Section 088 · Chapter 12, Data, Bias, Raters, and Incentives
Dataset Bias and Coverage Gaps
A clean-looking evaluation can still be wrong if the sample misses the people, languages, risks, and workflows that matter.
What to do
- Define runnable checks that exercise RAG, dataset bias, and dataset bias coverage gaps.
- Set acceptable outcomes and blocker failures for RAG, dataset bias, and dataset bias coverage gaps before running the evaluation.
- Run representative cases for RAG, dataset bias, and dataset bias coverage gaps and preserve the failures that would change the decision.
Evidence to preserve
- Preserve the inputs, versions, configurations, raw outcomes, and results for RAG, dataset bias, dataset bias coverage gaps needed to reproduce work on Dataset Bias and Coverage Gaps.
- Report results for RAG, dataset bias, dataset bias coverage gaps by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
At scale, maintain a coverage matrix with population share, risk weight, sample count, pass rate, confidence interval, known exclusions, label quality, and production drift. Make omissions explicit instead of letting them become hidden assumptions. A small but high-risk slice may deserve more samples than its traffic share would suggest.
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Cite this page
Jason Arbon. "Dataset Bias and Coverage Gaps." Testing AI Knowledge Edition, section 88.
https://jarbon.ai/testing-ai/knowledge/ch088-dataset-bias-coverage-gaps.html