#AI #GenerativeAI #AISafety #SafetyFrameworks: "To provide a concrete foundation for this analysis, I primarily focus on Anthropic's safety framework (version 1.0), which stands as the most comprehensive public document of its kind to date. I then outline how this analysis extends to and informs other safety frameworks. By employing a measurement modeling lens, I identify six neglected problems (See Table 1) that are crucial to address through collaboration among diverse expert perspectives. First, a collection of models or an embedded model in larger AI ecosystems might trigger catastrophic events. Second, indirect or contributing causes can bring about catastrophic events via complex causal chains. Third, the open-ended characterization of AI Safety Levels (ASLs) can introduce uncertainties into the effective governance of AI catastrophic risks. Fourth, the lack of rigorous justification in setting specific quantitative thresholds presents obstacles in reliably defining and measuring catastrophic events. Fifth, the validity of AI safety assessments can be compromised by the fundamental limitations inherent in red-teaming methodologies. Lastly, mechanisms to ensure developer accountability in ASL classification, particularly for false negatives, are needed to address the risk of AI systems being deployed with inadequate safety measures due to inaccurate catastrophic risk evaluations."

https://www.techpolicy.press/measurement-challenges-in-ai-catastrophic-risk-governance-and-safety-frameworks/