Glossary
Updated
Drift occurs when the statistical properties of the data the model sees in production no longer match the data it was trained on. AI governance requires post-deployment monitoring to detect drift, as a model that was accurate and fair at launch can become unreliable or biased as external conditions change.
Example
A retail AI trained on 2019 spending patterns experiences model drift during the 2020 pandemic because consumer behavior changed so radically that the model's stock predictions became useless.