Labeling
Last Updated: July 29, 2026 | By Mihail Sebastian | AI Dictionary
The process of tagging raw data with the correct answers so supervised machine learning models can learn from examples, done by humans or algorithms.
What is Labeling?
Labeling is the process of attaching the correct answer (a category, tag, or value) to each example in a dataset so a machine learning model can learn from it. A photo tagged “dog”, a review tagged “negative”, an X-ray tagged “pneumonia”: each label tells the model what its output should have been.
The labels become the ground truth that supervised learning treats as fact. Mislabeled examples teach the model to reproduce the mistake, so label quality sets a ceiling on model quality.
Types of Labeling
- Manual labeling: Human annotators assign each label. Accuracy is high; so are cost and time.
- Automated labeling: An algorithm assigns labels at scale. Errors rise, cost falls, and large datasets become feasible.
- Semi-supervised labeling: A small hand-labeled seed set propagates labels across a much larger unlabeled pool.
- Active learning: The model selects the examples it is least certain about and routes only those to human annotators. Annotation effort goes where the model is weakest.
Whatever the method, labels look objective but encode judgment. For a category like “toxic comment”, trained annotators disagree on a real share of examples, and every one of those judgment calls flows straight into the model’s behavior.
Example of Labeling
A company wants a sentiment analysis model that sorts product reviews into positive, negative, and neutral. Annotators read 20,000 reviews and tag each one. A second annotator relabels a sample to check agreement, and cases where they disagree (“Great product, broke after a week”) go to a third reviewer.
The finished labels turn the raw reviews into a training set. The model that trains on them will be exactly as consistent as those 20,000 human judgments, no more.
Related AI terms: Training Set · Ground Truth · Supervised Learning · Imbalanced Data · Data Quality
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Mihail Sebastian — Writes about AI governance, regulation, and the technology behind them. Placeholder bio — replace with a real credential line. About