--- name: tai-ch045-bonus-testing-bias-in-productization description: 'Apply chapter 45 of Testing AI, Bonus: Testing Bias in Productization, as a workflow for evaluating AI and non-deterministic systems. Use for test planning, eval design, quality review, release evidence, examples, or coaching related to bonus: testing bias in productization.' --- # Bonus: Testing Bias in Productization Skill name: `tai-ch045-bonus-testing-bias-in-productization` Based on **Testing AI: Engineering Confidence in AI Systems** by **Jason Arbon**. ## Purpose Bias is not finished when the model scores an output. The user interface, ranking metric, latency, and reliability shape what users actually experience. ## Use This Workflow - Identify the AI behavior or release decision being evaluated. - Define realistic cases, slices, unacceptable outcomes, and evidence needed for confidence. - Choose measurements that match the risk: rubric scores, samples, intervals, traces, human review, deterministic checks, or production monitors. - Report uncertainty, severe failures, and decision impact instead of only a pass/fail result. ## Key Guidance Productization turns model output into user experience. That translation creates its own bias because users see interfaces, rankings, delays, omissions, and actions, not raw model scores. For example, a search model may score ten results, but users mostly experience the first few links. A metric like NDCG captures some of that top-heavy experience, but it also encodes assumptions about what kinds of queries matter. ## Apply The Approach Create representative cases, score them with explicit criteria, review severe failures separately, report uncertainty, and connect the evidence to a concrete decision. ## Expert Notes At expert level, productization bias testing combines relevance metrics with operational telemetry and UX inspection. Track metric fit, position bias, latency by segment, fallback behavior, exposure fairness, and whether business rules override model output in ways users cannot see.