Verify AI-detected patterns with the same rigor as your own
When AI identifies patterns in your reflective archive, treat AI-generated patterns with the same verification rigor as your own: check counterexamples, count instances, demand 3+ independent occurrences, and test for narrative imposition on random variation.
When This Fires
- After Use AI to analyze routine execution logs… or Share completed reflective writing with AI and ask… when AI has analyzed your reflective data and reported patterns
- When an AI-identified pattern feels compelling and you're tempted to act on it immediately
- When AI analysis of personal data produces "insights" you hadn't considered
- Complements Three-pass pattern spotting (three-pass pattern analysis) with the AI-specific verification layer
Common Failure Mode
Uncritical AI acceptance: "The AI found a pattern of conflict avoidance — it must be true since it analyzed 200 entries." The AI found a narrative it could construct from the data. Whether it's a real pattern or a well-told story requires the same verification you'd apply to your own initial impressions.
The Protocol
(1) When AI reports a pattern, write it down as a hypothesis, not a finding. (2) Count instances: how many independent entries support this pattern? Demand 3+ genuinely separate occurrences (not the same event described in multiple entries). (3) Check counterexamples: search for entries that contradict the pattern. If counterexamples exist, refine or discard the pattern. (4) Test for narrative imposition: "Is the AI constructing a story arc that my data doesn't actually contain?" Check whether the pattern depends on temporal ordering or causal connections the AI assumed. (5) Only promote to "validated pattern" if it survives all three checks. An AI-detected pattern that survives verification is valuable; one that doesn't was a compelling-sounding false positive.