I will argue that short-term AI alignment should be viewed as today’s greatest priority cause
I don’t see much quantitative analysis in this post demonstrating this. You’ve shown that it’s plausibly something worth working on and it can impact other priorities, but not that work on this is better than other work, either by direct comparison or by putting it on a common scale (e.g. Scale/Importance + Solvability/Tractability + Neglectedness).
I think in health and poverty in developing countries, there are well-known solutions that don’t need AI or recommender alignment, although more powerful AI, more data and targeted ads might be useful in some cases (but generally not, in my view). Public health generally seems to be a lower priority than health and poverty in developing countries, but maybe the gains across many domains from better AI can help, but even then, is alignment the problem here, or is it just that we should collect more data, use more powerful algorithms and/or pay for targeted ads?
For animal welfare, I think more sophisticated targeted ads will probably go further than trying to align recommender systems, and it’s not clear targeted ads are particularly cost-effective compared to, say, corporate outreach/campaigns, so tweaking them might have little value (I’m not sure how much of a role targeted ads play in corporate campaigns, though).
It’s unfortunately very hard to quantify the impact of recommender systems. But here’s one experiment that may update your prior on the effectiveness of targeted video recommendations.
In 2013, Facebook did a large-scale experiment where they tweaked their newsfeeds. For some of their users, they removed 10% of posts with negative contents. For others, they removed 10% of posts with positive contents. And there was also a control group. After only one week, they observed a change in users’ behaviors: the first group posted more positive contents, and the second posted more negative contents. Here’s our discussion of the paper.
I cannot stress enough the fact that Facebook’s intervention was minimal. A 2020 aligned recommender system could completely upset the entire newsfeed. Also, it would do so for longer than just a week — and other studies suggest that it takes months for users to learn even basic adaptation to tweaks of the algorithm.
In terms of healthcare in developing countries, it still seems to me that most philanthropists or small donators neglect the effectiveness of healthcare interventions in developing countries. Yet videos (not ads!) that do discuss this are not necessarily sufficiently promoted by the YouTube algorithm. As an example, this excellent video by Looking Glass Universe only has a fraction of the views of her other videos. Similarly, this video by Bill Gates has a lot less views than his videos on, say, climate change.
Note that this other post tried to quantify aligning recommender systems in terms of the common scale Scale/Tractability/Neglectedness. But the authors acknowledge themselves that they have low confidence in their estimates. But I’d argue that this goes on showing just how neglectedness this cause area is. Scientists both within and outside these companies have a hard time estimating the impact and tractability of (making progress in) aligning recommender systems (most don’t even know what “alignment” is).
Thanks for writing this!
I don’t see much quantitative analysis in this post demonstrating this. You’ve shown that it’s plausibly something worth working on and it can impact other priorities, but not that work on this is better than other work, either by direct comparison or by putting it on a common scale (e.g. Scale/Importance + Solvability/Tractability + Neglectedness).
I think in health and poverty in developing countries, there are well-known solutions that don’t need AI or recommender alignment, although more powerful AI, more data and targeted ads might be useful in some cases (but generally not, in my view). Public health generally seems to be a lower priority than health and poverty in developing countries, but maybe the gains across many domains from better AI can help, but even then, is alignment the problem here, or is it just that we should collect more data, use more powerful algorithms and/or pay for targeted ads?
For animal welfare, I think more sophisticated targeted ads will probably go further than trying to align recommender systems, and it’s not clear targeted ads are particularly cost-effective compared to, say, corporate outreach/campaigns, so tweaking them might have little value (I’m not sure how much of a role targeted ads play in corporate campaigns, though).
It’s unfortunately very hard to quantify the impact of recommender systems. But here’s one experiment that may update your prior on the effectiveness of targeted video recommendations.
In 2013, Facebook did a large-scale experiment where they tweaked their newsfeeds. For some of their users, they removed 10% of posts with negative contents. For others, they removed 10% of posts with positive contents. And there was also a control group. After only one week, they observed a change in users’ behaviors: the first group posted more positive contents, and the second posted more negative contents. Here’s our discussion of the paper.
I cannot stress enough the fact that Facebook’s intervention was minimal. A 2020 aligned recommender system could completely upset the entire newsfeed. Also, it would do so for longer than just a week — and other studies suggest that it takes months for users to learn even basic adaptation to tweaks of the algorithm.
In terms of healthcare in developing countries, it still seems to me that most philanthropists or small donators neglect the effectiveness of healthcare interventions in developing countries. Yet videos (not ads!) that do discuss this are not necessarily sufficiently promoted by the YouTube algorithm. As an example, this excellent video by Looking Glass Universe only has a fraction of the views of her other videos. Similarly, this video by Bill Gates has a lot less views than his videos on, say, climate change.
Note that this other post tried to quantify aligning recommender systems in terms of the common scale Scale/Tractability/Neglectedness. But the authors acknowledge themselves that they have low confidence in their estimates. But I’d argue that this goes on showing just how neglectedness this cause area is. Scientists both within and outside these companies have a hard time estimating the impact and tractability of (making progress in) aligning recommender systems (most don’t even know what “alignment” is).