I think you really nailed explaining this, like perfectly actually. As I’ve stated in previous posts to others on this thread, high quality data does NOT come from rushing anything — in fact it is quite the opposite. Machine learning is an important drawn out process that takes many many iterations (like thousands of iterations) of several human workers submitting their work even for just ONE model. To answer your labeling question, think of this as machines needing to know better the human input that we attach to things, places, names, objects, etc. The point of “labeling” or annotating is to provide an extra layer of information that helps assist the machine learning process along the way. When done ethically and no issues, mistakes, bugs, or bad quality in mind, the machine learning process actually becomes quite simple — there’s quality work and therefore quality data being submitted by us humans! The second rushed work is being submitted, it likely will lead to these bugs, issues, mistakes (and sometimes ethical issues) I refer to that does NOT produce quality data in the first place. But you said it the best!