Data annotation
Data annotation is the process of attaching information that explains or categorizes data for a specific purpose. A label can identify an object in an image, the intent of a request, or a step in a process. An annotation is an interpretation made under a scheme, not automatically an objective fact.
How it works
Define the task and label meanings, create instructions with edge cases, and choose how annotations will be produced and checked. Human reviewers, automated tools, or combinations of both can be involved. Sampling, disagreement review, and versioning help identify inconsistent interpretations.
Keep labels connected to their source records and document how uncertainty is represented. Changing the instructions can change the meaning of a label even when its name stays the same.
Why it matters for licensing
Annotations may make a dataset useful for a particular training or evaluation task, but they also add work, assumptions, and rights questions. A recipient needs to know who or what created them, how they were checked, and whether the license covers both source material and annotations.
Example
Fictional example: Reviewers classify service requests by the action needed next. They distinguish “requires inspection” from “ready for repair” using written rules and flag ambiguous requests rather than forcing every case into a confident category.
Limitations and misconceptions
More labels do not necessarily mean better data. Inconsistent instructions, missing context, or automated errors can create misleading targets. A label such as “successful” needs a precise definition and evidence of the underlying outcome.
Questions to ask
- What exactly does each label mean, including uncertain cases?
- How were annotations checked and disagreements resolved?
- Are the source records and annotation rights documented?
Sources
- Google Developers — Machine Learning Glossary · Accessed
- Gebru et al. — Datasheets for Datasets · Accessed
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