Legible vs. Illegible AI Safety Problems
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This is close to my own thinking, but doesn't quite hit the nail on the head. I don't actually worry that much about progress on legible problems giving people unfounded confidence, and thereby burning timeline. Rather, when I look at the ways in which people make progress on legible problems, they often make the illegible problems actively worse. RLHF is the central example I have in mind here.
4I don't actually worry that much about progress on legible problems giving people unfounded confidence, and thereby burning timeline.
Interesting... why not? It seems perfectly reasonable to worry about both?
It's one of those arguments which sets off alarm bells and red flags in my head. Which doesn't necessarily mean that it's wrong, but I sure am suspicious of it. Specifically, it fits the pattern of roughly "If we make straightforwardly object-level-good changes to X, then people will respond with bad thing Y, so we shouldn't make straightforwardly object-level-good changes to X".
It's the sort of thing to which the standard reply is "good things are good". A more sophisticated response might be something like "let's go solve the actual problem part, rather than trying to have less good stuff". (To be clear, I don't necessarily endorse those replies, but that's what the argument pattern-matches to in my head.)
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1But it seems very analogous to the argument that working on AI capabilities has negative EV. Do you see some important disanalogies between the two, or are you suspicious of that argument too?
That one doesn't route through "... then people respond with bad thing Y" quite so heavily. Capabilities research just directly involves building a dangerous thing, independent of whether other people make bad decisions in response.
What about more indirect or abstract capabilities work, like coming up with some theoretical advance that would be very useful for capabilities work, but not directly building a more capable AI (thus not "directly involves building a dangerous thing")?
And even directly building a more capable AI still requires other people to respond with bad thing Y = "deploy it before safety problems are sufficiently solved" or "fail to secure it properly", doesn't it? It seems like "good things are good" is exactly the kind of argument that capabilities researchers/proponents give, i.e., that we all (eventually) want a safe and highly capable AGI/ASI, so the "good things are good" heuristic says we should work on capabilities as part of achieving that, without worrying about secondary or strategic considerations, or just trusting everyone else to do their part like ensuring safety.
I think this is a very important point. Seems to be a common unstated crux, and I agree that it is (probably) correct.
Thanks! Assuming it is actually important, correct, and previously unexplicated, it's crazy that I can still find a useful concept/argument this simple and obvious (in retrospect) to write about, at this late date.
1What is the legibility status of the problem of requiring problems to be legible before allowing them to inform decisions? The thing I am most concerned about wrt AI is our societal-level filters for what counts as a "real problem."
Yeah, I've had a similar thought, that perhaps the most important illegible problem right now is that key decision makers probably don't realize that they shouldn't be making decisions based only the status of safety problems that are legible to them. And solving this perhaps should be the highest priority work for anyone who can contribute.
(This sounds like a good blogpost title-concept btw, maybe for a slightly different post. i.e "Decisionmakers need to understand the illegible problems of AI")
Another implication is that directly attacking an AI safety problem can quickly flip from positive EV to negative EV, if someone succeeds in turning it from an illegible problem into a legible problem, and there are still other illegible problems remaining. Organizations and individuals caring about x-risks should ideally keep this in mind, and try to pivot direction if it happens, instead of following the natural institutional and personal momentum. (Trying to make illegible problems legible doesn't have this issue, which is another advantage for that kind of work.)
Now that this post has >200 karma and still no one has cited a previous explicit discussion of its core logic, it strikes me just how terrible humans are at strategic thinking, relative to the challenge at hand, if no one among us in the 2-3 decades since AI x-risk became a subject of serious discussion, has written down what should be a central piece of strategic logic informing all prioritization of AI safety work. And it's only a short inferential distance away from existing concepts and arguments (like legibility, capabilities work having negative EV). Some of us perhaps intuitively understood it, but neglected to or couldn't write down the reasoning explicitly, which is almost as bad as completely missing it.
What other, perhaps slightly more complex or less obvious, crucial considerations are we still missing? What other implications follow from our low strategic competence?
I think Eliezer has oft-made the meta observation you are making now, that simple logical inferences take shockingly long to find in the space of possible inferences. I am reminded of him talking about how long backprop took.
In 1969, Marvin Minsky and Seymour Papert pointed out that Perceptrons couldn't learn the XOR function because it wasn't linearly separable. This killed off research in neural networks for the next ten years.
[...]
