The focus for alignment is to avoid bad PR specifically the kind of headlines written by major media houses like NYT, WSJ, WaPo. You could imagine the headlines

The focus for alignment is to avoid bad PR specifically the kind of headlines written by major media houses like NYT, WSJ, WaPo. You could imagine the headlines like "Google's AI produced a non-diverse output on occasions" when a researcher/journalist is trying too hard to get the model to produce that. The hit on Google is far bigger than say on Midjourney or even Open AI till now (I suspect future models will be more nerfed than what they are now) For the cases you mentioned, initially those were the examples. It gets tricky during red teaming where they internally try out extreme prompts and then align the model for any kind of prompt which has a suspect output. You train the model first, then figure out the issues, and align the model using "correct" examples to fix those issues. They either went to extreme levels doing that or did not test it on initial correct prompts post alignment.