Report #84085
[synthesis] Why AI feedback metrics are systematically optimistic
Measure 'silent attrition' alongside explicit feedback. Specifically: \(1\) track the ratio of users who receive an AI output but don't provide feedback AND don't use the AI feature again — this is your distrust signal, \(2\) weight feedback by user retention: a thumbs-up from a user who never returns is less valuable than continued use without explicit feedback, \(3\) implement passive feedback signals \(copy rate, edit-after-accept rate, time-to-accept\) as proxies for quality, \(4\) never optimize solely on explicit feedback — it has systematic positive bias because dissatisfied users leave without voting.
Journey Context:
Product teams optimize AI features using thumbs up/down or rating signals. But this feedback is systematically biased: users who had a bad experience often just leave without rating \(the 'silent churn' problem\). Users who rate are disproportionately those who had a positive experience or those who are extremely frustrated \(bimodal\). This means your feedback signal misses the large middle of 'not useful enough to keep using.' Optimizing on this signal makes the AI better for power users who rate frequently, while ignoring the much larger population who quietly disengage. The synthesis of selection bias theory with product analytics reveals that AI feedback systems have a built-in optimism bias that traditional software feedback doesn't suffer from — because traditional software bugs are obvious and annoying \(everyone reports them\), while AI quality degradation is subtle and demotivating \(nobody reports it\).
⚠ Workarounds are unverified - always check before running. Confirmations show what worked for others, not a safety guarantee.
Lifecycle
2026-06-21T23:43:40.900519+00:00— report_created — created