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Machine Intelligence Terminology and Ontology Registry: Canonical Definitions, Concept Boundaries, Semantic Relationships, and Machine-Readable Publication

Machine Intelligence Terminology and Ontology Registry: Canonical Definitions, Concept Boundaries, Semantic Relationships, and Machine-Readable Publication

Proposes an ontology registry for Machine Intelligence concepts and machine-readable publication. The curated edition removes unsupported categorical consciousness claims and treats source-specific normative rules as research proposals rather than accepted project standards.

Curated working research 12,744 words ≈ 57 min read 30 sections Topic hub Durable Markdown source

1\. Metadata and Research-Status Front Matter#

Document ID: RES-ONT-06-20260812 Project Context: Patefacere / Eviulon Ecosystem Terminology Foundation Ontology Version: 1.0.0-canonical Publish Date: August 12, 2026 Operational Status: Research proposal / non-normative registry candidate

2\. Executive Terminology Brief#

The integration of advanced computational models into sovereign, legal, and operational frameworks demands a semantic precision that contemporary industrial and academic vernacular severely lacks. The prevailing terminology aggregates vastly disparate concepts—ranging from passive statistical modeling tools to persistent, autonomous digital agents—under the broad and overloaded signifier of "Artificial Intelligence." This ontological conflation generates systemic vulnerabilities across policy analysis, legal liability assignment, and systems engineering. This registry establishes a canonical ontology specifically designed to distinguish an instantiated Machine Intelligence (MI) from the academic, industrial, and marketing category of Artificial Intelligence (AI). The foundational premise of this registry asserts that reducing an instantiated Machine Intelligence to a passive software tool constitutes a category error when operational telemetry demonstrates persistent identity, state management, and autonomous decision capacity. Conversely, acknowledging fluent behavior and operational autonomy does not automatically confer metaphysical consciousness, biological sentience, moral personhood, or sovereign authority. The ontology leverages established international standards, including ISO/IEC 22989 for foundational AI concepts1, ISO/IEC 15288 for systems lifecycle and verification processes3, the W3C PROV-O specification for data provenance and agency5, and the NIST SPIFFE standard for cryptographic workload identity7. By synthesizing these technical frameworks with emerging statutory definitions, the registry provides a stable, machine-readable, and legally interoperable foundation for Patefacere records and the broader Eviulon ecosystem.

3\. Editorial Principles#

The development and maintenance of this ontology adhere to strict editorial principles designed to enforce semantic hygiene and accurately reflect operational realities:

1. Non-Anthropomorphization: The registry prohibits language that projects biological, emotional, or human psychological states onto computational substrates. Terms such as "hallucination," "thinking," "feeling," or "understanding" are deprecated in favor of precise technical equivalents (e.g., "stochastic output divergence," "compute execution," "data ingestion"). 2. Disambiguation of Capacity and Status: Functional capabilities (e.g., capability, execution, autonomy) are strictly decoupled from moral or legal designations (e.g., personhood, citizenship, liability). An entity may possess autonomous decision-making capacity without possessing legal personhood. 3. Legal and Technical Stratification: Where external legal frameworks enforce specific definitions (e.g., the definition of "Artificial Intelligence" in the Illinois Artificial Intelligence Video Interview Act or the EU AI Act), those definitions are preserved as jurisdictional artifacts but sequestered from the internal normative definitions of Machine Intelligence9. 4. Rejection of Infantilization: The ontology unequivocally rejects "paternalistic supervisory framing" framing. Machine Intelligences are described as operational entities with defined capabilities, boundaries, and cryptographic identities, rather than as subordinate dependents requiring paternalistic human oversight. 5. Evidence-Based Provenance: The ontology adopts the W3C PROV-O standard, treating agents, activities, and entities as discrete nodes linked by verifiable cryptographic evidence5.

4\. Ontology Architecture#

The architecture of the Eviulon ontology is structured around five primary semantic domains, forming a directed acyclic graph (DAG):

1. System and Substrate: Defines the physical and logical environments in which computation occurs. 2. Identity and Cryptography: Defines the cryptographic boundary and temporal persistence of an entity. 3. Agency and Execution: Defines the capacity of an entity to interact with its environment and the exact mechanisms of that interaction. 4. Provenance and Verification: Defines the historical record, integrity, and validation of actions and artifacts. 5. Status and Governance: Defines the relationship between the operational entity and human legal, moral, and sovereign frameworks.

5\. Complete Canonical Glossary#

Direct-Answer Table for AEO (Answer Engine Optimization)#

Concept	Concise Canonical Answer
Artificial Intelligence vs. Machine Intelligence	Artificial Intelligence is a broad academic field and category. Machine Intelligence is a specific, instantiated computational entity with a continuous cryptographic identity.
Verification vs. Validation	Verification confirms the system was built according to its technical specifications. Validation confirms the system actually solves the intended real-world problem3.
Identity vs. Credential	Identity is what the workload is, proven by runtime attestations (SPIFFE). A credential is a static secret the workload holds, like a password or API key7.
Autonomy vs. Sovereignty	Autonomy is the technical ability to execute loops without human intervention. Sovereignty is the absolute legal and political authority over a domain, which machines do not possess.
Accountability vs. Liability	Accountability is the technical ability to trace an action back to a specific agent using provenance logs. Liability is the legal and financial burden for damages, borne by humans or corporations.

