Animal stereotypes are deeply embedded in human culture and language. They often shape our perceptions and expectations of various species. Our study investigates how animal stereotypes manifest in vision-language models during the task of image generation. Through targeted prompts, we explore whether DALL-E perpetuates stereotypical representations of animals, such as "owls as wise," "foxes as unfaithful," etc. Our findings reveal significant stereotyped instances where the model consistently generates images aligned with cultural biases. The current work is the first of its kind to examine animal stereotyping in vision-language models systematically and to highlight a critical yet underexplored dimension of bias in AI-generated visual content.
View on arXiv@article{aman2025_2501.12433,
title={ Owls are wise and foxes are unfaithful: Uncovering animal stereotypes in vision-language models },
author={ Tabinda Aman and Mohammad Nadeem and Shahab Saquib Sohail and Mohammad Anas and Erik Cambria },
journal={arXiv preprint arXiv:2501.12433},
year={ 2025 }
}