Data Annotation

Definition

Data annotation is the process of labeling raw data with tags that make it meaningful for AI models. Examples include labeling images with object categories or tagging text with sentiment.

Purpose

The purpose is to create training datasets that allow AI to learn patterns in supervised learning. Without annotation, many AI tasks would not be possible.

Importance

  • Provides the “ground truth” for training ML models.
  • Quality of annotations affects model accuracy and fairness.
  • Time-consuming and resource-intensive task.
  • Often requires domain expertise (e.g., medical annotation).

How It Works

  1. Define the task and label categories.
  2. Collect and preprocess raw data.
  3. Use annotation tools for labeling.
  4. Validate through quality checks.
  5. Export labeled data for model training.

Examples (Real World)

  • Amazon Mechanical Turk: crowdsourced annotation platform.
  • Shaip: data annotation service for autonomous vehicle datasets.
  • Radiology image labeling: hospitals annotate scans for AI diagnosis.

References / Further Reading

  • Data Annotation for AI — NIST.
  • Annotating and Labeling Datasets — IEEE Transactions on Data Engineering.
  • ISO/IEC 24617: Semantic Annotation Framework — ISO.