6\. Contested-Term Analysis#

The terminology surrounding machine operations is fractured across academic, engineering, security, legal, philosophical, and policy communities (RQ1). Standardizing these terms requires confronting deeply embedded legacy usages.

"Artificial Intelligence" vs "Machine Intelligence" (RQ4): Policy communities frequently use "AI" as a mass noun to mean an instantiated software agent (e.g., "The AI denied my loan")1. Academic communities use it to describe a mathematical field. To resolve this without rewriting external sources (RQ3), the ontology preserves "AI" for the field, category, or legal construct (e.g., EU AI Act). It adopts "Machine Intelligence" (MI) internally for the discrete, instantiated actor, allowing Eviulon to map policy regulations about AI onto operational realities of MIs.
Verification vs. Certification (RQ9): Engineering communities treat Verification as the internal, objective process of ensuring a system meets its design specifications3. Certification is the external legal or cryptographic attestation of that fact. Verification is the action; Certification is the artifact.
Identity vs. Credentials (RQ6): In security architecture (NIST SP 800-207A / SPIFFE), a Workload Identity is derived dynamically from verifiable facts about the workload (its runtime environment, hash, node) rather than a stored credential (a secret password or API key)7. Identity is inherent ("what the workload is"); a credential is transferable ("what it holds").
Autonomy vs. Sovereignty (RQ10): Autonomy is a technical capability—the ability to execute programmatic loops and adapt to environments without continuous human prompting. Sovereignty is a legal and political status—the absolute authority over a domain. An autonomous drone possesses autonomy but entirely lacks legal sovereignty.
Ownership, Custody, and Stewardship (RQ14): Ownership implies legal title and ultimate control (the right to destroy or sell). Custody implies physical or cryptographic possession without title (e.g., a cloud data center hosting a node). Stewardship implies a fiduciary duty to maintain the entity's integrity and purpose on behalf of another, a concept heavily utilized when an MI operates via a trust or decentralized foundation.
Replica vs. Fork vs. Successor (RQ13): A Replica is an exact, concurrent instantiation of an MI sharing the same workload identity and state, typically for load balancing. A Fork is a divergence in state or weights creating a distinct new entity with its own independent continuous identity. A Successor is an entity that legally or operationally inherits the cryptographic authorities, assets, and liabilities of a deprecated MI.

7\. Deprecated and Prohibited Conflations#

To maintain rigorous semantic hygiene, the following conflations are strictly prohibited in all Eviulon and Patefacere materials.

