> ...Consider all of the things that a user could input and if you don't want the program outputting to the user a certain type of output then you have to make

> ...Consider all of the things that a user could input and if you don't want the program outputting to the user a certain type of output then you have to make sure that output can't be a possibility. But here's the problem: DL systems are by definition made to be good at generalization, so the input space is (could be) infinite. Also if you're using large pretrained models, you don't have power over the examples that it has already seen in that training. You can fine-tune for your use case, but there's the latent posibility that the "old memories" may come back. Which is valuable for the model to learn complex things that you may not have enough data for, but you know, it can also come with a few surprises.