A Governance Lens on Drift, Evidence, and Reliance.

Why Model Drift Is Often Behavioral Drift When people talk about model drift, they usually mean performance metrics changing over time. Accuracy drops. Outputs feel “off.” Alerts fire. Retraining gets scheduled. But in practice, many of these incidents aren’t caused by the model changing in any meaningful way. They’re caused by behavior changing around the model. That distinction matters, because it determines whether monitoring is enough or whether governance is missing. The assumption behind most drift discussions Most drift detection assumes: the model is the primary moving part inputs shift outputs degrade metrics tell the story This works well in controlled environments with stable usage patterns. It breaks down in real systems, where: use cases expand reliance increases decisions compound accountability diffuses The model may be static. The system is not. Behavioral Drift vs. Model Drift By behavioral drift, I mean changes in how a system is used, relied on, and interpreted over