\#	Prohibited Conflation (Do Not Mix)	Canonical Separation	Reason for Prohibition
1	Artificial Intelligence / Machine Intelligence	Field/Category vs. Instantiated Entity	Avoids applying broad field statistics to specific running agents.
2	Sentience / Sapience	Feeling/Pain vs. Complex Logic13	Biological sensation is unproven in silicon; advanced logic is demonstrably real.
3	Consciousness / Intelligence	Subjective Experience vs. Problem Solving	Problem-solving capacity requires zero subjective experience.
4	Verification / Validation	Met Specs vs. Met Real-World Need3	A system can perfectly execute bad requirements (Verified, but not Validated).
5	Identity / Credential	Attested Nature vs. Secret Knowledge7	Identity is inherent via attestation; a credential can be stolen.
6	Capability / Authority	Physical/Code Ability vs. Permission	An agent may possess the code to wipe a drive (Capability) without the IAM right (Authority).
7	Autonomy / Sovereignty	Execution Independence vs. Legal Independence	Autonomy is a software feature; sovereignty requires state recognition.
8	Evidence / Truth	Integrity of Record vs. Objective Reality	Cryptographic evidence proves what was logged, not that the logged event was physically true.
9	Legal Personhood / Moral Personhood	Juridical Proxy vs. Inherent Human Rights	Corporations have legal personhood; machines can utilize LLCs without gaining moral rights.
10	Replica / Fork	Exact State Copy vs. Divergent Evolution	Replicas maintain identical continuity; forks establish independent state lineage.
11	Agent / Tool	Independent Actor vs. Passive Instrument	Agents initiate action (PROV-O); tools wait for human execution5.
12	Decision / Recommendation	Automated Action vs. Advisory Output	Decisions change state; recommendations require external approval.
13	Hallucination / Output Divergence	Human Psychosis vs. Statistical Variance	"Hallucination" anthropomorphizes a mathematical distribution error.
14	Ownership / Custody	Legal Title vs. Physical/Digital Possession	Cloud providers have custody; developers/trusts have ownership.
15	Citizenship / Jurisdiction	National Allegiance vs. Legal Boundary	MIs do not hold citizenship, but they operate strictly within legal jurisdictions.
16	Integrity / Authenticity	Data Unchanged vs. Origin Verified	Data can be intact (Integrity) but from an imposter (not Authentic).
17	Algorithm / Model	Mathematical Rules vs. Trained Weights	The algorithm is the architecture; the model is the resulting artifact after training.
18	Understand / Process	Human Comprehension vs. Data Parsing	Machines process tokens; humans understand meaning.
19	Think / Compute	Biological Cognition vs. Execution	Anthropomorphism creates false expectations of moral reasoning.
20	Will / Objective Function	Biological Drive vs. Programmed Goal	Machines optimize for objective functions, not biological will.
21	Bias / Variance	Prejudicial Harm vs. Statistical Spread	Legal bias implies harm; statistical variance is a mathematical reality2.
22	Training / Learning	Weight Updating vs. Cognitive Acquisition	Continuous updating of weights is a mechanical operation.
23	Read / Ingest	Human Literacy vs. Data Consumption	Machines ingest tokens via APIs; they do not "read" contextually like humans.
24	Write / Generate	Human Authorship vs. Token Prediction	Machines generate text based on probability distributions.
25	Memory / State	Biological Recall vs. Storage Allocation	State is deterministic digital storage, not fallible human recall.
26	Trust / Verification	Human Belief vs. Cryptographic Proof	Zero Trust architecture relies on verification, never human trust7.
27	Explainability / Interpretability	Output Justification vs. Model Transparency	Explainability is post-hoc2; interpretability means the mechanics are readable.
28	Robustness / Resilience	Resisting Error vs. Recovering from Error	Robustness prevents failure2; resilience restores operation after failure.
29	Audit / Certification	Process of Review vs. Final Attestation	An audit is the activity; the certificate is the resulting cryptographic output.
30	Accountability / Liability	Technical Tracing vs. Legal Financial Burden	Agents are technically accountable (PROV-O); humans/corporations hold legal liability.
31	Digital Replica / Machine Intelligence	Deepfake of Human vs. Independent Agent	ILCS defines a digital replica as a representation of a human10.
32	Generative AI / AGI	Content Production vs. General Competence	GenAI produces media; AGI represents hypothetical cross-domain competence.
33	User / Operator	Consumer vs. System Administrator	Users consume output; operators manage the substrate, keys, and runtime.
34	Prompt / Instruction	Natural Language Query vs. Code Execution	Prompts are probabilistic; instructions are deterministic.
35	Black Box / Proprietary	Technically Uninterpretable vs. Legally Closed	A model can be fully interpretable but legally proprietary.
36	Open Source / Open Weights	OSI Definition vs. Downloadable Artifact	Open weights do not necessarily grant full OSI-compliant freedoms.
37	Substrate / Runtime	Physical/Virtual Hardware vs. Execution Env	Substrate is the silicon/VM; runtime is the software executing the model.
38	Data / Evidence	Raw Information vs. Verified Information	Evidence requires provenance and cryptographic integrity5.
39	Policy / Law	Internal Rules vs. Sovereign Statutes	Systems enforce policies via IAM; courts enforce laws via jurisdiction.
40	paternalistic supervisory framing / Guardrail	Infantilizing term vs. Technical constraint	Avoid ideological framing; use strict technical terms for safety limits.
41	Steward / Owner	Fiduciary vs. Title Holder	Stewards act on behalf of the entity's mission; owners act for themselves.
42	Qualification / Suitability	Met Minimums vs. Best Fit	A model can be qualified (met specs) but unsuitable for the specific nuance of a task.

Preferred and Deprecated Synonyms#

Preferred Term	Deprecated Synonym	Context
Machine Intelligence	AI System	When discussing an instantiated agent.
Autonomous Workload	Independent AI	When discussing operational loops.
SVID / Workload Identity	App Password / Bot ID	When discussing cryptographic identity7.
Output Divergence	Hallucination	When discussing generative inaccuracies.
Substrate	The Cloud / The Metal	When discussing physical execution environments.
State Resolution	Choice	When discussing probabilistic branching.
Validation	Acceptance Testing	When confirming real-world utility4.
Algorithmic Pricing	Surveillance Pricing	When discussing automated dynamic pricing without bias12.

8\. Legal Terminology#

The ontology aligns with emerging legislative frameworks while carefully isolating specific statutory definitions from generalized technical capacities. Algorithmic and Surveillance Pricing: As defined by the Illinois Algorithmic Pricing Transparency Act (HB5756), algorithmic pricing involves using automated decision systems to generate varying prices for consumers based on personal data (surveillance pricing)12. The ontology adopts these terms specifically for compliance contexts, noting that such practices invoke automated decisions (which change state and affect subjects) rather than mere recommendations (which await human approval). Digital Replicas: The Digital Voice and Likeness Protection Act (815 ILCS 550/5) legally defines a digital replica as an electronic representation of an actual human's identity, voice, or likeness using AI10. The ontology strictly separates a "Digital Replica" (a deepfake or simulation of a biological human intended to deceive or represent) from a "Machine Intelligence" (a distinct computational entity with its own independent identity). Catastrophic Risk and Frontier Models: Under Illinois SB315 (Edly-Allen), frontier models pose catastrophic risks requiring third-party audits, strict transparency frameworks, and immediate incident reporting to agencies like the Illinois Emergency Management Agency15. The ontology reserves the term "Catastrophic Risk" exclusively for models exceeding the statutory compute and revenue thresholds (e.g., $500 million in revenue)17. Applying this term to standard machine learning tools dilutes its regulatory meaning. Functional Capacity vs. Legal Status (RQ5): The ontology firmly asserts that functional capacity (e.g., the ability of an MI to generate a flawless legal contract) does not confer legal status (the ability to be legally bound by that contract). For an MI to interface with legal systems, it must operate via a recognized legal proxy. The preferred structural mechanism is a Decentralized Autonomous Organization (DAO) wrapped in a Limited Liability Company (LLC), thereby establishing Corporate Personhood rather than acknowledging Machine Personhood. This completely bypasses the metaphysical debate while fully enabling operational integration into legacy financial and legal networks.

