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Most existing forecasting platform questions are for very clearly verifiable questions:
But many of the questions we care about are much less verifiable:
One solution attempt would be to have an "expert panel" assess these questions, but this opens up a bunch of issues. How could we know how much we could trust this group to be accurate, precise, and understandable?
The topic of, "How can we trust that a person or group can give reasonable answers to abstract questions" is quite generic and abstract, but it's a start.
I've decided to investigate this as part of my overall project on forecasting infrastructure. I've recently been working with Elizabeth on some high-level research.
I believe that this general strand of work could be useful both for forecasting systems and also for the more broad-reaching evaluations that are important in our communities.
One concrete topic that's easily studiable is evaluation consistency. If the most respected philosopher gives wildly different answers to "Is moral realism true" on different dates, it makes you question the validity of their belief. Or perhaps their belief is fixed, but we can determine that there was significant randomness in the processes that determined it.
Daniel Kahneman apparently thinks a version of this question is important enough to be writing his new book on it.
Another obvious topic is in the misunderstanding of terminology. If an evaluator understands "transformative AI" in a very different way to the people reading their statements about transformative AI, they may make statements that get misinterpreted.
These are two specific examples of questions, but I'm sure there are many more. I'm excited about understanding existing work in this overall space more, and getting a better sense of where things stand and what the next right questions are to be asking.
Making long term predictions is hard. That's a fundamental problem. Having proxies can be convenient, but it's not going to tell you anything you don't already know.