It sounds like you are concerned that these inferences are over-fitting the training. But it is important to note that once you have a highly trained data set -

It sounds like you are concerned that these inferences are over-fitting the training. But it is important to note that once you have a highly trained data set -- maybe one that can't produce anything novel but does extremely well at doing what humans agree on -- then it is probably very easy to relax your process to produce novel results through iteration. At least then you know you've built a robust enough system to get things right in a sterile setting. On the other hand, demanding novelty from a system that can't even get the basics right isn't likely to give you useful novelties so much as it will give you random mish-mash. To find a local maximum, you'll want to overshoot your target slightly and then back up slowly.