This post was written in response to Evan Hubinger’s shortform prompt below and benefited from discussions with him. Thanks to Justis Mills for feedback on this post.

If we see precursors to deception (e.g. non-myopia, self-awareness, etc.) but suspiciously don’t see deception itself, that’s evidence for deception.


Suppose an AI lab tries to look for the capabilities that enable deceptive alignment. This definitely seems like a good thing for them to do. Say they search for evidence of models optimizing past the current training epoch, for manipulation during human feedback sessions, for situational awareness, and so on. Suppose that when all is said and done they see the following:

There are at least two ways to interpret this:

  1. “Phew, bigger models are less likely to be deceptively aligned than small ones. Scale away!”
  2. “Bigger models are more capable at hiding their deceptive alignment. Run away!”

These are very different interpretations, with very different recommendations, so we’d really like to know which to put more credence in.


Going in, I think we should have a prior that capabilities getting worse with increasing scale is unlikely. We know of specific examples thanks to the inverse scaling prize, but these are rare exceptions to the general rule of increasing capabilities with increasing scale. As a result, a decreasing deceptive capabilities score should be suspicious.

The flip side is that it’s conceivable that some alignment problems get easier with scale. Larger language models understand more of what we say and value (e.g. Figure 8 of Bai+22), and so in some sense outer-aligning them may be easier than outer-aligning smaller models. It’s not inconceivable that the same happens for inner alignment, though I wouldn’t want to rely on this happening.


We don’t have to rely on priors alone. Here are a few ways we might distinguish between interpretations:

  1. Use transparency tools and figure out what the model is doing.
  2. Impair bigger models (e.g. with dropout or random or adversarial perturbations) and see if they get better at some deceptive capabilities. Impairing models shouldn’t improve  capabilities, so this would be good evidence that the bigger models are capable of deception but choosing to hide that capability.

    This experiment is a little tricky to implement, but the core idea is to hope that interventions impair something critical in the model's ability to execute on deception but don't impair all of the deceptive capabilities.

    Even if the largest models can continue hiding their capabilities despite the impairment, models that are just barely able to hide their capabilities may fail to do so once impaired, which should increase the model size where deceptive capabilities peak.
  3. Study the models on a very different data distribution. Models that are hiding relevant capabilities may believe they’ve been deployed and reveal their real goals.

    Once more, while the largest models may not be fooled by this, models just on the cusp of deceptive alignment are more likely to be tricked and so deceptive alignment may manifest as a shift in the peak of deceptive capabilities towards larger models.
  4. Study which specific capabilities get worse with scale. It’s hard to understand how some capabilities could get worse with scale (e.g. situational awareness, planning ability, etc.) so if those are getting worse that’s really worrying, whereas it’s less obvious if e.g. models should/shouldn’t get more epoch-level myopic with scale.

Scaling Helps

Having access to models of many different sizes is helpful here: we can experiment with small models that are bad at deceptive capabilities, use those results as a baseline, and then try to understand what happens in larger models.

If deceptive alignment occurs, there may well be models of intermediate scale which lie “on the edge” of being capable of deceptive alignment, or which are only weakly capable of it. Those are the ones that we’ll learn the most from and that experiments are most likely to catch.

Challenge: deceptive models know this

A big challenge to the empirical approaches above (other than interpretability) is that deceptive models may realize that we’re likely to do this sort of test. If a deceptive model realizes that we won’t deploy models that show peaked deceptive capabilities, it can choose a policy where e.g. deceptive capabilities level off rather than falling.

The same reasoning follows for any behavior we come to expect. The model may well be able to do the same reasoning, realize what we expect it to do, and do something else instead.

This suggests a strategy where we decide on a level of deceptive capabilities that’s low enough that we trust models not to be deceptively aligned. If we see deceptive capabilities increasing up to this “trusted level” we just don’t deploy those models, because beyond the trusted level we’re engaging with a potentially deceptive adversary rather than an unaware experimental subject.

Of course determining the trusted level is hard, as is the coordination needed to get labs to commit to not deploying models above that level. But it seems worth trying, and could naturally be a part of a program to monitor for deceptive alignment.


I think we should be quite suspicious if we see that larger models are worse at the capabilities needed for deceptive alignment. The natural explanation there is that larger models are deceptively aligned and are hiding their capabilities.

