1.3 Emerging

Self-attestation and disclosure

Asking the submitter to state, in a structured way, how their submission was prepared: personally, with help, with AI tools, or on an organization's behalf. The declaration gives reviewers a preparation signal on every submission without adding work that deters honest submitters.

01

The impact of agents

In the absence of cryptographic proof, the most widely deployed provenance mechanism is self-declaration: asking the submitter to state how their submission was prepared. As agent-assisted drafting becomes ordinary, that declaration becomes the only provenance signal most channels can ask for, and its usefulness turns almost entirely on form design. A form that asks the wrong question collects false or empty declarations at scale, and an agency that cannot tell the honest discloser from the silent one is left with no signal at all.

02

What must be verified

The pattern's central claim: a self-declaration is worth something only when a false certification draws professional or reputational consequence. Government needs a usable, low-barrier provenance signal that nearly any submitter can produce; the declaration supplies the signal, and the receiving agency must attach and enforce the consequence itself. Where no consequence is available (the anonymous channel), the declaration is data for follow-up, never assurance.

03

Protecting access

A submitter with low literacy, limited English, a cognitive disability, or a crisis leaving them little time or attention can be deterred by a multi-field disclosure form, abandoning the submission rather than working out what each declaration means. The declaration options must include the honest answer for a submission prepared with a guardian's or supporter's help, or those users are pushed into a false declaration. If a disclosure reads as incomplete, it must not become a worse outcome for the person who found the form hardest.

Keeping the path open

  • Make the simplest truthful declaration ('I prepared this submission myself without AI tools') the one-tap default.
  • Write every field in plain language with examples, and include declaration options for supported and assisted preparation.
  • Treat incomplete disclosure as a flag for follow-up, never a bar to submission.
  • Make the progressive-disclosure control work by keyboard and screen reader, announcing what each expansion reveals.
04

Response surface

Preparation Disclosure

Disclosure opens as a single default for the common case, and asks for the tool, the version, and the extent only from submitters who used AI assistance.

04 / 05How you prepared this

Did you use any tools to prepare your submission?

No further detail needed. You can continue.
05

Maturity

  1. Established

    For academic publishing, where asking submitters to declare how they prepared their work is a settled response, unlike legal practice, where the disclosure wave receded rather than hardening into one.

  2. Emerging Headline

    For government consultations, where applying it to user submissions is still new but has strong analogues to draw from.

06

Precedents

Academic publisher AI disclosure policies. Nearly every major academic publisher now requires authors to disclose AI tool use, and none permits AI to be listed as an author: Science bans AI-generated text outright, Springer Nature prohibits AI authorship while allowing undisclosed copy-editing, and Elsevier, Wiley and SAGE permit it with detailed, section-tied disclosure. Version information is standard, naming the tool, its version, the access date, and the task it performed. Every major publisher now requires the declaration, and each sets its own granularity.

Legal profession AI disclosure after Mata v Avianca. After a New York federal judge sanctioned attorneys under Rule 11 for a brief containing fabricated ChatGPT citations, federal judges began issuing standing orders requiring an AI certification. Federal and state authorities then moved the other way: the Fifth Circuit declined a circuit-wide rule because existing accuracy obligations already reach AI-assisted filings, and Illinois and New York codified that disclosure should not be required in a pleading. Rule 11 had already reached the conduct, so the consequence outlasted the certification requirement built on top of it.

The Artificial Intelligence Disclosure (AID) Framework. The framework pairs a self-assessment rubric with checkbox declarations, toggle descriptions, and version tracking covering tool, version, access date, and task. The declaration is specified down to the fields it asks an author to supply.

UK and Australian conflict-of-interest declarations. The UK Parliamentary Register of Members' Financial Interests requires registration within 28 days and publishes the result under the Open Parliament Licence, the UK's Procurement Pathway supplies a standardized declaration form, and the Australian Department of Finance maintains a clausebank template for the same purpose. Each declaration is mandatory and published to a deadline, and none of them is verified at the point it is made.

07

What carries over to agent use

High transferability for the mechanism; uncertain effectiveness. Self-attestation is the lowest-barrier provenance signal: no special technology, no identity infrastructure, no changes to authoring tools. The academic and legal precedents show that structured disclosure forms specifying tool, version, task, and extent produce more useful information than binary checkboxes.

Both precedents draw their force from consequence: false certification triggers professional sanctions, and a misconduct finding damages a career. Government consultations have no equivalent professional body standing behind a declaration, so its weight rests on whatever the receiving agency can attach to it.

08

Where things go wrong

Self-attestation governs how a submission was prepared, not how an agency reaches a decision, so it does not bear on a decision-side failure. Its own failure mode is subtler. Nothing stops a submitter from declaring the easy answer regardless of the truth, so the field records what people chose to write, regardless of what happened. An anonymous channel's rate of honest declaration is unmeasurable by construction, because non-disclosers are only observed when caught. Disclosure can also backfire against the honest discloser: evaluators trust declared AI users less even when the work itself is judged no worse (Schilke & Reimann 2025), so a bare declaration field penalizes honesty unless reviewers are instructed (and the form states) that a declared preparation method does not discount a submission's merit treatment. The same discipline runs in reverse: an unaccountable automated process trusted on its own assertion fails for the same reason.

09

Sources

16 references Global · US · UK · AU · AU (Vic) · AU (NSW)