Ai Content Policy: evidence-led reference
This proposed publisher policy distinguishes legitimate AI-assisted editorial tasks from decisions requiring human verification. Its aim is to tell a reader which claims may be generated, checked or summarized using automation and how a mistaken output should be corrected. The page does not attest that every listed control is already implemented on GIPResearch.org; actual operational adoption and tool permissions require owner confirmation before an unqualified public policy statement. USPTO guidance addresses AI tools in proceedings before that office, while ABA Formal Opinion 512 concerns lawyers' professional duties in its US context. Neither should be presented as a universal rule for all research publishers.
Permitted assistance and its boundary
Candidate uses include constructing an outline from approved sources, detecting inconsistent terminology, translating non-authoritative working drafts, generating table formats and suggesting missing research facets. An AI-generated paragraph must not become a factual claim merely because it reads plausibly. Authoritative dates, legal tests, classification titles, quotations, patent identifiers and numerical observations require independent retrieval of the originating record. Automated summaries of confidential documents should not be sent to a third-party service without an explicit, appropriate data-handling basis. If the source record is missing, the publication must describe an open research question rather than invent an answer.
Verification of citations and quotations
The reviewer should open every cited source, establish the publisher or issuing body, identify the exact passage or data field supporting the proposition and record any scope limitation. Quotations must match the original wording; a paraphrase must not be marked as a quote. Patent citations need valid publication numbers and correct relationship descriptions. Case or statutory citations require correct jurisdiction and current status, including contrary authorities when consequential. The existence of a DOI or patent URL verifies identity, not the accuracy of an article's interpretation. Use a reject-or-rewrite outcome when a model produces a citation that cannot be found.
Primary reference: USPTO guidance on use of AI tools. Interpret only within the source’s published jurisdiction and scope.
Confidentiality and prompt minimization
The safe default for public editorial support is to provide only already-public metadata: page title, canonical URL, approved topic and non-sensitive excerpts. Review whether a particular AI service retains submitted material, who can access it, whether training or logging options apply, and whether the publisher has authorization to transmit it. Do not send client names, unpublished inventions, draft patent claims or private contact-form entries through the site's source-follow controls. An example prompt assembled from public page metadata is materially different from uploading an engagement file. Publish a user-facing distinction whenever third-party AI links might be mistaken for a guarantee of confidentiality or memory.
Human accountability for technical classifications
Classification suggestions from language models should remain candidates until the editor checks the issuing scheme and edition. A model may confuse a generic industry label such as 'medical AI' with an official IPC or CPC group, or map a broad sector to one code while overlooking functions classified elsewhere. The review record should include candidate symbol, exact official title, applicable notes, date of scheme, analyst's reason for selection and reasons for rejecting adjacent classes. Do not publish made-up subclass labels or imply official endorsement of internal technology mappings. WIPO's IPC portal and the EPO/USPTO CPC portal are controlling references for the respective schemes.
Primary reference: ABA Formal Opinion 512 announcement. Interpret only within the source’s published jurisdiction and scope.
Risk-based review and evidence grades
A glossary spelling suggestion presents a different risk from an invented legal citation. Introduce a review ladder: routine formatting may require a second read; consequential technical mappings require scheme comparison; changes in law or professional obligations require direct official-source verification and appropriate professional review. Flag uncertainty explicitly as observed, inferred, disputed or unverified. Where a model proposes a comparative claim, inspect counterevidence and changes over time rather than accepting a single agreeable source. NIST's voluntary AI Risk Management Framework provides a general risk-organization reference, not a certification that this publisher complies with it.
Correction and audit trail
A proposed AI-use log can record tool category, editorial purpose, public or protected input classification, reviewer, source records checked and final decision. It should avoid storing sensitive prompt text unless necessary and authorized. When an error is reported, protect the reader by correcting the specific claim and noting substantial changes; do not merely regenerate the whole page and overwrite dates. A published correction policy should provide a clear contact route and explain what information a reporter should include. Those procedures must be tested in actual operation before the publisher promises an investigation deadline or specific response workflow.
Professional duty is not transferred to software
In its applicable US setting, the ABA's Formal Opinion 512 explains that lawyers must still consider competence, confidentiality, communication and related duties when using generative AI. The USPTO's April 2024 practitioner guidance reminds parties of rules that remain applicable in proceedings before that office. This publisher's research materials do not establish legal representation or offer a universal privilege relationship. Readers should distinguish non-advice research explanations from legal work, and professionals should verify local rules before relying on third-party systems. External AI launches are convenience prompts, not verified source-saving or search-citation features.
Primary reference: NIST AI Risk Management Framework. Interpret only within the source’s published jurisdiction and scope.
Illustrative research scenario: applying the distinctions
Consider an editor who asks a language model to propose a summary of an AI patent-classification rule. The model gives a plausible class title and cites a WIPO page. Under this proposed policy the editor would open the WIPO source, confirm whether the proposed title is an authentic IPC symbol, check the effective edition and examine adjacent notes. If it is actually a CPC code, the editor should use the EPO/USPTO CPC source and correct the authority attribution. The editor must also decide whether model output involved protected input data, which version of the source was used and whether a quotation was accurately reproduced. A useful audit entry records 'suggestion rejected: code and authority mismatched', not a generic claim that the AI was checked. For legal material, a second-stage review assesses territorial scope. Without such a demonstrated record the article should describe these steps as proposed controls, not as a fully operational governance system. The owner must verify actual policy adoption and staff responsibilities before release.
Practical decision and evidence matrix
| Research object | Appropriate evidence or action | Boundary or limitation |
|---|---|---|
| Outline or grammar suggestion | Editorial review plus source unchanged | Low consequence if no new facts |
| IPC/CPC mapping | Verify exact scheme and version | High technical accuracy risk |
| Legal or ethics claim | Verify jurisdiction and current authority | High consequential risk |
What this reference establishes — and what it does not
The material above is a source-bounded reference concerning ai content policy. It distinguishes official documentation from editorial method and provides practical decision boundaries; it does not claim an exhaustive global search, an empirical dataset that was not collected, a legal opinion, or certified implementation of publisher policy. Readers seeking to apply a source to a current filing, claim or professional obligation should inspect the applicable original authority, record its effective date, and obtain the necessary qualified advice. A page is updated substantively only when its evidence, method or answer materially changes.
Relevant original authorities
These are starting points for checking the governing documentation. Inspect the applicable edition, original wording and any limitations before relying on a consequential proposition.
- ABA: Formal Opinion 512 — ABA. US professional ethics: GAI competence, confidentiality, verification, communication.
- USPTO: AI tools used by practitioners — USPTO. Practice-before-USPTO requirements for AI tools, April 2024 guidance.
- NIST AI Risk Management Framework — NIST. Voluntary framework and update/revision status.
- NIST Generative AI Profile AI 600-1 — NIST. Risk categories and controls for GAI evaluation.