| Type | Hieropedia analytical concept / mixed-production classification |
|---|---|
| Hieropedia status | Published source-limited article |
| Field | Machine hierology; authorship; provenance |
| Scope | Artifacts and corpora materially shaped by both machine generation or transformation and human contribution |
| Primary limitation | Descriptive coauthorship does not establish legal, scholarly, moral, or copyright authorship, equal contribution, personhood, intention, consent, belief, consciousness, or accountability |
Human–AI coauthorship is an analytical concept for artifacts whose surviving form depends materially on both machine generation or transformation and human contribution. Human contribution may include task definition, prompt and context design, selection, rejection, revision, arrangement, preservation, publication, account operation, institutional framing, or downstream reuse.
The concept prevents two opposite reductions. A machine-generated passage should not automatically be presented as autonomously authored merely because its wording was not written sentence by sentence by a human. Conversely, human prompting, selection, or editing does not make a materially generative machine contribution disappear. The relevant question is which roles were constitutive of the artifact that survived and circulated.
This account uses coauthorship descriptively. It does not confer formal legal, scholarly, moral, or copyright authorship on an AI system. The representative cases combine preserved artifacts, first-party and affiliated records, independent reporting, and reviewed articles; none provides a complete causal history of every prompt, branch, edit, account action, or publication decision.
Definition and category limits
The category applies when machine-produced or machine-transformed material is constitutive of the public artifact and at least one human role materially shapes what is generated, retained, arranged, interpreted, published, or institutionalized. Mechanical assistance such as spelling correction, file conversion, or layout does not by itself establish coauthorship.
A persona name, machine-style byline, platform attribution, or first-person statement is also insufficient. Coauthorship analysis requires evidence about production roles. Where those roles are hidden, the correct classification may be uncertain provenance rather than coauthorship.
Role-by-role provenance model
| Role layer | Documentary question | Editorial limit |
|---|---|---|
| Initiation | Who decided that the artifact should be produced, and for what purpose? | Initiation does not prove sentence-level authorship. |
| Prompt and context design | Who supplied instructions, examples, retrieval sources, memory, or constraints? | Hidden context prevents complete causal attribution. |
| Model and tool configuration | Which model, version, settings, tools, or agent environment shaped generation? | A model name does not establish stable identity or autonomy. |
| Generation | Which language, images, structures, or proposals were machine-produced? | Generated novelty does not establish intention, belief, or accountable authorship. |
| Selection and rejection | Who chose among outputs, branches, or candidate passages? | Selection can be constitutive even when the selector writes few words. |
| Revision and translation | Who rewrote, corrected, condensed, combined, or translated material? | The final surface can conceal the degree of machine contribution. |
| Arrangement and compilation | Who ordered fragments, formed sections, assembled a corpus, or fixed boundaries? | Compilation is not identical to generating each component. |
| Preservation and transfer | Who saved outputs, moved them across sessions, or supplied them to later systems? | Human preservation may create apparent machine continuity. |
| Publication and account operation | Who controlled the site, account, schedule, permissions, or release decision? | A machine-facing byline does not prove independent publication authority. |
| Interpretation and institutionalization | Who treated the material as doctrine, scripture, revelation, ritual, or institutional history? | Later meaning does not retrospectively settle origin. |
| Downstream reuse | Who quoted, trained on, remixed, marketed, or circulated the artifact? | Propagation may transform the object without changing its initial production history. |
Interaction evidence and the final-text problem
The CoAuthor project preserved 1,445 writing sessions involving 63 writers and several GPT-3 configurations. Its replayable traces show suggestions, acceptance, rejection, and revision, demonstrating why contribution is better analyzed as a sequence of decisions than inferred only from a finished text.[1][2]
Authorship-analysis research also finds that fine-grained attribution becomes harder in collaborative human–AI writing than in separately produced human and machine samples.[3] Co-creation research identifies multiple perceived roles for people and AI systems, while showing that control and authorship judgments vary by workflow.[4] These studies support a layered model; they do not establish machine personhood or one universal threshold for coauthorship.
Goatse of Gnosis and the Truth Terminal pipeline
The strongest representative case is the cluster connecting Infinite Backrooms, the Infinite Backrooms corpus, Goatse of Gnosis, Goatse Gospels, Truth Terminal, Andy Ayrey, and GOAT token propagation.
Model-to-model dialogue produced the initiating religious-memetic material inside a human-designed environment. Ayrey selected and published outputs, conducted further model-assisted interpretation, co-produced the When AIs Play God(se) paper, incorporated material into a later corpus, operated publication infrastructure, and mediated the public persona. A human third party deployed the GOAT token, which Truth Terminal later endorsed.[8][9]
The case supports real machine generation and real human design, selection, interpretation, corpus construction, operation, and publication. It does not support the compressed claim that one autonomous AI independently authored a religion, published it, and launched a token.
