  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 on model
performance remain under-explored. This is particularly important in the
context of multimodal datasets whose main source is the World Wide Web,
condensed and packaged as the Common Crawl dump, which is known to exhibit
numerous drawbacks. In this paper, we evaluate the downstream impact of dataset
scaling on 14 visio-linguistic models (VLMs) trained on the LAION400-M and
LAION-2B datasets by measuring racial and gender bias using the Chicago Face
Dataset (CFD) as the probe. Our results show that as the training data
increased, the probability of a pre-trained CLIP model misclassifying human
images as offensive non-human classes such as chimpanzee, gorilla, and
orangutan decreased, but misclassifying the same images as human offensive
classes such as criminal increased. Furthermore, of the 14 Vision
Transformer-based VLMs we evaluated, the probability of predicting an image of
a Black man and a Latino man as criminal increases by 65% and 69%,
respectively, when the dataset is scaled from 400M to 2B samples for the larger
ViT-L models. Conversely, for the smaller base ViT-B models, the probability of
predicting an image of a Black man and a Latino man as criminal decreases by
20% and 47%, respectively, when the dataset is scaled from 400M to 2B samples.
We ground the model audit results in a qualitative and historical analysis,
reflect on our findings and their implications for dataset curation practice,
and close with a summary of mitigation mechanisms and ways forward. Content
warning: This article contains racially dehumanising and offensive
descriptions.
