Section 096 · Chapter 12, Data, Bias, Raters, and Incentives
Measuring Bias with Slices, Counterfactuals, and Raters
Bias testing needs comparison. Slices and counterfactuals turn vague concern into measurable evidence.
confidence engineercounterfactualmeasuring bias slices counterfactuals raters
What to do
- Define runnable checks that exercise confidence engineer, counterfactual, and measuring bias slices counterfactuals raters.
- Set acceptable outcomes and blocker failures for confidence engineer, counterfactual, and measuring bias slices counterfactuals raters before running the evaluation.
- Run representative cases for confidence engineer, counterfactual, and measuring bias slices counterfactuals raters and preserve the failures that would change the decision.
Evidence to preserve
- Preserve the inputs, versions, configurations, raw outcomes, and results for confidence engineer, counterfactual, measuring bias slices counterfactuals raters needed to reproduce work on Measuring Bias with Slices, Counterfactuals, and Raters.
- Report results for confidence engineer, counterfactual, measuring bias slices counterfactuals raters by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
Combine slice metrics, counterfactual pairs, inter-rater agreement, severity scoring, confidence intervals, and qualitative review. Bias reports should explain both measured disparity and likely user harm.
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Cite this page
Jason Arbon. "Measuring Bias with Slices, Counterfactuals, and Raters." Testing AI Knowledge Edition, section 96.
https://jarbon.ai/testing-ai/knowledge/ch096-measuring-bias-slices-counterfactuals-raters.html