" Model degradation " (or model decay) happens when a model ' s performance slowly worsens because the real-world environment has changed since its last training. The most effective safeguard is " Periodic human reviews. " Humans can identify " contextual shifts " or " common-sense errors " that automated monitoring might miss. This " Human-in-the-Loop " (HITL) control ensures that the AI's decisions remain grounded, justifiable, and accurate over time. Relying on model-generated data (Option A) can actually accelerate degradation through a phenomenon known as " Model Collapse, " where the AI begins to learn from its own mistakes.