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AI data labeling is the process of adding descriptive labels to data so a machine-learning model has a target or context to learn from. Also called data annotation, it can mean assigning a simple category—such as “spam” or “not spam”—or marking detailed regions within an image. The label is not the model; it is information used to train or evaluate one.
What does AI data labeling mean?
A label describes a data record in terms relevant to a task. The record might be a message, photograph, audio clip, video, or another kind of data; its label could be a category, target value, transcript, or more detailed annotation. CNIL describes labels as descriptions assigned to data that provide “ground truth” for a model. Its English page is a courtesy translation, and CNIL says the French original prevails if the two differ. CNIL’s explanation of data annotation is dated 5 January 2026.
“Ground truth” here means the answer or description the model is intended to learn from. A label is therefore distinct from the input itself and from the model’s eventual prediction. The FDA glossary likewise describes labeling or annotation as attaching descriptive information to data: FDA Digital Health and Artificial Intelligence Glossary.
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How does data labeling work in supervised learning?
In supervised learning, training examples pair input features with a target answer. The model uses those pairs to learn a relationship, then applies what it learned to predict a target for new, unlabeled inputs. In an email classifier, for instance, message text and sender details are inputs; “spam” or “not spam” is the target label. AWS explains the role of labeled examples in Collecting Labeled Data.
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- Define the task. Specify what the model should predict or identify, and what counts as each possible answer.
- Prepare representative examples. Choose data that reflects the inputs the model is expected to encounter in use. A training set that differs substantially from deployment data may teach the wrong patterns.
- Assign labels. People, automated systems, or a combination of both attach the target or description to each record.
- Check the labeled data. Review whether labels follow the task definition and whether the data is suitable for the intended use before relying on it to train or assess a model.
These are the functional stages, not a guarantee that any particular project uses one fixed sequence or quality threshold. The appropriate checks depend on the task and the consequences of an incorrect label.
What can be labeled?
The label format and level of detail depend on what the model is meant to do. Examples include:
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| Data type | Possible label or annotation |
|---|---|
| Images | Whether an image contains a bird or car; for a more detailed task, the pixels belonging to an object can be marked. |
| Text | A topic for a news article or a sentiment category for a social post. |
| Audio | A transcript or identification of important spoken words. |
| Video | A genre or a description of relevant content. |
| Medical images | Whether an X-ray contains a tumor. |
These examples are described by AWS and Oracle. See AWS’s overview of data labeling and Oracle’s overview (updated 9 August 2023). A yes-or-no label answers a different question from pixel-level annotation: more detail is useful only when it matches the model task.
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Who or what assigns the labels?
Labeling may be performed manually, automatically, or through a workflow that combines automation with human review. In a human-in-the-loop process, software can suggest or apply labels, while people review uncertain or important cases. AWS SageMaker AI documents human-in-the-loop labeling, automated labeling, and annotation consolidation features in its data-labeling documentation. Those are workflow options, not evidence that automation removes the need for task-specific quality controls.
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Why do label quality and process matter?
A model learns from the examples and labels it is given. If labels do not match the task definition, or if the examples fail to represent likely inputs, the resulting model may learn an unsuitable relationship. There is no single universal accuracy threshold established for every labeling task; teams need checks appropriate to the data, task, and impact of errors.
For a broader process framework, ISO lists ISO/IEC 5259-4:2024, published in July 2024. It addresses data quality for analytics and machine learning across supervised, unsupervised, semi-supervised, and reinforcement learning, with attention to labeling, evaluation, and lifecycle management. It is process guidance for teams developing or using ML systems and data-labeling services, rather than a definition of an individual label.
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