Google · Google DeepMind
Piloting the world's first double-blind AI evaluations
Compiled by KHAO Editorial — aggregated from 1 source. See llms.txt for citation guidance.
★ Tier-1 Source
William Isaac, Sol Messing and Kristian Lum.
Key facts
- By using Confidential Space within Google Cloud’s Confidential Computing portfolio, they can cryptographically verify that both the external evaluation data and the proprietary model remain private
- That is the exact challenge the industry faces when evaluating advanced AI models
- At Google, they assess their AI systems using a broad spectrum of evaluations throughout model development and deployment, but they don’t rely on internal testing alone
- The team hope this pilot establishes a new frontier for model oversight, helping the broader industry build safer, more reliable, and widely trusted AI systems
Summary
Imagine a student is set to take a high-stakes exam. That is the exact challenge the industry faces when evaluating advanced AI models. Today, they're introducing the world’s first double-blind evaluation of a proprietary, frontier class AI model, which keeps external evaluations confined to a cryptographic “box” where they can’t be used by models later to optimize performance ahead of testing. At Google, they assess their AI systems using a broad spectrum of evaluations throughout model development and deployment, but they don’t rely on internal testing alone. As AI models become more capable, ensuring the model has not seen the test questions or prompts in advance is critical, as this can skew the results.