I want to quickly draw attention to a concept in AI alignment: Robustness to Scale. Briefly, you want your proposal for an AI to be robust (or at least fail gracefully) to changes in its level of capabilities. I discuss three different types of robustness to scale: robustness to scaling up, robustness to scaling down, and robustness to relative scale.

The purpose of this post is to communicate, not to persuade. It may be that we want to bite the bullet of the strongest form of robustness to scale, and build an AGI that is simply not robust to scale, but if we do, we should at least realize that we are doing that.

Robustness to scaling up means that your AI system does not depend on not being too powerful. One way to check for this is to think about what would happen if the thing that the AI is optimizing for were actually maximized. One example of failure of robustness to scaling up is when you expect an AI to accomplish a task in a specific way, but it becomes smart enough to find new creative ways to accomplish the task that you did not think of, and these new creative ways are disastrous. Another example is when you make an AI that is incentivized to do one thing, but you add restrictions that make it so that the best way to accomplish that thing has a side effect that you like. When you scale the AI up, it finds a way around your restrictions.

Robustness to scaling down means that your AI system does not depend on being sufficiently powerful. You can't really make your system still work when it scales down, but you can maybe make sure it fails gracefully. For example, imagine you had a system that was trying to predict humans, and use these predictions to figure out what to do. When scaled up all the way, the predictions of humans are completely accurate, and it will only take actions that the predicted humans would approve of. If you scale down the capabilities, your system may predict the humans incorrectly. These errors may multiply as you stack many predicted humans together, and the system can end up optimizing for some seeming random goal.

Robustness to relative scale means that your AI system does not depend on any subsystems being similarly powerful to each other. This is most easy to see in systems that depend on adversarial subsystems. If part of you AI system is suggest plans, and another part is trying to find problems in those plans, if you scale up the suggester relative to the verifier, the suggester may find plans that are optimized for taking advantage of the verifier's weaknesses.

My current state is that when I hear proposals for AI alignment that do not feel very strongly robust to scale, I become very worried about the plan. Part of this comes from feeling like we are actually very early on a logistic capabilities curve. I thus expect that as we scale up capabilities, we can get eventually get large differences very quickly. Thus, I expect that the scaled up (and partially scaled up) versions to actually happen. However, robustness to scale is very difficult, so it may be that we have to depend on systems that are not very robust, and be careful not to push them too far.

New Comment
7 comments, sorted by Click to highlight new comments since:

Robustness to scale is still one of my primary explanations for why MIRI-style alignment research is useful, and why alignment work in general should be front-loaded. I am less sure about this specific post as an introduction to the concept (since I had it before the post, and don't know if anyone got it from this post), but think that the distillation of concepts floating around meatspace to clear reference works is one of the important functions of LW.

(5 upvotes from a few AF users suggests this post probably should be nominated by an additional AF person, but unsure. I do apologize again for not having better nomination-endorsement-UI.

I think this post may have been relevant to my own thinking, but I'm particularly interested in how relevant the concept has been to other people who think professionally about alignment)

I think that the terms introduced by this post are great and I use them all the time

This essay makes a valuable contribution to the vocabulary we use to discuss and think about AI risk. Building a common vocabulary like this is very important for productive knowledge transmission and debate, and makes it easier to think clearly about the subject.

Rereading this post while thinking about the approximations that we make in alignment, two points jump at me:

  • I'm not convinced that robustness to relative scale is as fundamental as the other two, because there is no reason to expect that in general the subcomponents will be significantly different in power, especially in settings like adversarial training where both parts are trained according to the same approach. That being said, I still agree that this is an interesting question to ask, and some proposal might indeed depend on a version of this.
  • Robustness to scaling up and robustness to scaling down sounds like they can be summarized by: "does it break in the limit of optimality? and "does it only work in the limit of optimality?". Where the first gives us an approximation for studying and designing alignment proposals, and the second points out a potential issue in this approximation. (Not saying that this is capturing all of your meaning, though)

At the time I began writing this previous comment, I felt like I hadn't directly gotten that much use of this post. But then after reflecting a bit about Beyond Astronomical Waste I realized this had actually been a fairly important concept in some of my other thinking.

I've used the concepts in this post a lot when discussing various things related to AI Alignment. I think asking "how robust is this AI design to various ways of scaling up?" has become one of my go-to hammers for evaluating a lot of AI Alignment proposals, and I've gotten a lot of mileage out of that.