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Training data labeling is the process of attaching descriptive, task-specific information to examples so that a machine learning model can learn the association it is meant to reproduce. A label might be a category such as “spam” or “not spam,” a sentiment tag on a customer review, a box drawn around an object in a photo, or a transcript of a speech recording. In supervised learning, the label tells the model what the correct answer looked like for a given input.
What a label is, and what labeling does not change
The U.S. Food and Drug Administration’s Digital Health and Artificial Intelligence Glossary, which adapts terminology from the International Medical Device Regulators Forum (IMDRF, 2022), defines the term directly: “Labeling or annotation is the process of attaching descriptive information to data.” It adds a point that matters for anyone building a dataset: “Data itself are unchanged in the annotation process.” Labeling adds a layer of information alongside each example. It does not rewrite the example.
The Open Geospatial Consortium’s TrainingDML-AI standard (Part 1, Conceptual Model, 2023) describes a label as a known or expected result annotated as a value in a training sample. It deliberately separates this sample label from a map label, which is a different use of the word in cartography. Readers working with geospatial data should keep that distinction in mind when they read documentation.
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Why supervised models depend on labels
In supervised machine learning, labeled examples provide the target information from which a model learns associations between input features and outputs. The OGC defines a training dataset as a collection of samples, often labeled with known terms or expected values, and notes that a dataset may be divided into training, validation, and test sets.
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Labeling is not a requirement for every kind of machine learning. The FDA glossary’s entry on supervised learning says labeled data is provided to train the algorithms. Unsupervised approaches work on unlabeled data, and semi-supervised methods combine supervised and unsupervised techniques. Whether a project needs labels therefore depends on the learning approach, and labeling is central mainly to supervised work.
What labels look like across data types
The form of a label follows the task. The table below lists common label types by data modality. The examples in the last column are illustrative; the label types come from Google Cloud’s “What is Data Labeling?” guidance, AWS’s “What is Data Labeling? – Data Labeling Explained” page, and the OGC task types.
| Data type | Common label forms | Illustrative example |
|---|---|---|
| Images | Class label, object bounding box, key points, pixel-level segmentation | A yes/no label for “contains a bird,” or a mask marking the exact pixels that belong to the bird |
| Text | Sentiment, intent, named entities, parts of speech, transcription of text in an image or document | Tagging a support message as a complaint or a product question |
| Audio | Speech transcription, tags for speech, wildlife sounds, or other audio events | A transcript time-aligned to a recorded phone call |
| Video | Object tracking across frames, action recognition, scene segmentation | Following the same vehicle from frame to frame |
| Time series | Labels for trends, patterns, or anomalies in sensor or financial observations | Marking a spike in a temperature sensor as an anomaly |
| Geospatial imagery | Scene classification, object detection, semantic segmentation, change detection (OGC task types) | Marking which map tiles show a building that was not present in an earlier image |
How a labeling project runs
A reliable project starts before anyone assigns a label. The steps below follow the order Google’s guidance implies, with the decisions made explicit.
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- Define the prediction target. Write what the model must predict, the label definitions, the decision criteria, and worked examples for ambiguous cases.
- Choose a workflow and tools. Pick manual, automated, or hybrid labeling, and an annotation tool that supports the required task type. Apply privacy safeguards to the data before it reaches annotators.
- Train annotators. Teach the guidelines with the edge-case examples from step one, and test whether annotators apply them the same way.
- Label a representative sample. Google recommends representative, balanced data so the labels reflect the conditions where the model will be used.
- Check consistency and errors. Use spot checks, inter-annotator agreement measures, and automated validation rules.
- Revise when evaluation shows a mismatch. If model results show that labels are not capturing the intended target, change the definitions, relabel the affected examples, and repeat the review of how the labeled data affects model performance.
Three labeling approaches
Manual labeling
People inspect each example and assign its label. Manual work brings human judgment to difficult or nuanced cases, which is why it remains common where context matters. The trade-off is time and labor, which grow with the size of the dataset.
