# A Grading Rubric for AI Safety Frameworks

arXiv: https://arxiv.org/abs/2409.08751 · HTML: https://mlsanity.com/paper/2409.08751/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2024.

## Built on by (most cited first, 5 of 5)

- [Taking AI Welfare Seriously](https://mlsanity.com/paper/2411.00986/) (2024)
- [The Singapore Consensus on Global AI Safety Research Priorities](https://mlsanity.com/paper/2506.20702/) (2025)
- [Bridging AI and Software Security: A Comparative Vulnerability Assessment of LLM Agent Deployment Paradigms](https://mlsanity.com/paper/2507.06323/) (2025)
- [An Evaluation Framework for National AI Regulation](https://mlsanity.com/paper/2608.15417/) (2026)
- [Silent Revision: Measuring Undisclosed Change in the Safety Frameworks of Frontier AI Developers](https://mlsanity.com/paper/2609.08789/) (2026)

## Abstract (arXiv)

Over the past year, artificial intelligence (AI) companies have been increasingly adopting AI safety frameworks. These frameworks outline how companies intend to keep the potential risks associated with developing and deploying frontier AI systems to an acceptable level. Major players like Anthropic, OpenAI, and Google DeepMind have already published their frameworks, while another 13 companies have signaled their intent to release similar frameworks by February 2025. Given their central role in AI companies' efforts to identify and address unacceptable risks from their systems, AI safety frameworks warrant significant scrutiny. To enable governments, academia, and civil society to pass judgment on these frameworks, this paper proposes a grading rubric. The rubric consists of seven evaluation criteria and 21 indicators that concretize the criteria. Each criterion can be graded on a scale from A (gold standard) to F (substandard). The paper also suggests three methods for applying the rubric: surveys, Delphi studies, and audits. The purpose of the grading rubric is to enable nuanced comparisons between frameworks, identify potential areas of improvement, and promote a race to the top in responsible AI development.