Researchers have created a machine learning system that they claim can determine a person's political party, with reasonable accuracy, based only on their face. TechCrunch reports: The study, which appeared this week in the Nature journal Scientific Reports, was conducted by Stanford University's Michal Kosinski. Kosinski made headlines in 2017 with work that found that a person's sexual preference could be predicted from facial data. [...] The algorithm itself is not some hyper-advanced technology. Kosinski's paper describes a fairly ordinary process of feeding a machine learning system images of more than a million faces, collected from dating sites in the U.S., Canada and the U.K., as well as American Facebook users. The people whose faces were used identified as politically conservative or liberal as part of the site's questionnaire. The algorithm was based on open-source facial recognition software, and after basic processing to crop to just the face (that way no background items creep in as factors), the faces are reduced to 2,048 scores representing various features -- as with other face recognition algorithms, these aren't necessary intuitive things like "eyebrow color" and "nose type" but more computer-native concepts. The system was given political affiliation data sourced from the people themselves, and with this it diligently began to study the differences between the facial stats of people identifying as conservatives and those identifying as liberal. Because it turns out, there are differences. Of course it's not as simple as "conservatives have bushier eyebrows" or "liberals frown more." Nor does it come down to demographics, which would make things too easy and simple. After all, if political party identification correlates with both age and skin color, that makes for a simple prediction algorithm right there. But although the software mechanisms used by Kosinski are quite standard, he was careful to cover his bases in order that this study, like the last one, can't be dismissed as pseudoscience. The most obvious way of addressing this is by having the system make guesses as to the political party of people of the same age, gender and ethnicity. The test involved being presented with two faces, one of each party, and guessing which was which. Obviously chance accuracy is 50%. Humans aren't very good at this task, performing only slightly above chance, about 55% accurate. The algorithm managed to reach as high as 71% accurate when predicting political party between two like individuals, and 73% presented with two individuals of any age, ethnicity or gender (but still guaranteed to be one conservative, one liberal).Read more of this story at Slashdot.
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