Let’s do AI phrenology again

Every few months, another highly educated academic asks: What if I tried to do 18th century race science, but with AI?

U the last entry in the AI ​​phrenology portfolio comes from a group of economics professors who say they have developed a method to algorithmically analyze a single photo of a person’s face to calculate their personality and predict their educational and career outcomes.

Other recent academic forays into AI phrenology– such as algorithms that claim to predict a person’s sexuality or the likelihood that they will commit a crime based on their facial features – have been widely criticized and burst out. Investigations have also shown that commercial AI tools that claim to measure personality traits are extremely unreliable.

However, Marius Guenzel and Shimon Kogan, of the Wharton School of the University of Pennsylvania; Marina Niessner, of Indiana University; and Kelly Shue, from Yale University, decided that a photo of a person’s face can determine their personality. They have received funding for their research from several AI research funds and finance at Wharton and have presented their results at financial technology conferences and universities around the world, according to their paper.

The authors collected the LinkedIn profile photos of 96,000 MBA program graduates and ran them through a facial analysis algorithm that purportedly measured how the person would score on the Big Five personality test, which assesses people on their perceived openness, conscientiousness, extraversion, agreeableness and neuroticism.

Then they measure the correlation between these extracted personality scores and the prestige of the MBA program they have completed and their eventual compensation in the workforce (as estimated by a proprietary model that analyzes LinkedIn data).

Based on this analysis, the authors conclude that personality plays an “important role” in predicting whether a person will attend a school with a highly ranked MBA program and how much they will earn in their first job after graduation. For example, men in the top 20 percent of “desirable” personalities attended MBA programs ranked 7.3 percent higher and had estimated incomes 8.4 percent higher than men whose personalities were in the bottom 20 percent of desirability. When the researchers controlled for factors such as a person’s race, age and attractiveness (all of which were inferred), the effects became smaller.

In particular, the authors do not appear to have made an independent effort to establish that the Big Five personality scores that their algorithm extracted from LinkedIn headshots were accurate. None of the people whose profile pictures were analyzed took a Big Five personality test to confirm the algorithm’s conclusions.

The professors wrote that their findings highlight “the critical role of non-cognitive skills in shaping career outcomes” and that using AI to analyze faces, rather than actually administer tests of personality to people, “presents new avenues for academic inquiry … (and invites) further exploration of the ethical, practical and strategic considerations inherent in exploiting such technologies”.

At the same time, they wrote that the technique they just demonstrated should not be used for labor market screening and that “the extraction of personality from faces represents statistical discrimination in its most fundamental form.”

In other words, the scientists stopped to think about whether they should, concluded that it was discriminatory, and then did it anyway.