Can this finally be the light at the end of the tunnel? A group of researchers from Brown University and University of Victoria believe that one of the simplest ways to reduce racial bias is to enable people to differentiate between faces of individuals of a different race.
The researchers revealed that they learnt this through a new measurement system and protocol they developed to train Caucasian subjects to recognize different African American faces.
Advertisement"The idea is this that this sort of perceptual training gives you a new tool to address the kinds of biases people show unconsciously and may not even be aware they have," said Michael J. Tarr, a Brown cognitive neuroscientist, and a senior author of the paper published online in PLoS ONE.
"There is a strong connection between the way we perceive and categorize the world and the way we end up making stereotypes and generalizations about social entities," Tarr added.
According to the researchers, training people to recognize facial differences among individuals of other races may blunt the effect of racial bias.
They hope that their training program may someday be used to train anyone who comes into contact with other races - police officers, social workers or immigration officials.
Overall, the researchers used 20 Caucasian subjects for the study, which incorporated a measurement developed at Brown and dubbed the Affective Lexical Priming Score (ALPS).
The ALPS measure is similar to, and builds on, a test developed at Harvard University known as the Implicit Association Test (IAT), which helps to identify unconscious social biases.
Each subject was first shown a series of pictures of different races, such as African American and Caucasian faces. All the faces were shown in black and white, so that subjects would focus on facial features rather than skin color.
On each ALPS trial, each test subject was shown a picture of a face, which then disappeared. The test subject then saw a word that could be real or nonsense - "tree" or "malk", for example - and had to decide whether the word was a real word or nonsense word.
The researchers said that real words would imply something positive or negative.
Study's lead author Sophie Lebrecht, a third-year PhD student in the Department of Cognitive and Linguistic Sciences and a member of Tarr's lab, found that prior to training, the subjects more quickly responded if the word was negative and followed an African-American face.
Lebrecht revealed that the subjects responded more slowly if the word was positive, and followed an African-American face.
The subjects later participated in about 10 hours of facial recognition training: half learnt to tell apart individual African-American faces, while the remaining learnt simply to tell whether the faces were African-American or not.
The researchers noted that the subjects who had improved their ability to tell the difference between separate Africa-American faces also showed the greatest reduction in their implicit racial bias, as measured by the ALPS system.
Their positive associations with African-American faces increased, according to the researchers, and they had fewer negative associations with African-American faces.
The team conceded that it would not be right to claim that their approach could eliminate racial bias, but they suggested that teaching people to tell the difference better between individual faces of a different race might be at least one way to help reduce that bias.
Lebrecht said that developing a system that teaches people to make those distinctions should be helpful in reducing generalizations based on social stereotypes.
"If you give people the tools to start individuating, maybe they will make more individual (rather than stereotypical) attributions," she said.
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