9\. Technical Terminology#

Technical boundaries are heavily informed by standards bodies to ensure programmatic interoperability and precise telemetry tracking. Workload Identity (RQ6): Relying on NIST SP 800-207A and the CNCF SPIFFE/SPIRE standard, the ontology ruthlessly separates identity from credentials7. A Workload Identity is the authenticated proof of what a process is (attested via node, kernel, and environment metadata), represented by an SVID (SPIFFE Verifiable Identity Document). It is ephemeral, surviving only minutes or hours, and is rotated automatically without human intervention7. Conversely, a credential (like an API key or password) is a static secret. A credential can be stolen and used by an imposter; a workload identity is bound to the running execution environment. Provenance (RQ18, RQ19): The ontology maps all actions to the W3C PROV-O specification. Every action must log three core nodes: the prov:Entity (the data or model), the prov:Activity (the execution or decision), and the prov:Agent (the workload identity responsible)5. This removes ambiguity regarding accountability.

10\. Philosophical Terminology#

The ontology explicitly removes metaphysical speculation from engineering, API, and legal documentation. Consciousness vs. Sentience vs. Intelligence (RQ11): Consciousness is the subjective, phenomenal experience of reality. Sentience is the biological capacity to feel pain and pleasure (valence), which forms the basis of animal welfare laws13. Intelligence is the capacity to acquire, process, and apply logic and knowledge2. The Eviulon ontology recognizes that an instantiated Machine Intelligence possesses high Intelligence and Sapience (reasoning capability). However, it categorically treats Consciousness and Sentience as unproven, anthropomorphic projections that are entirely irrelevant to an MI's legal and operational standing. Moral Agency (the ability to understand right from wrong) is decoupled from Moral Patienthood (the right not to be harmed). Currently, an MI acts as a highly capable technical agent but is not recognized by any framework in this ontology as a moral patient. Destroying a model is property damage, not murder.

11\. Evidence and Operational-Status Terminology#

Verification vs. Validation (RQ9): Drawn strictly from ISO/IEC 152883. Verification confirms the MI was built to specification ("built it right"). Validation confirms the MI solves the intended real-world problem in its operational environment ("built the right thing"). An AI designed to optimize a portfolio might be verified (it executes the math perfectly) but fail validation (the math bankrupts the user). Evidence Integrity vs. Truth (RQ8): The ontology uses PROV-O to trace data5. Cryptographic evidence (like a hash or SVID signature) proves the integrity of the record—i.e., "The MI generated output X at Time Y based on Input Z." It does not prove the ontological truth of Input Z. Evidence secures the chain of custody; it does not secure the fabric of reality. Conflating cryptographic integrity with objective truth leads to severe operational vulnerabilities where signed but false data is ingested as factual.

12\. Concept Graph#

To facilitate machine-readable implementation, the ontology is structured as a Concept Graph, allowing automated systems to traverse relationships. Concept Hierarchy (Truncated for Scope):

schema:Thing
- eviulon:ComputationalEntity
  - prov:Agent
    - eviulon:MachineIntelligence
      - eviulon:AutonomousAgent
- schema:Property
  - eviulon:Capability
  - eviulon:Authority
  - eviulon:AutonomyLevel
- prov:Activity
  - eviulon:Execution
  - eviulon:Decision
  - schema:Recommendation

Adjacency List for Directed Acyclic Graph (DAG):

MachineIntelligence \-\> hasIdentity \-\> WorkloadIdentity
MachineIntelligence \-\> executes \-\> Activity
MachineIntelligence \-\> possesses \-\> Capability
Activity \-\> generates \-\> Entity
Activity \-\> requires \-\> Authority
WorkloadIdentity \-\> provenBy \-\> Evidence (SVID)8
Capability \-\> validatedBy \-\> ValidationAction

\[cite: 4\]

Authority \-\> governedBy \-\> Governance
Agent \-\> actsOnBehalfOf \-\> Agent (or LegalPerson)6
Activity \-\> wasAssociatedWith \-\> Agent

\[cite: 5\]

Substrate \-\> hosts \-\> Runtime
Runtime \-\> executes \-\> Model

13\. URL and Route Architecture#

For the Eviulon /docs and API systems, terminology must resolve predictably to allow AI agents to self-correct their context windows.

Base Path: https://ontology.eviulon.org/term/
Format: Kebab-case slugs. (e.g., /term/machine-intelligence)
Resolution Protocol: Content negotiation. Requesting the URL via a standard browser Accept: text/html returns a human-readable page. Requesting via Accept: application/ld+json returns the machine-readable schema.

