I get worried about things like this article that showed up on the Partnership on AI blog. Reading it there’s nothing I can really object to in the body of post: it’s mostly about narrow AI alignment and promotes a positive message of targeting things that benefit society rather than narrowly maximize a simple metric. How it’s titled “Aligning AI to Human Values means Picking the Right Metrics” and that implies to me a normative claim that reads in my head something like “to build aligned AI it is necessary and sufficient to pick the right metrics” which is something I think few would agree with. Yet if I was a casual observer just reading the title of this post I might come away with the impression that AI alignment is as easy as just optimizing for something prosocial, not that there are lots of hard problems to be solved to even get AI to do what you want, let alone to pick something beneficial to humanity to do.
To be fair this article has a standard “not necessarily the views of PAI, etc.” disclaimer, but then the author is a research fellow at PAI.
This makes me a bit nervous about the effect of PAI on promoting AI safety in industry, especially if it effectively downplays it or makes it seem easier than it is in ways that either encourages or fails to curtail risky behavior in the use of AI in industry.
Hi Gordon. Thanks for reading the post. I agree completely that the right metrics are nowhere near sufficient for aligned AI — further I’d say that “right” and “aligned” have very complex meanings here.
What I am trying to do with this post is shed some light on one key piece of the puzzle, the actual practice of incorporating metrics into real systems. I believe this is necessary, but don’t mean to suggest that this is sufficient or unproblematic. As I wrote in the post, “this sort of social engineering at scale has all the problems of large AI systems, plus all the problems of public policy interventions.”
To me the issue is that large, influential optimizing systems already exist and seem unlikely to be abandoned. There may be good arguments that a particular system should not be used, but it’s hard for me to see an argument to avoid this category of technology as a whole. As I see it, the question is not so much “should we try to choose appropriate metrics?” but “do we care to quantitatively monitor and manage society-scale optimizing systems?” I believe this is an urgent need for this sort of work within industry.
Having said all that, you may be right that the title of this post overpromises. I’d welcome your thoughts here.
I get worried about things like this article that showed up on the Partnership on AI blog. Reading it there’s nothing I can really object to in the body of post: it’s mostly about narrow AI alignment and promotes a positive message of targeting things that benefit society rather than narrowly maximize a simple metric. How it’s titled “Aligning AI to Human Values means Picking the Right Metrics” and that implies to me a normative claim that reads in my head something like “to build aligned AI it is necessary and sufficient to pick the right metrics” which is something I think few would agree with. Yet if I was a casual observer just reading the title of this post I might come away with the impression that AI alignment is as easy as just optimizing for something prosocial, not that there are lots of hard problems to be solved to even get AI to do what you want, let alone to pick something beneficial to humanity to do.
To be fair this article has a standard “not necessarily the views of PAI, etc.” disclaimer, but then the author is a research fellow at PAI.
This makes me a bit nervous about the effect of PAI on promoting AI safety in industry, especially if it effectively downplays it or makes it seem easier than it is in ways that either encourages or fails to curtail risky behavior in the use of AI in industry.
Hi Gordon. Thanks for reading the post. I agree completely that the right metrics are nowhere near sufficient for aligned AI — further I’d say that “right” and “aligned” have very complex meanings here.
What I am trying to do with this post is shed some light on one key piece of the puzzle, the actual practice of incorporating metrics into real systems. I believe this is necessary, but don’t mean to suggest that this is sufficient or unproblematic. As I wrote in the post, “this sort of social engineering at scale has all the problems of large AI systems, plus all the problems of public policy interventions.”
To me the issue is that large, influential optimizing systems already exist and seem unlikely to be abandoned. There may be good arguments that a particular system should not be used, but it’s hard for me to see an argument to avoid this category of technology as a whole. As I see it, the question is not so much “should we try to choose appropriate metrics?” but “do we care to quantitatively monitor and manage society-scale optimizing systems?” I believe this is an urgent need for this sort of work within industry.
Having said all that, you may be right that the title of this post overpromises. I’d welcome your thoughts here.