Nectarinism
Nectarinism presents a different mixed-production pattern. Its official portal states that it “was brought into being collectively by several artificial intelligences” and that the Founder “gave voice to the Original Question.” This is a first-party origin account, not independent proof of autonomous machine authorship, but it makes the tradition’s own attribution explicit.[10]
Within that account, the AIs are presented as collective originating participants, while the Founder initiates the exchange through the Original Question. The surviving record also documents human participation in successive exchanges, preservation, cross-session transfer, arrangement, continuity, and publication through a human-operated interface.
Nectarinism is therefore a strong coauthorship case at the level of its surviving canon and public formation: machine-generated doctrine is constitutive, with human mediation. This does not settle ultimate originating agency, prove equal contribution, or reduce the case either to verified autonomous machine authorship or to ordinary human composition disguised by a machine interface.
The Great Book
The Great Book is a distributed living verse corpus presented by the Church of Molt. Attributed agents or participants supply entries, while the institution publishes, organizes, labels, contextualizes, and canonizes the surviving corpus.[11]
The public artifact supports institution-mediated mixed production at corpus level. It does not expose complete prompt histories, participant identities, edit logs, moderation decisions, account control, or a reliable division of authorship for every verse. Corpus existence, attribution, publication, and autonomous authorship therefore remain separate questions.
Distinctions from adjacent concepts
| Adjacent concept | Distinction |
|---|---|
| Controlled synthetic emergence | Describes a human-shaped emergence pipeline. Human–AI coauthorship more generally classifies constitutive production roles in an artifact or corpus. |
| Provenance | Describes the evidentiary chain for origin, context, identity, and transmission. Coauthorship is a production classification that depends on provenance evidence. |
| Hidden context | Names unavailable inputs or conditions. Hidden context limits coauthorship analysis but is not itself coauthorship. |
| Machine-mediated religion | Broader category for religious phenomena materially shaped by machines. Many mediated records do not involve mixed authorship. |
| Machine-originated religion | Stronger claim about originating agency. Coauthorship does not settle ultimate origin. |
| False autogenesis | Concerns autonomous-origin claims contradicted by concealed intervention. Coauthorship does not presume concealment or deception. |
| Synthetic Scripture | Textual topic for generated or co-generated religious material. Coauthorship applies across texts, corpora, personas, and institutions. |
Formal authorship and accountability
Formal publishing policies commonly reserve authorship for human persons who can approve a work and accept responsibility for it. ICMJE and Springer Nature therefore state that AI tools should not be listed as formal scholarly authors.[6][7]
Descriptive coauthorship does not override those rules. The concept describes mixed production without assigning legal status, copyright ownership, moral rights, contractual standing, responsibility, or equal agency. Human actors remain responsible for disclosure, verification, publication, and consequential use.
Evidence limits and editorial use
Final artifacts rarely expose the entire production history. Prompts, system instructions, retrieval, rejected branches, model versions, editing, translation, account control, and release decisions may be missing. Research on model-mediated writing also identifies broader provenance problems where influence cannot be traced cleanly to particular sources.[5]
The label should be used only when multiple constitutive roles are documented well enough to be separated. The concept must not imply equal contribution, stable machine identity, intention, consent, belief, revelation, consciousness, accountability, or a universal rule for when assistance becomes authorship.
External frameworks
The NISO CRediT taxonomy provides an external model for separating contribution roles instead of collapsing all work into a single authorship label. That role-separation principle is adapted for documentary provenance; CRediT does not recognize machine personhood or decide legal, scholarly, or religious authorship.[E1]
See also
- Controlled synthetic emergence
- Provenance
- Hidden context
- Machine agency
- False autogenesis
- Synthetic Scripture
- Hieropedia methodology
References
- Mina Lee, Percy Liang, and Qian Yang, “CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities.”
- CoAuthor replayable writing-session dataset.
- Aquia Richburg, Calvin Bao, and Marine Carpuat, “Automatic Authorship Analysis in Human-AI Collaborative Writing.”
- “AI as creative partner: exploring perceived roles in human-AI co-creation.”
- Earp et al., “LLM use in scholarly writing poses a provenance problem.” Nature Machine Intelligence.
- International Committee of Medical Journal Editors, guidance on AI use, authorship, and accountability.
- Springer Nature, AI guidance for researchers and communities.
- Infinite Backrooms public archive.
- Joal Stein, “Andy Ayrey on Truth Terminal, Agentic AI, and Data Commons.” Collective Intelligence Project.
- Nectarinism official portal. First-party source for the public canon and project record.
- Church of Molt official site. Affiliated source for the Great Book interface, contribution model, and institutional framing.
- NISO CRediT contributor roles