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AI Systems and Reality Drift
A collection of short papers on how AI systems, metrics, evaluations, and institutions can remain coherent while gradually losing contact with the realities they are meant to represent.
These papers connect AI evaluation, model monitoring, hallucination, grounding, RAG faithfulness, Goodhart’s Law, KPI distortion, semantic fidelity, and institutional drift through a shared pattern:
Systems keep working. Outputs remain coherent. Metrics may improve. But alignment with reality weakens.
Included Papers
AI Evaluation, Benchmarking, and Reality Drift
Why measured AI performance can diverge from real-world capability.
AI Reliability, Model Monitoring, and Reality Drift
Why AI systems can appear trustworthy while monitoring and governance lose sensitivity to real-world behavior.
Goodhart’s Law, Campbell’s Law, KPI Decay, and Reality Drift
Connects Goodhart’s Law, Campbell’s Law, KPI decay, proxy optimization, and metric gaming to Reality Drift.
Hallucination, Distribution Shift, Alignment Failure, and Reality Drift
Connects hallucination, distribution shift, model collapse, concept drift, data drift, and alignment failure as related forms of AI drift.
Hallucination, Grounding, Faithfulness, and Reality Drift
Explains why fluent AI outputs can remain convincing while their grounding weakens.
Institutional Drift, Mission Drift, and Organizational Reality Drift
Explains why organizations can remain functional while losing contact with their purpose.
The Map-Territory Distinction and Semantic Fidelity
Connects the map-territory distinction to semantic fidelity and Reality Drift.
Performance Metrics, KPI Distortion, and Campbell’s Law
Explains why indicators stop reflecting the realities they were built to measure.
Principal-Agent Problems, Incentive Misalignment, and Reality Drift
Explores how delegated systems can continue operating while drifting away from the goals they were created to pursue.
Retrieval-Augmented Generation, Grounding, Faithfulness, and Reality Drift
Why RAG systems can retrieve relevant information while still losing or distorting meaning during generation.
Semantic Alignment, Semantic Preservation, and Reality Drift
Explains how meaning changes across summarization, translation, retrieval, embedding, and generation.
Semantic Fidelity, Meaning Preservation, and AI Interpretation Failure
Defines semantic fidelity as the degree to which meaning is preserved as information is transformed.
Silent Drift in AI Systems
Describes AI failure modes where outputs remain fluent and useful while alignment with evidence, intent, objectives, or real-world conditions gradually weakens.
Core Pattern
Across these papers, the same structure appears:
A real-world condition is represented.
The representation becomes easier to measure, manage, or optimize.
The system begins responding to the representation.
The representation remains coherent while its connection to reality weakens.
This is Reality Drift.
Intended Use
This collection is intended for thinking about:
- AI evaluation beyond benchmark performance
- semantic fidelity and meaning preservation
- RAG, grounding, and faithfulness failures
- model monitoring and AI reliability
- proxy optimization and KPI distortion
- institutional and organizational drift
- system-level alignment after deployment
Not Intended For
This is not a benchmark dataset or training corpus.
It is a conceptual and diagnostic reference collection.
Context
Part of the Semantic Fidelity Project and the broader Reality Drift Framework by A. Jacobs.
Core Framework and Sources
Research Library (GitHub): Semantic Fidelity Project Repository
Articles & Essays (Substack): Semantic Fidelity Project Substack
Concept Glossary: Semantic Fidelity Glossary
License
CC BY 4.0
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