The Myth of Culturally Agnostic AI Models
This work addresses the problem of cultural bias in AI for researchers and developers, but it is incremental as it builds on existing studies of model outputs.
The paper examines the trade-offs between culturally agnostic and culturally specific AI models by analyzing outputs from DALL-E 2 and Stable Diffusion, finding that memorization and bias in generated images highlight the impossibility of achieving cultural agnosticism.
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.