> apply existing technology. There is a lot Facebook can do, but they are not, because they are lazy.

Mmmh. You do realise that the state of the art technology that you refer to was developed by Facebook, right? I mean, there are no public communications around whether and which type of image recognition is used to detect harmful content at scale for obvious reasons but… take a wild guess.

Do you _really_ think engineers at Facebook who love to solve problems at scale would prefer to have an army of contractors handle something hurtful if there was a solution involving smart code and a battery of servers?

So, if FB has the state of the art and they are still employing armies of contractors then maybe they are just using said contractors to create their training data set.

The contractors’ feedback is certainly considered a high-signal dataset. I am not directly familiar with that aspect of the company (I worked on things somehow related but not this) but I suspect there are several ways of training and assessing estimations that certain content breaks certain rules (gore, direct threat, child endangerment, etc.). There could be challenges (legal, quality issues) of using automated decision to block or not block different types of flagged content.

One thing that, as a data scientist, I noticed: none of those reports complains about seeing the same image repeatedly. I understand that posters are often tempted to spread new content, so I would expect there to be many repeats. It could be because they see so many similar and shocking things they don’t recognise. Or companies could use techniques to pool together content that is identical or similar enough.

> There is a lot Facebook can do, but they are not, because they are lazy.

Therefore they contract it out rather than automate it? Your comment makes no sense.

I'm saying they should be able to automate flagging of the really bad stuff because it will likely be copies of the same video (by virtue of being so bad) ...