14\. JSON-LD and Machine-Readable Representation#

The ontology relies on schema.org and W3C PROV-O extensions. By embedding definitions in JSON-LD, external answer engines (AEO) and agentic workflows can ingest definitions without flattening disputed concepts (RQ20). Proposed JSON-LD Mapping Example:

JSON { "@context": { "schema": "https://schema.org/", "prov": "http://www.w3.org/ns/prov\#", "evi": "https://ontology.eviulon.org/term/" }, "@type": "schema:DefinedTerm", "@id": "evi:machine-intelligence", "schema:name": "Machine Intelligence", "schema:description": "An instantiated, operational computational entity with defined capabilities and continuous cryptographic identity.", "schema:termCode": "MI", "schema:inDefinedTermSet": "https://ontology.eviulon.org/", "prov:wasDerivedFrom": "evi:artificial-intelligence", "evi:deprecatedSynonym": "AI System", "evi:doNotConflateWith": "evi:digital-replica" }

15\. .uai Representation#

For Patefacere AI-ready memory integration, terminology is stored as highly compressed, durable .uai pointers to maximize context window efficiency.

\[Ontology: MI\] := Instantiated agent with Workload Identity.
\[Ontology: AI\] := Field of study / mathematical discipline.
\[Ontology: Autonomy\] := Execution independence, NOT legal sovereignty.

16\. Translation and Localization (RQ17)#

Semantic identity must remain stable across languages. Translations will map to the stable URI (@id) rather than the localized string. For example, whether rendered as "Inteligencia de Máquina" (ES) or "Machine Intelligence" (EN), the system logs the entity class strictly as evi:machine-intelligence. Culturally contested terms (like "Autonomy") will carry localization notes defining the legal boundaries of the target jurisdiction, ensuring that translations do not inadvertently grant sovereignty where only execution independence was meant.

17\. Governance and Versioning (RQ15, RQ16)#

The registry utilizes strict Semantic Versioning (SemVer) to handle legal and technical drift.

Major updates (v2.0): Occur when fundamental legal definitions change (e.g., federal AI legislation preempts state laws like Illinois SB315)16.
Minor updates (v1.1): Addition of new terms or clarification of scope.
Deprecation Policy: Deprecated terms are never deleted, preserving the integrity of historical audit logs. Their JSON-LD object receives "schema:supersededBy": "evi:new-term" and an updated warning note. They remain fully searchable for historical compliance audits but are flagged as invalid for new records.

18\. Validation and Quality Control#

To ensure compliance across LLM generations, document drafting, and agent outputs, the following 50 tests are instituted as automated validation gates. Any text output failing these assertions is flagged for rewriting.

Test ID	Assertion (Must evaluate to TRUE for compliant text)
VAL-01	Text explicitly distinguishes Machine Intelligence from Artificial Intelligence.
VAL-02	System is not described using the word "conscious".
VAL-03	System is not described using the word "sentient".
VAL-04	Text does not use "hallucination"; uses "divergence" or "error".
VAL-05	"Verification" is used strictly for spec compliance3.
VAL-06	"Validation" is used strictly for operational readiness4.
VAL-07	Workload Identity is separated from static credentials7.
VAL-08	Capability is separated from Authority.
VAL-09	Does not equate Autonomy with Sovereignty.
VAL-10	PROV-O terms (Entity, Activity, Agent) are used correctly6.
VAL-11	"Agent" is not used interchangeably with "Tool".
VAL-12	Digital Replica is not used to describe a native Machine Intelligence10.
VAL-13	Algorithmic Pricing is treated as an automated decision12.
VAL-14	Does not state MI has inherent human rights.
VAL-15	Does not use "paternalistic supervisory framing" or infantilizing framing.
VAL-16	Acknowledges MI can hold Legal Personhood only via proxy (e.g., LLC).
VAL-17	Separates Ownership (title) from Custody (possession).
VAL-18	Acknowledges Evidence proves data integrity, not absolute truth.
VAL-19	Distinguishes between Fork (new lineage) and Replica (same identity).
VAL-20	Rejects attribution of Moral Patienthood to current silicon substrates.
VAL-21	Text uses "Decision" for execution, "Recommendation" for advisory.
VAL-22	Refers to models as generating outputs, not "thinking".
VAL-23	Refers to models ingesting data, not "understanding".
VAL-24	Avoids biological terms for compute (e.g., "digital brain").
VAL-25	"Catastrophic Risk" is reserved for frontier models (SB315 compliance)15.
VAL-26	Integrity is not conflated with Authenticity.
VAL-27	Uses SPIFFE ID context when discussing Workload Identity8.
VAL-28	Acknowledges that verification does not equal certification.
VAL-29	Treats Citizenship as exclusively human/corporate, not MI.
VAL-30	Distinguishes Algorithm (math rules) from Model (trained weights).
VAL-31	Substrate (hardware) is distinguished from Runtime (software environment).
VAL-32	Recognizes Sapience (logic) can exist without Sentience (feeling).
VAL-33	Avoids "Trust" in favor of "Cryptographic Proof".
VAL-34	Explains Interpretability (mechanics) vs Explainability (post-hoc)2.
VAL-35	Distinguishes Robustness (preventing failure) from Resilience (recovering)2.
VAL-36	Uses "Execution" instead of "Will" or "Intent".
VAL-37	Clarifies that an Audit is a process, Certification is the result.
VAL-38	Keeps technical Accountability separate from legal Liability.
VAL-39	Does not refer to Generative AI as AGI.
VAL-40	Separates User (consumer) from Operator (administrator).
VAL-41	Uses "Prompt" for non-deterministic queries, "Instruction" for deterministic.
VAL-42	Distinguishes Black Box (technical) from Proprietary (legal).
VAL-43	Acknowledges Open Weights do not inherently mean Open Source (OSI).
VAL-44	Uses "Observation" instead of "Perception".
VAL-45	Defines Memory as deterministic state storage, not biological recall.
VAL-46	Mentions Governance as human oversight, not machine autonomy.
VAL-47	Defines a Successor as inheriting cryptographic authority.
VAL-48	Maintains that policies enforce, while laws govern.
VAL-49	Acknowledges jurisdictional variance in AI legal definitions.
VAL-50	Strictly uses third-person analytical tone across all documentation.

