AAC™

The AI Attribution & Compensation (AAC) for the AIACTA™ Framework

Implementation Specifications for Publisher Visibility & Attribution Standards

Verified Genesis Hash (IPFS): bafybeieo34vvnbtcqnuqsupf6ykmjligoqlqr7k2kltbnfhbobc62hab24

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🌐 Vision: The Economic Bedrock for the AGI Era

The AAC™ (incorporating the AIACTA™ Specifications) is a proposed entity for the AIACTA™ Open Specification designed to solve the "Transparency Gap" in the Artificial Intelligence ecosystem. As we accelerate toward Artificial General Intelligence (AGI), our civilization requires a technical social contract that aligns the boundless potential of machine intelligence with the irreplaceable value of human expertise.

This framework provides the technical blueprint for a symbiotic future—where AI companies access high-fidelity training data at scale, and content creators are perpetually honored and compensated through a verifiable, cryptographic protocol.

🛠 Core Technical Components

• Standardized Attribution Schema: A universal metadata format for data provenance and model training logs.
• The AAC™ Webhook Gateway: A secure, real-time protocol for logging citation events and usage metrics.
• Dual-Pathway Compensation Models: Scalable economic structures including Revenue-Proportional Allocation (RPA) and Unitized Citation Fees (UCF).
• Verifiable Audit Trails: Cryptographic "Proof-of-Inference" using HMAC-SHA256 signatures to prevent fraud and ensure systemic integrity.

📄 Documentation

• White Paper (PDF Source): The full technical and economic specification.
• Contributor License Agreement: Mandatory terms for all specification contributions.

Contributor License Agreement (CLA)

By submitting a Pull Request or contributing to this specification, you agree to the AAC™ Contributor License Agreement. This ensures the specification remains unified and legally defensible under the Founder's stewardship.

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Community & Governance

• 📧 Contact the Author: contact@aiacta.org
• 📋 Governance: docs/governance/aac-governance.md

The AIACTA™ Foundation is being formed as a neutral non-profit to govern the specification, certification, and AAC distributions.

Founding Partners, Sponsors, and Board member advisors are welcome and encouraged at: [foundation@aiacta.org]

🤝 Join the Global Standard

We are seeking a "Founding Class" of contributors to refine V2.0 Reference Implementation.

1. Star the AIACTA™ Repo to show support for AI transparency.
2. Review the Specs: Open an Issue to discuss architectural improvements or edge cases.
3. Become a Partner: If you represent an AI Lab or a Major Publisher, contact the Founder for early-access pilot programs.

"We are not just building a protocol; we are designing the incentives that will allow human brilliance to scale alongside its greatest invention." — Eric Michel, PhD

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How to cite this work:

The AI Architecture for Content Transparency and Attribution (AIACTA) Framewok
Creator: Eric Michel, PhD
Date: March, 2026
Copyright © 2026 Eric Michel  
Licensed under the Apache License, Version 2.0

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⚖️ Intellectual Property & Licensing

To ensure the integrity of the standard and secure its future as a global utility, the following terms apply:

AIACTA™

AI Architecture for Content Transparency and Attribution

Apache License 2.0 · Copyright © 2026 Eric Michel

The AIACTA™ name and associated certification marks are trademarks of the Author. Any "AIACTA-Compliant" designation requires explicit authorization from the Author or the future governance body.

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