
That is not what that project could evidence. Nobody does anybody any favors by misrepresenting evidence or mindlessly consuming inept media blurbs made for clickbait. No shit gay men on dating websites and openly gay on facebook posting well groomed face pictures probably are easily identifiable by a model on the basis of grooming and expressions. There was no factor analysis done in this paper.
>>Importantly, we would like to warn our readers against misinterpreting or overinterpreting this study’s findings. First, the fact that the faces of gay men and lesbians are, on average gender atypical, does not imply that all gay men are more feminine than all heterosexual men, or that there are no gay men with extremely masculine facial features (and vice versa in the case of lesbians). The differences in femininity observed in this study were subtle, spread across many facial features, and apparent only when examining averaged images of many faces. Second, our results in no way indicate that sexual orientation can be determined from faces by humans. In fact, Study 4 confirms that humans are rather inaccurate when distinguishing between facial images of gay and heterosexual individuals. Finally, interpreting classification accuracy is not trivial and is often counterintuitive. The AUC = .91 does not imply that 91% of gay men in a given population can be identified, or that the classification results are correct 91% of the time. The performance of the classifier depends on the desired trade-off between precision (e.g., the fraction of gay people among those classified as gay) and recall (e.g., the fraction of gay people in the population correctly identified as gay). Aiming for high precision reduces recall, and vice versa.

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