`Scale the model, scale the data, scale the GPU-farms' is the reigning sentiment in the world of generative AI today. While model scaling has been extensively studied, data scaling and its downstream impacts remain under explored. This is especially of critical importance in the context of visio-linguistic datasets whose main source is the World Wide Web, condensed and packaged as the CommonCrawl dump. This large scale data-dump, which is known to have numerous drawbacks, is repeatedly mined and serves as the data-motherlode for large generative models. In this paper, we: 1) investigate the effect of scaling datasets on hateful content through a comparative audit of the LAION-400M and LAION-2B-en, containing 400 million and 2 billion samples respectively, and 2) evaluate the downstream impact of scale on visio-linguistic models trained on these dataset variants by measuring racial bias of the models trained on them using the Chicago Face Dataset (CFD) as a probe. Our results show that 1) the presence of hateful content in datasets, when measured with a Hate Content Rate (HCR) metric on the inferences of the Pysentimiento hate-detection Natural Language Processing (NLP) model, increased by nearly $12\%$ and 2) societal biases and negative stereotypes were also exacerbated with scale on the models we evaluated. As scale increased, the tendency of the model to associate images of human faces with the `human being' class over 7 other offensive classes reduced by half. Furthermore, for the Black female category, the tendency of the model to associate their faces with the `criminal' class doubled, while quintupling for Black male faces. We present a qualitative and historical analysis of the model audit results, reflect on our findings and its implications for dataset curation practice, and close with a summary of our findings and potential future work to be done in this area.

翻译：“扩展模型、扩展数据、扩展GPU集群”是当今生成式AI领域的主流观点。尽管模型扩展已被广泛研究，但数据扩展及其下游影响仍未被充分探索。这在以万维网为主要来源、经压缩打包为CommonCrawl转储数据的视觉-语言数据集背景下尤为关键。这种大规模数据转储存在诸多缺陷，却反复被挖掘，成为大型生成模型的数据宝库。本文中，我们：1) 通过比较审计LAION-400M和LAION-2B-en（分别包含4亿和20亿样本），研究扩展数据集对仇恨内容的影响；2) 通过测量使用芝加哥人脸数据集（CFD）作为探针训练模型的种族偏见，评估扩展对基于这些数据集变体训练的视觉-语言模型的下游影响。结果显示：1) 在数据集中的仇恨内容方面，使用Pysentimiento仇恨检测自然语言处理（NLP）模型推理的仇恨内容率（HCR）指标衡量时，其存在率增加了近12%；2) 在我们评估的模型中，社会偏见和负面刻板印象也随扩展加剧。随着规模增大，模型将人脸图像与“人类”类别（相对于其他7类冒犯性类别）关联的倾向降低了一半。此外，对于黑人女性类别，模型将其人脸与“罪犯”类别关联的倾向增加了一倍，而对于黑人男性类别则增加了四倍。我们对模型审计结果进行了定性和历史分析，反思了这些发现对数据集整理实践的影响，并总结研究结果及该领域未来的潜在工作。