Safety frameworks represent a significant development in AI governance: they are the first type of publicly shared catastrophic risk management framework developed by major AI companies and focus specifically on AI scaling decisions. I identify six critical measurement challenges in their implementation and propose three policy recommendations to improve their validity and reliability.

翻译：安全框架代表了人工智能治理领域的一项重要进展：这是由主要人工智能公司开发的首个公开共享的灾难性风险管理框架，并特别聚焦于人工智能规模化决策。本文识别了其实施过程中的六个关键测量挑战，并提出了三项政策建议以提升其有效性与可靠性。