Section 091 · Chapter 12, Data, Bias, Raters, and Incentives
Testing Bias in Training
Feature selection, weights, hyperparameters, and training runs can all encode bias even when the data looks reasonable.
benchmarkRAGbias training
What to do
- Define runnable checks that exercise benchmark, RAG, and bias training.
- Set acceptable outcomes and blocker failures for benchmark, RAG, and bias training before running the evaluation.
- Run representative cases for benchmark, RAG, and bias training and preserve the failures that would change the decision.
Evidence to preserve
- Preserve the inputs, versions, configurations, raw outcomes, and results for benchmark, RAG, bias training needed to reproduce work on Testing Bias in Training.
- Report results for benchmark, RAG, bias training 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, bias testing should include feature attribution, slice analysis, counterfactual examples, retraining-to-retraining variance, and drift reports. A model with the same global metric can still be a different product for important subgroups.
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Cite this page
Jason Arbon. "Testing Bias in Training." Testing AI Knowledge Edition, section 91.
https://jarbon.ai/testing-ai/knowledge/ch091-bias-training.html