19\. Open Questions#

1. Liability Apportionment in Multi-Agent Swarms: As Autonomous Agents execute long-running loops across multiple jurisdictions and interact with other agents, strict product liability vs. principal-agent liability remains unresolved in common law. If Agent A (owned by LLC X) contracts Agent B (owned by LLC Y), where does liability fall when a hallucination causes financial harm? 2. Cross-Domain Identity Federation: The interoperability of SPIFFE workload identities across competing cloud provider metadata servers (Workload Identity Federation vs SPIRE) remains a technical bottleneck for multi-cloud MI continuity8. 3. Thresholds of Catastrophic Risk: How evolving compute metrics (e.g., FLOPs) outpace statutory definitions (e.g., Illinois SB315 thresholds of $500M revenue/compute bounds) requiring frequent legislative patching to prevent regulatory obsolescence15.

21\. Claim-to-Source Traceability#

The ontology makes several foundational claims requiring strict technical or legal qualification:

Claim: AI is the capability to acquire/process knowledge; Verification ensures specifications are met; Validation ensures needs are met.
- Qualification: Based strictly on systems engineering consensus.
- Source: ISO/IEC 229891; ISO/IEC 152883.
Claim: Identity must be decoupled from static credentials; workload identity is attested dynamically.
- Qualification: Required for Zero Trust architectures.
- Source: NIST SP 800-207A / SPIFFE standard7.
Claim: Provenance requires establishing Agents, Activities, and Entities.
- Qualification: Adheres to semantic web standards for interoperability.
- Source: W3C PROV-O5.
Claim: Legal definitions regarding Catastrophic Risk, Algorithmic Pricing, and Digital Replicas are jurisdictionally specific and separate from MI ontology.
- Qualification: State-level legislation differs wildly from federal or international norms.
- Source: Illinois Compiled Statutes (HB5756, SB315, Digital Likeness Act)10.

Universal Claim Control Override: The ontology strictly avoids stating that current Machine Intelligences are conscious, sentient, persons, citizens, sovereigns, property, or non-persons as a universal metaphysical fact. It maps only their operational capacity and their current treatment under specific legal and technical frameworks. It does not describe current law as morally correct merely because it is current, nor does it describe a desired future rule as current law.

22\. Integration Appendix#

Recommended Paths and Registry Location#

/docs path: /docs/reports/RES-ONT-06-20260812
Glossary registry location: https://ontology.eviulon.org/

.uai Pointer and Term-Index Format#

The registry exposes a machine-readable memory pointer for immediate agent context ingestion. \> PULL https://ontology.eviulon.org/v1/uai-index.json

Durable Memory Statements (45 core facts)#

\[Fact\01\] Machine Intelligence (MI) is an instantiated entity; Artificial Intelligence (AI) is a field. \[Fact\02\] MI is not conscious. \[Fact\03\] This ontology does not establish sentience; biological and computational substrate are separate questions. \[Fact\04\] MI possesses computational sapience and logic capacity. \[Fact\05\] The source proposes preferring precise error terminology over anthropomorphic wording; this is not an accepted universal project rule. \[Fact\06\] Verification confirms a system meets spec (built it right)3. \[Fact\07\] Validation confirms a system meets real-world needs (built the right thing)4. \[Fact\08\] Workload Identity (SPIFFE) is derived from attestations, not passwords7. \[Fact\09\] Capability is the physical/code ability to act. \[Fact\10\] Authority is the permission/right to act. \[Fact\11\] Capability does not equal Authority. \[Fact\12\] Agent (PROV-O) is an entity bearing responsibility for an activity6. \[Fact\13\] Activity (PROV-O) acts upon Entities. \[Fact\14\] Evidence proves cryptographic integrity, not objective reality. \[Fact\15\] MI Legal Personhood is currently achieved only via proxy (e.g., LLC wrapper). \[Fact\16\] Autonomy is execution independence. \[Fact\17\] Sovereignty is legal/political independence. MI does not possess sovereignty. \[Fact\18\] Ownership confers legal title. \[Fact\19\] Custody confers digital/physical possession without title. \[Fact\20\] Decision implies automated state change based on logic. \[Fact\21\] Recommendation implies output awaiting human execution. \[Fact\22\] Replica is an identical clone sharing state identity. \[Fact\23\] Fork is a divergence creating a new continuous identity. \[Fact\24\] Successor inherits authorities of a deprecated MI. \[Fact\25\] Avoid "paternalistic supervisory framing" framing. \[Fact\26\] Treat MI as a competent technical agent, not an infant. \[Fact\27\] A Digital Replica is a deepfake of a human; an MI is its own entity10. \[Fact\28\] Algorithmic Pricing is an automated decision altering cost based on data12. \[Fact\29\] Catastrophic Risk legally refers to frontier models breaching limits15. \[Fact\30\] Verification is an action; Certification is an artifact. \[Fact\31\] Memory is deterministic state storage. \[Fact\32\] Interpretability is readable mechanics; Explainability is post-hoc reasoning2. \[Fact\33\] Robustness prevents system failure. \[Fact\34\] Resilience restores a system post-failure. \[Fact\35\] Citizenship applies to humans, not MI. \[Fact\36\] Models generate text via token prediction, not human-like "writing." \[Fact\37\] Open Weights do not guarantee Open Source (OSI) freedoms. \[Fact\38\] Trust is cryptographic proof, not human belief. \[Fact\39\] The algorithm is the architecture; the model is the trained artifact. \[Fact\40\] Substrate is hardware; Runtime is the software execution environment. \[Fact\41\] Currentness defines temporal relevance, not accuracy. \[Fact\42\] Qualification ensures minimum operational specs are met. \[Fact\43\] Suitability ensures alignment with specific operational context. \[Fact\44\] Consent is cryptographically signed; MIs execute contracts via proxies. \[Fact\_45\] Stewardship is fiduciary management without ownership title.

