I spent a lot of time laboring under the intuition that there's some "preference" thingie that summarizes all we care about, that we can "extract" from (define using a reference to) people and have an AI optimize it. In the lingo of meta-ethics, that would be "right" or "morality", and it distanced itself from the overly specific "utility" that also has the disadvantage of forgetting that prior is essential.

Then, over the last few months, as I was capitalizing on finally understanding UDT in May 2010 (despite having convinced a lot of people that I understood it long before that, I completely failed to get the essential aspect of controlling the referents of fixed definitions, and only recognized in retrospect that what I figured out by that time was actually UDT), I noticed that a decision problem requires many more essential parts than just preference, and so to specify what people care about, we need a whole human decision problem. But the intuition that linked to preference in particular, which was by then merely a part of the decision problem, still lingered, and so I failed to notice that now not preference, but the whole decision problem, is analogous to "right" and "morality" (but not quite, since that decision problem still won't be the definition of right, it can be judged in turn), and the whole agent that implements such decision problem is the best tool available to judge them.

This agent, in particular, can find itself judging its own preference, or its own inference system, or its whole architecture that might or might not specify an explicit inference system as its part, and so on. Whatever explicit consideration it's moved by, that is whatever module in the agent (decision problem) it considers, there's a decision problem of self-improvement where the agent replaces that module with something else, and things other than that module can have a hand in deciding.

Also, there's little point in distinguishing "decision problem" and "agent", even though there is a point in distinguishing a decision problem and what's right. Decision problem is merely a set of tricks that the agent is willing to use, as is agent's own implementation. What that set of tricks wants to do is not specified in any of the tricks, and the tricks can well fail the agent.

When we apply these considerations to humans, it follows that no human can know what they care about, they can only guess (and, indeed, design) heuristic rules for figuring out what they care about, and the same applies to when they construct FAIs. So extracting "preference" exactly is not possible, instead FAI should be seen as a heuristic, that would still be subject to moral judgment and probably won't capture it whole, just as humans themselves don't implement what's right reliably. Recognizing that FAI won't be perfect, and that things it does are merely ways of more reliably doing the right thing, looks like an important intuition.

(This is apparently very sketchy and I don't expect it to get significantly better for at least a few months. I could talk more (thus describing more of the intuition), but not clearer, because I don't understand this well myself. An alternative would have me write up some unfinished work that would clarify each particular intuition, but would be likely of no lasting value, and so should wait for a better rendition instead.)