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.
at Stevens Institute of Technology, in collaboration with Princeton
University and University of Chicago, have now taught an AIalgorithmto model thesefirst impressionsand accurately predict how people will be perceived based on aphotographof their face. The work appears today, in the April 21 issue of theProceedings of the National Academy of Sciences.
"There's a wide body of research that focuses on modeling thephysical appearanceof
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."
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 aneural networkto 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 whichstereotypesthey would project onto you when they see your face," Suchow explained.
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.
algorithm doesn't provide targeted feedback or explain why a given
image evokes a particular judgment," Suchow said. "But even so it can
help us to understand how we're seen—we could rank a series of photos
according to which one makes you look most trustworthy, for instance,
allowing you to make choices about how you present yourself."
Though originally developed to help psychological researchers generate face images for use in experiments on perception andsocial cognition,
the new algorithm could find real-world uses. People carefully curate
their public persona, for instance, sharing only the photos they think
make them look most intelligent or confident or attractive, and it's
easy to see how the algorithm could be used to support that process,
said Suchow. Because there's already a social norm around presenting
yourself in a positive light, that sidesteps some of theethical issuessurrounding the technology, he added.
troublingly, the algorithm can also be used to manipulate photos to
make their subject appear a particular way—perhaps making a political
candidate appear more trustworthy, or making their opponent seem
unintelligent or suspicious. While AI tools are already being used to
create "deepfake" videos showing events that never actually happened,
the new algorithm could subtly alter real images in order to manipulate
the viewer's opinion about their subjects.
the technology, it is possible to take a photo and create a modified
version designed to give off a certain impression," Suchow said. "For
obvious reasons, we need to be careful about how this technology is
safeguard their technology, the research team has secured a patent and
is now creating a startup to license the algorithm for pre-approved
ethical purposes. "We're taking all the steps we can to ensure this
won't be used to do harm," Suchow said.
the current algorithm focuses on average responses to a given face
across a large group of viewers, Suchow next hopes to develop an
algorithm capable of predicting how a single individual will respond to
another person's face. That could give far richer insights into the way
that snap judgments shape our social interactions, and potentially help
people to recognize and look beyond their first impressions when makingimportant decisions.
important to remember that the judgments we're modeling don't reveal
anything about a person's actual personality or competencies," Suchow
explained. "What we're doing here is studying people's stereotypes, and
that's something we should all strive to understand better."