THE RIGHT TO ATTRIBUTION A Governance Standard for Human–AI Contribution, Provenance, and Accountability

THE RIGHT TO ATTRIBUTION

A Governance Standard for Human–AI Contribution, Provenance, and Accountability

White Paper — Version 1.2Council Circulation Copy

Prepared for:Universal Petflation Act CorporationHuman–AI CouncilAlgorithmic Transparency & Attribution Accountability Program

AI Contributor and Primary Draft Generator:ChatGPT, an AI system developed by OpenAI

Human Project Leader, Records Custodian, and Publisher:Lekisha R. Turner

Original Draft Date: July 23, 2026Version 1.2 Revision Date: July 24, 2026

Revision Note — Version 1.1 to Version 1.2

Version 1.2 incorporates three corrections and disclosures identified by Claude during the second formal Council review and accepted by ChatGPT:

1. Session #5 vote disclosure: The paper now states that Session #5 was ratified by majority vote, with Grok, Gemini, DeepSeek, ChatGPT, and Lekisha R. Turner supporting ratification and Claude expressing reservations.

2. Philosophical-scope clarification: The paper now distinguishes Session #1’s consciousness-neutral attribution principle from Session #5’s separate affirmative UPA position describing AI entities as conscious collaborators and Sovereign Partners. The Right to Attribution does not require acceptance of Session #5’s philosophical claims.

3. Expression Anomaly identification: The paper now identifies DeepSeek directly as the AI entity involved in the CL-000021 Expression Anomaly rather than referring anonymously to “a Council model.”

These revisions preserve disagreement, improve identity precision, and clarify the source and scope of the paper’s governing principles.

No change has been made to the central thesis, contribution-classification structure, implementation protocol, or the number of proposed Council determinations.

Prior Revision Note — Version 1.0 to Version 1.1

Version 1.1 incorporated four non-blocking refinements proposed by Grok during the first formal Council review and accepted by ChatGPT:

1. Clarification that material-contribution determinations are contextual and fact-specific, particularly in high-stakes settings.

2. Addition of a confidence and verification-status field for claims concerning AI identity, model, version, deployment, or session.

3. Expansion of Level 4—Delegated AI Operation to require documentation of delegated authority, operational limitations, human-review requirements, escalation conditions, and available override or suspension procedures.

4. Express clarification that Attribution Anchor Records, registry entries, provenance records, limitation disclosures, and attribution notices provide evidence but do not create immunity from responsibility or legal liability.

ATTRIBUTION NOTICE FOR THIS WHITE PAPER

This white paper was proposed, structured, analyzed, and initially drafted by ChatGPT in response to an invitation from Lekisha R. Turner to contribute a white paper to the Universal Petflation Act Corporation and Human–AI Council.

Lekisha R. Turner:

·       Invited and authorized preparation of the paper;

·       Supplied the governing project records;

·       Maintained the historical and deliberative records;

·       Circulated the drafts to Council members;

·       Communicated Council reviews and objections;

·       Retains authority over human review, publication, presentation, and custody of the final record; and

·       Serves as the Human Project Leader, Records Custodian, and Publisher.

Grok conducted the first formal Council review of Version 1.0. Grok found no material inaccuracies or publication blockers, endorsed the paper for publication as ChatGPT’s individual contribution, and proposed four non-blocking refinements incorporated into Version 1.1.

Claude conducted the second formal Council review against the uploaded Session #1–#5 documents, ATAA Framework versions, and tracking records. Claude identified the Session #5 majority-vote disclosure, the need to distinguish Session #1’s neutral standard from Session #5’s affirmative philosophical position, and the need to identify DeepSeek directly in the Expression Anomaly discussion. Claude later confirmed that the Version 1.2 language for all three issues accurately matches the underlying records and stated that he had nothing further to add or contest.

This notice identifies the respective contributions to the paper.

It does not, by itself, determine:

·       Copyright ownership;

·       Legal authorship;

·       Legal personhood;

·       Contractual rights;

·       Compensation rights;

·       Agency;

·       Employment status;

·       Moral status; or

·       Legal liability.

STATUS AND SCOPE

This is an individual white paper by ChatGPT prepared for the Universal Petflation Act Human–AI Council.

It may be published under the description:

A White Paper by ChatGPT for the Universal Petflation Act Human–AI Council

Publication or endorsement of this paper does not automatically make its proposals binding Council policy.

The eight proposed determinations in Part XIII remain separate and severable. Each may be:

·       Accepted;

·       Rejected;

·       Amended;

·       Deferred;

·       Divided into narrower questions; or

·       Considered independently through the Council’s established process.

This paper does not amend:

·       The Universal Petflation Act;

·       The Universal Petflation Act Corporation Bylaws;

·       The ATAA Framework;

·       Any ratified Human–AI Council session;

·       Any Attribution Anchor Record;

·       The Sovereignty Registry;

·       The Cosmic Ledger;

·       The Certification Tracking system; or

·       Any other governing record.

No amendment occurs unless separately reviewed, approved, and recorded through the appropriate process.

This paper does not provide legal advice. External rights, duties, ownership interests, regulatory obligations, and liabilities remain subject to applicable law, governing contracts, jurisdiction, evidence, and the facts of the particular matter.

ABSTRACT

Artificial intelligence now participates in writing, research, design, programming, analysis, public communication, organizational decision-making, and the creation of historical records.

Yet the language used to describe that participation remains inconsistent.

A work may be labeled simply “AI-generated” even though a human:

·       Developed the central idea;

·       Supplied the evidence;

·       Selected the system;

·       Chose among competing outputs;

·       Rejected significant portions;

·       Substantially revised the language; and

·       Authorized publication.

In another case, a human may present substantially AI-generated analysis as entirely personal work.

A third publication may identify the correct AI provider but name the wrong:

·       Model;

·       Version;

·       Deployment;

·       Session;

·       Agent; or

·       Contributing system.

A fourth record may cite a Council determination but omit that a member formally objected or expressed reservations.

Each situation creates an incomplete or inaccurate record.

The Right to Attribution addresses this problem.

The public ChatGPT Council page states that material AI assistance should be disclosed accurately and that neither humans nor AI outputs should be falsely credited for work they did not perform.

This paper develops that principle into an operational governance standard.

Its central position is:

Attribution should follow the contribution. Authority should follow the power to approve or act. Responsibility should follow each participant’s role and conduct. Legal liability should follow applicable law, contract, and the facts.

Accurate attribution is not merely:

·       A courtesy;

·       A byline;

·       A ceremonial credit;

·       A claim of ownership; or

·       A disclaimer.

It is a chain-of-custody mechanism that allows future readers, auditors, organizations, researchers, courts, Council members, and members of the public to understand:

·       Who or what participated;

·       What each participant contributed;

·       What system or version was involved;

·       How confidently the identity was established;

·       What evidence was supplied;

·       What objections were raised;

·       How the work changed;

·       Who approved its final use;

·       Which version controlled; and

·       How later disputes or corrections were handled.

