Artificial intelligence algorithms can help predict a person’s political ideology based on their facial features, a study in Denmark found.
The technology found that right-wing politicians were more likely to have happy facial expressions in photos, while people photographed with neutral facial expressions were more likely to identify as left-wing, the study found.
The study, “Using Deep Learning to Predict Ideology from Facial Photographs: Expressions, Beauty, and Extrafacial Information,” found that AI can predict a person’s political ideology with 61% accuracy when ‘analyze 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 only on photographs, 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,” according to the researchers.
Humans are able to read another person’s face and make almost immediate judgments about personality, intelligence, and even political ideology. Study author Stig Hebbelstrup Rye Rasmussen of Aarhus University and colleagues explored whether computational neural networks (algorithms that mimic the structure and function of the human brain) can predict the political ideology of ‘a person from a single photo.
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Artificial intelligence algorithms can help predict a person’s political ideology based on their facial features, a study in Denmark found. (Smith Collection/Gado/Getty Images)
The scientists trained the neural network on thousands of photos of politicians from the 2017 municipal elections, noting that the elections were not very polarized or competitive, and referred to the politicians as the “ultimate political fans”.
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They removed any photos of candidates who were not explicitly left-wing or right-wing, were not of European ethnicity, or had been photographed with beards. The photos only showed the candidates’ facial features, not photos with backgrounds that could alter the predictions. The researchers were then left with 4,647 photos of political candidates, 1,442 of which depicted female politicians.
Experts argue that the difference between AI investment in China and the US is that the US model is driven by private companies, while China takes a government approach. (Josep Lago/AFP via Getty Images)
The researchers used Microsoft’s facial expression recognition technology to measure the emotional state seen 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 on May 26, 2019. (Ida Marie Odgaard via Reuters)
In all, the research found that AI trained on the data could accurately predict ideology up to 61 percent, showing that the algorithms could predict political affiliation better than 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 that was trained and validated exclusively on publicly available data, we were able to predict the ideology of the person in the photo approximately 60% of the time across two samples.”
The research found that female politicians who were more attractive were more likely to be conservative, while male attractiveness and masculinity were not linked to political ideology. Faces of both men and women that looked happier were also more likely to be right-wing, while neutral facial expressions meant politicians were more likely to be members of left-wing parties. The study added that, although rarer, women who showed disdain 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 connects with independently classifiable facial features,” the study said. “For women (but not men), high attractiveness ratings 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.”
