zlacker

>>benhur+(OP)
If you've got an automated vetting process with a 99.999% success rate, but are dealing with billions of accounts, that's still tens of thousands of false positives.

At that level, "percentage" is an insufficient measure. You want "permillionage", or maybe more colloquially "DPM" for "Defects Per Million" or even "DPB".

You'll still get false positives though, so you provide an appeal process. But what's to prevent the bad actors from abusing the appeal process while leaving your more clueless legitimate users lost in the dust?

(As the joke goes: "There is considerable overlap between the intelligence of the smartest bears and the dumbest tourists" [1])

Can you build any vetting process, and associated appeal process, that successfully keeps all the bad actors out, and doesn't exclude your good users? What about those on the edge? Or those that switch? Or those who are busy, or wary?

There's a lot of money riding on that.

[1] https://www.schneier.com/blog/archives/2006/08/security_is_a...

>>AceJoh+YM1
The lesson here is: you are too big. If you were smaller, you could manage these issues. But you choose to be big instead.

>>esja+UO1
Is this really true? If Gmail was replaced with a dozen competing services each with "only" 100M users each, would the total number of moderators be lower? How does the number of required human moderators per million users scale, and why?

>>wmf+i52
I agree: not true. The advantage of automation is you can do more for less which extends the reach in wealth and services available to the human race. Automation is a beautiful thing and gmail being too big to service with human support is not understanding that we'll never have enough intelligence power to police every square inch of existence + the net if we rely solely on human intelligence.

Problem is: can we cultivate machine learning intelligence to be as good as some of the best human arbiters?

https://www.youtube.com/watch?v=91TRVubKcEM

>>BingoA+kf2
I don't think anyone is proposing that moderation rely solely on humans. The question is about machine learning with human backup/appeals vs. Google's approach of machine learning with no appeals.