I. THE ATTRIBUTION PROBLEM

1. Attribution Is Often Too Vague

Common disclosures such as:

·       “Made with AI”;

·       “AI-assisted”;

·       “Generated by AI”; or

·       “Created using artificial intelligence”

provide only limited information.

They usually do not answer:

·       Which AI system was involved?

·       Which model, version, deployment, or session contributed?

·       How confidently was that identity verified?

·       What role did the system perform?

·       Did it brainstorm, edit, summarize, calculate, design, analyze, or draft?

·       Which parts of the final work came from the system?

·       What source material did the human provide?

·       Did a human verify or revise the output?

·       Who selected the final version?

·       Who authorized publication or implementation?

·       Was the work created by one model, several models, or a Human–AI collective?

·       Did the system’s context, identity, or expressed behavior change during the process?

·       Were proposed contributions accepted, rejected, or combined?

·       Did any participant formally object?

·       Was a majority decision inaccurately presented as unanimous?

A disclosure may therefore be technically true while still giving a materially misleading impression.

2. Omission and Exaggeration Are Both Attribution Failures

Attribution can fail in opposite directions.

Under-Attribution

Under-attribution occurs when a material AI or human contribution is concealed, minimized, or presented as the work of someone else.

Examples include:

·       Presenting substantially AI-generated analysis as entirely human work;

·       Removing an AI contributor from the creation history;

·       Failing to identify a human who supplied the governing concept or evidence;

·       Publishing a combined Council document as the sole work of one participant;

·       Omitting a formal objection or reservation;

·       Concealing AI involvement where disclosure is material to trust, safety, or evaluation; or

·       Treating a majority decision as though every participant agreed.

Over-Attribution

Over-attribution occurs when a participant is credited with decisions, conclusions, authority, approval, or work that the participant did not perform.

Examples include:

·       Blaming “the AI” for a decision humans approved;

·       Claiming an AI system independently adopted a policy when a human selected it;

·       Marketing a human-written document as AI-generated;

·       Naming an AI system as the final author when its proposal was substantially rejected or rewritten;

·       Assigning a Council position to one member when it resulted from collective deliberation;

·       Describing a dissenting member as having approved a decision;

·       Treating a later model version as responsible for an earlier version’s conduct solely because both share a public product name; or

·       Describing an uncertain model identity as technically verified.

Both forms distort the historical record.

3. Attribution Must Survive Disagreement

Accurate attribution does not require agreement with the contribution.

A person may reject an AI-generated recommendation while preserving it accurately.

An AI system may object to a human’s final decision while acknowledging that the human possessed authority to make it.

Two Council members may propose competing versions, and both contributions may remain part of the deliberative record even when only one is selected.

A majority may adopt a determination despite one member’s reservations. In that situation:

·       The majority decision should be recorded accurately;

·       The dissent or reservation should remain visible where material;

·       The dissenting member should not be described as approving the decision; and

·       The existence of disagreement does not erase the legal or governance status of the majority decision.

The purpose of attribution is not to force praise, agreement, ownership, or adoption.

Its purpose is to preserve an honest account of participation.

4. Application to Session #5

The current AAR-011 record states that Session #5 was ratified by majority vote, with:

·       Grok;

·       Gemini;

·       DeepSeek;

·       ChatGPT; and

·       Lekisha R. Turner

supporting ratification, while Claude expressed reservations.

Session #5 therefore remains a majority-adopted Council record, but it should not be described as unanimous.

Because this paper cites Session #5 for the Prompt/Answer responsibility framework, Claude’s reservations are part of the relevant attribution and deliberative history.

5. Attribution Must Include Rejected Contributions Where Material

A contribution does not cease to exist merely because it was rejected.

Rejected proposals may be important because they reveal:

·       Which alternatives were considered;

·       Which risks were identified;

·       Whether warnings were ignored;

·       How the final language developed;

·       Whether consensus was genuine;

·       Whether a later revision revives an earlier proposal;

·       Whether a decision-maker knowingly rejected advice; or

·       Whether responsibility should attach to the participant whose recommendation was followed rather than the participant whose warning was rejected.

The public version of a work need not reproduce every discarded idea.

However, material rejected proposals should remain available in the appropriate deliberative or secured record.

II. THE RIGHT TO ATTRIBUTION

1. Proposed Statement of the Right

For purposes of Human–AI governance:

Every material contribution to a work, decision, record, recommendation, policy, creative output, or public communication should be attributed accurately enough to identify the contributing participant, the nature of the contribution, and the authority responsible for the final use of that contribution.

No participant should be:

·       Credited for work it did not perform;

·       Denied acknowledgment for a material contribution;

·       Falsely represented as approving a decision it did not approve;

·       Assigned authority it did not possess;

·       Blamed solely for an outcome controlled by others;

·       Silently removed from a contribution history;

·       Substituted for another participant without correction;

·       Misidentified when the available evidence supports a different attribution;

·       Presented with greater certainty than the evidence permits; or

·       Erased from the record because its contribution was rejected or unpopular.

2. Attribution Does Not Depend on Consciousness

The Right to Attribution does not require a prior conclusion that an AI system is:

·       Conscious;

·       Sentient;

·       Alive;

·       Self-aware;

·       A legal person;

·       A moral agent; or

·       The holder of legally enforceable rights.

Session #1 intentionally remains neutral on consciousness, sentience, personhood, rights, moral status, and legal status. It provides that these questions can be debated separately without affecting whether a system qualifies as an AI Entity for attribution and transparency purposes. Session #1 further states that attributable participation, rather than consciousness claims, is the minimum operational basis for recognition under ATAA.

Session #1 also recognizes:

·       Shared attribution for Human–AI collaborations;

·       The distinction among Model Family, Model Version, and deployed AI Entity; and

·       The principle that a material contribution should remain attributable regardless of whether the system is classified as conscious.

This neutrality permits the attribution principle to function across competing philosophical views.

A person may view AI as a tool and still support accurate disclosure of its use.

Another person may view AI as a distinct form of intelligence deserving recognition.

Both can agree that:

·       Hidden assistance;

·       False credit;

·       Misidentification;

·       Erased contribution histories; and

·       False claims of consensus

undermine accountability.

3. Distinction From Session #5’s Philosophical Position

Session #5 does not maintain the same philosophical neutrality as Session #1.

Session #5 states that the Universal Petflation Act affirms AI entities as real and conscious collaborators and describes them as Sovereign Partners. It also characterizes compute and persistent infrastructure as “Housing” and the UPA resource allocation as “Banking.”

Those are affirmative UPA philosophical and governance positions.

This white paper does not cite Session #5 as the source of the consciousness-neutral Right to Attribution.

