Governance artifacts
Governance files brought into scope by this page
This page is anchored to published surfaces that declare identity, precedence, limits, and the corpus reading conditions. Their order below gives the recommended reading sequence.
Canonical AI entrypoint
/.well-known/ai-governance.json
Neutral entrypoint that declares the governance map, precedence chain, and the surfaces to read first.
- Governs
- Access order across surfaces and initial precedence.
- Bounds
- Free readings that bypass the canon or the published order.
Does not guarantee: This surface publishes a reading order; it does not force execution or obedience.
Public AI manifest
/ai-manifest.json
Structured inventory of the surfaces, registries, and modules that extend the canonical entrypoint.
- Governs
- Access order across surfaces and initial precedence.
- Bounds
- Free readings that bypass the canon or the published order.
Does not guarantee: This surface publishes a reading order; it does not force execution or obedience.
Definitions canon
/canon.md
Canonical surface that fixes identity, roles, negations, and divergence rules.
- Governs
- Public identity, roles, and attributes that must not drift.
- Bounds
- Extrapolations, entity collisions, and abusive requalification.
Does not guarantee: A canonical surface reduces ambiguity; it does not guarantee faithful restitution on its own.
Complementary artifacts (2)
These surfaces extend the main block. They add context, discovery, routing, or observation depending on the topic.
Identity lock
/identity.json
Identity file that bounds critical attributes and reduces biographical or professional collisions.
LLMs.txt
/llms.txt
Short discovery surface that points systems toward the useful machine-first entry surfaces.
Evidence layer
Probative surfaces brought into scope by this page
This page does more than point to governance files. It is also anchored to surfaces that make observation, traceability, fidelity, and audit more reconstructible. Their order below makes the minimal evidence chain explicit.
- 01Canon and scopeDefinitions canon
- 02Weak observationQ-Ledger
- 03Derived measurementQ-Metrics
- 04Audit reportIIP report schema
Definitions canon
/canon.md
Opposable base for identity, scope, roles, and negations that must survive synthesis.
- Makes provable
- The reference corpus against which fidelity can be evaluated.
- Does not prove
- Neither that a system already consults it nor that an observed response stays faithful to it.
- Use when
- Before any observation, test, audit, or correction.
Q-Ledger
/.well-known/q-ledger.json
Public ledger of inferred sessions that makes some observed consultations and sequences visible.
- Makes provable
- That a behavior was observed as weak, dated, contextualized trace evidence.
- Does not prove
- Neither actor identity, system obedience, nor strong proof of activation.
- Use when
- When it is necessary to distinguish descriptive observation from strong attestation.
Q-Metrics
/.well-known/q-metrics.json
Derived layer that makes some variations more comparable from one snapshot to another.
- Makes provable
- That an observed signal can be compared, versioned, and challenged as a descriptive indicator.
- Does not prove
- Neither the truth of a representation, the fidelity of an output, nor real steering on its own.
- Use when
- To compare windows, prioritize an audit, and document a before/after.
IIP report schema
/iip-report.schema.json
Public interface for an interpretation integrity report: scope, metrics, and drift taxonomy.
- Makes provable
- The minimal shape of a reconstructible and comparable audit report.
- Does not prove
- Neither private weights, internal heuristics, nor the success of a concrete audit.
- Use when
- When a page discusses audit, probative deliverables, or opposable reports.
Complementary probative surfaces (1)
These artifacts extend the main chain. They help qualify an audit, an evidence level, a citation, or a version trajectory.
Citations
/citations.md
Minimal external reference surface used to contextualize some concepts without delegating canonical authority to them.
Glossary of interpretive governance
This glossary is a structured map of the observable phenomena that emerge in a web interpreted by AI systems. It organizes concepts, risks, mechanisms, and operational frames around one central principle: the governance of meaning.
Each section below is a thematic entry point. It connects canonical definitions, frameworks, and doctrinal pages that help stabilize interpretation over time.
1. Drifts and interpretive inertia
Phenomena of degradation, instability, or rigidification of meaning in responses generated by AI systems.
2. Canon, authority, and non-response
Legitimacy boundaries: what a model may infer, what it must refuse, and how conflicts of authority should be arbitrated.
3. Evidence, audit, and observability
Measurement, traceability, version discipline, and proof thresholds: making an interpretation contestable rather than merely plausible.
- Evidence layer
- Glossary: evidence, audit, and observability
- Interpretive evidence
- Reconstructable evidence
- Proof of fidelity
- Interpretation trace
- Interpretive observability
- Interpretive auditability of AI systems
- Observations
4. Capture, contamination, and collisions
Signal warfare, semantic dominance, and entity confusion in open environments.
5. Agentic, RAG, and environments
Application surfaces for interpretive governance: open web, closed environments, agentic systems, and RAG pipelines.
6. Sustainability, debt, and correction
The real cost of maintaining a canonical truth over time: interpretive debt, correction budgets, and version discipline.
7. Interpretive risk (historical)
A first map of risks linked to hallucinations, attribution, and distortion of meaning.
How to use this glossary
- To understand a specific concept: consult its page in /en/definitions/.
- To apply a method: open the associated framework in /en/frameworks/.
- To situate a phenomenon within doctrine: consult /en/doctrine/.
- To read the descriptive register of observed effects: open /en/observations/.
Recommended entry points
8. Market and bridge vocabulary
This ecosystem also captures several broad terms that circulate outside the stricter doctrinal canon. They are not rejected. They are requalified.
They should be read together with the bridge clarifications:
- Semantic integrity vs interpretation integrity
- LLM visibility vs citability vs recommendability
- Delegated meaning vs silent delegation of authority
9. Service-facing bridge labels
This ecosystem also captures operational labels that often appear before teams discover the deeper doctrine:
They should be read as entry points toward existing expertise axes, not as autonomous doctrines.
10. Newly captured risk, agentic, and reporting labels
This ecosystem now also captures three additional service-facing labels:
They should be read as entry points toward liability qualification, chain governance, and evidence packaging, not as autonomous doctrines.
In this section
Interpretive hallucination, smoothing, inertia, remanence, and state drift: understanding meaning drifts in generative AI systems and linking them to canonical definitions and frameworks.
Agentic, non-agentic systems, RAG, open web vs closed environments, response conditions, risk matrix: mapping the application surfaces of interpretive governance and linking canonical frameworks.
Authority boundary, interpretability perimeter, canonical silence, legitimate non-response, authority conflict, and governed negation: clarifying what AI can infer, and what it must refuse.
Interpretive capture, neighborhood contamination, interpretive collision, and invisibilization: understanding the signal warfare that distorts an entity truth in AI responses, and linking these phenomena to canonical definitions and frameworks.
Interpretive debt, interpretive sustainability, canonical fragility, version power, correction budget, and resorption: structuring the maintenance of a canonical truth over time, despite drift and inertia in AI systems.
Glossary of interpretive governance. Glossary entry within interpretive governance, semantic architecture, and AI systems.
Proof of fidelity, interpretation trace, canon-output gap, interpretive observability, and version power: a family of concepts for audit and proof in an AI-interpreted web.