June 11, 2024, 4:42 a.m. | Philip Wootaek Shin, Jihyun Janice Ahn, Wenpeng Yin, Jack Sampson, Vijaykrishnan Narayanan

cs.CL updates on arXiv.org arxiv.org

arXiv:2406.05602v1 Announce Type: cross
Abstract: It has been shown that many generative models inherit and amplify societal biases. To date, there is no uniform/systematic agreed standard to control/adjust for these biases. This study examines the presence and manipulation of societal biases in leading text-to-image models: Stable Diffusion, DALL-E 3, and Adobe Firefly. Through a comprehensive analysis combining base prompts with modifiers and their sequencing, we uncover the nuanced ways these AI technologies encode biases across gender, race, geography, and region/culture. …

abstract amplify analysis arxiv bias biases comparative analysis control cs.cl cs.cv dall dall-e diffusion generative generative models image manipulation prompt stable diffusion standard study text text-to-image type uniform

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