1660286 comments

• Comment Link
  Shawn Garza

  Domingo, 25 Octubre 2026 19:06

  Really useful piece. The point about freezing the facts before any model sees them stood out to me, and I'd add that recording exact model versions alongside each output saves a lot of pain later when someone asks why a score changed https://wiki-spirit.win/index.php/South_Korea_AI_Basic_Act,_Effective_January_22,_2026:_What_Changed_and_What_It_Means_for_High-Impact_AI
• Comment Link
  Aaron Stephens

  Domingo, 25 Octubre 2026 18:42

  Really useful piece. The point about freezing the facts before any model sees them stood out to me, and so did recording exact model versions, since a score from last month can be impossible to reproduce after an update Additional resources
• Comment Link
  Dora Marshall

  Domingo, 25 Octubre 2026 18:34

  Really useful piece. The point about multi-LLM reviews in threat assessment feels right, but I'd add that teams should freeze the facts each model saw, record exact model versions, and avoid averaging scores since that can hide real disagreement how to implement AI governance
• Comment Link
  Sara Sherman

  Domingo, 25 Octubre 2026 18:29

  Really useful piece. One thing I'd add from working with similar review pipelines: freeze the source facts before any model sees them, and log the exact model versions with each run Take a look at the site here
• Comment Link
  Margaret Baldwin

  Domingo, 25 Octubre 2026 18:25

  Really useful piece. The point about freezing the facts before any model sees them stood out to me, and so did recording exact model versions, since a score you can't reproduce later is hard to defend ISO/IEC 42001:2023
• Comment Link
  Jeremiah Nguyen

  Domingo, 25 Octubre 2026 18:22

  Really useful piece. The point about multi-LLM reviews needing guardrails stood out to me. In my experience, the biggest risk is quietly averaging scores, which hides real disagreement compare AI model outputs side by side
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