Section 139 · Chapter 17, Personalized and Dynamic AI Products
Testing When Not to Personalize
The best personalized system knows when user preference should not control the answer.
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What to do
- Measure both personalization lift and personalization harm.
- Define runnable checks that exercise personalization and personalize.
- Set acceptable outcomes and blocker failures for personalization and personalize before running the evaluation.
Evidence to preserve
- Include preference-reversal tests, counterfactual profiles, safety and authority thresholds, exploration requirements, and protected domains where personalization must be limited.
- Preserve the inputs, versions, configurations, raw outcomes, and results for personalization, personalize needed to reproduce work on Testing When Not to Personalize.
- Report results for personalization, personalize 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, define a personalization override policy. Include preference-reversal tests, counterfactual profiles, safety and authority thresholds, exploration requirements, and protected domains where personalization must be limited. Measure both personalization lift and personalization harm.
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Cite this page
Jason Arbon. "Testing When Not to Personalize." Testing AI Knowledge Edition, section 139.
https://jarbon.ai/testing-ai/knowledge/ch139-personalize.html