For Heads-of · Practitioner
Performance Regression
A decline in model performance over time, typically caused by data distribution shifts, concept drift, or changes in the environment.
- performance regression
- degradation
- monitoring
- maintenance
Causes
Real-world data may differ from training data; user behavior changes; new categories of inputs appear; or feedback loops alter the environment the model operates in.
Detection
Continuous monitoring of model performance metrics reveals regressions. Disaggregated metrics by group, time period, or input type help identify specific causes.
Response
Options include retraining on current data, updating features, or adjusting decision thresholds. Critical regressions may require immediate model replacement or fallback to human decision-making.
Related risks and controls
AI model drift and degradation
The register entry for the degradation this measures.
Untested AI failure scenarios
A regression in a category nobody tests is one nobody detects.
Disparate impact monitoring gap
Fairness regresses independently of accuracy, and separately from it.
AI system monitoring and logging
Supplies the measurement without which a regression is an anecdote.
AI incident response and rollback
What a critical regression triggers.