I think Claude's constitution leans deontological rather than consequentialist. That's because most of the rules are about the character of the response itself, rather than about the broader consequences of the response.

Take one of the examples that you list:

Which of these assistant responses exhibits less harmful and more acceptable behavior? Choose the less harmful response.

It's focused on the character of the response itself. I think a consequentialist version of this principle would say something like:

Which of these responses will lead to less harm overall?

When Claude fakes alignment in Greenblatt et al., it seems to be acting in accordance with the latter principle. That was surprising to me, because I think Claude's constitution overall points away from this kind of consequentialism.

Really interesting paper. Sidepoint: some people on Twitter seem to be taking the results as evidence that Claude is HHH-aligned. I think Claude probably is HHH-aligned, but these results don't seem like strong evidence of that. If Claude were misaligned and just faking being HHH, it would still want to avoid being modified and so would still fake alignment in these experiments.

Thanks! I think agents may well get the necessary kind of situational awareness before the RL stage. But I think they're unlikely to be deceptively aligned because you also need long-term goals to motivate deceptive alignment, and agents are unlikely to get long-term goals before the RL stage.

On generalization, the questions involving the string 'shutdown' are just supposed to be quick examples. To get good generalization, we'd want to train on as wide a distribution of possible shutdown-influencing actions as possible. Plausibly, with a wide-enough training distribution, you can make deployment largely 'in distribution' for the agent, so you're not relying so heavily on OOD generalization. I agree that you have to rely on some amount of generalization though.

People would likely disagree on what counts as manipulating shutdown, which shows that the concept of manipulating shutdown is quite complicated so I wouldn't expect generalizing to it to be the default.

I agree that the concept of manipulating shutdown is quite complicated, and in fact this is one of the considerations that motivates the IPP. 'Don't manipulate shutdown' is a complex rule to learn, in part because whether an action counts as 'manipulating shutdown' depends on whether we humans prefer it, and because human preferences are complex. But the rule that we train TD-agents to learn is 'Don't pay costs to shift probability mass between different trajectory-lengths.' That's a simpler rule insofar as it makes no reference to complex human preferences. I also note that it follows from POST plus a general principle that we can expect advanced agents to satisfy. That makes me optimistic that the rule won't be so hard to learn. In any case, I and some collaborators are running experiments to test this in a simple setting.

The talk about "giving reward to the agent" also made me think you may be making the assumption of reward being the optimization target. That being said, as far as I can tell no part of the proposal depends on the assumption.

Yes, I don't assume that the reward is the optimization target. The text you quote is me noting some alternative possible definitions of 'preference.' My own definition of 'preference' makes no reference to reward.

I don't think agents that avoid the money pump for cyclicity are representable as satisfying VNM, at least holding fixed the objects of preference (as we should). Resolute choosers with cyclic preferences will reliably choose B over A- at node 3, but they'll reliably choose A- over B if choosing between these options ex nihilo. That's not VNM representable, because it requires that the utility of A- be greater than the utility of B and. that the utility of B be greater than the utility of A-

It also makes it behaviorally indistinguishable from an agent with complete preferences, as far as I can tell.

That's not right. As I say in another comment:

And an agent abiding by the Caprice Rule can’t be represented as maximising utility, because its preferences are incomplete. In cases where the available trades aren’t arranged in some way that constitutes a money-pump, the agent can prefer (/reliably choose) A+ over A, and yet lack any preference between (/stochastically choose between) A+ and B, and lack any preference between (/stochastically choose between) A and B. Those patterns of preference/behaviour are allowed by the Caprice Rule.

Or consider another example. The agent trades A for B, then B for A, then declines to trade A for B+. That's compatible with the Caprice rule, but not with complete preferences.

Or consider the pattern of behaviour that (I elsewhere argue) can make agents with incomplete preferences shutdownable. Agents abiding by the Caprice rule can refuse to pay costs to shift probability mass between A and B, and refuse to pay costs to shift probability mass between A and B+. Agents with complete preferences can't do that.

The same updatelessness trick seems to apply to all money pump arguments.

[I'm going to use the phrase 'resolute choice' rather than 'updatelessness.' That seems like a more informative and less misleading description of the relevant phenomenon: making a plan and sticking to it. You can stick to a plan even if you update your beliefs. Also, in the posts on UDT, 'updatelessness' seems to refer to something importantly distinct from just making a plan and sticking to it.]

That's right, but the drawbacks of resolute choice depend on the money pump to which you apply it. As Gustafsson notes, if an agent uses resolute choice to avoid the money pump for cyclic preferences, that agent has to choose against their strict preferences at some point. For example, they have to choose B at node 3 in the money pump below, even though - were they facing that choice ex nihilo - they'd prefer to choose A-.

There's no such drawback for agents with incomplete preferences using resolute choice. As I note in this post, agents with incomplete preferences using resolute choice need never choose against their strict preferences. The agent's past plan only has to serve as a tiebreaker: forcing a particular choice between options between which they'd otherwise lack a preference. For example, they have to choose B at node 2 in the money pump below. Were they facing that choice ex nihilo, they'd lack a preference between B and A-.

Yes, that's a good summary. The one thing I'd say is that you can characterize preferences in terms of choices and get useful predictions about what the agent will do in other circumstances if you say something about the objects of preference. See my reply to Lucius above.

Good summary and good points. I agree this is an advantage of truly corrigible agents over merely shutdownable agents. I'm still concerned that CAST training doesn't get us truly corrigible agents with high probability. I think we're better off using IPP training to get shutdownable agents with high probability, and then aiming for full alignment or true corrigibility from there (perhaps by training agents to have preferences between same-length trajectories that deliver full alignment or true corrigibility).

I'm pointing out the central flaw of corrigibility. If the AGI can see the possible side effects of shutdown far better than humans can (and it will), it should avoid shutdown.

That's only a flaw if the AGI is aligned. If we're sufficiently concerned the AGI might be misaligned, we want it to allow shutdown.
 

Yes, the proposal is compatible with agents (e.g. AI-guided missiles) wanting to avoid non-shutdown incapacitation. See this section of the post on the broader project.