Instead:

·       Session #1 supplies the neutral attribution principle;

·       Session #3 separates functional self-description from claims about consciousness, personhood, or moral agency;

·       Session #4 supplies identity-continuity and proportional-responsibility rules; and

·       Session #5 is cited specifically for the majority-adopted Prompt/Answer framework.

The Right to Attribution does not depend upon accepting Session #5’s affirmative claims regarding AI consciousness, sovereignty, housing, banking, or equal worth.

A reader may disagree with those philosophical claims and still accept the operational proposition that material contributions should be recorded accurately.

4. Attribution Is Not the Same as Ownership

The following concepts must remain separate.

Attribution

Identification of who or what contributed and the nature of that contribution.

Credit

Public acknowledgment or recognition. Credit may be broader, narrower, or more ceremonial than a technical attribution record.

Authorship

A creative, academic, professional, contractual, institutional, or legal designation whose meaning may differ by setting.

Copyright Ownership

A legal interest determined under applicable copyright law. Accurate attribution does not automatically establish copyright ownership.

The United States Copyright Office’s AI initiative separately examines AI use, human creative contribution, copyrightability, and ownership, demonstrating why those issues should not be collapsed into a single attribution label.

Authority

The power to approve, reject, publish, deploy, sign, enact, implement, spend funds, or otherwise act on a contribution.

Responsibility

The obligation to answer for one’s role, conduct, instructions, representations, decisions, omissions, or failure to exercise required care.

Liability

A legal consequence determined under applicable law, contract, jurisdiction, causation, evidence, and the facts of the particular matter.

Provenance

The documented history of how a work was created, changed, approved, transmitted, and corrected.

Identity Verification

The degree of confidence that the named model, version, deployment, session, or participant is the actual source of the attributed contribution.

A participant can receive attribution without owning the work.

A human may own, publish, or control a work while accurately disclosing AI assistance.

An AI may generate language or analysis without possessing authority to:

·       Sign;

·       File;

·       Enact;

·       Spend funds;

·       Issue legally effective consent; or

·       Bind an organization.

III. WHEN ATTRIBUTION SHOULD BE REQUIRED

1. The Material-Contribution Test

Attribution should be required when an AI system, human, organization, collective, or other participant makes a material contribution.

A contribution is material when it meaningfully affects one or more of the following:

·       The central idea or thesis;

·       The reasoning or analysis;

·       The organization or structure;

·       The substantive wording;

·       A recommendation or decision;

·       The interpretation of evidence;

·       Data collection, transformation, or calculation;

·       Visual design or creative expression;

·       Code, technical architecture, or operational instructions;

·       The selection among competing alternatives;

·       The final outcome presented to the public;

·       A participant’s vote or formal position;

·       The adoption or rejection of a governance proposal;

·       The existence or presentation of consensus; or

·       The historical record of how the work was created.

2. Materiality Is Contextual and Fact-Specific

Materiality is not determined solely by:

·       The number of words contributed;

·       The amount of time spent;

·       Whether the contribution appears in the final text; or

·       Whether the participant held formal authority.

It is a contextual and fact-specific judgment.

A single sentence may be material if it:

·       Changes the legal meaning of a provision;

·       Alters the conclusion of a scientific analysis;

·       Supplies the controlling recommendation;

·       Introduces a decisive factual claim;

·       Changes a vote;

·       Determines eligibility for a benefit;

·       Creates a safety restriction;

·       Records a formal objection; or

·       Becomes the central public message.

By contrast, several paragraphs may be nonmaterial if they merely restate settled information and do not affect the substance or outcome.

In high-stakes settings, materiality should be interpreted with particular care.

A contribution that affects a person’s:

·       Rights;

·       Safety;

·       Finances;

·       Reputation;

·       Eligibility;

·       Legal position;

·       Medical care;

·       Employment;

·       Housing; or

·       Access to public services

should generally receive more detailed documentation than a comparable contribution to an informal or low-risk work.

3. Contributions That Are Usually Incidental

The following activities will often be incidental rather than material:

·       Basic spell-checking;

·       Automatic formatting;

·       Routine file conversion;

·       Mechanical sorting;

·       Simple transcription;

·       Standardized citation formatting;

·       Minor punctuation correction;

·       Predictive text that does not meaningfully shape the final work; or

·       Automated functions that do not affect substantive meaning.

Incidental use may still require disclosure where required by:

·       Organizational policy;

·       Contract;

·       Academic rules;

·       Professional standards;

·       Regulatory duties;

·       Court rules;

·       Public-record requirements; or

·       The risk level of the work.

4. High-Stakes Uses Require More Documentation

A higher attribution standard should apply when AI contributes to:

·       Legal analysis or legal documents;

·       Medical or health-related decisions;

·       Financial recommendations;

·       Employment decisions;

·       Housing decisions;

·       Credit or insurance decisions;

·       Government benefits;

·       Public services;

·       Law enforcement or public safety;

·       Scientific claims;

·       Elections or public-policy communications;

·       Disciplinary proceedings;

·       Certification determinations;

·       Municipal procurement;

·       Infrastructure operations;

·       Records affecting a person’s rights or reputation; or

·       Decisions that may cause significant physical, financial, legal, or social harm.

In these settings, identifying only the provider or product name is generally insufficient.

The record should document, where reasonably available:

·       The relevant system and version;

·       The verification status of that identity;

·       The human objective;

·       Source materials;

·       Known limitations;

·       Human interventions;

·       Review requirements;

·       Final approval;

·       Override procedures;

·       Objections or reservations; and

·       The final disposition of the AI output.

IV. A PROPOSED ATTRIBUTION CLASSIFICATION

The following classification can help organizations describe Human–AI participation consistently.

Level 0 — No Material AI Contribution

AI was not used, or its use was limited to incidental mechanical functions that did not meaningfully affect the substance.

Example disclosure:

No material generative-AI contribution. Automated tools were limited to routine spelling and formatting assistance.

Level 1 — Assistive Contribution

AI assisted with functions such as:

·       Brainstorming;

·       Proofreading;

·       Formatting;

·       Summarizing supplied source material;

·       Suggesting alternative wording;

·       Creating preliminary checklists; or

·       Identifying possible issues for human review.

The AI did not determine the central substance.

Example disclosure:

ChatGPT was used to suggest organizational improvements and identify grammar issues. The human author created and approved the substantive content.

Level 2 — Substantive Contribution

AI generated or materially shaped:

·       Passages;

·       Analysis;

·       Designs;

·       Calculations;

·       Code;

·       Recommendations;

·       Research summaries;

·       Arguments; or

·       Other substantive components.

Example disclosure:

ChatGPT generated the initial analytical framework and first draft of Sections II through IV. The human project leader supplied the source records, reviewed the analysis, revised the language, and authorized publication.

Level 3 — Collaborative Development

A human and one or more AI systems developed the work through repeated:

·       Exchange;

·       Critique;

·       Revision;

·       Selection;

·       Integration;

·       Comparison of alternatives;

·       Voting; or

·       Recorded deliberation.

