AI Can Make Personality Judgments Based on Our Photographs
By: Sai Srihaas Potu
Artificial intelligence is already altering the world and raising important questions for society, the economy, and governance. Despite its widespread lack of familiarity, AI is a technology that is transforming every walk of life. It is a wide-ranging tool that enables people to rethink how we integrate information, analyze data, and use the resulting insights to improve decision-making. The healthcare industry is undergoing a major transformation. Researchers, thinkers, developers, and scientists are raking a dive to understand and improve the healthcare sector using a host of different technologies such as artificial intelligence (AI).
Recently, Russian researchers have demonstrated that artificial intelligence can infer people’s personalities from ‘selfie’ photographs better than human raters do.
Physiognomists from Ancient Greece to Cesare Lombroso have tried to link facial appearance to personality, but the majority of their ideas failed to withstand the scrutiny by modern science. The few established associations of specific facial features, such as facial width-to-height ratio, with personality traits are quite weak. Studies asking human raters to make personality judgments based on photographs have produced inconsistent results, suggesting that our judgments are too unreliable to be of any practical importance.
Nevertheless, there are strong theoretical and evolutionary arguments to suggest that some information about personality characteristics, particularly, those essential for social communication, might be conveyed by the human face. After all, face and behavior are both shaped by genes and hormones, and social experiences resulting from one’s appearance may affect one’s personality development. However, the recent evidence suggests that instead of looking at specific facial features, the human brain processes images of faces in a holistic manner.
Researchers from Russia have trained a cascade of artificial neural networks to make reliable personality judgments based on photographs of human faces. The performance of the resulting model was far better than the ones discovered in previous studies that used machine learning or human raters. The artificial intelligence was able to make above-chance judgments about conscientiousness, neuroticism, extraversion, agreeableness, and openness based on ‘selfies’ the volunteers uploaded online. The resulting personality judgments were consistent across different photographs of the same individuals.
The study was done in a sample of 12 thousand volunteers who completed a self-report questionnaire measuring personality traits based on the “Big Five” model and uploaded a total of 31 thousand ‘selfies’. The respondents were randomly split into a training and a test group. A series of neural networks were used to preprocess the images to ensure consistent quality and characteristics and exclude faces with emotional expressions, as well as pictures of celebrities and cats. Next, an image classification neural network was trained to decompose each image into 128 invariant features, followed by a multi-layer perceptron that used image invariants to predict personality traits. The average effect size of r = .24 indicates that AI can make a correct guess about the relative standing of two randomly chosen individuals on a personality dimension in 58% of cases as opposed to the 50% expected by chance.
In comparison with the meta-analytic estimates of correlations between self-reported and observer ratings of personality traits, this indicates that an artificial neural network relying on static facial images outperforms an average human rater who meets the target in person without prior acquaintance. Conscientiousness emerged to be more easily recognizable than the other four traits. Personality predictions based on female faces appeared to be more reliable than those for male faces.
There are a vast number of potential applications to be explored. The recognition of personality from real-life photos can complement the traditional approaches to personality assessment in situations where high speed and low cost are more important than high accuracy.