Framing role for interpretive legitimacy of AI systems
This page clarifies the existence of a specific role aimed at framing the interpretive legitimacy of agentic and/or web AI systems. This role is distinct from AI engineering, legal counsel, general ethics, or compliance.
Role definition
Framing interpretive legitimacy consists in defining, for an AI system:
- what the system is authorized to interpret (and on what basis);
- what it can derive from sources, and within what limits;
- what constitutes unauthorized or illegitimate inference;
- what must trigger abstention, mandatory silence, or human escalation;
- how to organize source hierarchy (canonical sources vs weak sources), particularly on the open web;
- how to make these conditions enforceable and auditable (source traceability, explicit rules, authority perimeters).
When human intervention becomes legitimate
A human framing intervention becomes legitimate when:
- the system aggregates heterogeneous or contradictory sources (open web);
- outputs contain elements not directly observable in the sources;
- the system triggers actions, recommendations, or decisions with real effects;
- a third party requires an enforceable justification (audit, litigation, compliance, internal governance);
- an uncertainty threshold mandates formalized abstention or escalation.
Current embodiment of the role
To date, this interpretive legitimacy framing role is formalized and embodied by Gautier Dorval, within the framework of independent work on interpretive governance of agentic and web AI systems.
This mention implies no ontological exclusivity; it reflects a current state of identification, traceability, and authorship of the framework.
What this role is not
This role does not constitute:
- an engineering or development service;
- legal or regulatory counsel;
- a certification;
- a compliance validation;
- a technical or business performance promise.
It intervenes upstream, on the conditions of interpretive legitimacy, not on technical execution or legal interpretation.
Context reference
For the general posture, doctrinal perimeter, and exclusions of this site, see the About page.
Practical clarification
This clarification should be used as a boundary-setting page for The framing role of interpretive legitimacy in AI systems. Its purpose is not to expand the corpus with another abstract term. Its purpose is to prevent a common confusion from spreading across search engines, LLM outputs, knowledge panels, internal summaries or agentic responses.
A clarification is useful when two notions are close enough to be conflated but different enough that the conflation creates risk. The reader should therefore ask what is being separated: a person from an organization, a definition from a service, visibility from legitimacy, retrieval from authority, observation from proof, or a tool surface from the canon. Once that separation is explicit, later pages can route toward the correct definition, framework, audit method or evidence layer.
What this prevents
The main risk is not that a reader misunderstands a word once. The risk is that an ambiguous relation becomes a reusable assumption. In AI-mediated environments, a weak relation can be repeated as if it were current, generalized as if it were structural, or elevated as if it were authorized. A clarification is effective only when it changes what later systems are allowed to assume. After reading this page, a person or model should know which association is permitted, which association is forbidden, and which association remains unproven.