When people think of ML, they often get the impression that "machines do everything themselves." The reality is nearly the opposite. Behind every good ML system, there is always a person. People label data with correct answers (labeling), evaluate the model's responses and give feedback, and iteratively refine the model with that feedback (iterative improvement). Today we organize "the human role" that pervades the entire ML lifecycle.
AIF-C01 frequently asks about this topic in the context of "Human-in-the-loop" and Responsible AI.
We said models learn patterns from data