This algorithm has opinions about your face
When two people meet, they instantly size each other up, making snap judgments about everything from the other person’s age to their intelligence or trustworthiness based solely on the way they look. Those first impressions, though often inaccurate, can be extremely powerful, shaping our relationships and impacting everything from hiring decisions to criminal sentencing.
Researchers at Stevens Institute of Technology, in collaboration with Princeton University and University of Chicago, have now taught an AI algorithm to model these first impressions and accurately predict how people will be perceived based on a photograph of their face. The work appears today, in the April 21 issue of the Proceedings of the National Academy of Sciences.
“There’s a wide body of research that focuses on modeling the physical appearance of people’s faces,” said Jordan W. Suchow, a cognitive scientist and AI expert at the School of Business at Stevens. “We’re bringing that together with human judgments and using machine learning to study people’s biased first impressions of one another.”
Suchow and team, including Joshua Peterson and Thomas Griffiths at Princeton, and Stefan Uddenberg and Alex Todorov at Chicago Booth, asked thousands of people to give their first impressions of over 1,000 computer-generated photos of faces, ranked using criteria such as how intelligent, electable, religious, trustworthy, or outgoing a photograph’s subject appeared to be. The responses were then used to train a neural network to make similar snap judgments about people based solely on photographs of their faces.
“Given a photo of your face, we can use this algorithm to predict what people’s first impressions of you would be, and which stereotypes they would project onto you when they see your face,” Suchow explained.
Many of the algorithm’s findings align with common intuitions or cultural assumptions: people who smile tend to be seen as more trustworthy, for instance, while people with glasses tend to be seen as more intelligent. In other cases, it’s a little harder to understand exactly why the algorithm attributes a particular trait to a person. More