We have now entered the era of trillion parameter machine learning models
trained on billion-sized datasets scraped from the internet. The rise of these
gargantuan datasets has given rise to formidable bodies of critical work that
has called for caution while generating these large datasets. These address
concerns surrounding the dubious curation practices used to generate these
datasets, the sordid quality of alt-text data available on the world wide web,
the problematic content of the CommonCrawl dataset often used as a source for
training large language models, and the entrenched biases in large-scale
visio-linguistic models (such as OpenAI's CLIP model) trained on opaque
datasets (WebImageText). In the backdrop of these specific calls of caution, we
examine the recently released LAION-400M dataset, which is a CLIP-filtered
dataset of Image-Alt-text pairs parsed from the Common-Crawl dataset. We found
that the dataset contains, troublesome and explicit images and text pairs of
rape, pornography, malign stereotypes, racist and ethnic slurs, and other
extremely problematic content. We outline numerous implications, concerns and
downstream harms regarding the current state of large scale datasets while
raising open questions for various stakeholders including the AI community,
regulators, policy makers and data subjects.

本文报道了一个规模为 400M 的过滤了不良内容的图像 - alt 文本数据集，该数据集在生成大规模机器学习模型时可能存在的问题和潜在涉及的利益攸关者（包括 AI 社区、监管机构、政策制定者和数据主体）的问题。