Methodology

Treat AI As Assistance, Not Authority

AI may summarize, sort, compare, translate, or draft, but a human editor owns the finding. Keep an AI-use ledger for meaningful assistance, check hallucination risk, and never cite model output as proof of fact.

Publication information

Content type
Supporting Page
Factual status
Reference material
Last substantive review
Evidence summary
AI may summarize, sort, compare, translate, or draft, but a human editor owns the finding. Keep an AI-use ledger for meaningful assistance, check hallucination risk, and never cite model output as proof of fact.

Section 04

Context

Treat AI As Assistance, Not Authority asks whether the public record supports this proposition: AI may summarize, sort, compare, translate, or draft, but a human editor owns the finding. Keep an AI-use ledger for meaningful assistance, check hallucination risk, and never cite model output as proof of fact.

AI may summarize, sort, compare, translate, or draft, but a human editor owns the finding. Keep an AI-use ledger for meaningful assistance, check hallucination risk, and never cite model output as proof of fact.

Start with public records, policies, contracts, meeting materials, correction logs, or published source notes that can be checked without unauthorized access.. Record what it establishes, what it does not establish, and which additional evidence would change the assessment.

Evidence Snapshot

Issue Treat AI As Assistance, Not Authority asks whether the public record supports this proposition: AI may summarize, sort, compare, translate, or draft, but a human editor owns the finding. Keep an AI-use ledger for meaningful assistance, check hallucination risk, and never cite model output as proof of fact.
Documented incentives or beneficiaries Relevant incentives, institutional interests, commercial beneficiaries, or decision-making advantages should be identified from records and attributed evidence rather than assumed.
Documented or plausible impacts Documented or plausible effects should be tied to a named decision, affected group, time period, and evidence source; unverified harms remain labeled as such.
First verification step Start with public records, policies, contracts, meeting materials, correction logs, or published source notes that can be checked without unauthorized access.. Record what it establishes, what it does not establish, and which additional evidence would change the assessment.
Priority record Public records, policies, contracts, meeting materials, correction logs, or published source notes that can be checked without unauthorized access.

Reader Verification Path

Separate stated purpose from records that can independently support the claim.

  • Look for the decision point: what gets published, what gets corrected, what stays private, and what needs more proof.
  • Check whether the page names the record, date, uncertainty, correction path, or right-of-reply issue that would change the conclusion.
  • Use the outside links to compare the method against journalism, records, and AI-governance standards.

Relevant Public Records

Claim file

For Treat AI As Assistance, Not Authority, keep the claim file visible: the exact public claim, the source class, the confidence state, the date checked, and the condition that would change the language.

Evidence boundary

Use public records, policies, contracts, meeting materials, correction logs, or published source notes that can be checked without unauthorized access. but state its limit. A contract may prove purchase without proving use. A statement may explain intent without proving performance. A correction may repair one line while leaving the larger system untouched.

Correction path

Preserve the answer to this review question: what source class supports the claim, and could that source change, disappear, or be missing context? A method page earns trust when a reader can see what would confirm, narrow, dispute, or repair the page.

Minimization note

Look for the public-interest version of the evidence: policy clause, institution, date range, source class, and decision point. Private identifiers should appear only when the accountability point genuinely depends on them.

Verify And Read Further

These are direct references, not off-site search results. They point to official organizations, public records references, legal sources, civil-liberties material, or technical governance pages worth reading on their own.

  • GIJN About Direct reference page for checking records, rights, governance, privacy, AI risk, or public accountability context.
  • GIJN Membership Direct reference page for checking records, rights, governance, privacy, AI risk, or public accountability context.

How To Read The Record

Claim shape

When reading Treat AI As Assistance, Not Authority, underline the claim, the source class, the confidence state, and the exact phrase that would need to change if better evidence appears.

Limit language

Good method writing says what a record proves and what it cannot prove. That distinction is where trust is built: purchase is not use, intended function is not performance, and a denial is not always a full answer.

Repair trigger

Look for the correction trigger: subject response, new primary record, stale source, overbroad language, unnecessary private detail, or a disputed inference that should be marked more clearly.

Keep Reading

Relevant Public Records Verify And Read Further How To Read The Record The Failure Evidence To Request The Record Trail Revision Conditions Receipts And Limits

Issue and documented effects

Treat AI As Assistance, Not Authority asks whether the public record supports this proposition: AI may summarize, sort, compare, translate, or draft, but a human editor owns the finding. Keep an AI-use ledger for meaningful assistance, check hallucination risk, and never cite model output as proof of fact.

