Expanding on this—this whole area is probably best known as “AI Control”, and I’d lump it under “Control the thing” as its own category. I’d also move Control Evals to this category as well, though someone at RR would know better than I.
Yep, indeed I would consider “control evaluations” to be a method of “AI control”. I consider the evaluation and the technique development to be part of a unified methodology (we’ll describe this more in a forthcoming post).
It’s “a unified methodology” but I claim it has two very different uses: (1) determining whether a model is safe (in general or within particular scaffolding) and (2) directly making deployment safer. Or (1) model evals and (2) inference-time safety techniques.
(Agreed except that “inference-time safety techiques” feels overly limiting. It’s more like purely behavioral (black-box) safety techniques where we can evaluate training by converting it to validation. Then, we imagine we get the worst model that isn’t discriminated by our validation set and other measurements. I hope this isn’t too incomprehensible, but don’t worry if it is, this point isn’t that important.)
Expanding on this—this whole area is probably best known as “AI Control”, and I’d lump it under “Control the thing” as its own category. I’d also move Control Evals to this category as well, though someone at RR would know better than I.
Yep, indeed I would consider “control evaluations” to be a method of “AI control”. I consider the evaluation and the technique development to be part of a unified methodology (we’ll describe this more in a forthcoming post).
(I work at RR)
It’s “a unified methodology” but I claim it has two very different uses: (1) determining whether a model is safe (in general or within particular scaffolding) and (2) directly making deployment safer. Or (1) model evals and (2) inference-time safety techniques.
(Agreed except that “inference-time safety techiques” feels overly limiting. It’s more like purely behavioral (black-box) safety techniques where we can evaluate training by converting it to validation. Then, we imagine we get the worst model that isn’t discriminated by our validation set and other measurements. I hope this isn’t too incomprehensible, but don’t worry if it is, this point isn’t that important.)