The reason these companies don't fix these systems is because they don't know how. It is easier to remove certain outputs or retire the whole system. There is no line of code they can tweak.
It does seem worse that way.
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.
If you want to argue for the general case, you can simply prove the negation is false. Since it is incorrect to say that a network trained with a tiny percentage of possible inputs will never misclassify, it is true that a network trained in such a way will eventually misclassify. This is bolstered by training any network and seeing they always will misclassify something.
> Yes they did, I said that.
You didn't say that. You said a misclassification would happen on some inputs. That is different from saying on these specific inputs.
Not to imply the problem is unsolvable, just that if an institution has zero tolerance for this mistake, the fix your describing is no guarantee it won't occur.