Binding individual input to final text
An optional composition timeline and per-section source-annotation affordance that lets a submitter (but never requires them to) show how a piece was built and where each part came from.
Tools that assemble polished text from multiple contributors, AI assistance, or a campaign template are now widely available, so the final submission increasingly obscures the contribution of individual thought. As that becomes the norm, decision-makers cannot tell genuine deliberation from assembly-line production by reading the submitted text alone.
Government needs to be able to tell genuine deliberation from assembly-line production, so a decision-maker can read individual thought in a submission that may have been shaped by multiple contributors, AI assistance, or a campaign template. The signal has to set a disclosure norm rather than depend on technical enforcement, because a determined submitter can defeat any detection.
'Show your working' requirements risk recreating academic peer review, where detailed process documentation favors well-resourced, institutionally supported submitters over individuals writing from lived experience who should not need to document a 'methodology.' Make these features available and encouraged but never mandatory, and design the UX so the simplest truthful declaration ('I wrote this myself based on my own experience') is the fastest path through the form.
Because disclosure works here as a norm rather than as enforcement, the response is a voluntary 'annotate your sources' control that is encouraged and visible, while the unannotated 'I wrote this myself' path stays the fastest route to submit. Showing your working is something a submitter chooses to do, and choosing not to costs them no extra steps.
No surface has been built yet; the approach above is the brief for one.
Frontier. Letting a submitter show how a piece was built is a response no government consultation platform yet offers. The building blocks (document timelines, contribution attribution, source annotation) exist in collaborative editing tools but have not been adapted to the submission intake context. The academic disclosure model is the closest analogue, but it works after the fact in a published paper rather than at the point of submission, so the response still has to be designed for intake.
Document audit trails and version history. Modern document management systems record a sequential history of every action: upload, edit, share, approve. The distinction between version history (what changed in the content) and audit trail (who accessed, viewed, shared, or approved it) is well-established, and immutable event logs create a defensible chain of custody. Platforms like Google Docs, SharePoint, and Notion maintain per-character authorship attribution. See Folderit and Audit Trails for Accountability in LLMs.
AI disclosure verb patterns. Emerging UX research suggests disclosure labels with specific verbs ("Summarized with AI," "Rewrote with AI," "Translated with AI") are more informative than generic "AI" markers. The Shape of AI pattern library documents disclosure patterns communicating the nature and extent of AI involvement at a granular level. See Shape of AI.
Academic "use of AI" statements as structured metadata. The emerging standard requires tool name and version, access date, specific task, and which sections were affected, converting a binary disclosure into structured process metadata ("I used GPT-4o via ChatGPT on 15 January 2026 to generate an initial literature summary in Section 2, which I then substantially revised"). The shift from binary to structured is the key UX insight. See InstaText.
Interoperable architecture for AI agent identity delegation. A 2025 research paper proposes binding AI agent actions to the delegating human's verifiable credentials through blockchain-based audit trails, creating a cryptographic "show your working" chain from principal (human) through agent (AI) to action (submission), using W3C DIDs and Verifiable Credentials. See the arXiv paper.
Medium transferability. The document audit trail model works when drafting happens within a controlled environment. A consultation platform could implement a lightweight feature: a composition timeline ("Draft started 14:32; 847 words typed over 45 minutes; 312 words pasted at 15:05"), optional source annotation ("this section draws on [organization]'s position paper"), and a contribution breakdown for organizational submissions.
The critical limitation is that "show your working" is voluntary: a submitter using AI can simply type the AI-generated text rather than pasting it, defeating paste detection. The pattern works as a norm-setting device (making disclosure culturally expected) rather than technical enforcement. The legal profession model shows this can work: once disclosure is expected, non-disclosure becomes the anomalous behavior that attracts scrutiny.
'Show your working' targets the provenance of inbound submissions, so on its own it does not constrain the agency side. Applied to the agency's own reasoning, the same principle (an inspectable chain from each input to the final output) is precisely what exposes an unjustified leap, such as deriving a fortnightly liability from annual data, before it stands as fact.