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ArXiv paper: https://arxiv.org/abs/2403.10462 

The idea for this paper occurred to me when I saw Buck Shlegeris' MATS stream on "Safety Cases for AI." How would one justify the safety of advanced AI systems? This question is fundamental. It informs how RSPs should be designed and what technical research is useful to pursue.

For a long time, researchers have (implicitly or explicitly) discussed ways to justify that AI systems are safe, but much of this content is scattered across different posts and papers, is not as concrete as I'd like, or does not clearly state their assumptions.

I hope this report provides a helpful birds-eye view of safety arguments and moves the AI safety conversation forward by helping to identify assumptions they rest on (though there's much more work to do to clarify these arguments).

Thanks to my coauthors: Nick Gabrieli, David Krueger, and Thomas Larsen -- and to everyone who gave feedback: Henry Sleight, Ashwin Acharya, Ryan Greenblatt, Stephen Casper, David Duvenaud, Rudolf Laine, Roger Grosse, Hjalmar Wijk, Eli Lifland, Oliver Habryka, Sim ́eon Campos, Aaron Scher, Lukas Berglund, and Nate Thomas.

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I really liked this report! I think it fills a natural and important hole in the public writing about AI control and makes interesting claims/recommendations.

A few innovations I liked:

  • Risk cases - I think that's plausibly an amazing policy?
  • Calling it "black swans" when an AI decides it's time to take over (even if the distribution has not meaningfully shifted) ("Black swans are rare inputs that cause AI systems to catastrophically misbehave. Black swans may be rare enough to where they are not sampled during evaluation but are common enough to occur in deployment").
  • Calling the crazy bootstrapping hopes "deference". I like the vibes of this word. Deference is still somewhat counterintuitive to me. My first reaction when reading “AI advisors assert that other AI systems do not pose a catastrophic risk, and these AI advisors are at least as credible as human decision-makers.” was to think "human-level judgement is a super low bar when you're speaking of the safety of super powerful systems", but the default mistrust of super powerful AIs would naturally be taken into account by AI advisors if they are at least as credible as human decision-makers.
  • Linking GSN diagrams and other things that are common-practice in other industries with AI safety.
  • The report is beautiful, I like the little inline figures, and Figure 14 is a nice illustration of catch-then-shutdown approaches.
  • You mention unlearning, a subfield that lacks love and attention. I think its characterization as medium-practical and medium-strength is fair (especially given how generous he is with other approaches), but it's not very scalable imo (it breaks when AIs can derive things from scratch quite well).

Some elements I disagree with:

  • I basically disagree with most hopes described in the trustworthiness section:
    • The level of practicality you assign to some approaches is just insanely high. Neither modeling generalization nor externalized reasoning, and certainly not testbeds seem to "strongly practical" (defined as "Applies to arguments that do not require fundamental research advances, but might require"). The only one that is justifiably "strongly practical" to me is "absence of precursor abilities".
    • I disagree with putting testbeds and amplification in trustworthiness (at least not as strong or even medium practicality), I think they usually don't help to know if AIs can be trusted (and in particular they don't prevent any forms of alignment faking).
    • "Claim 4: AI systems are not faking alignment" is just extremely hard to assess. I think that the focus of trustworthiness assessment on "Claim 3: All enumerated causes of correlated infractions are unlikely aside from alignment faking." is somewhat unwarranted.
    • I think the description of macrosystem risks was not very convincing, aside from risks that make systems less trustworthy - so I think macrosystem should maybe have been folded into trustworthiness.
  • The strength of almost all "high strength" measures is overrated, almost nothing is "strong" (where "strong" is defined as "Applies to arguments that could bring catastrophic risk below acceptable levels without needing other arguments."). Control is not strong, monitoring is not strong, externalized reasoning is not strong (for control and trustworthiness), ... Even with a notion of acceptable that is quite generous (e.g. 2% p(doom)) I think nothing is strong, but given the stakes I think we shouldn't use "acceptable" for anything above 1% (and ideally reserve that word for 0.1% or below).

Thanks, this is good feedback!

Addressing the disagreements:

The level of practicality you assign to some approaches is just insanely high. Neither modeling generalization nor externalized reasoning, and certainly not testbeds seem to "strongly practical" (defined as "Applies to arguments that do not require fundamental research advances, but might require"). The only one that is justifiably "strongly practical" to me is "absence of precursor abilities."

Externalized reasoning: just a capability argument, no?
Testbeds: I think the best testbed ideas rn just require training a bunch of models to have particular policies, which seems pretty straightforward. I think these are good enough to make a moderate-strength argument (which is the bar where practicality is evaluated).
Modeling generalization: I definitely think that versions of this aren't very practical, but e.g. looking at efficiency inductive biases is pretty straightforward -- and this is sufficient to make a moderate strength not-alignment-faking argument.

