  As AI systems' sophistication and proliferation have increased, awareness of
the risks has grown proportionally (Sorkin et al. 2023). In response, calls
have grown for stronger emphasis on disclosure and transparency in the AI
industry (NTIA 2023; OpenAI 2023b), with proposals ranging from standardizing
use of technical disclosures, like model cards (Mitchell et al. 2019), to
yet-unspecified licensing regimes (Sindhu 2023). Since the AI value chain is
complicated, with actors representing various expertise, perspectives, and
values, it is crucial that consumers of a transparency disclosure be able to
understand the risks of the AI system the disclosure concerns. In this paper we
propose a risk profiling standard which can guide downstream decision-making,
including triaging further risk assessment, informing procurement and
deployment, and directing regulatory frameworks. The standard is built on our
proposed taxonomy of AI risks, which reflects a high-level categorization of
the wide variety of risks proposed in the literature. We outline the myriad
data sources needed to construct informative Risk Profiles and propose a
template-based methodology for collating risk information into a standard, yet
flexible, structure. We apply this methodology to a number of prominent AI
systems using publicly available information. To conclude, we discuss design
decisions for the profiles and future work.
