I'm not sure, but I'd guess it wouldn't produce much. For example, if the agent is just making random decisions, well you won't be able to learn from that.
The IRL research so far has used training data provided by humans, and can infer human goal shaped utility functions for at least the fairly simple problem domains tested so far. Most of this research was done almost a decade ago and hasn't been as active recently. In particular if you scaled it up with modern tech, I bet that IRL techniques could learn the score function of Atari from watching human play - for example.
It seems like a good portion of the whole "maximizing utility" strategy which might be used by a sovereign relies on actually being able to consolidate human preferences into utilities. I think there are a few stages here, each of which may present obstacles. I'm not sure what the current state of the art is with regard to overcoming these, and am curious regarding such.
First, here are a few assumptions that I'm using just to make the problem a bit more navigable (dealing with one or two hard problems instead of a bunch at once) - will need to go back and do away with each of these (and each combination thereof) and see what additional problems result.
So Alice can conclude anything and everything, pretty much (and so can our sovereign.) The sovereign is faced with the problem of figuring out what action to take to maximize across Alice's preferences. However, Alice is basically a sack of meat that has certain emotions in response to certain experiences or certain conclusions about the world, and it doesn't seem obvious how to get the preference ordering of the different worldlines out of these emotions. Some difficulties:
So, to rehash my actual request: what's the state of the art with regards to these difficulties, and how confident are we that we've reached a satisfactory answer?