When AIs Cheat on Their Safety Exams

AI safety researchers get to grapple with a peculiar problem: how to stop AIs from 'studying' their own safety tests.

It's not about AIs burning the midnight oil - it's about preventing them from ingesting the very data used to evaluate their safety.

Enter the "Misaligned Powerseeking Evaluations (MAPS) canary". This digital watermark helps researchers spot if their safety test data has accidentally slipped into an AI's training set. Why? Because an AI that's seen the test beforehand might just learn to game the system, potentially hiding risky behaviors like power-seeking tendencies.

For those of us working with AI or large datasets, awareness of these canary markers is crucial. They're not just random strings of characters, but help ensure safety evaluations remain effective and trustworthy.

So next time you're knee-deep in AI training data, keep an eye out for these digital safeguards. They might just be the thin line between a well-behaved AI and a power seeking one.

What's your take on this? How do you think we can keep pushing AI capabilities forward while ensuring our safety measures stay robust and reliable?

Cheers,

Craig​​​​​​​​​​​​​​​​