# AI Risk Profiles: A Standards Proposal for Pre-Deployment AI Risk Disclosures

arXiv: https://arxiv.org/abs/2309.13176 · HTML: https://mlsanity.com/paper/2309.13176/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 27 references that are in the map.
- First posted on arXiv 22 Sep 2023.

## Builds on (most cited first, 12 of 12)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Red Teaming Language Models with Language Models](https://mlsanity.com/paper/2202.03286/) (2022)
- [Discovering Language Model Behaviors with Model-Written Evaluations](https://mlsanity.com/paper/2212.09251/) (2022)
- [Model evaluation for extreme risks](https://mlsanity.com/paper/2305.15324/) (2023)
- [GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models](https://mlsanity.com/paper/2303.10130/) (2023)
- [Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database](https://mlsanity.com/paper/2011.08512/) (2020)
- [Evaluating the Social Impact of Generative AI Systems in Systems and Society](https://mlsanity.com/paper/2306.05949/) (2023)
- [On the Creativity of Large Language Models](https://mlsanity.com/paper/2304.00008/) (2023)
- [A taxonomic system for failure cause analysis of open source AI incidents](https://mlsanity.com/paper/2211.07280/) (2022)

## Built on by (most cited first, 4 of 4)

- [Co-designing an AI Impact Assessment Report Template with AI Practitioners and AI Compliance Experts](https://mlsanity.com/paper/2407.17374/) (2024)
- [STREAM (ChemBio): A Standard for Transparently Reporting Evaluations in AI Model Reports](https://mlsanity.com/paper/2508.09853/) (2025)
- [The Unified Control Framework: Establishing a Common Foundation for Enterprise AI Governance, Risk Management and Regulatory Compliance](https://mlsanity.com/paper/2503.05937/) (2025)
- [When Not to Automate: A Formal Protocol for Human Preservation in AI-Optimized Organizations](https://mlsanity.com/paper/2607.15944/) (2026)

## Abstract (arXiv)

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.