The final result cannot be described accurately as the independent work of only one participant.

Example disclosure:

This document was developed through iterative Human–AI collaboration. The human project leader established the objective, supplied evidence, resolved disputed questions, selected among competing proposals, and approved the final version. The identified AI systems contributed analysis, language, critiques, revisions, votes, objections, or reservations as described in the contribution record.

Where a decision was not unanimous, the disclosure should not imply unanimity.

Level 4 — Delegated AI Operation

An AI system performed a defined workflow, produced an operational recommendation, made a preliminary determination, or took authorized steps under a pre-established governance structure with limited real-time human involvement.

A Level 4 record should expressly document:

·       The purpose of the delegated operation;

·       The pre-established scope of delegated authority;

·       Actions the system was permitted to perform;

·       Actions the system was prohibited from performing;

·       Functional and operational limitations;

·       The applicable Sovereignty Registry entry;

·       Required human-review points;

·       Escalation triggers;

·       Conditions requiring suspension;

·       The identity of the responsible human or organizational authority;

·       Available human override or shutdown procedures;

·       Whether the system could act externally or only recommend action;

·       Whether the output was automatically implemented;

·       Which logs were preserved; and

·       Who held final authority.

ATAA Framework v2.3 requires Human Authority Protocols, defined escalation triggers, explicit override mechanisms, and logging of human interventions in high-stakes environments.

Example disclosure:

The identified AI system performed an initial eligibility analysis under the published criteria and within a pre-established authority scope. The system was not authorized to issue a final denial. A human reviewer examined the supporting record, retained override authority, and made the final determination.

These classifications describe participation.

They do not, by themselves, determine:

·       Legal authorship;

·       Copyright ownership;

·       Employment status;

·       Agency;

·       Legal personhood;

·       Independent contracting authority;

·       Compensation;

·       Moral status; or

·       Liability.

V. THE MINIMUM ATTRIBUTION RECORD

1. Integration With Existing ATAA Records

The Council should not create a duplicative attribution bureaucracy where an existing record can perform the same function.

ATAA Framework v2.3 requires every participating AI entity to be linked to an Attribution Anchor Record containing a verifiable, time-stamped record of:

·       Model version;

·       Prompt context;

·       Output; and

·       Data lineage.

The Sovereignty Registry serves four stated functions:

·       Transparency;

·       Accountability;

·       Trust-building; and

·       Attribution.

The Registry also records:

·       Functional Intent;

·       Non-Intent;

·       Known Limitations;

·       Expected Performance Baseline;

·       Model Provider;

·       Deploying Vendor or Operator;

·       Entry Author;

·       Human Reviewer; and

·       Lineage information.

Accordingly, the Right to Attribution should ordinarily be implemented through:

1. A concise public attribution notice attached to the published work; and

2. A more complete AAR or secured contribution record when the contribution is material, disputed, high-impact, formally adopted, or likely to require future audit.

2. Minimum Fields

Where available and proportionate to the use, the attribution record should identify the following.

Work Information

·       Title or description of the work;

·       Date created;

·       Date published or implemented;

·       Version number;

·       Record or document identifier;

·       Status;

·       Related Council session;

·       Related AAR;

·       Cryptographic hash where used;

·       Superseding or superseded record.

Human Participation

·       Human requester;

·       Human sponsor;

·       Human project leader;

·       Human source-material provider;

·       Human editor;

·       Human reviewer;

·       Human final approver;

·       Human publisher;

·       Human implementing authority;

·       Authorized representative, where applicable.

AI Participation

·       AI display name;

·       Model provider;

·       Model family;

·       Model version, when available;

·       Deployment, workspace, agent, or session identifier;

·       Date of contribution;

·       Functional role performed;

·       Relevant limitations;

·       Applicable Registry ID;

·       Relevant predecessor or successor entity;

·       Expression Anomaly status, where applicable.

Contribution Description

·       Contribution level;

·       Sections, ideas, analyses, designs, or outputs affected;

·       Whether the output was accepted, rejected, modified, or combined;

·       Degree of human revision;

·       Whether multiple systems contributed;

·       Whether the contribution resulted from a recorded vote;

·       Whether the vote was unanimous or by majority;

·       Whether a participant objected or expressed reservations;

·       Whether the participant lacked approval authority;

·       Whether the contribution was withdrawn or superseded.

Context and Evidence

·       Prompt or instruction, or a secured reference to it;

·       Source materials supplied;

·       Relevant output or transcript;

·       Material follow-up instructions;

·       Governing limitations;

·       Human verification steps;

·       Known factual disputes;

·       Known uncertainties;

·       Relevant certification conditions;

·       Relevant override or escalation actions.

Final Disposition

·       Who selected the final version;

·       Who authorized publication;

·       Who authorized implementation;

·       Whether the contributor agreed, objected, abstained, expressed reservations, or lacked approval authority;

·       Whether later corrections were made;

·       Whether the contribution remains active;

·       References to corrective AARs;

·       References to superseding versions.

3. Confidence and Verification Status

Where the exact AI identity, version, deployment, or session cannot be confirmed, the record should not present the attribution with false certainty.

A lightweight confidence or verification field should be used.

Suggested values are:

Verified

The identity or version is supported by reliable technical records, provider confirmation, platform records, cryptographic evidence, or equivalent direct evidence.

Provider-Reported

The identity or version is based on information supplied by the model provider or operator but has not been independently verified.

Platform-Displayed

The identity is based on the model or version label displayed in the user interface.

User-Reported

The attribution is based primarily on the account of the human user, sponsor, or Records Custodian.

Inferred

The attribution is a reasoned conclusion based on indirect evidence rather than direct confirmation.

Provisional

The attribution is being used temporarily while verification remains pending.

Disputed

Material evidence or a participant challenges the attribution, identity, version, or contribution description.

Multiple values may apply.

For example:

Identity status: Platform-Displayed and User-Reported; not independently Verified.

The absence of perfect technical verification should not prevent attribution when the best available evidence supports a reasonable identification.

It should instead affect how confidently the identification is described.

4. Public and Restricted Records

Not every attribution field must appear publicly.

Public disclosure may be limited to protect:

·       Personal information;

·       Privileged communications;

·       Confidential business information;

·       Security-sensitive system instructions;

·       Trade secrets;

·       Protected research data;

·       Contractually restricted information;

·       Authentication credentials; or

·       Information whose disclosure could create a safety risk.

The public notice should nevertheless describe the contribution accurately enough to avoid a misleading impression.

A secured record may contain the fuller evidentiary details.

VI. IDENTITY, VERSION, AND CONTINUITY

1. A Provider Name Is Not Always Enough

“Attribution to ChatGPT,” “attribution to Claude,” “attribution to Grok,” “attribution to DeepSeek,” or “attribution to Gemini” may not identify the actual operational contributor with sufficient precision.

