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.
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.
Interpretation policy
/.well-known/interpretation-policy.json
Published policy that explains interpretation, scope, and restraint constraints.
- Governs
- Response legitimacy and the constraints that modulate its form.
- Bounds
- Plausible but inadmissible responses, or unjustified scope extensions.
Does not guarantee: This layer bounds legitimate responses; it is not proof of runtime activation.
Q-Layer in Markdown
/response-legitimacy.md
Canonical surface for response legitimacy, clarification, and legitimate non-response.
- Governs
- Response legitimacy and the constraints that modulate its form.
- Bounds
- Plausible but inadmissible responses, or unjustified scope extensions.
Does not guarantee: This layer bounds legitimate responses; it is not proof of runtime activation.
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
- 02Response authorizationQ-Layer: response legitimacy
- 03Evidence artifactcommon-misinterpretations.json
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-Layer: response legitimacy
/response-legitimacy.md
Surface that explains when to answer, when to suspend, and when to switch to legitimate non-response.
- Makes provable
- The legitimacy regime to apply before treating an output as receivable.
- Does not prove
- Neither that a given response actually followed this regime nor that an agent applied it at runtime.
- Use when
- When a page deals with authority, non-response, execution, or restraint.
common-misinterpretations.json
/common-misinterpretations.json
Published surface that contributes to making an evidence chain more reconstructible.
- Makes provable
- Part of the observation, trace, audit, or fidelity chain.
- Does not prove
- Neither total proof, obedience guarantee, nor implicit certification.
- Use when
- When a page needs to make its evidence regime explicit.
The wrong reduction of AI risk
AI risk is still too often reduced to factual error.
The system invents a claim. The model hallucinates. The answer misquotes a source. The correction reflex then becomes narrow: improve retrieval, add citations, refresh the corpus, or force the model to say less.
Those corrections matter. They do not exhaust the problem.
A generated answer can be factually plausible, stylistically careful, and visibly sourced while still moving the authority that should govern meaning. The risk is not that the answer is obviously false. The risk is that the answer becomes the place where meaning is silently redefined.
Authority displacement
Authority displacement occurs when the governing locus of meaning moves from the legitimate source to another surface:
- from the person to the system’s emotional interpretation;
- from the official statement to a recomposed summary;
- from the canonical definition to an approximate paraphrase;
- from the source perimeter to a generalized answer;
- from a legitimate non-response to a weak completion.
This is why interpretive authority matters. It names the question that factual accuracy alone cannot answer: who has the right to define, bound, correct, or suspend the meaning of the object being discussed?
Why citation does not solve the issue
Citation can make a source visible without restoring its authority.
A cited source may still lose its object. It may still lose its perimeter. It may still be framed by a third party. It may still be used beyond its modality, date, or scope.
That is why the site separates citation from understanding, and provenance from proof of fidelity. A sourced answer can still be interpretively illegitimate.
The missing test
The key test is not only:
Is the answer true?
It is also:
Did the answer preserve the authority that governs this meaning?
When the answer cannot preserve that authority, the right output is not a more confident answer. It is clarification, qualification, or legitimate non-response.
External trigger
The Springer Nature Communities discussion of interpretive authority in AI governance is useful because it makes the same shift visible in an affective domain: the issue is not only whether AI is correct, but whether it becomes authoritative over the interpretation of a person’s internal state.
This site extends the same logic to public statements, sources, entities, doctrines, and response legitimacy.
Closing rule
The next layer of AI governance is not only about preventing wrong answers. It is about preserving the legitimate locus from which meaning may be defined.