Section 090 · Chapter 12, Data, Bias, Raters, and Incentives

Testing Bias in Labeling

Labels are human judgment turned into training data. That judgment carries instructions, incentives, disagreement, and demographics.

bias labeling

What to do

Ask multiple raters to label the same item, then measure where they agree and where they diverge.
Use agreement metrics, entropy analysis, rater demographics, guideline A/B tests, and adjudication logs.
Do not let the cleanup process erase the very users the system needs to serve.

Evidence to preserve

Preserve the inputs, versions, configurations, raw outcomes, and results for bias labeling needed to reproduce work on Testing Bias in Labeling.
Report results for bias labeling by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.

Expert note

In a real release review, labeling tests should separate harmful inconsistency from meaningful plurality. Use agreement metrics, entropy analysis, rater demographics, guideline A/B tests, and adjudication logs. Do not let the cleanup process erase the very users the system needs to serve.

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Cite this page

Jason Arbon. "Testing Bias in Labeling." Testing AI Knowledge Edition, section 90.

https://jarbon.ai/testing-ai/knowledge/ch090-bias-labeling.html