The key is building the right abstraction layers - clean separation of responsibilities into black boxes. Outside of security, no one complains about trusting the heap of abstractions that interpreted languages depend on... interpreters, compilers, operating systems, hardware... clear layers.

I don't think machine learning and NLP has reached that point. Largely because the field is based on probabilities and tunable parameters which are hard to expose without complexity, and hard to trust when they aren't exposed.