  The unstructured nature of data used in foundation model development is a
challenge to systematic analyses for making data use and documentation
decisions. From a Responsible AI perspective, these decisions often rely upon
understanding how people are represented in data. We propose a framework
designed to guide analysis of human representation in unstructured data and
identify downstream risks. We apply the framework in two toy examples using the
Common Crawl web text corpus (C4) and LAION-400M. We also propose a set of
hypothetical action steps in service of dataset use, development, and
documentation.