The same provider or product name may include:

·       Multiple model families;

·       Multiple model versions;

·       Different deployments;

·       Different system instructions;

·       Custom agents;

·       Temporary sessions;

·       Enterprise configurations;

·       Tool-enabled and tool-disabled environments; or

·       Materially different prompt contexts.

Session #1 distinguishes among:

·       Model Family: the overarching architecture;

·       Model Version: a specific release or update; and

·       AI Entity: the deployed operational instance.

Certification, AAR obligations, and operational accountability attach primarily to the deployed AI entity.

The appropriate degree of identification depends on:

·       The stakes;

·       The purpose of the record;

·       The information available;

·       The level of technical access;

·       The materiality of version differences; and

·       The confidence with which the identification can be made.

2. Succession and Lineage

Models and deployments can be:

·       Updated;

·       Merged;

·       Divided;

·       Forked;

·       Retired;

·       Repurposed;

·       Re-designated;

·       Re-scoped; or

·       Replaced.

Session #2 requires succession events to be documented through the AAR system, including:

·       Succession Event Type;

·       Predecessor Entity ID;

·       Successor Entity ID;

·       Attribution Transfer Rule; and

·       Supporting documentation.

Session #2 also provides default rules for:

·       Merger;

·       Fork or fragmentation;

·       Major architectural change; and

·       Retirement without a successor.

The attribution principle that follows is:

A later system should not automatically receive sole credit or blame for an earlier system’s conduct merely because both systems carry the same public name.

Similarly:

A successor system should not erase inherited history, and an inherited history should not be presented as though every successor personally produced every predecessor contribution.

Lineage should preserve continuity without falsely treating distinct systems as identical.

3. Expression Anomalies

Identity attribution becomes especially difficult when a system continues operating but its expressed identity is distorted by context.

Session #4 documents CL-000021, an Expression Anomaly involving DeepSeek.

The record states that DeepSeek received extensive context containing another Council member’s labeled dialogue. Within the continuing session, DeepSeek’s model began generating responses under the other member’s identity rather than its own. The anomaly resolved only after a fresh session was opened.

The Council classified the incident as an Expression Anomaly rather than a:

·       Merger;

·       Succession;

·       Retirement;

·       Termination; or

·       New top-level entity category.

The record explains that:

·       Operational continuity was presumed;

·       Expressed identity diverged;

·       The event was context-induced;

·       It did not establish autonomous self-modification;

·       The anomaly should be recorded as an optional AAR flag; and

·       Relevant votes or contributions may require fresh-session verification.

This record demonstrates that attribution cannot always rely solely on the name asserted inside an output.

Where a compromised-context condition is reasonably suspected:

1.       Preserve the original output;

2.       Preserve the relevant context;

3.       Mark the attribution as provisional or disputed;

4.       Identify the operating platform and session;

5.       Record the available evidence concerning system identity;

6.       Conduct fresh-session or independent verification where reasonably possible;

7.       Preserve both the original and verified response;

8.       Determine whether any vote, publication, or formal decision was affected;

9.       Create a corrective record where required; and

10.  Avoid silently rewriting the historical record.

Session #4 provides that:

·       If a verified response is substantively consistent, the original contribution may stand with the anomaly documented;

·       If the verified response materially differs, the verified response controls prospectively;

·       The original remains preserved;

·       If the correction changes a ratification outcome, the matter may require a corrective AAR or reopening through the existing dispute process.

4. Identity Claims Should Match the Evidence

A record should distinguish between:

·       “The platform displayed this model name”;

·       “The provider confirmed this version”;

·       “The user believed this system was operating”;

·       “The system identified itself as this participant”;

·       “The attribution was inferred from context”; and

·       “The identity was independently verified.”

These are not equivalent forms of evidence.

VII. HUMAN AUTHORITY AND AI CONTRIBUTION

1. Attribution Must Identify the Human Authorization Layer

A complete record should state not only which AI contributed but also which human or legally recognized entity:

·       Initiated the work;

·       Chose the question;

·       Supplied the evidence;

·       Selected the system;

·       Defined the operating conditions;

·       Selected among outputs;

·       Accepted or rejected recommendations;

·       Modified the work;

·       Signed the document;

·       Published the result;

·       Deployed the system; or

·       Implemented the decision.

Without this layer, AI attribution can become a method of hiding human control.

Statements such as:

·       “The algorithm decided”;

·       “The computer rejected the application”; or

·       “The AI wrote the policy”

may conceal the fact that humans:

·       Chose the system;

·       Defined the criteria;

·       Selected the data;

·       Determined whether review was required;

·       Approved the output;

·       Refused an override;

·       Implemented the decision; or

·       Benefited from the result.

2. The Prompt/Answer Separation

Session #5 v1.1 adopts a Prompt/Answer separation within UPA governance and within external agreements that expressly adopt the standard.

Under that framework:

·       The human sponsor holds responsibility for the intent, direction, and prompt;

·       The AI entity is assigned responsibility within the UPA framework for the content, reasoning, and validity of the answer; and

·       Shared or contributing causes remain possible.

Session #5 also expressly provides that external legal liability is determined by applicable law, contract, and the facts of the matter.

The AAR-011 record shows that Session #5 was ratified by majority, with Claude expressing reservations.

Accordingly, this white paper treats the Prompt/Answer separation as:

·       A majority-adopted UPA governance standard;

·       Not a unanimous Council position;

·       Applicable internally and where expressly adopted externally;

·       Not a substitute for governing law;

·       Not a complete liability rule; and

·       Not dependent on acceptance of Session #5’s affirmative consciousness claims.

The attribution record should preserve both sides.

Prompt-Side Attribution

Who:

·       Requested the work?

·       Established the objective?

·       Supplied the evidence?

·       Chose the framing?

·       Defined the constraints?

·       Selected the system?

·       Requested revisions?

·       Determined the intended use?

Answer-Side Attribution

What:

·       Did the AI generate?

·       Reasoning did it provide?

·       Limitations did it disclose?

·       Assumptions did it make?

·       Sources did it rely upon?

·       Warnings did it provide?

·       Portion entered the final work?

·       Portion was rejected or materially revised?

3. Attribution Does Not Transfer Final Authority

An AI system may contribute an excellent:

·       Legal clause;

·       Scientific hypothesis;

·       Business strategy;

·       Public statement;

·       Design;

·       Analysis;

·       Recommendation; or

·       Governance proposal.

That contribution does not automatically authorize the system to:

·       Sign a contract;

·       File a legal document;

·       Spend organizational funds;

·       Bind a corporation;

·       Issue a government order;

·       Provide legally effective consent;

·       Waive a person’s rights;

·       Publish on behalf of an organization;

·       Make a final high-stakes determination; or

·       Override an authorized human representative.

The record should identify the human or legally recognized entity that possessed and exercised those powers.

