The order in which key properties emerge is important and often glossed over.

Thanks to Wil Perkins, Grant Fleming, Thomas Larsen, Declan Nishiyama, and Frank McBride for feedback on this post. Any mistakes are my own.

Note: I have now changed the second post into this sequence into a standalone post that incorporates the key points from this post. The comments here are valuable, so I'm leaving this post, but I recommend going straight to the next post.

This is the first post in a sequence about deceptive alignment. The second post describes my personal views about the likelihood of deceptive alignment for TAI. I’m separating the key considerations and editorial content so others can more independently update their own views. I intend this sequence to be a submission to the Open Philanthropy AI Worldviews Contest.

Deceptive alignment is a core part of many AI x-risk scenarios. You can find a highly cited, foundational walkthrough of the deceptive alignment argument here. I’m specifically discussing the concept of deceptive alignment described in that post, in which a proxy-aligned model becomes situationally aware and acts cooperatively in training so it can escape oversight and defect to pursue its proxy goals. There are several existing arguments for why this might be the default outcome for highly capable models. There are other ways a model could be manipulative or deceptive that are not covered in this sequence.

In this post, I discuss four key precursors of deceptive alignment, which I will refer to in this sequence as foundational properties. I then argue that the order in which these foundational properties develop is crucial for estimating the likelihood that deceptive alignment will emerge for prosaic transformative AI (TAI).

In this sequence, I use the term “differential adversarial examples” to refer to adversarial examples in which a non-deceptive model will perform differently depending on whether it is aligned or proxy aligned. The deceptive alignment story assumes that differential adversarial examples exist. The model knows it’s being trained to do something out of line with its goals during training and plays along temporarily so it can defect later. That implies that differential adversarial examples exist in training.

Foundational properties for deceptive alignment

There are four foundat...

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