论文标题
文化不可知的AI模型的神话
The Myth of Culturally Agnostic AI Models
论文作者
论文摘要
本文讨论了大型视觉模型作为经验文化研究感兴趣的对象的潜力。该论文着重于两个流行的文本到图像合成模型DALL-E 2和稳定扩散的输出的比较分析，该论文试图解决努力努力朝着文化上不可知论与文化特定的AI模型努力的利弊。本文讨论了生成的产出中记忆和偏见的几个例子，这些示例展示了降低风险和文化特异性之间的权衡，以及开发文化不可知论模型的总体不可能。
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.