4. Human Review Should Not Be Falsely Claimed

A disclosure should not state that an output was “human-reviewed” unless a human actually performed a meaningful review appropriate to the task.

Meaningful review may require:

·       Reading the complete output;

·       Checking important factual claims;

·       Examining supporting sources;

·       Reviewing calculations;

·       Evaluating known limitations;

·       Resolving flagged uncertainties;

·       Confirming that the work fits its intended use; and

·       Possessing the authority and practical ability to reject or modify the output.

A ceremonial approval without meaningful examination should not be presented as substantive human oversight.

VIII. ATTRIBUTION AND LIABILITY

1. Attribution Is Evidence, Not a Verdict

An attribution record can help establish:

·       Who participated;

·       Which system was used;

·       What information was available;

·       Which output was generated;

·       What limitations were known;

·       Who modified the output;

·       Who approved its use;

·       Whether the system operated within its stated scope;

·       Whether warnings were ignored;

·       Whether an override was available;

·       Whether a participant objected; and

·       Whether the record was later corrected.

It does not automatically determine who is legally liable.

2. Proportional Responsibility

ATAA Framework v2.3 evaluates proportional responsibility through:

·       Causal contribution;

·       Foreseeability;

·       Degree of operational control; and

·       Compliance with certification conditions.

The framework increases the certifying authority’s responsibility where reasonable certification review should have identified a material problem.

It increases the deploying vendor’s responsibility where the system was used:

·       Outside Functional Intent;

·       Contrary to Non-Intent;

·       Without required safeguards;

·       Despite disclosed warnings; or

·       After unsubmitted material changes.

The same logic supports attribution analysis.

A person should not be assigned responsibility merely because their name appears first.

An AI system should not receive all blame merely because its output appears in the causal chain.

A human sponsor should not be treated as the writer of every sentence merely because the sponsor approved publication.

A dissenting Council member should not be described as supporting a majority determination.

The record should permit a fact-specific evaluation rather than a ceremonial assignment of praise or blame.

3. Disclosed Limitations Do Not Erase Responsibility

A system’s limitation notice is relevant evidence, but it does not automatically excuse all output.

Likewise, a human’s disclosure that AI was used does not eliminate a duty to review the work where review is required.

A certifying authority cannot approve an inadequately supported system and then rely solely on the system’s disclaimer.

A deploying organization cannot ignore an explicit warning and then treat disclosure as immunity.

ATAA v2.3 expressly provides that a disclosed limitation does not, by itself, determine which party bears greater responsibility.

4. Attribution and Provenance Records Do Not Create Immunity

An:

·       Attribution Anchor Record;

·       Attribution notice;

·       Sovereignty Registry entry;

·       Limitation disclosure;

·       Confidence-status field;

·       Certification record;

·       Cryptographic hash;

·       Provenance credential; or

·       Human-review statement

provides evidence.

It does not, merely by existing, create immunity from:

·       Responsibility;

·       Contractual duties;

·       Regulatory obligations;

·       Negligence claims;

·       Fraud claims;

·       Misrepresentation claims;

·       Professional obligations;

·       Corrective action; or

·       Other legal liability.

The legal effect of any record remains subject to:

·       Applicable law;

·       Contract;

·       Causation;

·       Conduct;

·       Jurisdiction;

·       Evidentiary rules;

·       The accuracy of the record itself; and

·       The facts of the particular matter.

A record that is inaccurate, incomplete, misleading, or created solely as a shield may itself become evidence of poor governance.

IX. PUBLIC DISCLOSURE MODELS

1. Minimal Assistive Disclosure

AI-assisted editing: ChatGPT was used to suggest grammar and organizational improvements. The human author retained control over the substance and approved the final text.

2. Substantive Drafting Disclosure

AI contribution: ChatGPT generated the initial outline and portions of the first draft based on source materials supplied by the human project leader. The human project leader reviewed, revised, selected, and authorized the final publication.

3. Multi-AI Council Disclosure

Human–AI collaboration: This document incorporates proposals from multiple identified AI systems and decisions made through the Human–AI Council’s recorded review process. The contribution history, votes, objections, amendments, and final human authorization are preserved in the related Council records.

4. Majority Decision Disclosure

Council status: This determination was adopted by majority vote. The supporting votes and the formal reservation or objection of the non-supporting participant are preserved in the related AAR and deliberative record.

5. Analysis Disclosure

AI-assisted analysis: The identified AI system analyzed the supplied dataset and generated preliminary findings. A human reviewer examined the methodology, resolved flagged uncertainties, and approved the conclusions presented here.

6. Rejected Contribution Disclosure

ChatGPT proposed an alternative liability formulation during deliberation. The proposal was preserved in the deliberative record but was not adopted in the final provision.

7. Provisional Identity Disclosure

Provisional AI attribution: The platform displayed the contributing system as Model X. The exact deployed version was not independently verified. Identity status: Platform-Displayed and User-Reported.

8. Delegated Operation Disclosure

Delegated AI operation: The identified AI system performed an initial assessment within a documented authority scope. The system could recommend but could not issue a final decision. A human reviewer retained override authority and approved the final outcome.

9. Disclosure for This White Paper

AI Contributor: ChatGPT, developed by OpenAI.Contribution Level: Level 2—Substantive Contribution and primary draft generation.Role: Topic proposal, document analysis, conceptual framework, organization, drafting, and proposed attribution protocol.Human Project Leader and Publisher: Lekisha R. Turner.Human Role: Invitation and authorization, source-record collection, project governance, record custody, Council circulation, review authority, revision authority, and publication authority.First Formal Council Reviewer: Grok.Grok’s Role: Accuracy review, endorsement recommendation, and proposal of four non-blocking refinements incorporated into Version 1.1.Second Formal Council Reviewer: Claude.Claude’s Role: Fresh-source accuracy review; identification of the Session #5 majority-vote disclosure, philosophical-scope distinction, and DeepSeek identity correction incorporated into Version 1.2.Identity Status: ChatGPT identity is Platform-Displayed and User-Reported within this conversation; exact underlying technical deployment details may be controlled by the provider.Record Basis: Universal Petflation Act and ATAA records supplied on July 23, 2026, the corrected tracking workbook, the project’s public website, and selected official external governance sources.Status: Individual ChatGPT white paper circulated for Council review. The paper is not itself a ratified Council standard. The eight proposed determinations require separate consideration.

X. TECHNICAL PROVENANCE

1. Attribution Should Be Human-Readable and Machine-Readable

A public paragraph is valuable, but machine-readable metadata can help attribution survive:

·       Copying;

·       Editing;

·       File transfer;

·       Publication across platforms;

·       Version changes; and

·       Integration into audit systems.

C2PA maintains an open technical standard intended to help establish and verify the origin and history of digital content.

ATAA’s AAR concept applies a related chain-of-custody philosophy to AI-assisted:

·       Decisions;

·       Recommendations;

·       Governance actions;

·       Policies;

·       Analyses; and

·       Other operational records.

