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Annotation Drift

Annotation drift changes label meaning over time. Learn how calibration, agreement checks, sample reviews, and human oversight preserve data quality.

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In the full article

  1. What is annotation drift?
  2. Why does annotation drift happen?
  3. How can teams detect annotation drift?
  4. How can teams prevent annotation drift?
  5. How does AI-assisted annotation affect drift?
  6. Key takeaways
  7. How PulseLake helps
  8. Frequently asked questions
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Maintaining Label Consistency

Label consistency keeps annotations comparable as data, teams and guidelines change. Learn how calibration, quality review and governance prevent drift.

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Annotation Quality Control

Annotation quality control keeps labeled data accurate and consistent through ongoing review, error detection, guideline updates, and human oversight.

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AI Assisted Annotation

AI-assisted annotation uses models to suggest labels and flag uncertain cases, helping human reviewers scale labeling without surrendering quality control.

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