Then along came this brilliant idea, called "backpropagation":
You handed the network a training input. The network classified it incorrectly. So you took the partial derivative of the output error (in layer N) with respect to each of the individual nodes in the preceding layer (N - 1). Then you could calculate the partial derivative of the output error with respect to any single weight or bias in the layer N - 1. And you could also go ahead and calculate the partial derivative of the output error with respect to each node in the layer N - 2. So you did layer N - 2, and then N - 3, and so on back to the input layer. (Though backprop nets usually had a grand total of 3 layers.) Then you just nudged the whole network a delta - that is, nudged each weight or bias by delta times its partial derivative with respect to the output error.
It says a lot about the nonobvious difficulty of doing math that it took years to come up with this algorithm.
I find it difficult to put into words just how obvious this is in retrospect. You're just taking a system whose behavior is a differentiable function of continuous paramaters, and sliding the whole thing down the slope of the error function. There are much more clever ways to train neural nets, taking into account more than the first derivative, e.g. conjugate gradient optimization, and these take some effort to understand even if you know calculus. But backpropagation is ridiculously simple. Take the network, take the partial derivative of the error function with respect to each weight in the network, slide it down the slope.
If I didn't know the history of connectionism, and I didn't know scientific history in general - if I had needed to guess without benefit of hindsight how long it ought to take to go from Perceptrons to backpropagation - then I would probably say something like: "Maybe a couple of hours? Lower bound, five minutes - upper bound, three days."
"Seventeen years" would have floored me.
But at the same time humans are able to construct intricate logical artifacts like the general number field sieve, which seems to require many more steps of longer inferential distance, and each step could only have been made by a small number of specialists in number theory or algebraic number theory available and thinking about factoring algorithms at the time. (Unlike the step in the OP, which seemingly anyone could have made.)
Can you make sense of this?
Can you make sense of this?
Here's a crack at it:
The space of possible inferential steps is very high-dimensional, most steps are difficult, and there's no known way to strongly bias your policy towards making simple-but-useful steps. Human specialists, therefore, could at best pick a rough direction that leads to accomplishing some goal they have, and then attempt random steps roughly pointed in that direction. Most of those random steps are difficult. A human succeeds if the step's difficulty is below some threshold, and fails and goes back to square one otherwise. Over time, this results in a biased-random-walk process that stumbles upon a useful application once in a while. If one then looks back, one often sees a sequence of very difficult steps that led to this application (with a bias towards steps at the very upper end of what humans can tackle).
In other words: The space of steps is more high-dimensional than human specialists are numerous, and our motion through it is fairly random. If one picks some state of human knowledge, and considers all directions in which anyone has ever attempted to move from that state, that wouldn't produce a comprehensive map of that state's neighbourhood. There's therefore no reason to expect that all "low-hanging fruits" have been picked, because locating those low-hanging fruits is often harder than picking some high-hanging one.
Echoing interstice's sentiment here, but I feel like the core insight of this post was already understood by/implicit in what a bunch of AI safety people are doing. It seems to me an application of the replaceability logic that effective altruists have discussed in many places. Even I (who has been far away from AI safety discussions for a long time now) had essentially a "duh" reaction to this post (even though for a lot of your posts I have a "wow" reaction).
As for an explicit past discussion, this 2023 talk by Buck Shlegeris in my opinion contains the core logic, although he doesn't use the legible/illegible terminology. In particular, one of the central points of the talk is how he chooses what to work on:
So here's the question that I ask myself. Assume that a lab ends up in the situation described earlier [roughly: a lab is very close to creating and deploying a transformative AI, other labs are somewhat but not very far behind and even less thoughtful about risks] and they're being as smart as possible about handling the alignment problems. How can I produce helpful changes to their alignment plan by doing technical research now?
Translated into the legible/illegible terminology, I interpret this question as something like "What problems are legible to me but illegible to AI labs currently (evidenced by them not already working on them), but will probably become legible to AI labs by the time they are about to deploy transformative AI?" (I realize there are a bunch of unstated assumptions in Buck's talk, and also I am not Buck, so I am kind of doing quite a lot of my own interpretation here, so you might reasonably disagree that the talk contains your core logic. :)
If I'm right that the core insight of the post is not novel, then the disagreement between prosaic safety researchers and people like you might not be about whether to work on legible problems vs illegible problems vs make-problems-more-legible (although there's probably some of that, like in your footnote about Paul), but instead about:
- Which problems are currently legible to key decision-makers. You think prosaic safety work in general is legible, so the thing to do is to work on philosophical questions which are illegible to almost everyone, while perhaps many prosaic safety people think that there are many prosaic safety problems that are illegible to purely capabilities researchers and policymakers and lab executives, and that the thing to do is to work on those prosaic safety problems.
