Interpretive risk
Who is responsible when AI answers without legitimacy?
Published 2026-02-26 · Based on work from 2026-01-27 (source) · French version · Topic hub: Interpretive risk · Position: Doctrinal note · Lane: Governance boundaries and decision risk
The responsibility question often arrives too early. Before asking who will bear the cost of error, an institution must ask where the answer drew its legitimacy from: which source carried authority, what scope authorised the response, and which actor could actually stand behind it. Without that chain, responsibility disperses.
Reading markers — Interpretive risk- Reconstruct legitimacy before assigning responsibility.
- Identify the actors who publish, authorise, relay, or execute.
- See why a disclaimer alone is not enough.
Legitimacy comes before liability
An answer may look convincing without being legitimate. It may reuse plausible fragments while lacking a sufficient basis to assert, recommend, classify, or exclude.
Assigning responsibility without reconstructing legitimacy means looking for a culprit before identifying the authority actually mobilised by the output.
Reconstructing the authority chain
At minimum four levels have to be separated: the canonical source, secondary sources, the system that arbitrates between them, and the actor that exposes or operationalises the answer.
When those levels are blurred, responsibility fragments. Each layer points to another layer to shed the burden.
- who publishes the canonical statement
- who was allowed to supplement or summarise
- who exposed the answer to a third party
Why the disclaimer fails
A generic warning does not neutralise a detailed answer that still looks authoritative. The more precise the output, the more likely users are to assign decision status to it.
A disclaimer is at best a rhetorical brake. It does not replace source hierarchy, scope boundary, or explicit non-answer rules.
What must be possible to own
A defensible organisation does not only try to reduce error. It makes legible what it accepts to stand behind, what must remain conditional, and what should never have been asserted.
That legibility is what later makes a meaningful discussion of responsibility possible, rather than a vague contractual argument.
Publication boundary
InferensLab publishes doctrine, limits, vocabulary, and machine-readable signals here. Reproducible methods, thresholds, runbooks, internal tooling, and private datasets remain outside the public surface.
Topic compassContinue from this note
This note belongs to the Interpretive risk hub. Use this topic when the output has consequences: legal exposure, false certainty, silent misclassification, decision risk, and interpretive debt.
Lane: Governance boundaries and decision risk · Position: Doctrinal note · Active corpus: 16 notes
Go next toward
- AI governance — Policies, boundaries, proof obligations, change control, and machine-first publication.
- Interpretation phenomena — Recurring phenomena: fusion, smoothing, invisibilization, coherent hallucinations, etc.
- Agentic era — Agents, delegation, non-answers, safety, and proxy governance.
Source lineage
This note builds on a post published on gautierdorval.com (2026-01-27). This InferensLab edition reframes the material for institutional legibility, public doctrine, and machine-first indexing.
Related machine-first surfaces
