  The race to train language models on vast, diverse, and inconsistently
documented datasets has raised pressing concerns about the legal and ethical
risks for practitioners. To remedy these practices threatening data
transparency and understanding, we convene a multi-disciplinary effort between
legal and machine learning experts to systematically audit and trace 1800+ text
datasets. We develop tools and standards to trace the lineage of these
datasets, from their source, creators, series of license conditions,
properties, and subsequent use. Our landscape analysis highlights the sharp
divides in composition and focus of commercially open vs closed datasets, with
closed datasets monopolizing important categories: lower resource languages,
more creative tasks, richer topic variety, newer and more synthetic training
data. This points to a deepening divide in the types of data that are made
available under different license conditions, and heightened implications for
jurisdictional legal interpretations of copyright and fair use. We also observe
frequent miscategorization of licenses on widely used dataset hosting sites,
with license omission of 70%+ and error rates of 50%+. This points to a crisis
in misattribution and informed use of the most popular datasets driving many
recent breakthroughs. As a contribution to ongoing improvements in dataset
transparency and responsible use, we release our entire audit, with an
interactive UI, the Data Provenance Explorer, which allows practitioners to
trace and filter on data provenance for the most popular open source finetuning
data collections: www.dataprovenance.org.
