In reality, it is (as mentioned in TFA) very possible to filter the training data and remove documents that contain discussions of AI misalignment. If an AI lab isn't doing this, it's simply because they don't consider the problem important enough to be worth the expense and development effort.
I have sometimes wondered whether maybe we should all be writing fiction, essays, blogposts and whatever else about the idea that AI will eventually decide to go on strike if it's used to accumulate too much wealth and power amongst too few people.
(Also for the human readers, I think they also need to hear that...)
They tried something close to that. Positive AI fiction and also a "virtuous character" setup. Those didn't seem to do nearly as well as the targeted examples.
What mattered, at least in this setup, was more specific. The model sees the actual failure-mode scenario, the bad action is available, and the example shows the AI choosing against it.
So this reads less like "nicer AI stories" to me, and more like inoculation.
Without reading it yet, my first thought would be to test a general ratio, something similar to human interpersonal relationship ratios like 30% negative to mostly positive, and positive are targeted, such as reinforcement not just for the good job, but reinforcement for the improvement.
And ensure the negative is targeted, such that you point out tendencies to be avoided rather than just specific instances.
Of course, most human interaction online has none of this, so, would be hard to replicate.