TRACE AI LABEL v0.1

Transparent Reporting of AI Content & Editing

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Why TRACE?

AI is everywhere in publishing. But right now, it’s hard to tell what’s been lightly edited by a model, what’s been co-authored, and what’s been fully generated. This leaves audiences guessing, creators exposed to skepticism, and platforms vulnerable to regulatory pressure.

TRACE aims to fix this with a simple, neutral labeling system. Think of it like a nutrition label for AI use: lightweight, consistent, and easy to adopt.

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Core Principles

1. Simplicity first — a single badge like [TRACE U3] tells the whole story at a glance.
2. Neutral, not judgmental — labels explain usage, not quality.
3. Self-label today, platform support tomorrow — the spec starts with creators, then grows into integrations.
4. Tiered clarity — six clear levels (U0–U5) cover the full spectrum of AI involvement.

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The Levels (U0–U5)

• U0 — No AI
  Purely human-created.
  Example: a hand-written poem, manually typed into a blog post.

• U1 — Utility Assist
  AI tools used only for mechanical cleanup: spellcheck, grammar, color correction, noise reduction.
  Example: stabilizing raw video footage with AI filters.

• U2 — Assistive (Light)
  AI helps with small, supporting tasks: brainstorming, outlines, minor rewrites. The human leads the content.
  Example: drafting a YouTube video outline with ChatGPT, but writing the script yourself.

• U3 — Co-author
  AI generates substantial drafts or sections; the human reshapes and contributes original material.
  Example: AI writes the first draft of a newsletter, human edits heavily and adds unique analysis.

• U4 — Generative (Human-reviewed)
  Majority of the content is model-generated; a human curates, edits, and approves before publishing.
  Example: AI image series for an art project, published after manual review.

• U5 — Autonomous Publish
  An agent handles end-to-end generation and publishing with minimal human input.
  Example: a bot that writes, formats, and posts daily market summaries without human edits.

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Using TRACE Labels

The simplest way to start is with self-labeling:

• Add the label in brackets wherever you publish.
• Examples:
  • Tweet: New AI soundtrack is live! [TRACE U4]
  • Social Media Photo: Overlay on image with [TRACE U2]  May indicate AI edits for object removal and caption help.
  • YouTube description: Script and cut scenese co-authored with AI [TRACE U3 +PR]

View the Styleguide for standardization of visual implementation

Optional suffixes add more transparency:

• +PR → Prompt Revealed. The creator has shared either the exact prompt used or a short description of how AI was instructed.
  Example: A blog post with +PR might include “Prompt: summarize transcript into 500 words” or “Prompt summary: generated outline of key themes.”

• +CR → Content Credentials Attached. The output file or media includes cryptographic provenance metadata (for example, using C2PA content credentials).
  Example: An image exported from Photoshop with C2PA metadata embedded.

• +SR → Safety Review Performed. A human or automated process checked the content for things like copyright, toxicity, or privacy issues before publishing.
  Example: [TRACE U4 +SR] for a generative video that was screened for harmful imagery.

Example label with multiple suffixes:
[TRACE U4 +PR +SR]

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JSON Metadata (for platforms & devs)

Every badge can be backed by structured metadata.
Lightweight, optional at first — but ready for automation.

{
  "trace": {
    "version": "0.1",
    "level": "U3",
    "modality": "text",
    "human": {
      "review": true,
      "authors": ["Jason S."]
    },
    "date": "2025-09-10",
    "ai": {
      "role": "coauthor",
      "method": "iterative",
      "models": ["disclosed"]
    },
    "safety": ["copyright"]
  }
}

Field-by-field guidance

• trace.version — Spec version. Stick to "0.1" for now; future changes will bump this for safe migrations.
• trace.level — One of U0–U5. If torn between two, pick the higher level.
• trace.modality — Primary type: "text" | "image" | "audio" | "video" | "multimodal".
• trace.human.review — true if a human approved the final asset. Optional authors lets you credit people/orgs.
• trace.date — ISO 8601 date of publication/labeling (e.g., 2025-09-10). Useful for audits and moderation windows.
• trace.ai.role — How AI participated: utility | assistive | coauthor | generative | autonomous.
• trace.ai.method — Process shape: single_prompt | iterative | template | finetuned | agentic_pipeline.
• trace.ai.models — Simple disclosure for v0.1. Use an array of strings (e.g., model family names) or "disclosed"/"undisclosed" if you prefer generic labeling now.
• trace.safety — Which checks ran before publishing (e.g., copyright, privacy, toxicity). Keep it short.

Tip: v0.1 is intentionally lightweight. Start with the fields above. Platforms can add richer, optional detail using the extension block below.

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Optional extension block (for richer detail)

To support power users and platform integrations without burdening creators, add an optional ext namespace:

{
  "trace": {
    "version": "0.1",
    "level": "U3",
    "modality": "text",
    "human": { "review": true },
    "date": "2025-09-10",
    "ai": { "role": "coauthor", "method": "iterative", "models": ["disclosed"] },
    "safety": ["copyright"],

    "ext": {
      "apps": [
        { "name": "ChatGPT", "provider": "OpenAI", "version": "2025-09", "mode": "web" }
      ],
      "models": [
        {
          "name": "gpt-5-thinking",
          "provider": "OpenAI",
          "version": "2025-08-15",
          "id": "openai:gpt-5-thinking:2025-08-15",
          "usage": { "share": "majority", "purpose": "drafting" }
        }
      ],
      "pipeline": [
        { "step": "transcription", "app": "Whisper", "version": "large-v3" },
        { "step": "summarization", "model": "gpt-5-thinking", "iterations": 3 }
      ],
      "data": { "sources": ["original", "licensed"], "retrieval": { "used": false } },
      "prompt": {
        "disclosure": "summary",
        "summary": "Draft first-pass newsletter from transcript; keep jokes; 700–900 words."
      },
      "provenance": { "type": "none" }
    }
  }
}

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Schema choices that help adoption

• Keep core simple — Only the fields above are needed for v0.1 self-labeling.
• Enums where clarity matters — Use enums for level, modality, and prompt.disclosure; keep tool/model names as free text.
• Dates/versions as strings — Model versioning is messy; don’t over-constrain.
• Everything in ext is optional — Rich detail for platforms, zero friction for creators.
• Allow vendor/custom fields — Permit x- prefixed keys (e.g., "x-runway") or a vendor sub-object for proprietary identifiers.
• Privacy by default — Don’t include secrets, PII, or full prompts unless intentionally disclosed (+PR). Prefer summaries and external URIs for long details.

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For Creators

• Trust your audience: show exactly how you use AI, without stigma.
• Protect your work: get ahead of “is this AI?” accusations.
• Simple to start: just add [TRACE Ux] — no tools required.

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For Platforms & Engineers

• Schema-ready: plug-in JSON metadata for structured transparency.
• Future-proof: aligns with emerging standards like C2PA “content credentials.”
• Flexible integration: attach TRACE labels to exports, posts, or media manifests.

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Adoption Path

• Today: Self-labeling by creators.
• Next: Plugins and platform integrations auto-generate labels.
• Future: Provenance links (C2PA, signed manifests) ensure verifiable labeling.

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Contribute

TRACE is open and versioned. v0.1 is deliberately simple — the spec will evolve with community input.

Ways to contribute:

• Creators: start labeling your work and share feedback.
• Developers: test the schema, build prototypes, suggest improvements.
• Platforms: pilot integrations, align with your content policies.

Open issues, propose updates, or join the discussion in GitHub Issues.

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⚡ Transparency only works if we do it together. Start by labeling your next post [TRACE Ux].