OOF™ Origin Open Foundation™

Independent Methodological Authority

OriginID: OOF-OID-AI-ARTD-2026-02-22-0012
Category: AI & Data Integrity Standards (AI)
Subcategory: Data Integrity Classification

Version: 1.0
Status: Canonical · Binding
Effective Date: 22 February 2026

Compatibility:
MTVF™ · Methodology OS™ · AI Governance Frameworks

Authority: OOF™ Origin Open Foundation™
Protection: MIP™ — Methodological Intellectual Property
Canonical Language: English (UCL™)

A. Standard Abstract

The ArtData™ Standard defines a minimum structural integrity
classification for datasets used in artificial intelligence training, testing, and
validation environments.

The standard establishes baseline conditions for dataset traceability,
structural identity, modification transparency, and declared responsibility.

ArtData™ does not define dataset quality or ethical evaluation.
It defines the minimum structural accountability required for reliable AI
data pipelines.

The standard is designed to be technology-neutral and immediately
applicable across research, commercial, and governance environments.

B. Canonical Definition

ArtData™ is a structural integrity classification for digital datasets that
meet defined minimum requirements of traceability, identity anchoring,
modification transparency, and responsible entity declaration.

ArtData™ defines the minimum structural accountability conditions
required for datasets used in AI systems.

It is independent of dataset type, format, domain, or size.

C. Purpose

The purpose of the ArtData™ Standard is to:

• establish traceable dataset origin structures
• enable transparent dataset lifecycle tracking
• reduce operational and regulatory risk in AI data pipelines
• support reproducible training and evaluation environments
• introduce structural accountability in AI dataset management

ArtData™ establishes minimum structural transparency, not dataset
quality guarantees.

D. Structural Scope

The ArtData™ Standard applies to datasets used in:

• AI training environments
• machine learning testing systems
• AI validation datasets
• simulation and model evaluation datasets
• AI research datasets
• automated decision-system training pipelines

The standard may be applied across public, commercial, academic, or
institutional environments.

E. Minimum Structural
Requirements

A dataset may be designated ArtData™ Compliant only when the
following conditions are satisfied.

1. Origin Disclosure

The dataset must have a documented origin source.

Minimum requirement:

• identifiable source category
  or
• documented acquisition method.

2. Identity Anchor

The dataset must possess a unique structural identifier.

Minimum requirement:

• dataset identifier
• dataset version reference
• cryptographic hash or equivalent integrity reference.

3. Time Continuity

Dataset creation and modification history must be recorded.

Minimum requirement:

• initial creation timestamp
• modification timestamp records.

4. Modification Transparency

All transformation, filtering, labeling, or preprocessing operations must be
documented.

Minimum requirement:

• transformation description
• responsible entity or system reference.

5. Responsible Entity Declaration

A legally identifiable entity must declare dataset responsibility.

Minimum requirement:

• organization or responsible entity name
• contact reference
• declaration of responsibility.

F. Designation Rule

The designation

“ArtData™ Compliant Dataset”

may only be used when all minimum structural requirements defined by
this standard are satisfied and documented.

Partial compliance does not qualify.

G. What ArtData™ Is Not

ArtData™ does not guarantee:

• absence of dataset bias
• ethical neutrality
• legal compliance across jurisdictions
• dataset quality superiority.

ArtData™ guarantees traceability and structural accountability, not
dataset perfection.

H. Governance Position

ArtData™ functions as a foundational data integrity classification layer
within AI data pipelines.

It may operate independently or in compatibility with broader validation
frameworks including MTVF™.

The standard establishes a structural baseline for trustworthy AI dataset
management.

Canonical Closing Statement

Reliable AI systems require accountable data foundations.

ArtData™ establishes the minimum structural integrity required for
datasets used in AI environments.

AD-P — ArtData™ Provenance Module
AD-I — ArtData™ Integrity Module
AD-AI — ArtData™ AI Training Module
AD-S — ArtData™ Synthetic Data Module
AD-C — ArtData™ Certification Module
AD-AC — Agent Communication Module

→ About ArtData™ Standard
→ Minimum Implementation Framework (MIF)