Focusing more on data governance:
GovAI now has a full-time researcher working on compute governance. Chinchilla's Wild Implications suggests that access to data might also be a crucial leverage point for AI development. However, from what I can tell, there are no EAs working full time on how data protection regulations might help slow or direct AI progress. This seems like a pretty big gap in the field.
What's going on here? I can see two possible answers:
- Folks have suggested that compute is relatively to govern (eg). Someone might have looked into this and decided data is just too hard to control, and we're better off putting our time into compute.
- Someone might already be working on this that I just haven't heard of.
If anyone has an answer to this I'd love to know!
The Cannonball Problem:
Doing longtermist AI policy work feels a little like aiming heavy artillery with a blindfold on. We can’t see our target, we’ve no idea how hard to push the barrel in any one direction, we don't know how long the fuse is, we can’t stop the cannonball once it’s in motion, and we could do some serious damage if we get things wrong.
Longtermist legal work seems particularly susceptible to the Cannonball Problem, for a few reasons:
Underlying all of this are huge, unanswered questions in political philosophy about where we want to end up. A lack of knowledge about our final destination makes it harder to come up with ways to get there.
I think this goes some way to explaining why longtermist lawyers only have a few concrete policy asks right now despite admirable efforts from LPP, GovAI and others.
I agree. It seems like a highly impactful thing, with a high level of uncertainty. The normal way of reducing uncertainty is to run small trials. My understanding of this concept from the business world is the idea of Fire Bullets, Then Cannonballs. But (as someone with zero technical competence in AI) I suspect that small trials might simply not be feasible.