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Big Tech's AI auditors will only see what they're shown

To be fair to OpenAI, it allowed the testing in the first place. But it shows how much rides on what a company chooses to share.

Samuel Vance-Law

Publisher CoinDesk

Oct 16, 2026 at 2:58 PM UTC · Updated 2日前 · 1 分で読める

Big Tech's AI auditors will only see what they're shown
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$100M+ AI model production cost

Last Updated

2日前

翻訳中…

To be fair to OpenAI, it allowed the testing in the first place. But it shows how much rides on what a company chooses to share.

A trained AI model is essentially a very large file of numbers, often costing more than $100 million to produce, and whoever has a copy has the model. Handing copies to outsiders is a security risk. Until recently, that left testers with an awkward choice, which Google DeepMind has described well: either the testers hand over their questions, so the company sees the exam in advance, or the company hands over its model.

In August, DeepMind showed a way around that. Working with Singapore's AI Safety Institute and three other outside groups, it ran what it called the world's first double-blind test of an AI model, on the same principle as a double-blind drug trial. The test ran on sealed computer hardware, so the outside testers never saw the model and Google never saw their questions. Google says each side can check mathematically that its material stayed private.

It also moves the trust onto the hardware. Last year, researchers broke into this kind of sealed hardware with less than $1,000 of equipment, although they needed physical access to the server to do it. That's worth taking seriously. But it's a much smaller problem than "we can't let anyone near the model," and it can be managed by being careful about who controls the machines.

Why this matters

Independent AI safety audits could remain constrained by what model developers choose to expose, limiting confidence in assessments of powerful systems. The hardware-based approach could improve auditor independence, but the cited physical-access breach suggests audit infrastructure must be secured as rigorously as the models it is designed to test.

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Originally reported by CoinDesk

NewsLayer coverage based on externally reported material.

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