Social media companies have very successfully deployed and protected their black box recommendation algorithms despite massive negative societal consequences
Having actually worked for a tech giant on recommendation systems (specifically, for music), they are very much not black boxes to the people building them. They us fairly old and quite understandable ML techniques to predict engagement, from every obvious signal that the engineers involved can think of that might help do so, and they’re tweaked a lot, and every tweak is A/B tested at huge scale. It’s a very obvious learning algorithm, with a lot of hand-engineering involved. Getting a 0.5% increase in a secondary metric that your data scientists have shown is correlated to your north-star metric is a major win. The only element of all this that’s in any way hard to predict is the social side effects of maximizing engagement. So the recommendation algorithms might be a black box to users, but by LLM standards they’re practically transparent.
I left a couple of years ago. At that time, for the music aspect of the company that I was working for, the main recommender was a great many carefully-crafted input signals (many having already been processed by a wide variety of ML models) fed into a small tower of MLP layers with multiple output heads attempting to predict different aspects of engagement with the item, feeding into a data-scientist-derived formula. I gather the main video recommender then used something comparable. Quite old-school ML. Since almost all of our inputs generally weren’t in the form of meaningful sequences, there weren’t many obvious problems that a transformer could help with — for those that were, I’d expect it to be applied only to that portion of the data (e.g. chunks of text). Indeed, that was starting to happen with I was there (e.g. for search where the user input actually is a chunk of text). In general, they put a lot of effort into having data scientists understand what was going on inside the system in as much detail as possible.
They also did actually try to think about the possible social consequences of their algorithms — for example, they maximized account retention, not total engagement, which turned out to mean that past a certain point total engagement had very diminishing returns. They also classified content into buckets, including certain ones of which they did NOT want to encourage engagement with, even if it was still available for users actively looking for it (less of an issue for music, admittedly), and other highly promoted ones which seemed to correlate with people’s self-reported long-term happiness with using the site — which tended to be quite “worthy” (again, rare for music). (Note that I am explicitly NOT claiming all social media companies were then acting like that.)
Having actually worked for a tech giant on recommendation systems (specifically, for music), they are very much not black boxes to the people building them. They us fairly old and quite understandable ML techniques to predict engagement, from every obvious signal that the engineers involved can think of that might help do so, and they’re tweaked a lot, and every tweak is A/B tested at huge scale. It’s a very obvious learning algorithm, with a lot of hand-engineering involved. Getting a 0.5% increase in a secondary metric that your data scientists have shown is correlated to your north-star metric is a major win. The only element of all this that’s in any way hard to predict is the social side effects of maximizing engagement. So the recommendation algorithms might be a black box to users, but by LLM standards they’re practically transparent.
is that still the case? I had the impression that the youtube recommender may be a transformer now? I’m not sure why I had this hunch
I left a couple of years ago. At that time, for the music aspect of the company that I was working for, the main recommender was a great many carefully-crafted input signals (many having already been processed by a wide variety of ML models) fed into a small tower of MLP layers with multiple output heads attempting to predict different aspects of engagement with the item, feeding into a data-scientist-derived formula. I gather the main video recommender then used something comparable. Quite old-school ML. Since almost all of our inputs generally weren’t in the form of meaningful sequences, there weren’t many obvious problems that a transformer could help with — for those that were, I’d expect it to be applied only to that portion of the data (e.g. chunks of text). Indeed, that was starting to happen with I was there (e.g. for search where the user input actually is a chunk of text). In general, they put a lot of effort into having data scientists understand what was going on inside the system in as much detail as possible.
They also did actually try to think about the possible social consequences of their algorithms — for example, they maximized account retention, not total engagement, which turned out to mean that past a certain point total engagement had very diminishing returns. They also classified content into buckets, including certain ones of which they did NOT want to encourage engagement with, even if it was still available for users actively looking for it (less of an issue for music, admittedly), and other highly promoted ones which seemed to correlate with people’s self-reported long-term happiness with using the site — which tended to be quite “worthy” (again, rare for music). (Note that I am explicitly NOT claiming all social media companies were then acting like that.)