Content filters on ML feel so silly. I assume the goal is to avoid bad press? Because the... "attack" would be someone generating offensive material, which they could just write themselves, not to mention I have serious doubts that any filter is going to be a serious barrier. For images/ video I can see merit, ex: using that nudity inference project on images of children, but text seems particularly pointless.
For example, a couple years ago, there was a big hubbub over a Google Image labeler that labeled a black man and woman as "gorillas". A mistake for sure, but the headlines about the algorithm being "racist" were wrong. The algorithm was certainly incorrect, and it could probably have been argued that one reason it was wrong is that its training set contained fewer black people than white people, but the algorithm was certainly unaware of the historical context around this being a racist description.
Similarly, in the early days of Google driving directions I remember one commenter saying something along the lines of "You can tell that no black engineers work at Google" because it pronounced "Malcolm X Boulevard" as "Malcolm 10 Boulevard". Of course, the vast majority of time you see a lone "X" in a street address it is pronounced "ten".
It's kind of analogous to the "uncanny valley" problem in graphics. When the algorithm gets things mostly right, people think of it as "human-like", and so when it makes a mistake, people attribute human logic to it (it's quite safe to assume that a human labeling a picture of black people as gorillas is racist), as opposed to the plain statistical inferences ML models make.