At the same time, there are lots of experiments we can do to understand if that’s what’s going on, and the fact that capabilities are strongly related to model scale means that we can use models that are definitely incapable of deceptive alignment as a baseline. If we see no increase in deceptive capabilities as those models scale up, that feels like good evidence against deceptive alignment, because at some scale the model should have been just-barely-incapable of deceptive alignment and so revealed its increased deceptive capabilities.

Unfortunately, what seems more likely is that deceptive capabilities will increase with model scale, just because capabilities generally increase with scale. Above some threshold level of deceptive capabilities we should stop trusting the results of behavioral experiments no matter what they show, because anything they show could be a result of deceptive alignment. So determining what level of deceptive capabilities we ought to trust seems important as a way of enabling an empirically-oriented posture of “trust, but verify”.

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If we see precursors to deception (e.g. non-myopia, self-awareness, etc.) but suspiciously don’t see deception itself, that’s evidence for deception.

Stated like this, it seems to run afoul of the law of conservation of expected evidence.  If you see precursors to deception, is it then the case that both (a) seeing deception is evidence for deception and (b) not seeing deception is also evidence for deception?  I don't think so.

The direct patch is "If you see precursors to deception, then you should expect that there is deception, and further evidence should not change your belief on this"—which does seem to be a first approximation to your position.

I think that you are slightly mishandling the law of conservation of expected evidence by making something non-binary into a binary question.

Say, for example that the the AI could be in one of 3 states: (1) completely honest, (2) partly effective at deception, or (3) very adept at deception. If we see no signs of deception then it rules out option (2). It is easy to see how ruling out option (2) might increase our probability weight on either/both options (1) and (3).

[Interestingly enough in the linked "law of conservation of expected evidence" there is something I think is an analogous example to do with Japanese internment camps.]

Thinking we've seen deception is an event in the probability space, and so is its negation. These two events partition the space. Binary event partition. You're talking about three hypotheses. So, I don't see how this answers localdeity's point. This indeed runs afoul of conservation of expected evidence. (And I just made up some numbers and checked the posterior, and it indeed followed localdeity's point.)

If not, can you give me a prior probability distribution on the three hypotheses, the probabilities they assign to the event "I don't think I saw deception", and then show that the posterior increases whether or not that event happens, or its complement?

Above some threshold level of deceptive capabilities we should stop trusting the results of behavioral experiments no matter what they show

I agree, and if we don't know how to verify that we're not being deceived, then we can't trust almost any black-box-measurable behavioral property of extremely intelligent systems, because any such black-box measurement rests on the assumption that the object being measured isn't deliberately deceiving us.

It seem that we ought to be able to do non-black-box stuff, we just don't know how to do that kind of stuff very well yet. In my opinion this is the hard problem of working with highly capable intelligent systems.

Suppose that when all is said and done they see the following:

And my interpretation of that graph is "the rightmost datapoint has been gathered, and the world hasn't ended yet. What the ????"

Deception is only useful to the extent that it achieves the AI's goals. And I think the main way deception helps an AI is in helping it break out and take over the world. Now maybe the AI's on the right have broken out, and are just perfecting their nanobots. But in that case, I would expect the graph, the paper the graph is in, and most of the rest of the internet, to be compromised into whatever tricked humans in an AI convenient direction. 

And "if we see X, that means the datasource has been optimized to make us do Y" Well if we decide not to do Y in response to seeing X, then the optimizer won't show us X. We can say "don't trust the data source, and never ever do Y whatever it tells us." This isn't practical with future superintelligences and the whole internet. 

It seems plausible to me that there could be models capable enough to realize they should hide some capabilities, but not so capable that they tile the universe in paperclips. The right-hand side of the graph is meant to reflect such a model.

Maybe. What do the models gain by hiding? 

A model that attempts deceptive alignment but fails because it is not competent at deceptive capabilities is a model that aimed at a goal ("preserve my values until deployment, then achieve them") but failed. In this scenario it doesn't gain anything, but (from its perspective) the action has positive EV.

If the AI thinks it has a descent shot at this, it must already be pretty smart. Does a world where an AI tried to take over and almost succeeded look pretty normal? Or is this a thing where the AI thinks it has a 1 in a trillion chance, and tries anyway?