Artificial intelligence algorithms can help predict a person’s political ideology based on their facial characteristics, a study conducted in Denmark found.
The tech found right-wing politicians were more likely to have happy facial expressions in photos while people pictured with neutral facial expressions were more likely to identify as left-wing, the study said.
The study, “Using deep learning to predict ideology from facial photographs: expressions, beauty, and extra-facial 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 where computer scientists teach computers to learn and process information similar to humans, can be used to make predictions about people based on photographs alone, the researchers explained in their paper, which was published in Scientific Reports.
The scientists tried to pin down exactly “what information contributes to the predictive success of these techniques,” according to researchers.
Humans are able to read another person’s face and make judgments almost immediately about personality, intelligence and even political ideology. Stig Hebbelstrup Rasmussen, an author of the study at Arhus University, and his co-authors tested whether computational neural networks (algorithms that simulate brain structure and function) could predict political ideologies from a photo.
They did away with any photos of candidates who were not explicitly left- or right-wing, were not of European ethnic origin or had been photographed with a beard. The photos only depicted the facial features of the candidates, not photos with backgrounds that could alter predictions. The researchers were then left with 4,647 photos of political candidates, 1,442 of which depicted female politicians.
The research team used Microsoft’s facial expression recognition software to assess the emotion in photos. They also applied other algorithms for determining attractiveness, and masculinity. The researchers also tested the accuracy of their algorithm using a few photos of Danish Parliamentarians.
In all, the research found that the AI trained on the data could accurately predict ideology to the tune of 61% – showing the algorithms could predict political affiliation better than pure chance.
“Our results confirmed the threat to privacy posed by deep learning approaches,” the researchers wrote. “Using a pre-developed and readily available network that was trained and validated exclusively on publicly available data, we were able to predict the ideology of the pictured person roughly 60% of the time in two samples.”
The research found female politicians who were more attractive were more likely conservative, while attractiveness and masculinity for men was not tied to political ideology. Faces of both men and women who appeared happier were also more likely to be right-wing, while neutral facial expressions meant the politicians were more likely members of left-wing parties. The study added that though it was more rare, women who showed contempt on their faces were more likely left-leaning.
“We also provide the first demonstration that model-predicted ideology connects to independently classifiable features of the face,” the study said. “For females (though not males), high attractiveness scores were found among those the model identified as likely to be conservative. These results are credible given that previous research using human raters has also highlighted a link between attractiveness and conservatism.”
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