Quite honestly, in a team that's stressed and run down to the line, checking for that particular classification in a model that has hundreds of classification targets can be really tricky.

Say you have 1000 classification targets. You have to produce a model that checks, for each target, the odds of it being classified as one of other 999.

You have to check, specifically, for "adult male as primate" out of a million potential combinations. And apply secondary business rules or optimizations to prevent that classifications.

So yes it's possible, but it's not cheap, simple or easy.

Facebook just decided to shove the model out the door and not worry about the consequences.

Quality engineering work, costs money and time. Facebook didn't spend it.