Your AI's training data might make it more “evil” and more able to circumvent your security, monitoring, and control measures. Evidence suggests that when you pretrain a powerful model to predict a blog post about how powerful models will probably have bad goals, then the model is more likely to adopt bad goals. I discuss ways to test for and mitigate these potential mechanisms. If tests confirm the mechanisms, then frontier labs should act quickly to break the self-fulfilling prophecy.

Research I want to see

Each of the following experiments assumes positive signals from the previous ones:

1. Create a dataset and use it to measure existing models
2. Compare mitigations at a small scale
3. An industry lab running large-scale mitigations

Let us avoid the dark irony of creating evil AI because some folks worried that AI would be evil. If self-fulfilling misalignment has a strong [...]

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