AI doesn't need to simply look more diverse. It needs to actually cultivate more diverse training sets.
out of Arizona State University discovered that GANs, when tasked with generating faces of engineering professors, both lightened the “skin color of non-white faces” and transformed “female facial features to be masculine.” Without diversity to start with, these generators were unequipped to create it—More concerningly, the biases contained within these synthetic images would be incredibly difficult to detect. After all, computers don’t “see” the way we do.
These synthetic images might also contain details capable of mistraining these tools that are entirely invisible to the human eye. If these systems were to associate these concealed synthetic features with non-white subjects, they would become susceptible to a range of malfunctions we’d be poorly equipped to deal with given our inability to see the relevant differences—an undetectable wrench thrust into the cogs.ironic contradiction that lurks within these synthetic images.
Early internet theorists were well attuned to the ways in which digital life was poised to reconfigure our understanding of race. Though some were cautiously optimistic— believing that these possibilities might prove liberating for marginalized groups—the most prescient critics were skeptical, noting that this malleability was, even in its primordial stages, largely reserved for those who already held power.
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