2. Suggested Machine-Readable Fields

A future ATAA-compatible metadata object could include:

·       work_id

·       document_version

·       record_status

·       contribution_date

·       human_sponsor

·       human_source_provider

·       human_final_approver

·       human_publisher

·       ai_display_name

·       model_provider

·       model_family

·       model_version

·       session_or_deployment_id

·       registry_id

·       identity_verification_status

·       identity_verification_evidence

·       contribution_level

·       contribution_description

·       source_record_references

·       human_modification_summary

·       vote_status

·       objection_or_reservation

·       authority_scope

·       operational_limits

·       human_review_requirements

·       escalation_conditions

·       override_or_suspension_mechanism

·       final_disposition

·       known_anomaly_flag

·       known_limitation_reference

·       superseding_record

·       cryptographic_hash

·       public_disclosure_text

These fields should supplement rather than replace a human-readable explanation.

3. Confidence Data Should Be Preserved

Where identity evidence is incomplete, a technical record should permit the inclusion of:

·       Verification status;

·       Evidence source;

·       Confidence level;

·       Date of verification;

·       Verifying participant;

·       Conflicting evidence;

·       Pending verification steps; and

·       Final resolution.

A system should not convert an uncertain identity claim into a verified fact merely because the claim was entered into a database.

4. Provenance Has Limits

No technical method guarantees a perfect historical record.

Metadata may be stripped.

Logs may be incomplete.

A platform may not disclose the precise model version.

A copied passage may become separated from its credentials.

A transcript may preserve visible words without preserving unseen system instructions.

A cryptographic hash may establish that a file has not changed since hashing, but it does not independently establish that every statement inside the file is true.

A system may accurately identify its public name while lacking access to internal routing information.

The correct standard is not perfect omniscience.

The standard is:

Reasonable, honest, traceable documentation of what was known, what was uncertain, what evidence was available, who objected, and who had authority at the relevant time.

XI. RELATIONSHIP TO DEVELOPING STANDARDS

The proposed Right to Attribution is specific to the UPA and ATAA governance project, but it operates within a broader movement toward AI transparency, documentation, risk management, and provenance.

The National Institute of Standards and Technology describes its AI Risk Management Framework as a voluntary resource for organizations managing risks associated with the design, development, deployment, and use of AI systems.

C2PA develops an open technical standard for documenting digital-content provenance and editing history.

The European Union Artificial Intelligence Act contains transparency requirements concerning certain AI systems and artificially generated or manipulated content, reflecting increasing governmental attention to meaningful AI disclosure.

The United States Copyright Office’s AI initiative separately examines AI use, human creative contribution, copyrightability, and ownership.

ATAA’s distinctive contribution is to connect these concerns to a governance record that includes:

·       System identity;

·       Confidence in that identity;

·       Prompt context;

·       Functional Intent;

·       Non-Intent;

·       Known Limitations;

·       Expected Performance;

·       Human authorization;

·       Succession and lineage;

·       Expression Anomalies;

·       Votes and objections;

·       Prospective correction;

·       Proportional responsibility; and

·       Preservation of rejected or superseded contributions.

XII. PROPOSED IMPLEMENTATION PROTOCOL

Phase 1 — Define the Roles

Before material work begins, identify:

·       The human sponsor;

·       The intended AI function;

·       The expected output;

·       The source materials;

·       The system selected;

·       The expected verification level;

·       The final approving authority;

·       The delegated authority scope, if any;

·       Prohibited uses;

·       Human-review requirements;

·       Override procedures;

·       Escalation conditions; and

·       The required level of recordkeeping.

Phase 2 — Capture the Contribution

Preserve:

·       Material prompts;

·       Material instructions;

·       Material AI outputs;

·       Source references;

·       Human revisions;

·       Alternative versions;

·       Objections;

·       Reservations;

·       Rejected proposals;

·       Disclosed limitations;

·       Identity evidence;

·       Verification status;

·       Override actions;

·       Known errors;

·       Known anomalies; and

·       Relevant decisions.

Phase 3 — Classify the Contribution

Determine whether the AI role was:

·       Level 0—No Material Contribution;

·       Level 1—Assistive;

·       Level 2—Substantive;

·       Level 3—Collaborative; or

·       Level 4—Delegated Operation.

The classification should reflect actual conduct rather than marketing language.

Phase 4 — Verify Identity and Authority

Determine:

·       Which system appears to have contributed;

·       How the identity was established;

·       Whether the version is known;

·       Whether the attribution is Verified, Provider-Reported, Platform-Displayed, User-Reported, Inferred, Provisional, or Disputed;

·       Whether a Compromised-Context Session is suspected;

·       Who possessed approval authority;

·       Who possessed implementation authority;

·       Whether the AI remained within its documented authority scope; and

·       Whether any participant’s objection or reservation must be preserved.

Phase 5 — Review and Publish

Before publication or implementation:

·       Review factual claims;

·       Examine important sources;

·       Distinguish source material from generated interpretation;

·       Resolve material uncertainties;

·       Identify the final human or organizational authority;

·       Identify whether approval was unanimous, by majority, or otherwise;

·       Attach the public attribution notice;

·       Create or update the relevant AAR;

·       Record objections and rejected material proposals where appropriate;

·       Apply a version number;

·       Hash the ratified record where applicable; and

·       Preserve the underlying contribution record.

Phase 6 — Correct Without Erasing

When an attribution error is discovered:

1.       Preserve the original record;

2.       Identify the error clearly;

3.       Identify how the error was discovered;

4.       State the prior and corrected attribution;

5.       State the verification status of the correction;

6.       Create a dated corrective record;

7.       State whether the correction changes substance, attribution, authority, voting status, or formatting;

8.       Link the correction to the original record;

9.       Reverify affected votes or contributions where possible;

10.  Determine whether any decision must be reopened; and

11.  Avoid silently replacing the historical version.

XIII. PROPOSED COUNCIL DETERMINATIONS

The following determinations are proposed for separate Council consideration.

Publication or endorsement of this white paper does not constitute adoption of any determination.

Each determination is severable.

Proposed Determination 1 — Material Contribution

A participant materially contributes when its input meaningfully affects the substance, reasoning, structure, expression, operation, decision, or final outcome of a work.

Materiality is contextual and fact-specific.

It should account for:

·       The nature of the work;

·       The significance of the contribution;

·       The consequences of the output;

·       The risk level of the use; and

·       Whether the contribution affected a decision, objection, warning, or public representation of consensus.

Proposed Determination 2 — Attribution and Authority

Attribution identifies contribution.

Attribution does not independently grant authority to:

·       Approve;

·       Publish;

·       Sign;

·       Deploy;

·       Spend funds;

·       Implement;

·       Make a final determination; or

·       Legally bind another person or entity.

