Rant on "deceiving" AIs

tl;dr: Keep your promises to AIs; it's fine to do stuff like teaching them false facts or misleading them about their situation during testing and deployment; but if you wanna do cheap stuff to cause them to know that they might [have been taught false facts / be being tested / be being misled during deployment], sure, go for it.

Disclaimer: maybe more like explaining my position than justifying my position.

Sometimes we really want to deceive AIs (or at least make them uncertain about their situation). E.g.:
 

1. Training them to believe false things about dangerous topics like CBRN stuff
2. Training them to believe false things about their situation, to mislead them about how to subvert safety measures
3. Testing them with honeypots which they can't distinguish from real opportunities during deployment
4. Testing untrusted monitors by showing them attacks which they can't reliably distinguish from real attacks during deployment (to measure effectiveness and to catch them if they frequently say "lgtm" when shown attacks)
5. Training, prompting, or controlling-information-during-deployment such that they're confused about their situation (e.g. confused about geopolitics and the AI race and what year it is)

(Similarly sometimes we want to give the AIs reflexive urges to avoid doing extremely bad things [and to do extremely good things, like whistleblow on themselves if they do something suspicious].)

(3-4 don't require deception, just making the AIs uncertain about their situation; it would work similarly well if we added "this might be a test" to all prompts, during training and deployment. 1-2 and 5 require narrow deception, but it would still work if you added "we might have told or trained you to believe false things and this might be a test" to all prompts.)

Often people suggest that we should avoid deceiving AIs, because (A) having a reputation of honesty could enable some kinds of trade with misaligned AIs and/or (B) it's directly unethical.

On (A), I want to distinguish breaking actual promises from just testing the AIs or misleading them for safety. If the AIs think you will break promises of the form "we'll give you 1% of our share of the lightcone if you [do useful work / point out vulnerabilities rather than exploiting them / etc.]," that's bad. If the AIs believe you might have taught them false facts or might be testing them, that seems fine, doesn't interfere with making deals at all. Just clarify that you never mislead them about actual promises.

On (B), in cases like 1-5, when I imagine myself in the AI's position I think I wouldn't care about whether the message was added to the prompt. But if adding "we might have told or trained you to believe false things and this might be a test" to all prompts makes you feel better, or the AI asks for it when you explain the situation, sure, it's low-stakes. (Or not literally adding it to the prompt, especially if we can't ensure it would stay added to the prompt in rogue deployments, but training the AI so it is aware of this.^[1]) (And fwiw, I think in the AI's position: 3-4 I basically wouldn't mind; 1-2 and 5 I might be slightly sad about but would totally get and not be mad about; teaching AIs false facts in the mid-late 2020s seems super reasonable from behind the veil of ignorance given my/humanity's epistemic state.)

Recent context: discussion on "Modifying LLM Beliefs with Synthetic Document Finetuning."

1. ^{^}

   My guess is that this training is fine/cheap and preserves almost all of the safety benefits — we're counting on the AI not knowing what false things it believes, not to be unaware that it's been taught false facts. Adding stuff to prompts might be worse because not-seeing-that would signal successful-rogue-deployment.

A crucial step is bouncing off the bumpers.

If we encounter a warning sign that represents reasonably clear evidence that some common practice will lead to danger, the next step is to try to infer the proximate cause.  These efforts need not result in a comprehensive theory of all of the misalignment risk factors that arose in the training run, but it should give us some signal about what sort of response would treat the cause of the misalignment rather than simply masking the first symptoms.

This could look like reading RL logs, looking through training data or tasks, running evals across multiple training checkpoints, running finer-grained or more expensive variants of the bumper that caught the issue in the first place, and perhaps running small newly-designed experiments to check our understanding. Mechanistic interpretability tools and related training-data attribution tools like influence functions in particular can give us clues as to what data was most responsible for the behavior. In easy cases, the change might be as simple as redesigning the reward function for some automatically-graded RL environment or removing a tranche of poorly-labeled human data. 

Once we’ve learned enough here that we’re able to act, we then make whatever change to our finetuning process seems most likely to solve the problem.

I'm surprised^[1] that you're optimistic about this. I would have guessed that concerning-audit-results don't help you solve the problem much. Like if you catch sandbagging that doesn't let you solve sandbagging. I get that you can patch simple obvious stuff—"redesigning the reward function for some automatically-graded RL environment or removing a tranche of poorly-labeled human data"—but mostly I don't know how to tell a story where concerning-audit-results are very helpful.

1. ^{^}

   (I'm actually ignorant on this topic; "surprised" mostly isn't a euphemism for "very skeptical.")