Here’s my take on why the distinction between inner and outer-alignment frame is weird/unclear/ambiguous in some circumstances: My understanding is that these terms were originally used when talking about AGI. So outer alignment involved writing down a reward or utility function for all of human values and inner alignment involves getting these values in the AI.
However, it gets confusing when you use these terms in relation to narrow AI. For a narrow AI, there’s a sense in which we feel that we should only have to define the reward on that narrow task. ie. if we want an AI to be good at answering questions, an objective that rewards it for correct answers and penalises itself for incorrect answers feels like a correct reward function for that domain. So if things go wrong and it kidnaps humans and forces us to ask it lots of easy questions so it can score higher, we’re not sure whether to say that it’s inner or outer alignment. On one hand, if our reward function penalised kidnapping humans (which is something we indeed want penalised) then it wouldn’t have done it. So we are tempted to say it is outer misalignment. On the other hand, many people also have an intuition that we’ve defined the reward function correctly on that domain and that the problem is that our AI didn’t generalise correctly from a correct specification. This pulls us in the opposite direction, towards saying it is inner misalignment.
Notice that what counts as a proper reward function is only unclear because we’re talking about narrow AI. If we were talking about AGI, then of course our utility function would be incomplete if it doesn’t specify that it shouldn’t kidnap us in order to do better at a question-answering task. It’s an AGI, so that’s in scope. But when we’re talking about narrow AI, it feels as though we shouldn’t have to specify it or provide anti-kidnapping training data. We feel like it should just learn it automatically on the limited domain, ie. that avoiding kidnapping is the responsibility of the training process, not of the reward function.
Hence the confusion. The resolution is relatively simple: define how you want to partition responsibilities between the reward function and the training process.
I'm definitely one of those non-experts who has never done actual machine learning, but AFAICT that article you linked both is tied to and does not explicitly mentioned that the 'principle of indifference' is about the epistemological taste of the reasoner, while arguing that the cases where the reasoner lacks knowledge to hold a more accurate prior means the principle itself is wrong.
The training of an LLM is not a random process, therefore indifference will not accurately predict the outcome of this process. This does not imply anything about other forms of AI, or about whether people reasoning in the absence of knowledge about the training process were making a mistake. It also does not imply sufficient control over the outcome of the training process to ensure that the LLM will, in general, want to do what we want it to want to do, let alone to do what we want it to do.
The section where she talks about how evolution's goals are human abstractions and an LLM's training has a well-specified goal in terms of gradient descent is really where that argument loses me, though. In both cases, it's still not well specified, a priori, how the well-defined processes cash out in terms of real-world behavior. The factual claims are true enough, sure. But the thing an LLM is trained to do is predict what comes next, based on training data curated by humans, and humans do scheme. Therefore, a sufficiently powerful LLM should, by default, know how to scheme, and we should assume there are prompts out there in prompt-space that will call forth that capability. No counting argument needed. In fact, the article specifically calls this out, saying the training process is "producing systems that behave the right way in all scenarios they are likely to encounter," which means the behavior is unspecified in whatever scenarios the training process deems "unlikely," although I'm unclear what "unlikely" even means here or how it's defined.
One of the things we want from our training process is to not have scheming behavior get called up in a hard-to-define-in-advance set of likely and unlikely cases. In that sense, inner-alignment may not be a thing for the structure of LLMs, in that the LLM will automatically want what it is trained to want. But, it is still the case that we don't know how to do outer-alignment for a sufficiently general set of likely scenarios, aka we don't actually know precisely what behavioral responses our training process is instilling.