  As training datasets become increasingly drawn from unstructured,
uncontrolled environments such as the web, researchers and industry
practitioners have increasingly relied upon data filtering techniques to
"filter out the noise" of web-scraped data. While datasets have been widely
shown to reflect the biases and values of their creators, in this paper we
contribute to an emerging body of research that assesses the filters used to
create these datasets. We show that image-text data filtering also has biases
and is value-laden, encoding specific notions of what is counted as
"high-quality" data. In our work, we audit a standard approach of image-text
CLIP-filtering on the academic benchmark DataComp's CommonPool by analyzing
discrepancies of filtering through various annotation techniques across
multiple modalities of image, text, and website source. We find that data
relating to several imputed demographic groups -- such as LGBTQ+ people, older
women, and younger men -- are associated with higher rates of exclusion.
Moreover, we demonstrate cases of exclusion amplification: not only are certain
marginalized groups already underrepresented in the unfiltered data, but
CLIP-filtering excludes data from these groups at higher rates. The
data-filtering step in the machine learning pipeline can therefore exacerbate
representation disparities already present in the data-gathering step,
especially when existing filters are designed to optimize a specifically-chosen
downstream performance metric like zero-shot image classification accuracy.
Finally, we show that the NSFW filter fails to remove sexually-explicit content
from CommonPool, and that CLIP-filtering includes several categories of
copyrighted content at high rates. Our conclusions point to a need for
fundamental changes in dataset creation and filtering practices.