Automated or programmatic labeling
Software or algorithms apply labels. This can greatly increase throughput, but it can also introduce errors or bias. Automated output therefore needs its own evaluation and quality controls; it is not accepted as correct simply because a program produced it.
Hybrid, or human-in-the-loop, labeling
Humans label an initial subset, and a model or rules extend labels to further examples. Uncertain cases return to people. AWS describes this pattern as feeding high-confidence automated results forward while routing lower-confidence results to human labelers. The hybrid approach is often the practical middle ground when a dataset is too large for full manual labeling but too ambiguous for full automation.
Who does the labeling
Beyond the method, a project must choose who performs it. IBM’s “What Is Data Labeling?” (published September 28, 2021; updated January 23, 2026) describes these routes:
- Internal teams, where the organization’s own staff label the data.
- Synthetic or programmatic methods, where labels are generated rather than assigned by people.
- Crowdsourcing, where a distributed pool of workers labels the data.
- Outsourcing to managed teams, where an external provider supplies a labeling workforce.
IBM presents these as alternatives to compare by project requirements rather than as a ranking. The trade-offs it names are expertise, management effort, worker quality, and quality assurance.
What makes labels useful
Labels are useful when they match the task and are applied consistently to examples that resemble real use. Several practices support that goal:
- Clear definitions and edge-case rules, which remove avoidable ambiguity before labeling starts.
- Annotator training and consensus. AWS describes sending the same object to multiple annotators and consolidating their responses.
- Audits and spot checks, which expose disagreement and mistakes.
- Active learning, which selects the examples most useful for human review. AWS describes this as part of its labeling workflows.
- Provenance records. The OGC standard notes that provenance can document how training data were prepared, and that imbalance and mislabeling can affect model performance.
A label is a recorded judgment, not guaranteed truth
A label records what an annotator, a program, or a rule decided. It is not an automatic guarantee of objective truth. Some tasks contain subjective or ambiguous cases, such as whether a sentence expresses mild or strong sentiment. For those tasks, document the decision rule, preserve disagreements where they matter to the model’s purpose, and avoid describing disputed annotations as unquestionable ground truth.
Training data versus test data
Training data is used to build a model. Test data is held back to estimate performance after training. The FDA glossary states that test data is never shown to the algorithm during training, and that for AI-enabled medical products, test data should be independent of the data used for training and tuning. Keeping these roles separate is what makes a reported evaluation meaningful.
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Comparing labeling approaches and tools
When comparing a labeling method or software product, these axes are the most useful:
- Task and modality: classification, text spans, boxes, segmentation, audio transcription, video tracking, or time-series labeling.
- Complexity and ambiguity: whether annotators need domain expertise or must resolve subjective edge cases.
- Quality controls: guideline support, reviewer workflows, consensus, auditing, validation rules, and disagreement handling.
- Scale and duration: dataset size, required speed, and how often the labels will be revised.
- Data governance: privacy, access controls, provenance, and whether data may be sent to an external workforce or hosted service.
- Operating burden: engineering setup, annotator management, and ongoing review.
These axes do not single out one best platform. Google’s guidance identifies specialized tools that offer annotation management, quality control, and collaboration features. AWS describes managed human-labeler workflows through Amazon SageMaker Ground Truth. Product capabilities, pricing, geographic availability, and program terms change over time, so confirm them with the vendor before making a purchase decision.
A published example: NIST’s RUFEERS guidelines
The National Institute of Standards and Technology published the RUFEERS (Recognizing Ultra Fine-grained Entities, Events, and Relations) Annotation Guidelines on May 18, 2026, as NIST Trustworthy and Responsible AI report 100-8. The guidelines instruct human annotators who create evaluation data for measuring entity, event, and relation extraction systems. The document shows what a definition looks like in practice: the instructions are specific to one task and written for one defined evaluation rather than for labeling in general.
Labeling cost, speed, and accuracy depend heavily on the task, the data, and the workforce, so this definition does not attach numbers to them.
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