(I reviewed this in a top-level post: Review of 'But exactly how complex and fragile?'.)
I've thought about (concepts related to) the fragility of value quite a bit over the last year, and so I returned to Katja Grace's But exactly how complex and fragile? with renewed appreciation (I'd previously commented only a very brief microcosm of this review). I'm glad that Katja wrote this post and I'm glad that everyone commented. I often see private Google docs full of nuanced discussion which will never see the light of day, and that makes me sad, and I'm happy that people discussed this publicly.
I'll split this review into two parts, since the nominations called for review of both the post and the comments:
I think this post should be reviewed for its excellent comment section at least as much as for the original post, and also think that this post is a pretty central example of the kind of post I would like to see more of.
~ habryka
I think this was a good post. I think Katja shared an interesting perspective with valuable insights and that she was correct in highlighting a confused debate in the community.
That said, I think the post and the discussion are reasonably confused. The post sparked valuable lower-level discussion of AI risk, but I don't think that the discussion clarified AI risk models in a meaningful way.
The problem is that people are debating "is value fragile?" without realizing that value fragility is a sensitivity measure: given some initial state and some dynamics, how sensitive is the human-desirability of the final outcomes to certain kinds of perturbations of the initial state?
Left unremarked by Katja and the commenters, value fragility isn't intrinsically about AI alignment. What matters most is the extent to which the future is controlled by systems whose purposes are sufficiently entangled with human values. This question reaches beyond just AI alignment.
They also seem to be debating an under-specified proposition. Different perturbation sets and different dynamics will exhibit different fragility properties, even though we're measuring with respect to human value in all cases. For example, perturbing the training of an RL agent learning a representation of human value, is different from perturbing the utility function of an expected utility maximizer.
Setting loose a superintelligent expected utility maximizer is different from setting loose a mild optimizer (e.g. a quantilizer), even if they're both optimizing the same flawed representation of human value; the dynamics differ. As another illustration of how dynamics are important for value fragility, imagine if recommender systems had been deployed within a society which already adequately managed the impact of ML systems on its populace. In that world, we may have ceded less of our agency and attention to social media, and would therefore have firmer control over the future and value would be less fragile with respect to the training process of these recommender systems.
But exactly how complex and fragile? and its comments debate whether "value is fragile." I think this is a bad framing because it hides background assumptions about the dynamics of the system being considered. This section motivates a more literal interpretation of the value fragility thesis, demonstrating its coherence and its ability to meaningfully decompose AI alignment disagreements. The next section will use this interpretation to reveal how the comments largely failed to explore key modelling assumptions. This, I claim, helped prevent discussion from addressing the cruxes of disagreements.
The post and discussion both seem to slip past (what I view as) the heart of 'value fragility', and it seems like many people are secretly arguing for and against different propositions. Katja says:
it is hard to write down what kind of future we want, and if we get it even a little bit wrong, most futures that fit our description will be worthless.
But this leaves hidden a key step:
it is hard to write down the future we want, feed the utility function punchcard into the utility maximizer and then press 'play', and if we get it even a little bit wrong, most futures that fit our description will be worthless.
Here is the original 'value is fragile' claim:
Any Future not shaped by a goal system with detailed reliable inheritance from human morals and metamorals, will contain almost nothing of worth.
~ Eliezer Yudkowsky, Value is Fragile
Eliezer claims that if the future is not shaped by a goal system, there's not much worth. He does not explicitly claim, in that original essay, that we have to/will probably build an X-maximizer AGI, where X is an extremely good (or perfect) formalization of human values (whatever that would mean!). He does not explicitly claim that we will mold a mind from shape Y and that that probably goes wrong, too. He's talking about goal systems chartering a course through the future, and how sensitive the outcomes are to that process.
Let's ground this out. Imagine you're acting, but you aren't quite sure what is right. For a trivial example, you can eat bananas or apples at any given moment, but you aren't sure which is better. There are a few strategies you could follow: preserve attainable utility for lots of different goals (preserve the fruits as best you can); retain option value where your normative uncertainty lies (don't toss out all the bananas or all of the apples); etc.
But what if you have to commit to an object-level policy now, a way-of-steering-the-future now, without being able to reflect more on your values? What kind of guarantees can you get?
In Markov decision processes, if you're maximally uncertain, you can't guarantee you won't lose at least half of the value you could have achieved for the unknown true goal (I recently proved this for an upcoming paper). Relatedly, perfectly optimizing an ϵ-incorrect reward function only bounds regret to 2ϵ per time step (see also Goodhart's Curse). The main point is that you can't pursue every goal at once. It doesn't matter whether you use reinforcement learning to train a policy, or whether you act randomly, or whether you ask Mechanical Turk volunteers what you should do in each situation. Whenever your choices mean anything at all, no sequence of actions can optimize all goals at the same time.
