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(CA) Blind deep-deployment evals for control & sabotage

AI safety evals could improve with new 'blind deep-deployment' method

By PulseAugur Editorial · [1 sources] ·

A proposal for "blind deep-deployment" evaluations aims to improve AI safety by allowing external auditors to specify control and sabotage tests without direct access to internal AI lab systems. Auditors would provide detailed prompts and code harnesses, which AI labs would then implement using their own resources and internal checkpoints. This method seeks to enhance the realism of safety evaluations and provide actionable insights to AI labs, even if the labs do not share proprietary information. AI

IMPACT This evaluation method could improve the rigor of AI safety testing, potentially leading to more robust AI systems.

RANK_REASON The item proposes a novel methodology for AI safety evaluation, akin to a research paper. [lever_c_demoted from research: ic=1 ai=1.0]

Read on LessWrong (AI tag) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI safety evals could improve with new 'blind deep-deployment' method

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0 / 100
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The item proposes a novel methodology for AI safety evaluation, akin to a research paper. [lever_c_demoted from research: ic=1 ai=1.0]
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
safety, paper
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High
Clearly on-topic for AI-industry coverage.
Story freshness
112 days old
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Full methodology in our editorial standards.

COVERAGE [1]

  1. LessWrong (AI tag) TIER_1 (CA) · Dylan Bowman ·

    Blind deep-deployment evaluations for control and sabotage

    <p><i><span>Thanks to </span></i><a href="https://www.lesswrong.com/users/ezra-newman" rel="noreferrer"><i><span>Ezra Newman</span></i></a><i><span> for initial ideation and various people at Apollo Research for feedback. This short personal piece does not necessarily reflect the…

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