As AI takes on more of our scoring tasks, we must maintain fairness and nuance in our approach. In this post, I share three best practices for designing annotation guidelines that keep human judgment front and center.
1. Define Clear, Behavior-Based Criteria
Break each scoring dimension into observable behaviors (e.g., “pauses appropriately” rather than “good fluency”). This makes guidelines actionable for both human raters and machine training data.
2. Use Layered Training & Calibration
Start with simple examples, then progressively introduce edge cases. Regular recalibration sessions help catch drift, keeping your rubric aligned with real performance.
3. Blend Automatic Checks with Human Review
Leverage automated scripts for speed (e.g., flagging outliers) but always have a human in the loop for ambiguous cases. This hybrid approach ensures both efficiency and integrity.
“Great guidelines don’t replace human judgment—they amplify it.”
Zaynab Martin 