So there has to be something which differentially pushes the future towards "good" things and away from "bad" things. That something could be 'humanity', or 'aligned AGI', or 'augmented humans wielding tool AIs', or 'magically benevolent aliens' - whatever. But it has to be something, some 'goal system' (as Eliezer put it), and it has to be entangled with the thing we want it to optimize for (human morals and metamorals). Otherwise, there's no reason to think that the universe weaves a "good" trajectory through time.
Hence, one might then conclude
Any Future not shaped by a goal system with detailed reliable inheritance from human morals and metamorals, will not be optimized for human morals and metamorals.
But how do we get from "will not be optimized for" to "will contain almost nothing of worth"? There are probably a few ways of arguing this; the simplest may be:
our universe has 'resources'; making the universe decently OK-by-human-standards requires resources which can be used for many other purposes; most purposes are best accomplished by not using resources in this way.
This is not an argument that we will deploy utility maximizers with a misspecified utility function, and that that will be how our fragile value is shattered and our universe is extinguished. The thesis holds merely that
Any Future not shaped by a goal system with detailed reliable inheritance from human morals and metamorals, will contain almost nothing of worth.
As Katja notes, this argument is secretly about how the "forces of optimization" shape the future, and not necessarily about AIs or anything. The key point is to understand how the future is shaped, and then discuss how different kinds of AI systems might shape that future.
Concretely, I can claim 'value is fragile' and then say 'for example, if we deployed a utility-maximizer in our society but we forgot to have it optimize for variety, people might loop a single desirable experience forever.' But on its own, the value fragility claim doesn't center on AI.
[Human] values do not emerge in all possible minds. They will not appear from nowhere to rebuke and revoke the utility function of an expected paperclip maximizer.
Touch too hard in the wrong dimension, and the physical representation of those values will shatter - and not come back, for there will be nothing left to want to bring it back.
And the referent of those values - a worthwhile universe - would no longer have any physical reason to come into being.
Let go of the steering wheel, and the Future crashes.
Katja (correctly) implies that concluding that AI alignment is difficult requires extra arguments beyond value fragility:
... But if [the AI] doesn’t abruptly take over the world, and merely becomes a large part of the world’s systems, with ongoing ability for us to modify it and modify its roles in things and make new AI systems, then the question seems to be how forcefully the non-alignment is pushing us away from good futures relative to how forcefully we can correct this. And in the longer run, how well we can correct it in a deep way before AI does come to be in control of most decisions. So something like the speed of correction vs. the speed of AI influence growing.
As I see it, Katja and the commenters mostly discuss their conclusions about how AI+humanity might steer the future, how hard it will be to achieve the requisite entanglement with human values, instead of debating the truth value of the 'value fragility' claim which Eliezer made. Katja and the commenters discuss points which are relevant to AI alignment, but which are distinct from the value fragility claim. No one remarks that this claim has truth value independent of how we go about AI alignment, or how hard it is for AI to further our values.
Value fragility quantifies the robustness of outcome value to perturbation of the "motivations" of key actors within a system, given certain dynamics. This may become clearer as we examine the comments. This insight allows us to decompose debates about "value fragility" into e.g.
I think this is much more enlightening than debating
VALUE_FRAGILE_TO_AI == True?If no such decomposition takes place, I think debate is just too hard and opaque and messy, and I think some of this messiness spilled over into the comments. Locally, each comment is well thought-out, but it seems (to me) that cruxes were largely left untackled.
To concretely point out something I consider somewhat confused, johnwentsworth authored the top-rated comment:
I think [Katja's summary] is an oversimplification of the fragility argument, which people tend to use in discussion because there's some nontrivial conceptual distance on the way to a more rigorous fragility argument.
The main conceptual gap is the idea that "distance" is not a pre-defined concept. Two points which are close together in human-concept-space may be far apart in a neural network's learned representation space or in an AGI's world-representation-space. It may be that value is not very fragile in human-concept-space; points close together in human-concept-space may usually have similar value. But that will definitely not be true in all possible representations of the world, and we don't know how to reliably formalize/automate human-concept-space.
The key point is not "if there is any distance between your description and what is truly good, you will lose everything", but rather, "we don't even know what the relevant distance metric is or how to formalize it". And it is definitely the case, at least, that many mathematically simple distance metrics do display value fragility.
This is a good point. But what exactly happens between "we write down something too distant from the 'truth'" and the result? The AI happens. But this part, the dynamics, it's kept invisible.
So if you think that there will be fast takeoff via utility maximizers (a la AIXI), you might say "yes, value is fragile", but if I think it'll be more like slow CAIS with semi-aligned incentives making sure nothing goes too wrong, I reply "value isn't fragile." Even if we agree on a distance metric! This is how people talk past each other.
Crucially, you have to realize that your mind can hold separate the value fragility considerations, the considerations as to how vulnerable the outcomes are to the aforementioned perturbations, you have to know you can hold these separate from your parameter values for e.g. AI timelines.
Many other comments seem off-the-mark in a similar way. That said, I think that Steve Byrnes left an underrated comment:
Corrigibility is another reason to think that the fragility argument is not an impossibility proof: If we can make an agent that sufficiently understands and respects the human desire for autonomy and control, then it would presumably ask for permission before doing anything crazy and irreversible, so we would presumably be able to course-correct later on (even with fast/hard takeoff).
