There's nothing inherently wrong with automating content discovery; it's the cost function being optimized that I think we would almost all take issue with.

Or rather, they knew the answer, but knew that it was the best way to maximize engagement and thus profit.

  A cop sees a guy crawling around under a streetlight and asks him, "Sir, what are you doing?"
  
  The man replies, "Well, officer, I dropped my car keys and I'm trying to find them."
  
  The policeman offers to help, and they search fruitlessly for ten minutes. 
  
  Finally, the officer says, "Are you sure this is where you dropped them?"
  
  "Oh, no, it's not. I dropped them way over there in the parking lot."
  
  Dumbfounded, the cop says, "WHY are you looking over HERE?"
  
  "Well, the light is better over here."

Tech companies should be able to explain and demonstrate the logic their systems use. These algorithms should probably be public. And any system which cannot be transparently explained should be shut down.

But what would even count as an explanation, and what does a "public" algorithm reveal?

There is no line of code that says "if (video.content == extreme) { show_to(EVERYONE) }".

A lot of the dangers are possible from a simple algorithm which merely performs A/B testing on whether a certain (randomly chosen, at first) video increases the amount of time a user spends on a site.

You could pass a law against A/B testing, or require companies to provide deliberately bad suggestions to make their users frustrated, but I'm not sure if that is a proportionate legislative response.

That sort of gets to the heart of it - saying "it's impossible to explain my algorithm because it is so complicated" no longer cuts it. It's the same as if I said to the FDA "This drug works in our testing for the indicated purpose but we have no idea if there are side effects" they aren't go to say "well, OK then, you can sell it". They'll say "come back when you have tested for all possible side effects and you can precisly define its behavior"

Currently Facebook, Google et al, will measure a narrow set of metrics to define the success of their algorithms (primarily relating to $$$) and then declare victory based on empirical optimisation of those metrics. They have to show that not only does the algorithm work for its stated purpose, it doesn't have undesirable side effects. They could do massive testing to demonstrate a huge range of side effects are not present. That would be similar to and probably as expensive as drug development. But an easier way to do that is to transparently explain what the algorithms do so that side effects can be predicted. If that is actually impossible for a given algorithm - well, maybe that algorithm shouldn't be used on the public at all.

Let me give you an example of a good system: Spam filters not operated by Google. Algorithms decide whether or not mail gets through my corporate mail filter. It determines it based on a score, which is tallied from a set of rules. And you can drill down and see the score a spam email received, and then you can see the rules and factors that created that score. As a user, you can even generally see this, because the results are included in the message's headers in your inbox. And if you're the admin, you can then adjust those rules to fix errant behavior.

That's how a recommendation system should work. A system which can't be analyzed in that matter should not exist, and the rules and scoring applied to such systems should be disclosed in some sort of header.

That's a valid argument for not using Gmail, but that in the case of Youtube, almost everyone wants a single place where they can watch all the user-generated videos.

I have my doubts this is true, so much as people are used to the relatively common controls/experience that comes with clicking a YouTube link. If I gave you a link to ocdtrekkietube.com/s8fsj2 (not a real link), and it worked as well as YouTube, I'm not sure any user would be particularly upset about it.

The biggest reason YouTube is as powerful as it is is that video hosting is expensive and few companies can eat that kind of bandwidth and storage without being an ad giant.

You believe it causes no harm, but that doesn't make for a good assumption, considering image recognition NNs have been repeatedly demonstrated to be racist. So you may find it's classification of dogs might work really well, but it might label other people's coworkers or family members as dogs.

Which is my main point, why should this apply to all tech when in some cases unexplainable mistakes are fine?

Then it should be illegal to use on consumer websites. It's that simple.

> If you want explainability it's going to require giving up a lot of innovation that could help people.

Four people died in the US Capitol because of this "innovation". It's time to stop pretending technology doesn't cause more harm than good in these cases.

The nuclear bomb is exactly why we need to block "innovation" that causes more harm than good.

The question is, how do you know something is bad before developing it? You oftentimes can't put the genie back in the bottle.

(This might be the very point you are making, rhetorically?)

Also there MUST be some randomization or more generalized content introduced. And I'm not talking Trending Now or Hit Songs That Just Came Out. I'm looking at you, YouTube and Facebook. Sometimes I actually want new stuff and it keeps dragging me back into a virtual rabbit hole.

- There's no simple decision tree, but an ML model with countless knobs that are mostly implicit. Not worth the money to make an UI to tweak it.

- The puprose of these platforms is to maximize your exposure to ads. Nothing more. Anything that detracts from it is a no-go.