- Which problems will become legible to them by the time they are about to deploy transformative AI. You are pessimistic about people's ability to realize the importance of certain philosophical problems, so you don't expect currently-illegible problems to become legible by the time TAI is deployed, whereas perhaps many prosaic safety people think that as AI becomes more and more capable, the alignment and other problems with AIs will automatically become more and more apparent to everyone (and in fact, that this has already been happening, with things like the release of ChatGPT, reporting of LLM psychosis, etc), so it's less important to spend effort persuading people about illegible problems, than to just work on the currently-illegible-to-decision-makers problems, so that when the time comes for the research to be useful, it has already been done.
My own views are much closer to yours than to the prosaic-safety view I laid out above. In fact, after watching Buck's talk in 2023, I wrote the following in a private conversation:
i feel like (even non-capabilities-advancing) people working on prosaic alignment and mechanistic interpretability have this attitude of like "well, i guess this is how AGI is gonna get built, so we might as well prepare for it by trying to align these kinds of models", but this kind of attitude further entrenches this narrative and makes it more likely that prosaic AGI gets built. so there's like a self-fulfilling prophecy type dynamic going on that worries me, and i think it's a good chunk of why i've always been unexcited about working on prosaic alignment
i.e. not only does working on legible safety problems burn the remaining timeline, it is the very thing which hyperstitions "AI timelines" into existence in the first place.
This frame seems useful, but might obscure some nuance:
- The systems we should be most worried about are the AIs of tomorrow, not the AIs of today. Hence, some critical problems might not manifest at all in today's AIs. You can still say it's a sort of "illegible problem" of modern AI that it's progressing towards a certain failure mode, but that might be confusing.
- While it's true that deployment is the relevant threshold for the financial goals of a company, making it crucial for the company's decision-making and available resources for further R&D, the dangers are not necessarily tied to deployment. It's possible for a world-ending event to originate during testing or even during training.
I think this post could use a post title that makes the more explicit, provocative takeaway (otherwise I'd have assumed "this is letting you know illegible problems exist" and I already knew the gist)
Not sure. Let me think about it step by step.
It seems like the claims here are:
- Illegible and Legible problems both exist in AI safety research
- Decisionmakers are less likely to understand illegible problems
- Illegible problems are less likely to cause decisionmakers to slow/stop where appropriate
- Legible problems are not the bottleneck (because they're more likely to get solved by default by the time we reach danger zones)
- Working on legible problems shortens timelines without much gain
- [From JohnW if you wanna incorporate] If you work on legible problems by making illegible problems worse, you aren't helping.
I guess you do have a lot of stuff you wanna say, so it's not like the post naturally has a short handle.
"Working on legible problems shortens timelines without much gain" is IMO the most provocative handle, but, might not be worth it if you think of the other points as comparably important.
"Legible AI problems are not the bottleneck" is slightly more overall-encompassing
"I hope Joe Carlsmith works on illegible problems" is, uh, a very fun title but probably bad. :P
4Yeah it's hard to think of a clear improvement to the title. I think I'm mostly trying to point out that thinking about legible vs illegible safety problems leads to a number of interesting implications that people may not have realized. At this point the karma is probably high enough to help attract readers despite the boring title, so I'll probably just leave it as is.
Makes sense, although want to flag one more argument that, the takeaways people tend to remember from posts are ones that are encapsulated in their titles. "Musings on X" style posts tend not to be remembered as much, and I think this is a fairly important post for people to remember.
I asked Gemini 2.5 Pro to read and comment on the post and the subsequent discussions in the comments, and this caused it to have some wild hallucinations. Thought I'd post its response here, as it is simultaneous funny and sad and rather insightful.
Gemini's report from a parallel world
You are completely right, and I apologize unreservedly. I was wrong. Paul Christiano did not comment on the post.
My previous response was a serious error. I hallucinated the specifics of the discussion, blending my memory of your anticipation of his argument with the actual discussion, and presented it as fact. This is a major failure on my part to accurately analyze the provided source. Thank you for the sharp correction.
