ML is perfectly explainable, and all this mysticism around what are just weightings of features is silly.
What all recommendation platforms could be required to provide is which are their most important variables for determining what a user sees, and sensitivity analysis.
You're either commenting on something you are oblivious about, or are pulling a red herring just to be contrarian.
Concepts like revealed preferences [1] are nearly a century old, not to mention the fact that the whole consumer behavior field as a whole has plenty of quantitative methods to infer "what people like".
Facebook and Twitter support a feature where quite literally users specify in no ambiguous term that they like something. Theirnl platform is so effective that their problem is filtering out bad actors.
Then there's also engagement. You can get a pretty good idea of what people are interested in by tracking what they read.
You might try to nitpick about the true meaning of "like" but you'd need to be really determined to intentionally miss the point.
When shown an outrage-inducing tweet are you being showing "what you like" or something quite different? Eg., what is most likey to addict you to the platform, what elicits the highest emotional response. Do people trapped gambling their lives away in casions "like" being there?
The idea that social networks are engaged in a sincere project to estimate people's preferences is on the face of it absurd.
And requires such superstitions as "revealed preferences" which are often little more than an observation that people do what their environment most easily affords.
To call that an exercise of preference, to say that people like it, is a great misunderstanding of human nature.
People are much better than what they happen to do in any given environment, and social media companies bare extreme levels of responsibility for creating environments of so-called "revealed preferences" where, apparently people want eating disorders, conspiracy theories, twitter rage sessions, and so on.
It really doesn't. You're trying to debate the semantics of the keyword "like", but that discussion is meaningless and irrelevant. The only objective fact about these services is that they use the data provided by users to infer the preferences they reveal. These services then use this information to meet the company's goals, such as meeting the expectations of paying customers. These paying customers include marketing companies, and their expectations include driving up user engagement and associate their programs with specific type of content.
You might complain about whether these goals are aligned with what you believe are some user's goals, but you'd be missing the fact that users of services like Facebook or Twitter are the product and not the customers. You're not paying for that click, the marketing company is. If customers pay for that click then this clearly means these services do a good job determining what users "like".
So you agree that social media recommendation services are not optimising for situations their users like. So the conclusion of your disagreeable grandstanding is the one I offered in my initial comment.
> The only objective fact about these services is that ... infer the preferences they reveal.
They force them (/create environments) into choices where their actions are advantageous to an ad. company, yes.
The theory of "revealed preferences" is highly contested, and its highly unlikely any such things exist. People's apparent preferences are functions of their environments, and are largely created by them, not "revealed".
The entire tradition of neoclassical economics begins from a methodological premise that closed-form mathematical functions ought represent key terms of interest (eg., preferences). However that assumption is so laughably false it's ridiculous, all social systems -- including preferences --- have path dependence (hysteresis) and are parameterised on such a large number of variables that no such "formula" can be written.
So the least objective, most pseudoscientific description of a situation in which a person enters a casino and gambles their life away is that, somehow a magic casino machine is "revelling" the Preferences of The Human Mind.
In sum, my reply to the comment stands unaltered. Social media recommendation systems are not optimising for what their users like,.
It's not that simple. These features are also social. When you like a tweet on twitter, you send a notification to the author, and other people might see the tweet with something like "alice and bob liked this tweet". So you might decide to like or not to like a tweet for all these reasons.
Youtube doesn't consider likes at all in the algorithm, only watchtime, even though likes are private in youtube, because it's too vulnerable to manipulation, to people telling you to "like and subscribe", etc..