Section 089 · Chapter 12, Data, Bias, Raters, and Incentives
Testing Bias in Data
Bias enters before the model exists. Sourcing, sampling, and train/test splits decide what the system can learn.
bias data
What to do
- Track provenance, sampling windows, exclusion rules, coverage gaps, leakage between train and test sets, and feedback loops from production behavior.
- Define runnable checks that exercise bias data.
- Set acceptable outcomes and blocker failures for bias data before running the evaluation.
Evidence to preserve
- Track provenance, sampling windows, exclusion rules, coverage gaps, leakage between train and test sets, and feedback loops from production behavior.
- Preserve the inputs, versions, configurations, raw outcomes, and results for bias data needed to reproduce work on Testing Bias in Data.
- Report results for bias data by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
Bias testing treats the dataset as a product surface. Track provenance, sampling windows, exclusion rules, coverage gaps, leakage between train and test sets, and feedback loops from production behavior. Every data-selection rule is also a product decision.
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Cite this page
Jason Arbon. "Testing Bias in Data." Testing AI Knowledge Edition, section 89.
https://jarbon.ai/testing-ai/knowledge/ch089-bias-data.html