The idea of a “basin of attraction around corrigibility” motivates much of prosaic alignment research. Essentially this is an abstract way of thinking about the process of iteration on AGI designs. Engineers test to find problems, then understand the problems, then design fixes. The reason we need corrigibility for this is that a non-corrigible agent generally has incentives to interfere with this process. The concept was introduced by Paul Christiano:
> ... a corrigible agent prefers to build other agents that share the overseer’s preferences — even if the agent doesn’t yet share the overseer’s preferences perfectly. After all, even if you only approximately know the overseer’s preferences, you know that the overseer would prefer the approximation get better rather than worse.
>
> Thus an entire neighborhood of possible preferences lead the agent towards the same basin of attraction. We just have to get “close enough” that we are corrigible, we don’t need to build an agent which exactly shares humanity’s values, philosophical views, or so on.
>
> In addition to making the initial target bigger, this gives us some reason to be optimistic about the dynamics of AI systems iteratively designing new AI systems. Corrigible systems want to design more corrigible and more capable successors. Rather than our systems traversing a balance beam off of which they could fall at any moment, we can view them as walking along the bottom of a ravine. As long as they don’t jump to a completely different part of the landscape, they will continue traversing the correct path.
Max Harms wrote about CAST, a similar strategy that relied on the same idea:
> This property of non-self-protection means we should suspect AIs that are almost-corrigible will assist, rather than resist, being made more corrigible, thus forming an attractor-basin around corrigibility, such that almost-corrigible systems gradually become truly corrigible by being modified by their creators.
This post is about