File size: 4,299 Bytes
fd49a93 a577166 9cf45c5 a577166 9cf45c5 a577166 9cf45c5 a577166 fd49a93 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 | ---
license: cc-by-4.0
task_categories:
- text-generation
- question-answering
- text-retrieval
language:
- en
tags:
- llm
- ai
- model-drift
- drift-detection
- evaluation
- ai-alignment
- monitoring
- mlops
- agents
- ai-governance
- ai-risk
- model-evaluation
- semantic-drift
- system-analysis
- reliability
pretty_name: 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.
---
## 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.
---
## Contents
- [LLM Drift Detection — Why AI Outputs Degrade Without Errors](./llm-drift-detection-why-ai-outputs-degrade-without-errors.pdf) \[[Academia.edu](https://www.academia.edu/166169350/Detecting_Silent_Model_Drift_in_LLM_Systems_Why_AI_Outputs_Degrade_Without_Errors)\] \[[GitHub](https://github.com/therealitydrift/semantic-fidelity-lab/blob/main/05_Drift_Detection_Frameworks/detecting-silent-model-drift-llm-systems.pdf)\]
- [AI Model Audit Checklist — Drift Detection in Production Systems](./ai-model-audit-checklist-drift-detection.pdf) \[[Academia.edu](https://www.academia.edu/166169323/AI_Model_Audit_Checklist_Drift_Detection_in_Production_Systems)\] \[[GitHub](https://github.com/therealitydrift/semantic-fidelity-lab/blob/main/05_Drift_Detection_Frameworks/drift-audit-checklist-ai-systems.pdf)\]
- [Model Drift Detection Framework — Machine Learning Systems](./model-drift-detection-framework-machine-learning.pdf) \[[Academia.edu](https://www.academia.edu/166169353/Model_Drift_Detection_Framework_Evaluating_and_Mitigating_Drift_in_Machine_Learning_Systems)\] \[[GitHub](https://github.com/therealitydrift/semantic-fidelity-lab/blob/main/05_Drift_Detection_Frameworks/drift-evaluation-framework-ai-systems.pdf)\]
- [Institutional Drift Detection Framework](./institutional-drift-detection-framework.pdf) \[[Academia.edu](https://www.academia.edu/166169346/Institutional_Drift_Detection_Framework_Diagnosing_Organizational_Misalignment)\] \[[GitHub](https://github.com/therealitydrift/semantic-fidelity-lab/blob/main/05_Drift_Detection_Frameworks/organizational-drift-detection-framework.pdf)\]
---
## 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)
---
## 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.
---
## 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
---
## 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.
---
## Core framework and sources
- Research Library (GitHub): [Semantic Fidelity Lab Repository](https://github.com/therealitydrift/semantic-fidelity-lab)
- Articles & Essays (Substack): [Reality Drift](https://therealitydrift.substack.com)
- Primary DOI Record: [Figshare Collection](https://figshare.com/)
- Concept Glossary: [Semantic Fidelity Glossary](https://offbrandguy.com/semantic-fidelity-glossary/)
---
## License
CC BY 4.0 |