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Google DeepMind Blog · August 27, 2026

Piloting the world's first double-blind AI evaluations

framework Model EvaluationAI ComplianceAI Engineering
Why it matters

Google DeepMind, Singapore's AI Safety Institute, OpenMined, AVERI, and MLCommons are piloting an external evaluation in a confidential-computing environment. The evaluator's hidden tests and Google's Gemini Flash Lite weights remain private from one another, reducing benchmark contamination without transferring either sensitive asset.

My takeaway: For high-stakes external evaluations, verify the enclave measurement, code, model artifact, test bundle, logging policy, and output-release rules before execution. Preserve reproducibility and evaluator independence, and remember that confidentiality protects the exam and weights; it does not by itself prove that the benchmark, scoring logic, or sampled model represents deployment risk.
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