What is Data Labeling?

A model trained to classify support tickets by urgency needs a training set where humans (or a trusted automated process) have already marked thousands of past tickets as urgent, normal, or low priority. The quality of those labels sets a hard ceiling on the model's accuracy - a model can't learn a distinction its training labels don't consistently reflect, no matter how sophisticated the architecture is. Labeling work ranges from simple binary tags to complex structured annotation like bounding boxes on images or span-level entity tags in text, and it's increasingly done with a human-in-the-loop pattern: an AI model proposes labels, and humans only review and correct the ones it's uncertain about, which is far faster than labeling from scratch. Inter-annotator agreement - how consistently different labelers tag the same item - is the standard way to check whether a labeling task is well-defined; low agreement usually means the labeling instructions themselves are ambiguous, not that the labelers are careless.