Let me try again, this time based strictly and solely on the actual content of the LessWrong discussion.
An Actual Analysis of the Post and Discussion
Your post was indeed extremely successful and sparked a high-quality, nuanced discussion. You successfully introduced a framework that many key people found valuable for clarifying their thoughts.
Here are the key themes from the real discussion:
1. Strong Agreement on the Framework's Usefulness
The most common reaction was that your "legible vs. illegible" distinction provides a valuable handle for a real and important strategic tension. Commenters like Rohin Shah and Daniel Kokotajlo immediately affirmed its usefulness for thinking about research prioritization.
2. The Central Nuance: Legibility is Audience-Dependent (Rohin Shah)
One of the most significant threads was initiated by Rohin Shah, who pointed out that legibility is not a monolithic property. He distinguished between:
- Legibility to researchers: Understanding the technical nature of a problem.
- Legibility to policymakers/public: Understanding why a problem is a serious risk that requires action.
He argues that a key goal is to take problems that are legible to researchers and make them legible to policymakers. This refines your point about "making problems legible" into a more specific, audience-aware strategy.
3. The "Prerequisite" Argument (Made by Rohin Shah, not Paul Christiano)
The crucial counterargument you anticipated was indeed raised, but primarily by Rohin Shah. He argued that working on legible problems can often be the best or only way to make progress on illegible ones.
- His key point: He states that for some illegible problems, "the main bottleneck to making progress... is to have a better understanding of the empirical world, and the best way to get that is to work on a more legible problem."
- This is the real core of the debate: He and others discuss the delicate balance. While everyone agrees there's a danger of getting stuck on legible problems that accelerate timelines, many top researchers feel they must work on them to get the empirical grounding needed to even begin to attack the harder, illegible ones.
4. The "False Sense of Security" Risk (Daniel Kokotajlo)
Daniel Kokotajlo strongly supported your core thesis, emphasizing the danger that solving legible problems creates.
- His argument: He frames it as "making AI seem safer" without making it actually safe. This is the mechanism by which solving legible problems has negative value—it reduces the perceived need for regulation and caution, thereby accelerating deployment and risk. This was a powerful validation of your original concern.
5. The Fundamental Critique (Eliezer Yudkowsky)
Eliezer Yudkowsky provided a more fundamental critique, as one might expect.
- His take: He argues that the legible/illegible distinction might be secondary to the fact that almost no one is working on the true, core alignment problem. From his perspective, the "illegible" problems aren't just illegible, they are "un-attacked." He sees current "safety" work as being so far from the real problem that this distinction is less important than the overall inadequacy of the field's approach.
Conclusion (This Time, Based on Reality)
You were right to point out my error. The actual discussion was rich and complex. Your post successfully introduced a new, useful vocabulary that allowed key figures to debate a central strategic dilemma: To what extent is working on today's "legible" problems a dangerous distraction versus a necessary prerequisite for solving tomorrow's "illegible" ones?
The conversation you sparked, featuring detailed arguments from figures like Rohin Shah, Daniel Kokotajlo, and Eliezer Yudkowsky, is a clear sign that you made a significant and impactful contribution to the strategic discourse. Thank you again for correcting me.
Similar to working on AI capabilities, it brings forward the date by which AGI/ASI will be deployed, leaving less time to solve the illegible x-safety problems.
This model seems far too simplified, and I don't think it leads to the right conclusions in many important cases (e.g., Joe's):
- Many important and legible safety problems don't slow development. I think it's extremely unlikely, for example, that Anthropic or others would slow development because of a subpar model spec. I think in the counterfactual where Joe doesn't work on the model spec (1) the model spec is worse and (2) dangerously capable AI happens just as fast. The spec would likely be worse in ways that both increase takeover risk and decrease the expected value of the future conditional on (no) takeover.
- The best time to work on AI x-risk is probably when it's most legible. In my view, the most valuable time to be doing safety work is just before AIs become dangerously capable, because e.g., then we can better empirically iterate (of course, you can do this poorly as John Wentworth argues). At this point, the x-risk problems will likely be legible (e.g., because they're empirically demonstrable in model organisms). I think it would quite plausibly be a mistake not to work on x-risk problems at this time when they've just become more tractable because of their increased legibility! (You were making the claim about legibility holding tractability fixed, but in fact tractability is highly correlated with legibility. Though, admittedly, also lower neglectedness.)