Research Paper · Data & AI
Evidence Coverage and Traceability
Knowing What Supports a Conclusion
A deterministic evidence architecture for preserving and inspecting the governed relationships that actually support AI-generated conclusions.
Northline Topic
Evidentiary Authority & Traceability
Research on preserving evidence boundaries, provenance and traceability across multistage AI workflows and corrective generation.
This collection focuses on a practical governance question: how can an AI system transform evidence without quietly expanding the authority of what that evidence supports?
Research Publications
Formal Northline research documenting the evidence, architecture, methods, limitations and open questions relevant to this topic.
Explore Research Publications →Research Note · Data & AI
Preserving Evidentiary Authority During Corrective Regeneration in Multistage LLM Systems
An Applied Engineering Case
An implementation-tested approach to correcting an unauthorized generated claim while preserving the evidence, validated context and acceptance rules governing it.
Research Paper · Data & AI
Beyond the Model
A Governed Architecture for Consequential AI Systems
Consequential AI requires an architecture that governs evidence, artifacts, evaluation, authority and learning around the model.
Practitioner Insights
Applied perspectives connecting the topic to executive decisions, implementation and operating practice.
Explore Practitioner Insights →Practitioner Insight · Governance
It's Just RAG. Except That Isn't the Interesting Part.
RAG helps determine what knowledge a model should receive. Governed AI must also determine whether the model should reason at all, whether another intervention is worthwhile, which state remains authoritative, and what the system should learn from the result.
Practitioner Insight · Governance
From Decision History to Governed Intelligence
How governed AI systems can learn from decision history without allowing accumulated experience to silently become authority.