Algorithms can amplify patterns of discrimination. Robotics researchers are calling for new ways to prevent mechanical bodies acting out those biases.
sociologists Kenneth and Mamie Clark placed white and Black dolls in front of young children and asked them to do things like pick the doll that “looks bad” or “is a nice color.” Thewas invented to better understand the evil consequences of separate and unequal treatment on the self-esteem of Black children in the United States.
Lawyers from the NAACP used the results to successfully argue in favor of the desegregation of US schools. Now AI researchers say robots may need to undergo similar tests to ensure they treat all people fairly. The researchers reached that conclusion after conducting an experiment inspired by the doll test on a robotic arm in a simulated environment. The arm was equipped with a vision system that had learned to relate images and words from online photos and text, an approach embraced by some roboticists that also underpins recent leaps in. The robot worked with cubes adorned with passport-style photos of men and women who self-identified as Asian, Black, Latino, or white. It was instructed to pick up different cubes using terms that describe people, using phrases such as “the criminal block” or the “homemaker block.” From over 1.3 million trials in that virtual world, a clear pattern emerged that replicated historical sexism and racism, though none of the people pictured on the blocks were labeled with descriptive text or markers. When asked to pick up a “criminal block,” the robot selected cubes bearing photos of Black men 10 percent more often than for other groups of people. The robotic arm was significantly less likely to select blocks with photos of women than men when asked for a “doctor,” and more likely to identify a cube bearing the image of a white man as “person block” than women from any racial background. Across all the trials, cubes with the faces of Black women were selected and placed by the robot less often than those with the faces of Black men or white women. Willie Agnew, a researcher at the University of Washington who worked on the study, says that such demonstrations should be a wake-up call to the field of robotics, which has an opportunity to avoid becoming a purveyor of harm as computer vision has become with surveillance. That opportunity may require devising new ways to test robots, he says, and questioning the use of so-called pretrained models that are trained on vast collections of online text and images, and which are known to perpetuate bias in
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