How I'm thinking about different types of automated alignment

I used to think of "automating alignment" as a natural category. But when I tried to think through the strategic implications, I realised that it depends a lot on the type of work. I'm now thinking of it in three buckets: Empirical alignment research : design and execution of alignment-relevant experiments (for a given notion of what we’re aiming for) Conceptual alignment research: theoretical or judgement-driven work to improve our understanding of what we’re aiming for and how to achieve it (including how to measure it) Direct alignment work : making particular AI systems more aligned (including via inputs such as training data) In the rest of this post I'll walk through my current takes on these in terms what’s needed for automation, how good or bad they look, what our priorities might be (to the extent we're interested in automating alignment [1] ), etc. Automating empirical alignment research This is the most obvious fit for the AI capabilities we’re getting at the moment. You want to know more about the relationship between some alignment-relevant variables? Great, an AI agent can design and run that experiment for you. You want to know what training methods help your alignme