Strong practicality ~refers to "does there exist an argument of this type of moderate strength that can be made without fundamental research." So, lots of arguments of these types will still require fundamental research, but if some don't, then that means they are strongly practical.

I disagree with putting testbeds and amplification in trustworthiness (at least not as strong or even medium practicality), I think they usually don't help to know if AIs can be trusted (and in particular they don't prevent any forms of alignment faking).

I agree that amplification arguments do not help to rule out alignment faking, but testbeds could help. I've read your post on protocol evaluations vs control. If I remember correctly, the main objection you had to testbeds ruling out alignment faking is that the testbed models could intentionally throw off the results. I think this is a knock-down problem. For example, consider a testbed where models have been trained to defect if they are confident they've been deployed to the real internet. Then, developers could try to tell these models from benign ones without being able to access the real internet. It is straightforward in this case to verify that the behavior of the testbed models is consistent with their intended policies (nice when not on the internet, and otherwise bad). So, the only the models can do to throw off the test is to change their internal states (e.g. think in simple ways when they know they could otherwise obfuscate their reasoning by thinking in convoluted ways).

"Claim 4: AI systems are not faking alignment" is just extremely hard to assess. I think that the focus of trustworthiness assessment on "Claim 3: All enumerated causes of correlated infractions are unlikely aside from alignment faking." is somewhat unwarranted.

I agree claim 4 is hard to assess. I'm not sure what you mean by 'unwarranted' so I can't comment much on this.

I think the description of macrosystem risks was not very convincing, aside from risks that make systems less trustworthy - so I think macrosystem should maybe have been folded into trustworthiness.

I don't think it would have made sense to fold macrosystem risks into trustworthiness. One reason for this is that some macrosystem risks relate to control / inability. e.g. models might combine their intelligence at scale and become much more capable.

Another reason for this is that I also think it is harder to assess trustworthiness arguments at larger scales. In particular, not alignment faking arguments claim that de-correlated misalignment is not going to be an issue. At a macrosystem level, this requires answering the question: "would it be bad if 1% of my AI population was misaligned?" This is pretty different from answering the question "if one model in X subsystem is misaligned, would that be bad?"

The strength of almost all "high strength" measures is overrated, almost nothing is "strong" (where "strong" is defined as "Applies to arguments that could bring catastrophic risk below acceptable levels without needing other arguments."). Control is not strong, monitoring is not strong, externalized reasoning is not strong (for control and trustworthiness), ... Even with a notion of acceptable that is quite generous (e.g. 2% p(doom)) I think nothing is strong, but given the stakes I think we shouldn't use "acceptable" for anything above 1% (and ideally reserve that word for 0.1% or below).

I suspect that this disagreement might be mostly semantic. The main reason I labeled capability arguments as strong is that developers could in principle demonstrate a large margin of error. e.g. it's clear that gpt-2 is not going to cause a catastrophe. Or it could be very clear that smarter models are controlled if they are sufficiently isolated.

Strength labels indicate an upper bound. It's on me for not making that more clear.
 

My bad for testbeds, I didn't have in mind that you were speaking about this kind of testbeds as opposed to the general E[U|not scheming] analogies (and I forgot you had put them at medium strength, which is sensible for these kinds of testbeds). Same for "the unwarranted focus on claim 3" - it's mostly because I misunderstood what the countermeasures were trying to address.

I think I don't have a good understanding of the macrosystem risks you are talking about. I'll look at that more later.

I think I was a bit unfair about the practicality of techniques that were medium-strength - it's true that you can get some evidence for safety (maybe 0.3 bits to 1 bit) by using the techniques in a version that is practical.

On practicality and strength, I think there is a rough communication issue here: externalized reasoning is practical, but it's currently not strong - and it could eventually become strong, but it's not practical (yet). The same goes for monitoring. But when you write the summary, we see "high practicality and high max strength", which feels to me like it implies it's easy to get medium-scalable safety cases that get you acceptable levels of risks by using only one or two good layers of security - which I think is quite wild even if acceptable=[p(doom)<1%]. But I guess you didn't mean that, and it's just a weird quirk of the summarization?

which feels to me like it implies it's easy to get medium-scalable safety cases that get you acceptable levels of risks by using only one or two good layers of security

I agree there's a communication issue here. Based on what you described, I'm not sure if we disagree.


> (maybe 0.3 bits to 1 bit)

I'm glad we are talking bits. My intuitions here are pretty different. e.g. I think you can get 2-3 bits from testbeds. I'd be keen to discuss standards of evidence etc in person sometime.