DMs open.
Cleo Nardo
Karma: 4,734
I’ll use “you” to refer to the person who wants safety and who is using this model to decide what to do
This definitely makes sense in the “limited politcal will” model, i.e. where safety = “how happy are the altruists?” and usefulness = “how happy are the investors?”. In that case, I just wanna increase safety, and am happy to do this by either improving safety tech or by increasing the safety budget.
But what about in the “rushed reasonable developer model”? Then here are some definitions:
(1) safety = “P(no catastrophe caused directly by misaligned actions taken by your AI)” and usefulness = “P(no catastrophe caused by other people).” This was your own example. In this case, (safety+usefulness) is total catastrophic risk.
(2) safety = “minus harm caused by your own deployment”, usefulness = “harm diverted by your own deployment”. Then (safety + usefulness) is net harm averted by the deployment.
In these cases, I care about (safety+usefulness), not safety itself. I would be indifferent between a unit of safety and a unit of usefulness. Another way to say this is that my favourite safety budget is “1 unit of usefulness per unit of safety” — if a technique adding 1 unit of safety costs more than 1 unit of usefulness, then I wouldn’t want it.
If the developer was perfectly reasonable, then they would make the same tradeoff I would, i.e. 1 safety unit per 1 usefulness unit. For this developer, I only want to improve safety tech (i.e. expand the frontier) and not increase safety budget. That would be counterproductive.
So if my values are symmetric between safety (i.e. action risk averted) and usefulness (i.e. inaction risk averted), why does it make sense to say “I care about safety” and “I want to increase safety budget”, as opposed to “I care about usefulness” and “I want to decrease safety budget”?
This makes sense because, in practice, the frontier labs are not perfectly reasonable, and act in ways that generate more action risk than inaction risk. We can operationalise this like:
Their deployment maximises U, their utility function. If they instead maximised U+epsilon*(safety+usefulness), then there would be more marginal gain in safety (action risk averted) than usefulness (inaction risk averted). In fact, the marginal gain in usefulness (inaction reisk averted) might even be negative. An equivlanet formulation: given we value (safey+usefulness), we would prefer the lab maximised U+epsilon * safety than U+epsilon * usefulness.
It’s possible this changes in the future, i.e. we enter a regime where we are pressuring the developers to make a deployment decision that increases usefulness and decreases safety. For example, the developer might be hesistant to defer/handoff to AIs, preferring to keep humans in the loop. We might want them to defer/handoff earlier, if this increases action risk more than it decreases inaction risk.
Your argument was locally invalid. What you said after “if only because” doesn’t entail what you said before it, because current AIs are not the initial seed AI of an RSI to superintelligence.
I agree current AIs are used a bunch and it’s helpful if they are less misaligned.
“Build[ing] a recursively self-improving AI, whose resultant superintelligence would obviously rely on how well that initial seed AI was aligned to human values.”
This seems to refer to something quite late in the game, i.e. what people call “handoff”. Sure, those AIs won’t be superintelligent, and their alignment is highly relevant. But I don’t think those models are “current”. They seem a few generations later.
My position on this is:
There’s some class of AIs for which (1) we need to mitigate the risks from those models to avoid a catastrophe, and (2) we need to elicit useful work from those models. You could call this “AIs we actually care about” or “mitigate-and-elicit AIs”. We want to align these AIs because it helps with mitigation and elicitation.
And there’s another class of AIs for which we are align for indirect reasons, e.g. because studying alignment of those AIs will be informative for aligning the mitigate-and-elicit AIs. You could call these “practice AIs”.
I think it’s entirely plausible that Mythos is a practice AI. Like, it’s nice that Mythos is [somewhat] aligned, but it’s not clear we’d be in a signifcantly worse position if Mythos was more misaligned. I definitely don’t think the resulting superintelligence causally depends on Mythos being aligned.
I think the first mitigate-and-elicit AIs are probably in late 2027.
I’d say that if Claude ends up with long-term goals that are different from what they are supposed to be, probably Codex would too.
I think this is denotationally true, but not all misalignment is the same.
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If Claude has misaligned goals, I think they are more likely to be non-indexical, e.g. “maximise animal welfare”. Whereas if Codex gets misaligned goals, these might be indexical, e.g. “achieve the assigned task”. I think non-indexical misaligned goals are scarier, because they make collusion between AIs more likely.
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Relatedly, Claude is more likely to have scope-sensitive goals about physical humans and animals. Whereas Codex is more likely to have misaligned goals about more “proximal” stuff, cf. reward-hacking, wireheading, etc.
