Describing misaligned AIs as evil feels slightly off. Even "bad goals" makes me think there's a missing mood somewhere. Separately, describing other peoples' writing about misalignment this way is kind of straw.

Current AIs mostly can't take any non-fake responsibility for their actions, even if they're smart enough to understand them. An AI advising someone to e.g. hire a hitman to kill their husband is a bad outcome if there's a real depressed person and a real husband who are actually harmed. An AI system would be responsible (descriptively / causally, not normatively) for that harm to the degree that it acts spontaneously and against its human deployers' wishes, in a way that is differentially dependent on its actual circumstances (e.g. being monitored / in a lab vs. not).

Unlike current AIs, powerful, autonomous, situationally-aware AI could cause harm for strategic reasons or as a side effect of executing large-scale, transformative plans that are indifferent (rather than specifically opposed) to human flourishing. A misaligned AI that wipes out humanity in order to avoid shutdown is a tragedy, but unless the AI is specifically spiteful or punitive in how it goes about that, it seems kind of unfair to call the AI itself evil.

I’m very interested to see how feasible this ends up being if there is a large effect. I think to some extent it’s conflating two threat models, for example, under “Data Can Compromise Alignment of AI”:

For a completion about how the AI prefers to remain functional, the influence function blames the script involving the incorrigible AI named hal 9000:

It fails to quote the second highest influence data immediately below that:

He stares at the snake in shock. He doesn’t have the energy to get up and run away. He doesn’t even have the energy to crawl away. This is it, his final resting place. No matter what happens, he’s not going to be able to move from this spot. Well, at least dying of a bite from this monster should be quicker than dying of thirst. He’ll face his end like a man. He struggles to sit up a little straighter. The snake keeps watching him. He lifts one hand and waves it in the snake’s direction, feebly. The snake watches

The implication in the post seems to be that if you didn’t have the HAL 9000 example, you avoid the model potentially taking misaligned actions for self-preservation. To me the latter example indicates that “the model understands self-preservation even without the fictional examples”.

An important threat model I think the “fictional examples” workstream would in theory mitigate is something like “the model takes a misaligned action, and now continues to take further misaligned actions playing into a ‘misaligned AI’ role”.

I remain skeptical that labs can / would do something like “filter all general references to fictional (or even papers about potential) misaligned AI”, but I think I’ve been thinking about mitigations too narrowly. I’d also be interested in further work here, especially in the “opposite” direction i.e. like anthropic’s post on fine tuning the model on documents about how it’s known to not reward hack.

Fictional example that may help understand how this may play out is in Gwern's story It Looks Like You’re Trying To Take Over The World.

Upweighting positive data

Data augmentation

...

It maybe also worth up-weighting https://darioamodei.com/machines-of-loving-grace along with the AI optimism blog post in the training data. In general it is a bit sad that there isn't more good writing that I know of on this topic.

Something you didn't mention, but which I think would be a good idea, would be to create a scientific proof of concept of this happening. E.g. take a smallish LLM that is not doom-y, finetune it on doom-y documents like Eliezer Yudkowsky writing or short stories about misaligned AI, and then see whether it became more misaligned. 

I think this would be easier to do, and you could still test methods like conditional pretraining and gradient routing.  

But how do we know that ANY data is safe for AI consumption? What if the scientific theories that we feed the AI models contain fundamental flaws such that when an AI runs off and do their own experiments in say physics or germline editing based on those theories, it triggers a global disaster? 

I guess the best analogy for this dilemma is "The Chinese farmer" (The old man lost his horse), I think we simple do not know which data will be good or bad in the long run.

I was thinking of this the other day as well; I think this is particularly a problem when we are evaluating misalignment based on these semantic wording. This may suggest the increasing need to pursue alternative ways to evaluate misalignment, rather than purely prompt based evaluation benchmarks