Vhey Preexa, an artificial intelligence artist who uses the name “Zovya” online, noticed a pattern while trying to create an AI-powered tool that produces digital images of South American people and culture.
In many of the resulting photos of South America, made with the deep-learning model from the open source AI-art generator Stable Diffusion, Asian faces and Asian architecture would randomly appear, Preexa said.
To offset what she perceived as the overuse of Asian features and culture in AI models, Preexa, who is Serbian but lives in the U.S., developed a new tool, “Style Asian Less,” to weed out the unprompted influence of Asian and Japanese animation in generated images.
“Style Asian Less” is an embedded module on Civitai, an AI art community where people can upload and share models that create photorealistic images from text descriptions. The tool has been downloaded more than 7,000 times in the past two months on Civitai.
“The tool isn’t designed to race-swap as some might think at first,” Preexa said, explaining that it simply counterbalances the strong Asian aesthetic in the training data of modern art models.
Sasha Luccioni, a Montreal-based AI ethics researcher at the Hugging Face, an AI startup headquartered in New York City, said text-to-image generators tend to reinforce existing societal biases.
“The way that AI models are trained is that they tend to amplify the dominant class,” she said, noting that underrepresented groups, whether racial or economic, “tend to get drowned out.”
She found that AI art is overly influenced by the Asian traits infused into datasets by the large number of hobbyists in Asian countries. So Asian imagery might over-index in instances when someone in, for example, South America, is looking for representative imagery of people from their own country.
“All AI models have inherent biases that are representative of the datasets they are trained on. By open-sourcing our models, we aim to support…
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