Fair enough, but I would disagree about the need for manual review. The metrics for which they claim '1 in 6000 rate of false positives' are purely subjective, and can't possibly be substantiated quantitatively or qualitatively. In other words, it is a provably false claim.
How do I know this? Because it is a morality argument.
Even if they use the most advanced machine learning, it will fall extremely short of being 99.9998% effective (1 in 6000). If for no other reason than the subjective nature of morality, hostility, and toxicity.
If that is their claim, I wouldn't just question the integrity of their claim, but the integrity of those who made the claim. Even in the context of the most advanced machine learning alogarithms, which do not have the ability to take negative connotation, morality, and extremely complex use of words associated with hostility, into consideration, tend to be vastly less accurate than that.
So.... Sorry. But if at some point they claimed the systems were that accurate, they lied blatantly.
In fact, there are a few TED talks about exactly this, and the increasing importance of the human ability to discern morality, since machine learning cannot do so accurately.
Complex and interesting topic, given that machine learning has become so integral in so many industries, recently.