Max Harms

What the heck is up with “corrigibility”? For most of my career, I had a sense that it was a grab-bag of properties that seemed nice in theory but hard to get in practice, perhaps due to being incompatible with agency. Then, last year, I spent some time revisiting my perspective, and I concluded that I had been deeply confused by what corrigibility even was. I now think that corrigibility is a single, intuitive property, which people can learn to emulate without too much work and which is deeply compatible with agency. Furthermore, I expect that even with prosaic training methods, there’s some chance of winding up with an AI agent that’s inclined to become more corrigible over time, rather than less (as long as the people who built it understand corrigibility and want that agent to become more corrigible). Through a slow, gradual, and careful process of refinement, I see a path forward where this sort of agent could ultimately wind up as a (mostly) safe superintelligence. And, if that AGI is in the hands of responsible governance, this could end the acute risk period, and get us to a good future. This is not the path we are currently on. As far as I can tell, frontier labs do not understand corrigibility deeply, and are not training their models with corrigibility as the goal. Instead, they are racing ahead with a vague notion of “ethical assistance” or “helpful+harmless+honest” and a hope that “we’ll muddle through like we always do” or “use AGI to align AGI” or something with similar levels of wishful thinking. Worse, I suspect that many researchers encountering the concept of corrigibility will mistakenly believe that they understand it and are working to build it into their systems. Building corrigible agents is hard and fraught with challenges. Even in an ideal world where the developers of AGI aren’t racing ahead, but are free to go as slowly as they wish and take all the precautions I indicate, there are good reasons to think doom is still likely. I think