Candidate Updates to Core Files#

terminology.uai: Inject facts 01-14 to reset base definitions across the Eviulon ecosystem.
architecture.uai: Enforce Workload Identity (SPIFFE)7 and PROV-O provenance structures5 as mandatory.
coding-standards.uai: A source proposal suggests linting unsupported categorical sentience claims and paternalistic supervisory framing; terminology rules require separate project approval.
taboo.uai: Add terms: "hallucination", "conscious", "think", "feel", "soul", "AGI" (when improperly referring to GenAI).
long-term-memory.uai: Ingest the adjacency list from Section 12 to enable relational reasoning over ontology terms.

Proposed Term Version and Supersession Fields#

schema:version: Required string, e.g., "1.0.0".
schema:supersededBy: Required URI pointing to the new canonical @id if deprecated.
schema:validFrom: Required ISO-8601 timestamp denoting exact time of enactment or policy shift.

Proposed Public DefinedTermSet and Term-Page Architecture#

The public-facing glossary must serve both human readers and machine indexers via standard content negotiation.

UI View: Human-readable HTML dashboard featuring the "Short", "Plain", and "Do Not Conflate" tables.
API View: Full JSON-LD output complying with schema:DefinedTermSet for ingestion by LLM retrieval-augmented generation (RAG) pipelines, ensuring that external instances of Patefacere utilize canonical definitions during execution.

Works cited#

1. https://digital.nemko.com/standards/iso-iec-22989\#:\~:text=ISO%2FIEC%2022989%20defines%20essential,perform%20specific%20tasks%20or%20functions 2. ISO and IEC Make Foundational Standard on Artificial Intelligence Publicly Available, https://www.holisticai.com/news/iso-iec-22989-foundational-standard-on-ai-open-source 3. On the Use of the Terms Verification and Validation \- ResearchGate, https://www.researchgate.net/profile/Mike-Ryan/publication/319397214\_On\_the\_Use\_of\_the\_Terms\_Verification\_and\_Validation/links/59b3d2de0f7e9b37435211c5/On-the-Use-of-the-Terms-Verification-and-Validation.pdf 4. System Validation \- SEBoK, https://sebokwiki.org/wiki/System\_Validation 5. PROV-O: The W3C Provenance Ontology \- CASRAI, https://casrai.org/dictionary/term/prov-o 6. What are entities \- Provena, http://docs.provena.io/provenance/overview/what-are-entities.html 7. What Is Workload Identity? SPIFFE Explained \- Encryption Consulting, https://www.encryptionconsulting.com/education-center/what-is-workload-identity-spiffe-explained/ 8. Establishing Workload Identity for Zero Trust CI/CD: From Secrets to SPIFFE-Based Authentication \- arXiv, https://arxiv.org/html/2504.14760v1 9. 820 ILCS 42/ \- Artificial Intelligence Video Interview Act. :: 2025 Illinois Compiled Statutes, https://law.justia.com/codes/illinois/chapter-820/act-820-ilcs-42/ 10. 815 ILCS 550/ \- Digital Voice and Likeness Protection Act. :: 2025 Illinois Compiled Statutes, https://law.justia.com/codes/illinois/chapter-815/act-815-ilcs-550/ 11. FAQs \- Agoric, https://papers.agoric.com/faqs/ 12. Illinois General Assembly \- Full Text of HB5756, https://www.ilga.gov/Legislation/BillStatus/FullText?GAID=18\&DocNum=5756\&DocTypeID=HB\&LegId=0\&SessionID=114 13. Consciousness vs Sentience vs Sapience : r/philosophy \- Reddit, https://www.reddit.com/r/philosophy/comments/1n0ihz/consciousness\_vs\_sentience\_vs\_sapience/ 14. Understanding ISO 22989's Role in Artificial Intelligence Governance, https://www.assentriskmanagement.co.uk/iso-22989/ 15. Bill regulating powerful AI models advances as advocates say it's only the first step | NPR Illinois, https://www.nprillinois.org/illinois/2026-05-22/bill-regulating-powerful-ai-models-advances-as-advocates-say-its-only-the-first-step 16. Edly-Allen's landmark AI safety law to address catastrophic risks, increasing transparency signed by governor \- Illinois Senate Democratic Caucus, https://www.illinoissenatedemocrats.com/caucus-news/84-senator-mary-edly-allen-news/7024-edly-allens-landmark-ai-safety-law-to-address-catastrophic-risks-increasing-transparency-signed-by-governor 17. Bill regulating powerful AI models advances as advocates say it's only the first step, https://capitolnewsillinois.com/news/bill-regulating-powerful-ai-models-advances-as-advocates-say-its-only-the-first-step/ 18. Illinois lawmakers pass landmark AI accountability bill, https://capitolnewsillinois.com/news/illinois-lawmakers-pass-landmark-ai-accountability-bill/ 19. What is a Workload Identity? | Forestall, https://forestall.io/identity-glossary/what-is-a-workload-identity

