Paul Bryant • 9/12/2026

AI Feedback Governance: Control What Becomes Learning

This article explores AI feedback governance, explaining how to control which observations become persistent changes, where those changes apply, and who can authorize them. It distinguishes between positive ratings, verified resolutions, and approved training examples, and proposes routing corrections to appropriate layers while preserving attribution and binding approvals to exact changes. The goal is to prevent local experiences from becoming unreviewed rules for everyone.

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#Machine Learning #llm #ai