AI Safety & Responsible AI
Scalable oversight
Ways to check AI work when people cannot easily examine every task themselves.
Example
A review process directs people toward difficult cases while simpler checks run automatically.
Why people use it
It helps people supervise more difficult or numerous AI tasks without checking every detail manually.
What you'll hear
“How can we check work that is too large to read line by line?”
What this means for you
Test whether serious mistakes actually reach someone who can respond.
Can you control it?
Developer-only
The people building or running the AI choose this setup. An everyday user generally needs their help to change how this part works.
Common questions
- Does scalable oversight mean removing human responsibility?
- No. It concerns extending oversight capacity, not eliminating accountability.
- Can an automated checker share the same blind spots?
- Yes. A second AI can miss the same kinds of mistakes as the first.
- Does checking a sample reveal every failure?
- No. Sampling can find patterns while still missing rare or unusual problems.