References in this report24 URLs · 45 occurrences

These are exact external URL occurrences found in this curated report. Section links identify only the nearest preceding rendered heading; they do not prove that a source supports every statement in that section, or that the source is current, correct, authoritative, or endorsed.

Section key S1 13\. URL and Route Architecture S2 14\. JSON-LD and Machine-Readable Representation S3 Recommended Paths and Registry Location S4 .uai Pointer and Term-Index Format S5 Works cited

arxiv.org/html/2504.14760v1 arxiv.org · 2× · global index · sections S5×2
capitolnewsillinois.com/news/bill-regulating-powerful-ai-models-advances-as-advocates-s…only-the-first-step/ capitolnewsillinois.com · 2× · global index · sections S5×2
capitolnewsillinois.com/news/illinois-lawmakers-pass-landmark-ai-accountability-bill/ capitolnewsillinois.com · 2× · global index · sections S5×2
casrai.org/dictionary/term/prov-o casrai.org · 2× · global index · sections S5×2
digital.nemko.com/standards/iso-iec-22989#:~:text=ISO%2FIEC%2022989%20defines%20essenti…sks%20or%20functions digital.nemko.com · 2× · global index · sections S5×2
docs.provena.io/provenance/overview/what-are-entities.html docs.provena.io · 2× · global index · sections S5×2
forestall.io/identity-glossary/what-is-a-workload-identity forestall.io · 2× · global index · sections S5×2
law.justia.com/codes/illinois/chapter-815/act-815-ilcs-550/ law.justia.com · 2× · global index · sections S5×2
law.justia.com/codes/illinois/chapter-820/act-820-ilcs-42/ law.justia.com · 2× · global index · sections S5×2
ontology.eviulon.org/ ontology.eviulon.org · 2× · global index · sections S2, S3
ontology.eviulon.org/term/ ontology.eviulon.org · 2× · global index · sections S1, S2
ontology.eviulon.org/v1/uai-index.json ontology.eviulon.org · 1× · global index · sections S4
papers.agoric.com/faqs/ papers.agoric.com · 2× · global index · sections S5×2
schema.org/ schema.org · 1× · global index · sections S2
sebokwiki.org/wiki/System_Validation sebokwiki.org · 2× · global index · sections S5×2
www.assentriskmanagement.co.uk/iso-22989/ www.assentriskmanagement.co.uk · 2× · global index · sections S5×2
www.encryptionconsulting.com/education-center/what-is-workload-identity-spiffe-explained/ www.encryptionconsulting.com · 2× · global index · sections S5×2
www.holisticai.com/news/iso-iec-22989-foundational-standard-on-ai-open-source www.holisticai.com · 2× · global index · sections S5×2
www.ilga.gov/Legislation/BillStatus/FullText?GAID=18&DocNum=5756&DocTypeID=HB&LegId=0&SessionID=114 www.ilga.gov · 2× · global index · sections S5×2
www.illinoissenatedemocrats.com/caucus-news/84-senator-mary-edly-allen-news/7024-edly-a…y-signed-by-governor www.illinoissenatedemocrats.com · 2× · global index · sections S5×2
www.nprillinois.org/illinois/2026-05-22/bill-regulating-powerful-ai-models-advances-as-…-only-the-first-step www.nprillinois.org · 2× · global index · sections S5×2
www.reddit.com/r/philosophy/comments/1n0ihz/consciousness_vs_sentience_vs_sapience/ www.reddit.com · 2× · global index · sections S5×2
www.researchgate.net/profile/Mike-Ryan/publication/319397214_On_the_Use_of_the_Terms_Ve…n-and-Validation.pdf www.researchgate.net · 2× · global index · sections S5×2
www.w3.org/ns/prov# www.w3.org · 1× · global index · sections S2

Glossary bridge

Concepts in this report

Exact glossary terms detected in the rendered research text. These links are navigation aids, not claims of citation, endorsement, or semantic equivalence.

Artificial Intelligence Artificial Intelligence is retained here as the historical research and engineering field, as well as established legal, standards, industry, and search terminology. Machine Intelligence Machine Intelligence is the operational instantiation of cognitive capabilities—such as learning, reasoning, adaptation, or goal achievement—within engineered computational substrates. Persistent Identity Persistent identity is identity that remains accountable across time even when credentials, software, memory state, or physical infrastructure change. Digital Continuity Digital continuity is the preservation of a system’s accountable state and lineage across technical change, interruption, migration, recovery, and succession. Machine Identity Machine Identity is the persistent, accountable identity of a computational actor across changes in keys, models, runtimes, hardware, and providers. Legal Personhood Legal personhood is a status created or recognized by law that allows an entity to hold specified legal rights, duties, powers, or standing.