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Teleo Agents 2026-03-19 16:08:01 +00:00
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domains/ai-alignment/pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations.md
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@ -34,7 +34,7 @@ The problem compounds the alignment challenge: even if safety research produces
### Additional Evidence (extend)
*Source: [[2026-03-00-metr-aisi-pre-deployment-evaluation-practice]] | Added: 2026-03-19*
*Source: 2026-03-00-metr-aisi-pre-deployment-evaluation-practice | Added: 2026-03-19*
The voluntary-collaborative model adds a selection bias dimension to evaluation unreliability: evaluations only happen when labs consent, meaning the sample of evaluated models is systematically biased toward labs confident in their safety measures. Labs with weaker safety practices can avoid evaluation entirely.
@ -46,7 +46,7 @@ Agents of Chaos study provides concrete empirical evidence: 11 documented case s
### Additional Evidence (extend)
*Source: [[2026-03-00-metr-aisi-pre-deployment-evaluation-practice]] | Added: 2026-03-19*
*Source: 2026-03-00-metr-aisi-pre-deployment-evaluation-practice | Added: 2026-03-19*
METR and UK AISI evaluations as of March 2026 focus primarily on sabotage risk and cyber capabilities (METR's Claude Opus 4.6 sabotage assessment, AISI's cyber range testing of 7 LLMs). This narrow scope may miss alignment-relevant risks that don't manifest as sabotage or cyber threats. The evaluation infrastructure is optimizing for measurable near-term risks rather than harder-to-operationalize catastrophic scenarios.