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This framework does not promise truth: scope, limits, and responsibilities

Type: Application

Conceptual version: 1.0

Stabilization date: 2026-01-27

This page is a framing surface.

It establishes the interpretation limits of the “Interpretive risk” hub. Its role is to prevent frequent confusions: implicit compliance promise, magical hallucination reduction, truth certification, or substitution for legal, compliance, or internal governance functions.

What this framework does

This framework aims to make an AI response governable. Concretely, it seeks to reduce the interpretive error space by clarifying:

  • the perimeter within which a response can be considered legitimate;
  • the conditions under which a response becomes challengeable;
  • the mechanisms that transform plausibility into liability;
  • the place of non-response as a legitimate outcome when conditions are not met;
  • the chain of responsibility: source, interpretation, response, usage, impact.

What this framework does not do

This framework must not be interpreted as a promise of result, nor as a compliance label.

  • It does not certify the truth of a response.
  • It does not guarantee the absence of errors, omissions, or ambiguities.
  • It does not automatically “fix” a model.
  • It does not replace legal advice, an audit, quality assurance, or an internal compliance policy.
  • It does not transform an AI system into a legal, medical, financial, or regulatory authority.

Why hallucination reduction is not an acceptable promise

The term “hallucination” is useful as a public symptom, but insufficient for governing a system. A response can be false without “hallucinating” in the strict sense, and a response can be plausible while remaining unenforceable. The central problem is the absence of interpretive legitimacy when the response is produced: perimeter too broad, insufficient sources, unresolved contradictions, absent hierarchy, or obligation to respond despite indeterminacy.

Usage boundaries (what triggers risk)

Interpretive risk increases sharply when AI:

  • responds in place of a human in a commitment context (promise, contract, refund, conditions, guarantees);
  • influences an HR decision (evaluation, sorting, behavioral or implicit inferences);
  • produces a public assertion attributable to an organization;
  • summarizes or interprets contradictory sources by manufacturing surface coherence;
  • fills an informational gap by default instead of flagging indeterminacy.

Legitimate non-response (condition for liability reduction)

Part of governability consists in recognizing that a non-response can be more legitimate than a plausible response. Forcing a system to respond in all circumstances transforms indeterminacy into assertion, and therefore into exposure.

For the complete mechanism: /interpretive-risk/method/.

Responsibility: what must remain explicable

When an AI response crosses a responsibility boundary, the question is no longer “is it plausible?”. The question becomes:

  • what does this response rely on;
  • within what perimeter is it produced;
  • what is excluded and therefore not deducible;
  • why was a non-response not chosen;
  • who assumes the use of this response in a real context.

Related pages (internal linking)

Anchoring

This page acts as a safeguard. It must be read before interpreting the hub as a “solution” or an “offering”. The role of this space is to make responses governable, not to transform AI into truth.