One very interesting thing that was mentioned in the interview is how much Facebook relies on deep learning right now; specifically, how hate speech detection went from 75% manual to something like 2.5% manual, and how manual false negatives detection allowed this improvement.
What I'm wondering is about false positive detection, which wasn't mentioned, and how much of this incredible decrease in false negatives came at the expense of an increase in false positives.
Anecdotally, FB's hate speech detector is pathetic. I have lots of friends run afoul of it for trivial things. It seems no more coherent than a bunch of regular expressions.
I had a post in a group get flagged by the algorithm for something "your politics shouldn't involve saying '[bad word] about [protected group]'".
I suspect the problem is there's nothing that would flag as wrong for a system that just defaults to such crudeness. So that's what happens.
I was once asked to use machine learning to make a record linkage system for some crappy dataset. I got no requirements, of course, so I set it up to have a reasonable balance of precision and recall. After all, the point of asking for an ML system must be to allow fuzzy matches that a simple exact matching system would miss, right?
But my boss apparently got complaints about bad matches, so he changed it to allow exact matches only.
The machine learning system ended up being a Rube Goldberg machine for linking people based on exact name match.
Anecdotally this is pretty typical of the evolution of mo systems.
Some heuristic/standard algorithm works pretty well, but people see cases it didn't catch and think it could be better. An engineer/scientist takes a look and realizes the extra features required need a more complex/ml algorithm to add support for. Years later a model has made it to production, but there are now complaints about too lenient matching.
In my experience, ML works best in scenarios where there is either such immense data volume that .2% improvement is a real benefit (these are rare) or the very notion of a heuristic method simply wouldn’t work.
A guy I know got banned this week because he posted a cute kitten that looked like it was suffocating someone with the icanhazcheezeburger like caption of "i kil you"
Banned for 24 hours, lol. Anyway. I think that is within bounds.
What I'm wondering is about false positive detection, which wasn't mentioned, and how much of this incredible decrease in false negatives came at the expense of an increase in false positives.