The concept of “interfaces of misalignment” does not mainly point to GovAI-style research here (although it also may serve as a framing for GovAI). The concrete domains separated by the interfaces in the figure above are possibly a bit misleading in that sense:
For me, the “interfaces of misalignment” are generating intuitions about what it means to align a complex system that may not even be self-aligned—rather just one aligning part of it. It is expanding not just the space of solutions, but also the space of meanings of “success”. (For example, one extra way to win-lose: consider world trajectories where our preferences are eventually preserved and propagated in a way that we find repugnant now but with a step-by-step endorsed trajectory towards it.)
My critique of the focus on “AI developers” and “one AI” interface in isolation is that we do not really know what the “goal of AI alignment” is, and it works with a very informal and a bit simplistic idea of what aligning AGI means (strawmannable as “not losing right away”).
While a broader picture may seem to only make the problem strictly harder (“now you have 2 problems”), it can also bring new views of the problem. Especially, new views of what we actually want and what it means to win (which one could paraphrase as a continuous and multi-dimensional winning/losing space).
The concept of “interfaces of misalignment” does not mainly point to GovAI-style research here (although it also may serve as a framing for GovAI). The concrete domains separated by the interfaces in the figure above are possibly a bit misleading in that sense:
For me, the “interfaces of misalignment” are generating intuitions about what it means to align a complex system that may not even be self-aligned—rather just one aligning part of it. It is expanding not just the space of solutions, but also the space of meanings of “success”. (For example, one extra way to win-lose: consider world trajectories where our preferences are eventually preserved and propagated in a way that we find repugnant now but with a step-by-step endorsed trajectory towards it.)
My critique of the focus on “AI developers” and “one AI” interface in isolation is that we do not really know what the “goal of AI alignment” is, and it works with a very informal and a bit simplistic idea of what aligning AGI means (strawmannable as “not losing right away”).
While a broader picture may seem to only make the problem strictly harder (“now you have 2 problems”), it can also bring new views of the problem. Especially, new views of what we actually want and what it means to win (which one could paraphrase as a continuous and multi-dimensional winning/losing space).