Thanks. Your post specifically is pretty helpful because it helps with one of the things that was tripping me up, which is what standard names people call different methods. Your names do a better job of capturing them than mine did.
You might be interested in this post I wrote recently that goes into significant detail on what I see as the major leading proposals for building safe advanced AI under the current machine learning paradigm.
Actually this post was not especially helpful for my purpose and I should have explained why in advance because I anticipated someone would link it. Although it helpfully lays out a number of proposals people have made, it does more to work out what's going on with those proposals rather than find ways they can be grouped together (except incidentally). I even reread this post before posting this question and it didn't help me improve on the taxonomy I proposed, which I already had in mind as of a few months ago.
My initial thought is that there are at least 3, which I'll give the follow names (with short explanations):
All of these are woefully underspecified, so improved summaries of these approaches that you think accurately explain these approaches also appreciated.
I think the last one seems odd / doesn't make much sense. All agents have a decision theory, including RL-based agents, so it's not a distinctive approach.
If you were attempting to describe MIRI's work, remember that they're trying to understand basic concepts of agency better (meta level, object level), not in order to directly put the new concepts into the AI (in the same way current AIs do not always have a section for the 'probability theory' to be written in) but in order to be much less confused about what we're trying to do.
So if you want to describe MIRI's work, you'd call it "getting less confused about the basic concepts" and then later building an AI via a mechanism we'll figure out after getting less confused. Right now it's not engineering, it's science.
I suspect that nobody is actually pursuing the third one as you've described it. Rather, my impression is that MIRI researchers tend to think of decision theory as a more fundamental problem in understanding AI, not directly related to human interests.
Based on comments/links so far it seems I should revise the names and add a fourth:
Oh, I forgot about emulation approaches, i.e. bootstrap AI by "copying" human brains, which you mention. Thanks!
I'm writing a post comparing some high-level approaches to AI alignment in terms of their false positive risk. Trouble is, there's no standard agreement on what various high-level approaches to AI alignment there are today, either in terms of what constitutes these high-level approaches or where to draw the line in categorizing various specific approaches.
So, I'll open it up as a question to get some feedback before I get too far along. What do you consider to be the high-level approaches to AI alignment?
(I'll supply my own partial answer below.)