When multiple data annotators work on the same machine learning project, consistency becomes critical. Learn how to measure and ensure uniform annotation quality across your labeling team.
Measuring Inter-Annotator Agreement
Use inter-annotator agreement (IAA) metrics to quantify labeling consistency in your training data:
- Cohen's Kappa - Measures agreement between two annotators, accounting for chance. Values above 0.8 indicate excellent agreement.
- Fleiss' Kappa - Extends to multiple annotators for classification tasks
- Krippendorff's Alpha - Works for any number of annotators and data types
- IoU (Intersection over Union) - Essential metric for bounding box and segmentation annotation quality
Improving Annotation Consistency
- Start with training sessions using gold standard labeled data
- Run weekly calibration exercises with your annotation team
- Create decision trees for edge cases in your labeling guidelines
- Use consensus labeling for difficult items requiring multiple annotators
Pro Tip: TigerLabel automatically calculates inter-annotator agreement metrics. Set up alerts when agreement drops below your quality threshold.