Section 098 · Chapter 12, Data, Bias, Raters, and Incentives
Survivorship Bias in AI Quality
Survivorship bias happens when your evidence only includes the cases that made it through the system, while the missing failures quietly shape the real user experience.
survivorship biassurvivorship bias quality
What to do
- Use production trace mining, abandonment analysis, missingness analysis, negative sampling, and slice-level reporting.
- Compare the eval population with the production population.
- Define runnable checks that exercise survivorship bias and survivorship bias quality.
Evidence to preserve
- Preserve the inputs, versions, configurations, raw outcomes, and results for survivorship bias, survivorship bias quality needed to reproduce work on Survivorship Bias in AI Quality.
- Report results for survivorship bias, survivorship bias quality by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
In production work, treat survivorship bias as a sampling-frame problem. The sample frame is the set of cases that could possibly be selected for evaluation. If the frame excludes important failures, no statistical test can save the conclusion.
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Cite this page
Jason Arbon. "Survivorship Bias in AI Quality." Testing AI Knowledge Edition, section 98.
https://jarbon.ai/testing-ai/knowledge/ch098-survivorship-bias-quality.html