It's common for people to be worried about recommender systems being addictive or promoting filter bubbles etc, but as far as I can tell, they don't have very good arguments for these worries. Whenever I talk to someone who seems to have actually studied the topic in depth, it seems they think that there are problems with recommender systems, but they are different from what people usually imagine.

I'll go through the articles I've read that argue for worrying about recommender systems, and explain why I find them unconvincing. I've only looked at the ones that are widely read; there are probably significantly better arguments that are much less widely read.

Aligning Recommender Systems as Cause Area. I responded briefly on the post. Their main arguments and my counterarguments are:

1. A few sources say that it is bad + it has incredible scale + it should be super easy to solve. (I don't trust the sources and suspect the authors didn't check them; I agree there's huge scale; I don't see why it should be super easy to solve even if there is a problem, especially given that many of the supposed problems seem to have existed before recommender systems.)
2. Maybe working on recommender systems would have spillover effects on AI alignment. (This seems dominated by just working directly on AI alignment. Also the core feature of AI alignment is that the AI system deliberately and intentionally does things, and creates plans in new situations that you hadn't seen before, which is not the case with recommender systems, so I don't expect many spillover effects.)

80K podcast with Tristan Harris. This was actively annoying for a variety of reasons:

1. I don't know what the main claim was. Ostensibly it was meant to be "it is bad that companies have monetized human attention since this leads to lots of bad incentives and bad outcomes". But then so many specific things mentioned have nothing to do with this claim and instead seem to be a vague general "tech companies are bad". Most egregiously, in section Global effects [01:02:44], Rob argues "WhatsApp doesn't have ads / recommender systems, so it acts as a control group, but it too has bad outcomes, doesn't this mean the problem isn't ads / recommender systems?" and Tristan says "That's right, WhatsApp is terrible, it's causing mass lynchings" as though that supports his point.
2. When Rob made some critique of the main argument, Tristan deflected with an example of tech doing bad things. But it's always vaguely related, so you think he's addressing the critique, even though he hasn't actually. (I'm reminded of the Zootopia strategy for press conferences.) See sections "The messy real world vs. an imagined idealised world [00:38:20]" (Rob: weren't negative things happening before social media? Tristan: it's easy to fake credibility in text), "The persuasion apocalypse [00:47:46]" (Rob: can't one-on-one conversations be persuasive too? Tristan: you can lie in political ads), "Revolt of the Public [00:56:48]" (Rob: doesn't the internet allow ordinary people to challenge established institutions in good ways? Tristan: Alex Jones has been recommended 15 billion times.) 
   
   US politics [01:13:32] is a rare counterexample, where Rob says "why aren't other countries getting polarized", and Tristan replies "since it's a positive feedback loop only countries with high initial polarization will see increasing polarization". It's not a particularly convincing response, but at least it's a response.
3. Tristan seems to be very big on "the tech companies changed what they were doing, that proves we were right". I think it is just as consistent to say "we yelled at the companies a lot and got the public to yell at them too, and that caused a change, regardless of whether the problem was serious or not, or whether the solution was net positive or not".

The second half of the podcast focuses more on solutions. Given that I am unconvinced about the problem, I wasn't all that interested, but it seemed generally reasonable.

(This post responds to the object level claims, which I have not done because I don't know much about the object level.)

There's also the documentary "The Social Dilemma", but I expect it's focused entirely on problems, probably doesn't try to have good rigorous statistics, and surely will make no attempt at a cost-benefit analysis so I seriously doubt it would change my mind on anything. (And it is associated with Tristan Harris so I'd assume that most of the relevant details would have made it into the 80K podcast.)

Recommender systems are still influential, and you could want to work on them just because of their huge scale. I like Designing Recommender Systems to Depolarize as an example of what this might look like.