Section 034 · Chapter 5, Judges, Humans, and Disagreement
Testing the Value of Data Labelers
Data labelers create the ground truth many AI evaluations depend on. Their value should be measured, not assumed.
data labelersearch relevancevalue data labelers
What to do
- Start by measuring agreement.
- Use chance-adjusted agreement when the stakes justify it.
- Measure labeler value against outcomes.
- Track disagreement by category.
- Test the guideline, too.
Evidence to preserve
- Track disagreement by category.
- Preserve the inputs, versions, configurations, raw outcomes, and results for data labeler, search relevance, value data labelers needed to reproduce work on Testing the Value of Data Labelers.
- Report results for data labeler, search relevance, value data labelers by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
When the system matters, measure marginal labeler value. Compare one-rater, two-rater, three-rater, and expert-adjudicated labels against downstream model ranking, judge calibration, release decisions, and production outcomes. Stop buying labels that make the dataset larger but not more trustworthy.
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Cite this page
Jason Arbon. "Testing the Value of Data Labelers." Testing AI Knowledge Edition, section 34.
https://jarbon.ai/testing-ai/knowledge/ch034-value-data-labelers.html