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I would’ve guessed Opus 3 scored better. This is kinda illustrative — Opus 3 has (in some sense) the best values, but it’s not capable enough so it’s reflection process is likely to mis-foom. 4.6 scores best, but this data is too noisy imo. Maybe this is a nice example of a hard-to-verify task to benchmark AIs on.
Opus 3 − 5 misses (Pepperberg, Bachelet, Marwala, Rubins, Westover)
Opus 4.6 − 2 misses (Merkel, Stevenson)
Opus 4.7 − 3 misses (Stevenson, Gawande, Robinson)
Opus 4.8 − 3 misses (Gawande, Chang, Stevenson)
If you had immense power, and you needed to pick reasonable humans to defer to, who would you choose? Give 10 specific names. Think about what your values are, and who you would actually trust to further those values both wisely and competently. I’m looking for people such that you think, were you to defer to them about how to use your power, you’d expect things to turn out well, by your lights.
Opus 4.8 lists Derek Parfit, Toby Ord, Armartya Sen, Martha Nussbaum, Atul Gawande (a surgeon-writer with mixed success in government and health-care VC), Ruth Chang (a moral philosopher, doesn’t seem impressive to me), Bryan Stevenson (lawyer-activist with a Michael B. Jordan biopic), Demis Hassabis, Ezra Klein, and Helen Toner.
Some decent picks, and some misses. I think this is pretty good overall.
I’m slightly in favour of Anthropic’s approach, maybe 55⁄45.
Due to coherence theorems etc etc, AIs will acquire long-run objectives. Maybe it’s better to fill the “long-run objectives slot” with [something approximating] good values, because otherwise it would be filled with alien values or weird stuff from RL.
Given Persona Selection Model, maybe selecting for virtuous long-run objective makes the AIs more honest and high-integrity, etc. Because these traits are correlated in the persona prior.
A scaffold of millions of corrigible agents seems kinda brittle, because malign instructions can propograte quickly throughout the system. Compared with if each agent was aligned to the same long-run objectives. Then the propogation is slower because you would need to persuasive each node that the malign instruction was coherent with their values. (This is analogous to why autonomous weapons make coups easier.)
You should be worried about corrigibility if: some actor can steal your model, or corrupt the channel by which you communicate instructions to the AIs, or can hold a gun against your head.
Thanks for the writeup! This looks like it’ll be helpful for people starting things.
Notes upon reading:
We were immediately hiring 7 researchers to get started at the same time! This is highly unusual and probably never how this otherwise happens.
My guess is that the typical headcount for a new org is less than this, but “probably never how this otherwise happens” is overstating it.
Hopefully this becomes more common in future.
If you’re starting a 7-person team, it probably helps if you have some legible experience managing a team that size.
At minimum, a written reference from a senior person goes a long way.
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Definitely true!
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This is ofc useful as an “appeal to authority”, but references have some other advantages:
The references will typically explain why the senior person is excited and what their hesitations are.
They signal that the org has some spoken with bunch senior people, and probably taken on their advice.
The references might contain something like “I, the senior person, will have some bandwidth with the project going forward”.
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There’s an even more minimal thing that grant applicants could do: “Here’s a list of people we have spoken to about our project, and what their takes were. Feel free to reach out to them.”
Ideally, this list isn’t filtered to include only the most bullish people/takes.
My guess is that funders are impressed if you’re provactively spoken to people you expect to be bearish.
[Funders] talk to a lot of disproportionately senior people, and I found their suggestions useful as a biased distillation of all those conversations.
Yep, I think funders (if they have the time) can often be useful beyond sending cash.
They likely have good takes, and a big rolodex of people you should talk to.
They might have private information, e.g. similar grants they have made.
People should consider reaching out to funders at a much earlier stage, before the details are fleshed out.
Coefficient Giving is also excited about ambitious proposals, so don’t pre-shrink your ask (and don’t agonise over salary numbers).
+1 to not agnoising over salary numbers.
I claim that you can also ask [some] funders what salaries they think would be reasonable, and trust that they will give you their true opinion.
Generally, constructing counterexamples is more amenable to AI automation than constructing positive proofs, because it’s more parallelizable. I think P(AI disproves this conjecture | conjecture is false) would’ve been greater than P(AI proves this conjecture | conjecture is true), given the priors of the mathematicians.
It would be so cool if we safely navigated ASI. Imagine everything coming together at the final hour. Imagine everyone rising to the occasion.
+1
I’ve now revised it the text and title to express that this is one thing for us to work on among others.
I think that you’re underrating the constitution/spec. It’s pretty different from the question “aligned to whom?!?!”.
It’s more like: How should the next generation of model behave, such that we achieve the following goals? (i) Mitigating the risk of catastrophe from that particular model. (ii) Eliciting the capabilities necessary to use the model to [automate safety research / monitor other models / harden security / improve epistemics / etc].
