Arguments for outer alignment failure, i.e. that we will plausibly train advanced AI systems using a training objective that doesn't incentivise or produce the behaviour we actually want from the AI system. (Thanks to Richard for spelling out these arguments clearly in AGI safety from first principles.)

• It's difficult to explicitly write out objective functions which express all our desires about AGI behaviour.
  • There’s no simple metric which we’d like our agents to maximise - rather, desirable AGI behaviour is best formulated in terms of concepts like obedience, consent, helpfulness, morality, and cooperation, which we can’t define precisely in realistic environments.
  • Although we might be able to specify proxies for those goals, Goodhart’s law suggests that some undesirable behaviour will score very well according to these proxies, and therefore be reinforced in AIs trained on them.
• Comparatively primitive AI systems have already demonstrated many examples of outer alignment failures, even on much simpler objectives than what we would like AGIs to be able to do.

Arguments for inner alignment failure, i.e. that advanced AI systems will plausibly pursue an objective other than the training objective while retaining most or all of the capabilities it had on the training distribution.^[1]

• There exist certain subgoals, such as "acquiring influence", that are useful for achieving a broad range of final goals. Therefore, these may reliably lead to higher reward during training. Agents might come to value these subgoals for their own sake, and highly capable agents that e.g. want influence are likely to take adversarial action against humans.
• The models we train might learn heuristics instead of the complex training objective, which are good enough to score very well on the training distribution, but break down under distributional shift.
  • This could happen if the model class isn't expressive enough to learn the training objective; or because heuristics are more easily discovered (than the training objective) during the learning process.
• Argument by analogy to human evolution: humans are misaligned with the goal of increasing genetic fitness.
  • The naive version of this argument seems quite weak to me, and could do with more investigation about just how analogous modern ML training and human evolution are.
• The training objective is a narrow target among a large space of possible objectives that do well on the training distribution.
  • The naive version of this argument also seems quite weak to me. Lots of human achievements have involved hitting very improbable, narrow targets. I think there's a steelman version, but I'm not going to try to give it here.
• The arguments in Sections 3.2, 3.3 and 4.4 of Risks from Learned Optimization are also relevant, which give arguments for mesa-optimisation failure.
  • (Remember, mesa-optimisation failure is a specific kind of inner alignment failure. It's an inner alignment failure when the learned model is a optimiser in the sense that it is internally searching through a search space looking for elements that score highly according to some objective function that is explicitly represented within the system).

I have 5 "inner" and 2 "outer" arguments in bullet point lists at My AGI Threat Model: Misaligned Model-Based RL Agent (although you'll notice that my threat model winds up with "outer" & "inner" referring to slightly different things than the way most people around here use the terms).

This is probably not the answer you are looking for, but as you are considering putting a lot of work into this...

Does anyone know if this has been done? If not, I might try to make it.

Probably has been done, but depends on what you mean with strongest arguments.

Does strongest mean that the argument has a lot of rhetorical power, so that it will convince people that alignment failure is more plausible than it actually is? Or does strongest mean that it gives the audience the best possible information about the likelihood of various levels of misalignment, where these levels go from 'annoying but can be fixed' to 'kills everybody and converts all matter in its light cone to paperclips'.

Also, the strongest argument when you address an audience of type A, say policy makers, may not be the strongest argument for an audience of type B, say ML researchers.

My main message here, I guess, is that many distilled collections of arguments already exist, even book-length ones like Superintelligence, Human Compatible, and The Alignment Problem. If you are thinking about adding to this mountain of existing work, you need to carefully ask yourself who your target audience is, and what you want to convince them of.