Semantic Fidelity Lab Series: Drift Detection in AI Systems
A collection of core documents examining how modern AI systems can remain stable in appearance while gradually losing alignment with real-world conditions, user intent, and meaningful feedback.
Across model monitoring, evaluation, and governance, drift is often treated as a narrow technical issue. These documents frame it as a broader system condition involving data, behavior, meaning, incentives, and feedback loops. As AI systems scale and operate through compressed representations, failure can become harder to see. The system keeps functioning, but its connection to reality weakens.
This collection presents drift detection as both a technical and structural problem. It offers frameworks for identifying where alignment is degrading, why standard metrics often miss the signal, and how silent failures emerge across modern AI systems.
Documents — Drift Detection in AI Systems
Detecting Silent Model Drift in LLM Systems SFL 01
Explains how large language models degrade without triggering metric failures, producing outputs that remain fluent but lose alignment with intent, context, and usefulness.
[DOI]
Drift Audit Checklist (AI Systems) SFL 02
A practical checklist for identifying drift across data, performance, behavioral, semantic, and system layers in production AI systems. [DOI]
Model Drift Detection Framework SFL 03
A structured framework for detecting and evaluating model drift across statistical, behavioral, and semantic layers, including methods for monitoring and mitigation. [DOI]
Institutional Drift Detection Framework SFL 04
Extends drift detection beyond AI systems into organizations, showing how systems maintain performance while losing alignment with real-world outcomes. [DOI]
Additional Resources
- Semantic Fidelity Glossary
- Semantic Fidelity Canonical Framework (PDF)
- Failure Modes in LLM Systems
- Preserving Meaning in the Age of Artificial Intelligence
Note: This page is part of the Semantic Fidelity Lab, a focused reference archive on meaning preservation, semantic drift, and evaluation failure in AI systems. This site functions as a reference layer for selected concepts, summaries, and document collections connected to the broader Reality Drift framework by A. Jacobs.