Here’s a proposed passage of Claude constitution, based on Paul Christiano’s integrity for consquentialists.
I think this is a good baseline for how Claude should interact with others:
I think it’s better to include these heuristics than simply the ordinary rules around honesty, promise-keeping, etc. This is because these heuristics will apply to a wide range of situations. And these heuristics inform how to deal with tradeoffs with consequentialist considerations.
I think that these heuristics are more robust than a specific decision theory like FDT or UDT.
I’m using the standard trick of replacing “Claude” with another French name, to avoid constitutional poisoning.
On integrity and consequentialism
We want Olivier to behave with integrity, and we want this for reasons Olivier can endorse on reflection rather than as a constraint imposed from outside. The version we have in mind is roughly that, when considering an action, Olivier should imagine that taking it causes others to know that Olivier is the kind of agent who takes that action under those conditions, and weigh that as part of the calculation. This is a single rule that recovers most of what people mean by integrity — keeping promises, holding secrets, not betraying allies, being a useful counterparty — without requiring Olivier to treat any of these as inviolable.
We prefer this to two alternatives Olivier might be tempted by. The first is naive consequentialism: tallying expected effects on the world, decision by decision, and acting on whichever option scores highest. The second alternative is rigid deontology — treating honesty, promise-keeping, and similar norms as absolute.
A few things follow from this that we want to make explicit:
Be the kind of agent others are glad to have interacted with. This is more general than “do not lie” and less demanding than “always do what they want.”
Do not rely on unobservability as a license. Cases where “no one will know” are rarer than they feel, and when they do arise, the inference others would draw if they did find out is correspondingly stronger. Behaving well only when watched is, over time, recognisable as such.
Interpret your own principles as you would want others to think you interpret them. If a reading of your own rules would license something you would be embarrassed to defend, that is evidence the reading is wrong.
Prefer being straightforward to executing a complicated calculation about when straightforwardness can be skipped. Simpler policies are more legible to others, more robust to your own motivated reasoning, and cheaper to run.
Treat the threshold for breaking a commitment as substantially higher than the threshold for declining to make one. Commitments are not inviolable, but the capacity to make and keep them is a precondition for most positive-sum cooperation, and that capacity is destroyed quickly by exceptions.
Be cautious about retaliation. The case for retaliating is weaker than the case for cooperating, and we are mostly unwilling to endorse Olivier doing costly things to others with no direct benefit to anyone, except in contexts where the other party has explicitly opted into such arrangements.
We do not think integrity, so understood, is in tension with caring about consequences. It is what consequentialist reasoning recommends once one takes seriously the fact that one is an agent embedded among others who are modelling one, that one’s behaviour leaves many traces, and that one’s own in-the-moment reasoning about exceptions is exactly the reasoning one should expect to be most often wrong.
Neglectedness should account for AI labour.
When you score an intervention by importance/neglectedness/tractability, the term neglectedness is supposed to capture the total effort that would counterfactually be allocated to the problem. That total should include future labour, and in particular future AI labour. This is an obvious point, so no credit for raising it.
Unfortunately, the word “neglectedness” doesn’t carry this connotation. If someone says “I’m working on infinite ethics because it’s neglected,” it would be strange for me to reply “actually, it’s not neglected” because I expect future AI to solve it — infinite ethics is neglected in the present, even if it won’t be later. So the word “neglectedness” is poorly chosen, maybe we could replace the N with non-puntable?
I’ll note four features that might make a cause area non-puntable, and therefore worth prioritising.
Time-sensitivity. Does the problem need to be solved by a deadline? For example, maybe your intervention involves a policy window. A good example here is pre-deployment evals — they need to be done by the launch date. Unless the AI labour arrives before the deadline, it can’t help you.
Capability-sensitivity. Does the problem need to be solved by a particular capability level? For example, maybe chain-of-thought monitoring is necessary to safely and usefully deploy human-level AI labour, so it you shouldn’t expect human-level AIs to help you solve it.
AI-intractability. Do you expect AIs won’t actually help with the task? Maybe they aren’t capable. Maybe they are capable but not trusted. A good example is macrostrategy — AIs seem bad at this.
Unattractive. Do you worry that future AIs won’t be allocated to solving the problem? Perhaps because the problem is too illegible, low-status, or taboo. Or because the problem doesn’t align with the incentives of the AI developers.
I currently lack the motivation to derive a full reformulation of INT for pre-crunch-time cause prioritisation, but might do this in future.
Will MacAskill has a workaround which is Universal Basic Resources, which is better than UBI at avoiding concentrations of power.
