Alternative text is imperative for making the internet accessible to blind and low-vision individuals. Vision-language models (VLMs) have the potential to improve alt text coverage. This study considers how social stereotypes of race and gender are manifested in the alt-text language generated by GPT-4.1, Claude-Sonnet-4.5, and Gemini-2.5-Pro. A total of 1200 images are generated uniformly by gender and skin tone, and then the VLMs are prompted to generate the alternative text. The analysis of the generated alt text shows that the text descriptions differ significantly depending on the skin tone and gender of the person in an image. The VLMs tend to assume that whiteness is the default identity and the descriptions of those with darker skin are more likely to contain racialized terms. This bias suggests that AI-generated alt-text should be used with caution in the interest of preserving equity and preventing discrimination.

Existing studies have shown that AI-generated images tend to reinforce social biases, including those related to race and gender. However, no studies have investigated weight bias, or fatphobia, in AI-generated images. This study utilizes DALL-E 3 to determine the extent to which anti-fat and pro-thin biases are present in AI-generated images, and examines stereotypical associations between moral character and body weight. Four-thousand images are generated using twenty pairs of positive and negative textual prompts. These images are then manually labeled with weight information and analyzed to determine the extent to which they reflect fatphobia. The findings and their impact are discussed and related to existing research on weight bias.