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The process of adjusting institutional reliance on AI systems following significant changes—model updates, retraining, vendor transitions, or failure events. Trust recalibration is not merely technical validation but institutional revalidation of delegation appropriateness.
After a model update, technical teams validate accuracy metrics. Trust recalibration goes further: reviewing whether the update changed which decisions the model influences, whether human override patterns should change, and whether accountability structures remain appropriate.
Section 5.3: Learning Asymmetry Management