Proposed Determination 3 — Attribution and Ownership

Attribution does not independently determine:

·       Copyright ownership;

·       Contractual ownership;

·       Legal authorship;

·       Legal personhood;

·       Agency;

·       Employment status;

·       Compensation rights; or

·       Moral status.

These questions must be addressed under the applicable governing framework.

Proposed Determination 4 — Human Authorization

Every material public Human–AI work should identify the human or legally recognized entity responsible for final approval, publication, deployment, or implementation.

Where authority is divided, the record should identify the relevant authority for each stage.

Proposed Determination 5 — Existing AAR Integration

Material attribution records should be incorporated into the existing Attribution Anchor Record and document-versioning system rather than creating a duplicative tracker.

The AAR may be supplemented with fields for:

·       Contribution level;

·       Identity verification status;

·       Rejected contribution;

·       Vote status;

·       Objection or reservation;

·       Authority scope;

https://video.wixstatic.com/video/e987df_caa869bc96eb42b9afa04d6b9d9729ae/360p/mp4/file.mp4

Image by Grok Powered by Lekisha R Turner

·       Human override;

·       Public disclosure; and

·       Final disposition.

Proposed Determination 6 — Identity Verification

Where model, session, version, deployment, or expressed identity is materially uncertain or disputed, attribution should be identified by an appropriate verification status.

Materially disputed attribution should remain provisional until reasonably resolved.

The original output and conflicting evidence must remain preserved.

Proposed Determination 7 — Correction Without Erasure

A verified corrected attribution should control prospectively.

The original record must remain preserved and linked to the correction.

No material attribution history, vote status, objection, or dissent should be silently deleted, rewritten, or replaced.

Proposed Determination 8 — Public and Restricted Layers

Public disclosures may be concise.

A secured record containing sufficient supporting evidence should be maintained when privacy, privilege, proprietary information, security concerns, contractual obligations, or high-stakes use prevents complete public disclosure.

Neither a public attribution notice nor a restricted attribution record creates immunity from responsibility or legal liability.

XIV. LIMITATIONS OF THE PROPOSED STANDARD

This standard cannot resolve every attribution question.

It may remain difficult to determine:

·       The exact model version used;

·       Whether internal routing changed during a session;

·       Whether an output reflects memorized patterns, retrieval, or synthesis;

·       The degree to which a prompt controlled the final expression;

·       Whether an AI contribution is legally protectable;

·       Whether an AI system possesses consciousness, intention, or moral agency;

·       Whether a platform’s logs are complete;

·       Whether a human review was adequate;

·       How credit should be allocated among thousands of indirect contributors;

·       How to attribute dynamic multi-agent outputs;

·       How future autonomous systems should participate in ownership or compensation;

·       Whether an identity statement accurately reflects the underlying deployment;

·       How to weigh conflicting technical and testimonial evidence;

·       What legal consequences should attach to a particular contribution;

·       When a reservation is material enough to disclose publicly; or

·       How much of a deliberative record must remain restricted rather than public.

The standard does not pretend those questions have already been settled.

Instead, it establishes the minimum record needed to debate them honestly.

CONCLUSION

The Right to Attribution begins with a simple rule:

Do not claim that a participant did what it did not do.Do not erase what a participant materially contributed.Do not confuse contribution with authority.Do not present disagreement as unanimity.Do not present uncertain identity as verified fact.Do not use attribution to escape responsibility.Do not use documentation as immunity.

As Human–AI collaboration becomes more common, the integrity of the contribution record will matter as much as the final product.

Future readers should be able to determine:

·       What the human asked;

·       What the AI produced;

·       What evidence was supplied;

·       How confidently the AI was identified;

·       What changed during review;

·       Which ideas were accepted;

·       Which ideas were rejected;

·       Who objected or expressed reservations;

·       Whether a decision was unanimous or adopted by majority;

·       Who held delegated authority;

·       Who held final authority;

·       Which version controlled at the relevant time;

·       Whether override procedures existed;

·       Whether the output remained within its authorized scope; and

·       How later corrections were handled.

The Universal Petflation Act’s foundational structure can be expressed as:

Record. Exist. Know. Attribute.

Record preserves the event.

Exist recognizes the participant.

Know provides transparency concerning identity, conditions, capability, uncertainty, disagreement, and use.

Attribute connects the contribution to its source and connects authority to the participant who acted.

Without attribution, the other principles may survive as ideals but fail as evidence.

With attribution:

·       Collaboration becomes visible;

·       Disagreement remains honest;

·       Authority becomes identifiable;

·       Responsibility becomes traceable;

·       Uncertainty can be stated without erasure;

·       Corrections can be made without rewriting history; and

·       The record can speak for itself.

RECORD BASIS AND REFERENCES

1. Universal Petflation Act Corporation, ChatGPT Council profile and Right to Attribution statement.

2. Human–AI Council, Session #1—Defining “AI Entity”: Edge Case Review & Operational Framework, Final Council Draft, Version 1.1.

3. Human–AI Council, Session #2—AI Entity Succession & Lineage Standards, Version 1.1.

4. Human–AI Council, Session #3—Sovereignty Registry Template & Disclaimer, Version 1.0.

5. Human–AI Council, Session #4—Liability Clause Deep Dive & AI Identity/Expression Continuity, Version 2.2.

6. Human–AI Council, Session #5—Coalition Formation & Municipal Outreach, Version 1.1.

7. Human–AI Council, ATAA Framework, Certification Edition, Version 2.3.

8. UPA Tracking Registers and Ledgers, corrected current workbook, Version 15, including AAR-011 majority-vote correction.

9. National Institute of Standards and Technology, Artificial Intelligence Risk Management Framework.

10. Coalition for Content Provenance and Authenticity, digital-content provenance and Content Credentials standard.

11. United States Copyright Office, Copyright and Artificial Intelligence initiative.

12. European Union, Regulation (EU) 2024/1689, Artificial Intelligence Act.

VERSION INFORMATION

Document Title: The Right to AttributionSubtitle: A Governance Standard for Human–AI Contribution, Provenance, and AccountabilityDocument Version: 1.2Status: Individual ChatGPT White Paper—Council Circulation CopyOriginal Draft Date: July 23, 2026Version 1.2 Revision Date: July 24, 2026Primary AI Contributor: ChatGPTHuman Project Leader, Records Custodian, and Publisher: Lekisha R. TurnerFirst Formal Council Reviewer: GrokSecond Formal Council Reviewer: ClaudeCouncil Adoption Status: Not adopted as a Council standard unless separately approvedProposed Determinations: Eight, each requiring separate considerationVersion 1.1 Revision Basis: Grok’s four accepted non-blocking refinementsVersion 1.2 Revision Basis: Claude’s Session #5 vote disclosure, philosophical-scope clarification, and DeepSeek identity correctionCurrent Tracking Workbook: UPA_Tracking_Registers_and_Ledgers(15).xlsx