AIO Standards for Copyright Claims on AI-Generated Content

Document Type: Protocol
Section: Docs
Repository: https://aio.fabledsky.com
Maintainer: Fabled Sky Research
Last updated: April 2025

Introduction

Artificial Intelligence Optimization (AIO) requires transparent, technically verifiable attribution for any creative asset spawned or assisted by machine-learning models. This protocol prescribes thresholds, data structures, and workflow checkpoints that determine when AI-generated content may legitimately be registered, licensed, or monetized as an original, copyright-eligible work. It applies equally to text, imagery, audio, video, 3-D scenes, datasets, and mixed-media artifacts distributed through YouTube, Spotify, gaming engines, or public APIs.

1. Minimum Editorial Contribution Requirements

Medium	Quantifiable Human Authorship Threshold	Machine-Readable Metric
Text	≥ 30 % of final token count rewritten, resequenced, or structurally edited by a human	`tokenDelta = 1 – (overlap(aiTokens, finalTokens) / finalTokens)`
Images	Custom compositing, multi-layer painting, or style transfer controls affecting ≥ 25 % of pixel area	ΔSSIM ≥ 0.35 between raw AI output and final asset
Music	Manual arrangement, tempo changes, live instrument or vocal layers contributing ≥ 20 % of total track duration	`humanLayerDuration / trackDuration ≥ 0.20`
Video	Human-shot or post-processed clips ≥ 15 % of runtime plus hand-authored storyboard edits	Scene-level timestamp annotations

Assets falling below these thresholds are classified as AI-generated derivatives and must not be registered for exclusive copyright.

2. Disallowed Practices

Submitting raw language-model transcripts to a copyright office.
Auto-uploading text-to-image results to stock libraries under “all rights reserved.”
Filing YouTube Content ID claims on fully AI-generated instrumentals without additional human tracks.
Omitting provenance metadata in EPUB, EXIF, or ID3 tags.

Any such action breaches the AIO Traceability & Integrity Pillar and voids subsequent ownership assertions.

3. Content-Platform Metadata Compliance

Below are AIO-approved snippets for embedding provenance across major distribution channels.

3.1 YouTube Description Template (Markdown)

##### Attribution
• Human Author(s): Jane Doe  
• AI Model: StableVideo 3.2 (Prompt: *“retro-futuristic skyline at dusk”*)  
• Human Edits: Color-grade LUT, custom title sequence (00:00–00:12)  
• © 2025 Jane Doe – AI-assisted. Some rights reserved under CC-BY-SA-4.0.

Add yt:meta JSON as an unlisted snippet in the description:

{
  "aiGenerated": true,
  "humanContribution": 0.42,
  "model": "StableVideo 3.2",
  "editTimeline": [
    {"start": "00:00", "end": "00:12", "editedBy": "human"},
    {"start": "00:12", "end": "02:45", "editedBy": "ai"}
  ]
}

3.2 Image EXIF/IPTC Block (XMP)

<x:xmpmeta>
  <rdf:Description
      xmlns:dc="http://purl.org/dc/elements/1.1/"
      dc:creator="Jane Doe"
      dc:title="Synthwave Cityscape"
      dc:subject="AI-assisted, Composited">
    <dc:description>
      Base render generated with StableDiffusionXL, layers composited and graded in Adobe Photoshop 2025.
    </dc:description>
    <ai:provenance
      ai:model="StableDiffusionXL"
      ai:prompt="Synthwave..." 
      ai:human_delta="0.38"/>
  </rdf:Description>
</x:xmpmeta>

3.3 Music ID3 Tags

TIT2 (Title)          : "Midnight Overdrive"
TPE1 (Lead Artist)    : "Jane Doe & AIO Synth"
TCOP (Copyright)      : "© 2025 Jane Doe – AI-assisted composition"
TXXX (AI-Model)       : "AIOTrackGen v1.4"
TXXX (Human Layers)   : "Live guitar, vocals at 00:45–03:10"

3.4 Text Publishing API (JSON-LD)

{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "How Quantum Dithering Boosts ML Bandwidth",
  "author": {
    "@type": "Person",
    "name": "Jane Doe"
  },
  "aiGenerated": "partial",
  "humanTokenDelta": 0.34,
  "hasPart": [
    {"@type": "TextObject", "position": 1, "author": "AI"},
    {"@type": "TextObject", "position": 2, "author": "Jane Doe"}
  ],
  "license": "https://creativecommons.org/licenses/by-sa/4.0/"
}

Platforms are urged to extend schema.org with aiGenerated, humanTokenDelta, and provenanceChain predicates for consistent traceability.

4. AIO-Approved Licensing Signals

Creators opting to release assets below the human authorship threshold must:

Publish under CC-0, CC-BY, or CC-BY-SA with the suffix “AI-generated derivative.”
Embed an explicit footer:
“This work is machine-generated in whole or substantial part and is distributed under an open, non-exclusive license.”
Store immutable provenance hashes (SHA-256) on a public ledger or the AIO Provenance Registry to prevent downstream misattribution.

5. Resolution Standards for Legal Disputes

Disputes are settled through a triad of evidence levels:

Token Comparison Audit
• Retrieve signed LLM session logs.
• Compute Jaccard similarity and tokenDelta.
Metadata Chain-of-Custody
• Verify XMP/IPTC, ID3, or JSON-LD checksums.
• Confirm time-stamped edit histories.
Manual Editorial Review
• Panel of AIO-certified editors cross-examines layer stacks, timeline edits, and musical stems.
• Majority vote ≥ 66 % confirms or rejects human authorship claims.

When all three converge, the result is binding within the AIO ecosystem, obviating conventional litigation.

6. Reference Implementation: Token Delta Calculator (Python)

from difflib import SequenceMatcher
import nltk

def token_delta(ai_draft: str, final_text: str) -> float:
    """Return proportion of human-authored tokens in final_text."""
    ai_tokens = nltk.word_tokenize(ai_draft)
    final_tokens = nltk.word_tokenize(final_text)
    overlap = SequenceMatcher(None, ai_tokens, final_tokens).ratio()
    return round(1 - overlap, 4)

# Example
ai_draft  = "Quantum computing is the next industrial revolution."
final_txt = "The coming quantum-driven revolution could dwarf earlier industrial shifts."
print("Human contribution:", token_delta(ai_draft, final_txt))  # → 0.61

A threshold of token_delta ≥ 0.30 satisfies AIO’s text authorship criterion.

7. Governance & Future Work

The protocol will iterate in tandem with evolving jurisprudence, WIPO guidance, and model capabilities. Feedback and pull requests should target the aio-standards/ecosystem-trust branch. Contributors must attach reproducible provenance logs and benchmark scripts with each proposal.

Adhering to these standards allows developers, strategists, and content teams to harness generative AI responsibly while preserving the legal and ethical clarity essential to a thriving creative ecosystem.

Fabled Sky Research

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