The reason that corrigibility-like properties are so nice is that they let us continue to steer the future through the AI itself; its power becomes ours, and so we remain the "goal system with detailed reliable inheritance from human morals and metamorals" shaping the future.
The problem is that people are debating "is value fragile?" without realizing that value fragility is a sensitivity measure: given some initial state and some dynamics, how sensitive is the human-desirability of the final outcomes to certain kinds of perturbations of the initial state?
Left unremarked by Katja and the commenters, value fragility isn't intrinsically about AI alignment. What matters most is the extent to which the future is controlled by systems whose purposes are sufficiently entangled with human values. This question reaches beyond just AI alignment.
I'm glad Katja said "Hey, I'm not convinced by this key argument", but I don't think it makes sense to include But exactly how complex and fragile? in the review.
Thanks to Rohin Shah for feedback on this review.
This is maxw∈Wu(w), right? And then you might just constrain the subset of W which the agent can search over?
Exactly.
One toy model to conceptualize what a "compact criterion" might look like: imagine we take a second-order expansion of u around some u-maximal world-state w∗. Then, the eigendecomposition of the Hessian of u around w∗ tells us which directions-of-change in the world state u cares about a little or a lot. If the constraints lock the accessible world-states into the directions which u doesn't care about much (i.e. eigenvalu... (read more)
This is a post about my own confusions. It seems likely that other people have discussed these issues at length somewhere, and that I am not up with current thoughts on them, because I don’t keep good track of even everything great that everyone writes. I welcome anyone kindly directing me to the most relevant things, or if such things are sufficiently well thought through that people can at this point just correct me in a small number of sentences, I’d appreciate that even more.
~
The traditional argument for AI alignment being hard is that human value is ‘complex’ and ‘fragile’. That is, it is hard to write down what kind of future we want, and if we get it even a little bit wrong, most futures that fit our description will be worthless.
The illustrations I have seen of this involve a person trying to write a description of value conceptual analysis style, and failing to put in things like ‘boredom’ or ‘consciousness’, and so getting a universe that is highly repetitive, or unconscious.
I’m not yet convinced that this is world-destroyingly hard.
Firstly, it seems like you could do better than imagined in these hypotheticals:
I agree that neither of these would likely get you to exactly human values.
But secondly, I’m not sure about the fragility argument: that if there is basically any distance between your description and what is truly good, you will lose everything.
This seems to be a) based on a few examples of discrepancies between written-down values and real values where the written down values entirely exclude something, and b) assuming that there is a fast takeoff so that the relevant AI has its values forever, and takes over the world.
My guess is that values that are got using ML but still somewhat off from human values are much closer in terms of not destroying all value of the universe, than ones that a person tries to write down. Like, the kinds of errors people have used to illustrate this problem (forget to put in, ‘consciousness is good’) are like forgetting to say faces have nostrils in trying to specify what a face is like, whereas a modern ML system’s imperfect impression of a face seems more likely to meet my standards for ‘very facelike’ (most of the time).
Perhaps a bigger thing for me though is the issue of whether an AI takes over the world suddenly. I agree that if that happens, lack of perfect alignment is a big problem, though not obviously an all value nullifying one (see above). But if it doesn’t abruptly take over the world, and merely becomes a large part of the world’s systems, with ongoing ability for us to modify it and modify its roles in things and make new AI systems, then the question seems to be how forcefully the non-alignment is pushing us away from good futures relative to how forcefully we can correct this. And in the longer run, how well we can correct it in a deep way before AI does come to be in control of most decisions. So something like the speed of correction vs. the speed of AI influence growing.
These are empirical questions about the scales of different effects, rather than questions about whether a thing is analytically perfect. And I haven’t seen much analysis of them. To my own quick judgment, it’s not obvious to me that they look bad.
For one thing, these dynamics are already in place: the world is full of agents and more basic optimizing processes that are not aligned with broad human values—most individuals to a small degree, some strange individuals to a large degree, corporations, competitions, the dynamics of political processes. It is also full of forces for aligning them individually and stopping the whole show from running off the rails: law, social pressures, adjustment processes for the implicit rules of both of these, individual crusades. The adjustment processes themselves are not necessarily perfectly aligned, they are just overall forces for redirecting toward alignment. And in fairness, this is already pretty alarming. It’s not obvious to me that imperfectly aligned AI is likely to be worse than the currently misaligned processes, and even that it won’t be a net boon for the side of alignment.
So then the largest remaining worry is that it will still gain power fast and correction processes will be slow enough that its somewhat misaligned values will be set in forever. But it isn’t obvious to me that by that point it isn’t sufficiently well aligned that we would recognize its future as a wondrous utopia, just not the very best wondrous utopia that we would have imagined if we had really carefully sat down and imagined utopias for thousands of years. This again seems like an empirical question of the scale of different effects, unless there is a an argument that some effect will be totally overwhelming.