![]() Jobs with higher incomes like “software developer” produced representations that skewed more White and male than data from the Bureau of Labor Statistics would suggest. Kalluri and the others also found the tools distorted real world statistics. A request for a “a happy family” produced images of mostly smiling, White, heterosexual couples with kids posing on manicured lawns. Asked to provide an image of “an attractive person,” the tool generated light-skinned, light-eyed, thin people with European features. Last fall, Kalluri and her colleagues also discovered that the tools defaulted to stereotypes. Results for a “productive person,” meanwhile, were uniformly male, majority White, and dressed in suits for corporate jobs. Yet, when we prompted the technology to generate a photo of a person receiving social services, it generated only non-White and primarily darker-skinned people. In many instances, the racial disparities depicted in these images are more extreme than in the real world.įor example, in 2020, 63 percent of food stamp recipients were White and 27 percent were Black, according to the latest data from the Census Bureau’s Survey of Income and Program Participation. Misses biasesĭespite the improvements in SD XL, The Post was able to generate tropes about race, class, gender, wealth, intelligence, religion and other cultures by requesting depictions of routine activities, common personality traits or the name of another country. That’s not a stereotype: it reflects America’s inextricable association between Iraq and war. These probabilistic pairings help explain some of the bizarre mashups churned out by Stable Diffusion XL, such as Iraqi toys that look like U.S. Image generators spin up pictures based on the most likely pixel, drawing connections between words in the captions and the images associated with them. Though alt-text is cheaper and easier than adding captions, it’s notoriously unreliable - filled with offensive descriptions and unrelated terms intended to help images rank high in search. ![]() Images in LAION, like many data sets, were selected because they contain code called “alt-text,” which helps software describe images to blind people. ![]() “Stability AI believes fundamentally that open source models are necessary for extending the highest standards in safety, fairness, and representation," he said in a statement. Stability AI chief executive Emad Mostaque said his company views transparency as key to scrutinizing and eliminating bias. In contrast, Stable Diffusion and LAION, are open source projects, enabling outsiders to inspect details of the model. ![]() Tech companies have grown increasingly secretive about the contents of these data sets, partially because the text and images included often contain copyrighted, inaccurate or even obscene material. Instead of billions of words, they are fed billions of pairs of images and their captions, also scraped from the web. Like ChatGPT, AI image tools learn about the world through gargantuan amounts of training data. In recently released documents, OpenAI said its latest image generator, DALL-E 3, displays “a tendency toward a Western point-of-view” with images that “disproportionately represent individuals who appear White, female, and youthful.”Īs synthetic images spread across the web, they could give new life to outdated and offensive stereotypes, encoding abandoned ideals around body type, gender and race into the future of image-making. Stable Diffusion is not alone in this orientation. “You don’t need a data science degree to infer this.” “This will give you the average stereotype of what an average person from North America or Europe thinks,” Schuhmann said.
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