Global Courant
Artificial intelligence algorithms can help predict a person’s political ideology based on their facial features, a study conducted in Denmark found.
The technology found that right-wing politicians were more likely to have happy facial expressions in photos, while people depicted with neutral facial expressions were more likely to identify as left-wing, the study found.
The study, “Using deep learning to predict ideology based on facial photos: expressions, beauty, and extra-face information,” found that AI can predict a person’s political ideology with 61% accuracy when analyzing a photo of a person.
Deep learning, a method in AI in which computer scientists teach computers to learn and process information similar to humans, can be used to make predictions about humans based on pictures alone, the researchers explained in their paper, which was published in Scientific Reports.
The scientists sought to determine exactly “what information contributes to the predictive success of these techniques,” researchers said.
People can read someone else’s face and make almost instantaneous judgments about personality, intelligence, and even political ideology. Study author Stig Hebbelstrup Rye Rasmussen of the University of Arhus and his colleagues investigated whether computational neural networks – algorithms that mimic the structure and function of human brains – can predict someone’s political ideology based on a single picture alone.
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Artificial intelligence algorithms can help predict a person’s political ideology based on their facial features, a study conducted in Denmark found. (Smith Collection/Gado/Getty Images)
The scientists trained the neural network with thousands of photos of politicians from the 2017 municipal elections, noting that the election was not highly polarized or competitive, and calling the politicians the “last amateurs in politics.”
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They abolished all photos of candidates who were not explicitly left or right, were not of European ethnicity, or were photographed with a beard. The photos only showed the candidates’ facial features, not photos with backgrounds that could alter predictions. The researchers then obtained 4,647 photographs of political candidates, of which 1,442 were female politicians.
Experts argue that the difference between AI investment in China and the US is the fact that the US model is driven by private companies, while China takes a government approach. (Josep Lago/AFP via Getty Images)
The researchers used facial expression recognition technology from Microsoft to measure the emotional state in the photos, as well as other algorithms to determine the candidates’ attractiveness and even masculinity. They also used a handful of photos of Danish parliamentarians to test the algorithm’s accuracy.
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People vote during the European Parliament elections in Copenhagen, Denmark, May 26, 2019. (Ida Marie Odgaard via Reuters)
Overall, the study found that the AI trained on the data was able to accurately predict ideology by 61% — demonstrating that the algorithms can predict political affiliation better than pure chance.
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“Our results confirmed the threat to privacy posed by deep learning approaches,” the researchers wrote. “Using a pre-developed and readily available network trained and validated solely on publicly available data, we were able to predict the ideology of the person depicted in two samples approximately 60% of the time.”
The study found that female politicians who were more attractive were more likely to be conservative, while attractiveness and masculinity for men were not tied to political ideology. Faces of both men and women who appeared happier were also more likely to be on the right, while neutral facial expressions meant that the politicians were more likely to be members of left-wing parties. The study added that while it was rarer, women who showed contempt on their faces were more likely to lean to the left.
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“We also provide the first demonstration that model-predicted ideology is associated with independently classifiable features of the face,” the study said. “For women (but not men), high attractiveness scores were found among those likely to be conservative according to the model. These results are credible given that previous research using human raters has also shown a link between attractiveness and conservatism.”
