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Generated Content Disclosure Gate

Disclosure gate — instantiates Use-Time Source Attribution Calibration

Holds internally- or model-generated content at the point of release until it carries a label saying it was generated and is phrased so a downstream reader can weight it as such.

Generated text reads exactly as fluently as observed fact, and once it crosses to an audience the reader has no way to tell the difference. Generated Content Disclosure Gate is a checkpoint at the release boundary: content whose provenance is internally or model-generated cannot pass until it is labelled as such and phrased so a downstream reader weights it accordingly. Its defining move is that it governs disclosure, not detection or truth — it presumes you already know an item was generated, and it refuses to let that item travel undisclosed. That distinguishes it from mechanisms that find hidden generation and from those that verify a claim; the gate's whole job is to make provenance visible at the moment of handoff.

Example

A bank's customer-content team drafts plain-language explanations of account rules with a language model. The disclosure gate sits just before anything reaches customers. A drafted passage states an early-withdrawal penalty as flat fact, but its provenance tag reads model completion — no advisor confirmed it. The gate blocks release. To pass, the passage must carry an honest label ("AI-assisted; reviewed by a licensed advisor") and be rephrased so any claim the model can't stand behind is hedged or routed for verification first. The customer ultimately sees either a checked statement or a clearly qualified one — never fluent model output dressed as settled policy. Regimes such as the EU AI Act's transparency obligations formalise exactly this expectation for AI-generated content.[1]

How it works

The gate keys on the item's source label rather than its content, which is what lets it act without judging truth. Two conditions must both be met before content crosses the boundary: the generated-provenance label must be present and honest, and the downstream phrasing must match the source's actual warrant — a model completion may not be asserted in the register of an observed fact. It is a release policy, not a verifier: it can demand that a claim be hedged or sent onward for checking, but it does not itself decide whether the claim is correct.

Tuning parameters

  • Gate placement — where in the pipeline the checkpoint sits: draft, review, or final publish. Later placement catches more but blocks flow closer to the deadline.
  • Disclosure granularity — a single blanket "AI-assisted" banner versus per-claim labelling. Finer labelling is more honest but heavier to maintain and easier to let rot.
  • Hedge strictness — how strongly generated claims must be qualified, or routed to verification, before release. Stricter protects the reader; too strict buries genuine content under caveats.
  • Exemption rules — which low-stakes content is allowed to skip the gate entirely, trading throughput against the risk of an unlabelled item slipping out.

When it helps, and when it misleads

Its strength is that it stops fluent generated text from being read as vetted fact, and it moves provenance from something only the author knows to something the reader can see. At a boundary where the source cue would otherwise vanish, it re-attaches one.

It misleads when labels stop meaning anything. Label fatigue — marking everything "AI-assisted" until readers tune the banner out — quietly restores the very confusion the gate exists to remove, and a label is never verification: disclosed is not true. The classic misuse is theatrical, a backwards run — a disclosure banner deployed to offload responsibility ("we told you it was AI") in place of doing the checking that high-stakes content needs. The discipline that keeps it honest is to pair disclosure with proportionate verification where stakes are high, and to keep labels scarce enough to stay legible.

How it implements the components

  • generated_output_source_label — it requires and checks the "generated" provenance label on the item, and refuses release when the label is missing or dishonest.
  • downstream_language_policy — it governs how generated content must be phrased and hedged for the reader who will consume it, so the register matches the source's warrant.

It does not detect whether unlabelled content was in fact generated — that's Hallucination Intrusion Triage and Memory Source Probe — nor verify a claim by fresh observation (Observation Recheck or Replication), nor preserve labels through summarising (Source-Label Preserving Summary Template).

  • Instantiates: Use-Time Source Attribution Calibration — it enforces attribution at the point content is released to a reader.
  • Sibling mechanisms: Hallucination Intrusion Triage · Memory Source Probe · Borrowed Idea Attribution Scan · Chain-of-Custody or Lineage Check · Observation Recheck or Replication · Provenance Lookup Before Publication · Reality Monitoring Checklist · Source Attribution Confidence Rubric · Source Attribution Training Set · Source Confusion Matrix Review · Source-Label Preserving Summary Template

References

[1] Transparency regimes such as the EU AI Act require that AI-generated content be disclosed to the people who encounter it — a real, named instance of the disclosure-at-release logic this gate implements. It is cited here as an example of the norm, not a legal specification. ↩