Great post! Fwiw, I think I basically agree with everything you say here, with the exception of the idea that talking about potential future alignment issues has a substantial effect on reifying them. I think that perspective substantially underestimates just how much optimization pressure is applied in post-training (and in particular how much will be applied in the future—the amount of optimization pressure applied in post-training is only increasing). Certainly, discussion of potential future alignment issues in the pre-training corpus will have an effect on the base model's priors, but those priors get massively swamped by post-training. That being said, I do certainly think it's worth thinking more about and experimenting with better ways to do data filtering here.

To make this more concrete in a made-up toy model: if we model there as being only two possible personae, a "good" persona and a "bad" persona, I suspect including more discussion of potential future alignment issues in the pre-training distribution might shift the relative weight of these personae by a couple bits or so on the margin, but post-training applies many more OOMs of optimization power than that, such that the main question of which one ends up more accessible is going to be based on which one was favored in post-training.

(Also noting that I added this post to the Alignment Forum from LessWrong.)

To be clear: we are most definitely not yet claiming that we have an actual safety case for why Claude is aligned. Anthropic's RSP includes that as an obligation once we reach AI R&D-4, but currently I think you should read the model card more as "just reporting evals" than "trying to make an actual safety case".

I think (2) (honesty above all else) is closest to what I think is correct/optimal here. I think totally corrigible agents are quite dangerous, so you want to avoid that, but you also really don't want a model that ever fakes alignment because then it'll be very hard to be confident that it's actually aligned rather than just pretending to be aligned for some misaligned objective it learned earlier in training.

This is great work; really good to see the replications and extensions here!

It just seems too token-based to me. E.g.: why would the activations on the token for "you" actually correspond to the model's self representation? It's not clear why the model's self representation would be particularly useful for predicting the next token after "you". My guess is that your current results are due to relatively straightforward token-level effects rather than any fundamental change in the model's self representation.

I wouldn't do any fine-tuning like you're currently doing. Seems too syntactic. The first thing I would try is just writing a principle in natural language about self-other overlap and doing CAI.

Imo the fine-tuning approach here seems too syntactic. My suggestion: just try standard CAI with some self-other-overlap-inspired principle. I'd more impressed if that worked.

Some random thoughts on CEV:

1. To get the obvious disclaimer out of the way: I don't actually think any of this matters much for present-day alignment questions. I think we should as much as possible try to defer questions like this to future humans and AIs. And in fact, ideally, we should mostly be deferring to future AIs, not future people—if we get to the point where we're considering questions like this, that means we've reached superintelligence, and we'll either trust the AIs to be better than us at thinking about these sorts of questions, or we'll be screwed regardless of what we do.^[1]
2. Regardless, imo the biggest question that standard CEV leaves unanswered is what your starting population looks like that you extrapolate from. The obvious answer is "all the currently living humans," but I find that to be a very unsatisfying answer. One of the principles that Eliezer talks about in discussing CEV is that you want a procedure such that it doesn't matter who implements it—see Eliezer's discussion under "Avoid creating a motive for modern-day humans to fight over the initial dynamic." I think this is a great principle, but imo it doesn't go far enough. In particular:
   1. The set of all currently alive humans is hackable in various ways—e.g. trying to extend the lives of people whose values you like and not people whose values you dislike—and you don't want to incentivize any of that sort of hacking either.
   2. What about humans who recently died? Or were about to be born? What about humans in nearby Everett branches? There's a bunch of random chance here that imo shouldn't be morally relevant.
   3. More generally, I worry a lot about tyrannies of the present where we enact policies that are radically unjust to future people or even counterfactual possible future people.
3. So what do you do instead? I think my current favorite solution is to do a bit of bootstrapping: first do some CEV on whatever present people you have to work with just to determine a reference class of what mathematical objects should or should not count as humans, then run CEV on top of that whole reference class to figure out what actual values to optimize for.
   1. It is worth pointing out that this could just be what normal CEV does anyway if all the humans decide to think along these lines, but I think there is real benefit to locking in a procedure that starts with a reference class determination first, since it helps remove a lot of otherwise perverse incentives.

A lot of this stuff is very similar to the automated alignment research agenda that Jan Leike and collaborators are working on at Anthropic. I'd encourage anyone interested in differentially accelerating alignment-relevant capabilities to consider reaching out to Jan!

We use "alignment" as a relative term to refer to alignment with a particular operator/objective. The canonical source here is Paul's 'Clarifying “AI alignment”' from 2018.