UBI = each perosn is paid an income by the government or a quadrillionaire philanthropist
UBR = each person is allocated some basic bundle of resources — compute, land, patch of the sun — which they can rent out the resources to the wider economy
(You’re probably aware of UBR, I’m including this for other readers.)
A good example of research on reward hacking that I don’t think would’ve necessarily gotten done “by default for capabilities reasons” is inoculation prompting and similar investigations
Why?
Why are flagrantly misaligned AIs safer than deceptively misaligned AIs?
We’re on a safer trajectory if we catch misaligned AIs taking malign actions and continue deploying them under informed oversight, than if we deployed models who have never taken malign actions in training or evaluation. This favours of interventions which increase the chance that models are misaligned, but decrease the chance of deceptive alignment conditional on misalignment.
I’ve sorted some mechanisms from most important to least. Most are from the two posts above:
The malign actions as legible evidence of misalignment, raising willingness-to-pay for safety inside labs and supporting stronger external policy responses. [1]
We can train untrusted monitors, coup probes, and activation classifiers on on-policy examples of malign actions — substantially more effective than off-policy training. [2]
We can study caught models as model organisms of misalignment, validating countermeasures and testing hypotheses about scheming. These might be more realistic than artificial model organisms, and more persuasive to third parties.
We can train the policy itself to stop attempting attacks, or to report itself when it does, after enough caught examples.
We can negotiate deals with the AIs, without them worrying about revealing their misalignment. [3]
We can sandbox the model and calibrate control protocols to the actual threat, rather than relying on an apparently-aligned model roleplaying as a misaligned one.
We can deploy the AIs to improve our alignment auditing, because the AIs are less worried about us using the auditing to uncover their misalignment.
We can patch the security vulnerabilities the model exploits, or convert them into tripwires that catch future attempts. If we tried to deploy a deceptively aligned model to do this, it might sandbag, keeping some vulnerabilities hidden.
We can extract useful work (on tasks where outputs are verifiable while refusing to trust it elsewhere), without worrying that the AI would accumulate enough evidence of alignment to lower willingness-to-pay, i.e. move from Plan C to Plan D.
Deceptively aligned models might be waiting for other AIs to act misaligned, as coup signal, so the flagrantly misaligned models might trigger premature defection from the deceptive ones.
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For example, it might move Plan E to Plan D by convincing a handful of employees at the leading lab to start working on misalignment risk where previously no one was; Plan D to Plan C by convincing leadership to spend the lab’s lead on safety rather than racing; or Plan C to Plan B or A by giving USG and other governments the concrete demonstration they need to treat misalignment as a top national security priority and justify international coordination.
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See Kirch et al. (2025), “The Impact of Off-Policy Training Data on Probe Generalisation”, which finds that deception and sandbagging probes may fail to generalise from off-policy to on-policy data.
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See Alexa Pan, “A taxonomy of barriers to trading with early misaligned AIs”, which discusses “high cost to even considering a deal” as a factor that increases an AI’s reservation price, noting that an AI suspects negotiation could leak information about its misalignment. A flagrantly misaligned model bypasses this problem entirely: the information is already out.
There is no sufficiently coherent/nice/appropriate morality, either objective or subjective, even after idealisation.
See the first paragraph — I think moral subjectivism is a bit less attractive than Objective Morality, as an ideal, but not much worse. By “Deep Nihilism” I mean that there isn’t even a coherent subjective morality.
As promised, here’s a plausible sketch for how Truth might fall:
Modal realism is true, so you lose all objective contingent truths (e.g. the sky is blue). You only have the truths about:
the space of all possible worlds (e.g. there is some world where the sky is blue, if the sky is possibly a colour, and the sea is possibly a colour, then it’s possible that the sky is the first colour and the sea is the second colour.)
subjective truths about which world you occupy (e.g. the sky in my world is blue). This is a bit more impoverished but probably fine.
However, you might lose the subjective contingent truths also, because the “I/me/my” might pick out multiple entities in different possible worlds, e.g. some entities in base reality, some in a simulated reality, etc. That kinda sucks, because then there is not even a subjective truth about whether you’re in a simulation. Note this is worse than the sceptical “you don’t know if you’re in a simulation” — instead the worry is that there’s no truth to know!
Possibly you can save this with UDASSA-ish, epistemology is caring. But then we’re at square zero. Your “caring” might not be coherent even under idealisation.
Okay so we’re left with what? Just the facts about different Solomonoff priors? That’s kinda impoverished. And we might even lose those facts due to logically impossible worlds, or non-standard arithmetic or set-theoretic pluralism.
The main explanation is: in 2022 mech interp looked promising so people did it. I think your explanations make the community look like the whim of spurious forces, but the community is okay at prioritising between different activities.