I think there’s a strong argument for deception being simpler than corrigibility. Corrigibility has some fundamental difficulties in terms of… If you’re imagining gradient descent process, which is looking at a proxy aligned model and is trying to modify it so that it makes use of this rich input data, it has to do some really weird things to make corrigibility work.
It has to first make a very robust pointer. With corrigibility, if it’s pointing at all incorrectly to the wrong thing in the input data, wrong thing in the world model, the corrigible optimizer won’t correct that pointer. It’ll just be like, “Well, I have this pointer. I’m just trying to optimize for what this thing is pointing for,” and if that pointer is pointing at a proxy instead, you’ll just optimize that proxy. And so you have this very difficult problem of building robust pointers. With deception, you don’t have this problem. A deceptive model, if it realizes the loss function is different than what it thought, it’ll just change to doing the new loss function. It’s actually much more robust to new information because it’s trying to do this instrumentally. And so in a new situation, if it realizes that the loss function is different, it’s just going to automatically change because it’ll realize that’s the better thing to do instrumentally.
A part I liked and thought was well-explained: