"In a paper titled "AI Personality Extraction from Faces: Labor Market Implications," authors Marius Guenzel (Wharton), Shimon Kogan (Reichman University), Marina Niessner (Indiana University), and Kelly Shue (Yale University) describe how they used the LinkedIn facial images of over 96,000 MBA graduates to extract subjects' Big Five personality traits - Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism."
"The machine learning algorithm used was originally described in a 2020 Scientific Reports paper titled "Assessing the Big Five personality traits using real-life static facial images," one of about two dozen papers cited as "ML-laundered junk science" in a 2024 paper titled "The reanimation of pseudoscience in machine learning and its ethical repercussions.""
"The algorithm "uses facial features to predict self-reported personality, rather than others' perceptions of personality based on visual appearance," according to Guenzel, Kogan, Niessner, and Shue. By applying this algorithm, the authors found "that personality traits inferred from facial features provide substantial incremental predictive power for labor market outcomes.""
LinkedIn facial images of over 96,000 MBA graduates were used to infer Big Five personality traits with a machine-learning algorithm trained to predict self-reported personality from static facial images. Inferred trait scores provided substantial incremental predictive power for labor-market outcomes and yielded accurate predictions for class rank among graduates. The algorithm has faced criticism as 'ML-laundered junk science' and raises ethical concerns because personality extraction from faces can be fundamentally discriminatory. Personality screening is already used in admissions and HR, and adoption of AI personality tools is accelerating, intensifying potential harms.
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