Model Drift in AI Systems: A Framework for Detection, Auditing, and Mitigation [pdf]
This paper presents a comprehensive framework for detecting, auditing, and mitigating model drift in AI systems, including concept drift, data drift, and semantic drift, to ensure long-term reliability.
Model Drift in AI Systems: A Framework for Detection, Auditing, and Mitigation [pdf]
Report an error
The correction channel is not available yet. You can copy the article reference below for later.
Correction instructionsSummary only
Under the source content permission, this page provides an editorial summary, key points, and a link to the original article.