I would just like to point out three "classical EA" arguments for taking recommender systems very seriously.
1) The dangerousness of AGI has been argued to be orthogonal from the purpose of AGI, as illustrated by the paperclip maximizers. If you accept this "orthogonality thesis" and if you are concerned about AGI, then you should be concerned about the most sophisticated maximization algorithms. Recommender systems seem to be today's most sophisticated maximization algorithms (a lot more money and computing power has been invested in optimizing recommender systems than in GPT-3). Given the enormous economic incentives, we should probably not discard the probability that they will remain the most sophisticated maximization algorithms in the future.
As a result, arguments of the form "I don't see how recommender systems can pose an existential threat" seem akin to arguments of the form "I don't see how AGI can pose an existential threat".
(of course, if you reject the latter, I can see why you could reject the former 🙂)
2) Yudkowsky argues that “By far the greatest danger of Artificial Intelligence is that people conclude too early that they understand it.” Today's recommender systems are typical examples of something "that people conclude too early that they understand it". Such algorithms learn from enormous amounts of data which will definitely bias them in ways that no one can understand, since no one can view even a iota of what the YouTube algorithm sees. After all, YouTube receives 500 hours of new video per minute (!!), which it processes at least for copyrights, hate speech filtering and automated captioning.
As a result, arguments of the form "I don't think the YouTube recommender system is intelligent/sophisticated" might be signs that, perhaps, you may be underestimating today's algorithms. If so, then you might be prey to Yudkowsky's "greatest danger". At the very least, discarding the dangerousness of large-scale algorithms without an adequate understanding of them should probably be regarded as a bad habit.
3) Toby Ord's latest book stresses the problem of risk factors. Typically, if everybody cared about political scandals while a deadly pandemic (much worse than COVID-19) is going on, then, surely, the probability of mitigating pandemic risks will be greatly diminish. Arguably, recommender systems are major risk factors, because they point billions of individuals' attentions away from the most pressing problems. Including the attention of the brightest of us.
Bill Gates seems to have given a lot of importance to the risk factor of exposure to poor information, or to the lack of quality information, as his foundation has been investing a lot in "solutions journalism". Perhaps more interestingly still, he has decided to be a YouTuber himself. His channel has 2.3M views (!!) and 450 videos (!!). He publishes several videos per week, especially during this COVID-19 pandemic, probably because he considers that the battle of information is a major cause area! At the very least, he seems to believe that this huge investment is worth this (very valuable) time.
I haven't watched the documentary, but I'm antecedently skeptical of claims that social media constitute an existential risk in the sense in which EAs use that term. The brief summary provided by the Wikipedia article doesn't seem to support that characterization:
While many of these effects are terrible (and concern about them partly explains why I myself basically don't use social media), they do not appear to amount to threats of existential catastrophe. Maybe the claim is that the kind of surveillance made possible by social media and big tech firms more generally ("surveillance capitalism") has the potential to establish an unrecoverable global dystopia?
Are there other concrete mechanisms discussed by the documentary?
The argument that concerned me most was that disinformation spreads 6 times faster than the truth.
The implication is that it’s becoming increasingly difficult for people to establish what the truth is. This undermines democracy and the ability to build consensus. I think we will see this play out with the results of the US election in November and the extent to which people believe and accept the result.
There are some studies suggesting fake news isn't quite the problem some think.
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3316768
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3107731
There are also a number of papers which are sceptical of there being pervasive social media "echo chambers" or "filter bubbles".
http://eprints.lse.ac.uk/87402/
https://www.sciencedirect.com/science/article/abs/pii/S0747563216309086
Cf also this recent book by Hugo Mercier, which argues that people are less gullible than many think.
I don't know this literature well and am not quite sure what conclusions to draw. My impression is, however, that some claims of the dangers of fake news on social media are exaggerated.
Cf also my comment on the post on recommender systems, relating to other effects of social media.
I would be interested to see any evidence on whether citizen knowledge has increased or not since social media formed. People often assert this but don't argue for it and the long-term trend isn't that clear.
I'm not sure this answers your question but the Edelman Trust Barometer has been tracking levels of trust in societal institutions (government, business, NGOs and media) for the last 20 years. The trend shows a widening division between the "Informed Public" and the "Mass Population" using the following definitions:
Informed Public
Mass Population