realitydriftproject/ai-drift-detection-frameworks

AI Drift Detection Frameworks

A structured collection of frameworks, checklists, and evaluation methods for detecting drift in AI systems, including large language models (LLMs), agent workflows, and production machine learning systems.

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Overview

Most AI systems do not fail abruptly. Outputs remain fluent, structured, and internally consistent.

But systems can degrade while still appearing to work.

This dataset documents a recurring pattern:

Systems preserve coherence while gradually losing alignment with intent, context, and real-world conditions.

Each document focuses on a different layer of drift detection and reframes model degradation as a structural issue rather than a visible failure.

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Contents

• LLM Drift Detection — Why AI Outputs Degrade Without Errors

• AI Model Audit Checklist — Drift Detection in Production Systems

• Model Drift Detection Framework — Machine Learning Systems

• Institutional Drift Detection Framework

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Drift Types Covered

• Data drift (input distribution changes)
• Performance drift (metric-level degradation)
• Behavioral drift (changes in system outputs)
• Semantic drift (loss of meaning or intent alignment)
• System drift (compounding misalignment across workflows)

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Core Idea

Standard evaluation focuses on accuracy and correctness.

This framework focuses on whether systems remain:

• aligned with user intent
• grounded in real-world conditions
• useful over time

Drift often emerges without triggering metrics, making it difficult to detect using traditional monitoring approaches.

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Intended Use

This dataset is useful for:

• monitoring LLMs and production AI systems
• designing evaluation frameworks beyond accuracy
• analyzing agent and multi-step system behavior
• implementing AI governance and risk frameworks
• detecting alignment failures in real-world deployments

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Not Intended For

This is not a benchmark dataset or training dataset.
It is a conceptual and diagnostic resource for understanding system behavior and detecting drift in deployed AI systems.

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Core framework and sources

• Research Library (GitHub): Semantic Fidelity Lab Repository
• Articles & Essays (Substack): Reality Drift
• Primary DOI Record: Figshare Collection
• Concept Glossary: Semantic Fidelity Glossary

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License

CC BY 4.0