Frontier AI Risk Management Framework in Practice: A Risk Analysis Technical Report
Shanghai AI Lab, Xiaoyang Chen, Yunhao Chen, Zeren Chen, Zhiyun Chen, Hanyun Cui, Yawen Duan, Jiaxuan Guo, Qi Guo, Xuhao Hu, Hong Huang, Lige Huang, Chunxiao Li, Juncheng Li, Qihao Lin, Dongrui Liu, Xinmin Liu, Zicheng Liu, Chaochao Lu, Xiaoya Lu, Jingjing Qu, Qibing Ren
2025-07-28
Summary
This paper talks about the Frontier AI Risk Management Framework, which is a structured approach to identify, analyze, and manage the big risks that come with powerful AI systems.
What's the problem?
As AI models become more advanced, they can create serious safety and security risks that current methods don’t fully handle, making it hard to predict and prevent potentially harmful outcomes.
What's the solution?
The framework uses a process called E-T-C analysis to categorize risks into green, yellow, and red zones based on how dangerous they are and early warning signs. It guides AI developers to spot risks early, set clear limits, evaluate them carefully, and take steps to reduce these risks throughout the AI development cycle.
Why it matters?
This matters because it helps make AI development safer by providing a clear way to track and control risks, protecting people and society from unexpected bad effects of advanced AI.
Abstract
The report assesses frontier risks of AI models using the E-T-C analysis framework, categorizing risks into green, yellow, and red zones based on intolerable thresholds and early warning indicators.