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.
What is your standard for evidence? To be worried about a possibility does not require that the possibility is an actuality. Say the question was something like “Are recommender systems not addictive or addictive?” Do you care more about type-I (label addictive when not addictive) or type-II errors (label not addictive when addictive)? Without knowing anything more, I’d think it’s reasonable to care more about type-II,
It seems clear that recommender systems could be used to make the UI more addictive and there are companies that survive based on selling addictive products.
So there is a decision to be made: we can either decide there is a problem and be wrong or say there is not a problem when there is one.
There is a much higher cost (to society) if we say there is no problem when there is one as compared to the case where there is a problem and when there isn’t.
Thus, the optimal decision, from a societal perspective, would be bias toward caution or expressing worry if your true belief was: “who knows if there’s a major problem with recommender systems or not”.
To change my belief you’d need to either show that the cost to society is more under type-I or have significant evidence against addiction, but you don’t seem to present that here.
Could you be concrete about what papers you consider newer and maybe also link to original deep-q paper you have in mind? (This might help someone answer the question)