  The paper discusses the potential of large vision-language models as objects
of interest for empirical cultural studies. Focusing on the comparative
analysis of outputs from two popular text-to-image synthesis models, DALL-E 2
and Stable Diffusion, the paper tries to tackle the pros and cons of striving
towards culturally agnostic vs. culturally specific AI models. The paper
discusses several examples of memorization and bias in generated outputs which
showcase the trade-off between risk mitigation and cultural specificity, as
well as the overall impossibility of developing culturally agnostic models.