Documented incentives or beneficiaries: Relevant incentives, institutional interests, commercial beneficiaries, or decision-making advantages should be identified from records and attributed evidence rather than assumed.

Documented or plausible impacts: Documented or plausible effects should be tied to a named decision, affected group, time period, and evidence source; unverified harms remain labeled as such.

Evidence To Request

Ask which record, method, audit, policy, or correction history would allow the claim to be independently checked.

  • What exact sentence is being claimed, and what record is strong enough to support it?
  • What does the source fail to prove: use, performance, motive, scope, error rate, downstream sharing, or repair?
  • What private detail can be removed while preserving the public-interest point?
  • What new record, subject response, or correction would force the page to change?
  • What record proves or contradicts this claim from Treat AI As Assistance, Not Authority: AI may summarize, sort, compare, translate, or draft, but a human editor owns the finding. Keep an AI-use ledger for meaningful assistance, check hallucination risk, and never cite model output as proof of fact.

The Record Trail

Verification starts with records that already exist: contracts, policy manuals, retention schedules, audit logs, denial letters, complaint files, vendor claims, court forms, and correction history.

  • Public records, policies, contracts, meeting materials, correction logs, or published source notes that can be checked without unauthorized access.
  • Confidence labels that separate confirmed facts, corroborated claims, inference, dispute, stale material, and unknowns.
  • Minimization notes showing which details are needed for public accountability and which details are withheld to protect people.
  • A correction path that lets affected readers, subjects, or institutions challenge the public claim.

Revision Conditions

Treat AI As Assistance, Not Authority changes when the record changes. A released contract can show the tool was never purchased. A policy can show a narrower rule than officials implied. An audit can prove error rates are tracked and repaired. A correction log can show whether people actually get relief.

A claim is more useful when it identifies the record or observation that would revise it. That makes uncertainty and correction part of the analysis rather than an afterthought.

  • What source class supports the claim, and could that source change, disappear, or be missing context?
  • What would confirm, narrow, dispute, or correct the claim?
  • Who could be harmed by over-publication, and what can be redacted without weakening the public-interest point?

Reference Notes

Best records Public records, policies, contracts, meeting materials, correction logs, or published source notes that can be checked without unauthorized access. Confidence labels that separate confirmed facts, corroborated claims, inference, dispute, stale material, and unknowns.
Confidence limit Treat AI As Assistance, Not Authority should not be treated as confirmed beyond the records named on the page. Purchase is not use, stated purpose is not performance, and a denial is not a complete audit.
Minimize harm Keep the focus on institutions, vendors, rules, records, and decision paths. Remove private identifiers unless the public-interest claim genuinely cannot be made without them.
Correction path To challenge Treat AI As Assistance, Not Authority inside Methodology, send the dated record, the exact sentence at issue, what it proves, and what wording should change.

Sources and limitations

Open questions
  • What source class supports the claim, and could that source change, disappear, or be missing context?
  • What would confirm, narrow, dispute, or correct the claim?
  • Who could be harmed by over-publication, and what can be redacted without weakening the public-interest point?
What stays out

This page is public education and civic accountability material. It does not provide instructions for unauthorized access, evasion, sensor triggering, stalking, doxxing, coercive influence, or attempts to provoke monitoring systems.

Related route

Section 01

Define The Intelligence Requirement

Begin with the question the public actually needs answered: who bought the system, what data enters it, who can query it, what rights are affected, what errors cost, and what record would change the conclusion.

 Section 02

Use A Source Ladder

Label claims as confirmed by a primary record, corroborated, likely, inferred, disputed, stale, unverified, or unknown. Confidence language should tell readers how strong the evidence is, not how strongly the writer feels.

 Section 03

Preserve Context Without Overpublishing

Record source, date seen, archive status, transformation, redaction, and uncertainty. Preserve enough to verify the claim while cutting unrelated identifiers, private addresses, victims, minors, medical details, credentials, and bystanders.

 Section 05

Build In Right Of Reply

When a page makes a material claim about an organization or named person, give a practical route to respond when feasible. A reply can confirm, narrow, dispute, correct, or add context to the public record.

 Section 06

Publish Corrections As Memory

Corrections, updates, privacy repairs, and source challenges should leave visible notes proportionate to the issue. Public memory is credible only when repair is part of the archive, not hidden after publication.