Human–AI Authorship & Composition Framework V1.0

HUMAN–AI AUTHORSHIP AND COMPOSITION CLASSIFICATION FRAMEWORK

Universal Policy Development Proposal for Musical Work Registration, Attribution, and Rights Administration

Addressed to: Policy and Member Services Teams — The Mechanical Licensing Collective (The MLC)

Prepared by: Tony Ramos Torres | RAPTIMETVTONY

Songwriter | Executive Producer | Cultural Archivist

Founder, RapTimeTV Cultural DNA Archive™

Date: October 12, 2026 | Version: 1.0 | Status: Research and Policy Proposal

Scope: Universal — All Music Creators, AI Platforms, Publishers, and Rights Administrators

Website: https://raptimetvtony.com

1. Executive Statement

This proposal introduces a universal framework for documenting, classifying, and administering musical works created through combinations of human creativity and generative artificial intelligence.

Its purpose is to support accurate attribution, transparent documentation, reliable musical-work identification, and responsible rights administration as music creation technologies evolve.

The framework is intended to apply equally to independent and established songwriters, composers, lyricists, producers, publishers, recording artists, music organizations, and creators using AI-assisted production technologies. It is technology-neutral and does not favor any particular commercial AI provider.

The proposal does not seek to redefine copyright law, establish new ownership rights, or treat artificial intelligence as a legal author. Instead, it recommends that music registration systems distinguish creative participation from legally recognized authorship and royalty entitlement.

Foundational Principle: Accurate documentation should recognize genuine human contributions without misrepresenting AI-generated material or creating unsupported ownership claims.

2. The Emerging Industry Challenge

Traditional music registration systems generally distinguish between composers, lyric authors, publishers, and rights holders. Generative AI introduces additional production arrangements that may not fit neatly within these categories. For example, a musical work may contain:

  • Human-authored lyrics and AI-generated melody.
  • Human-composed music and AI-generated lyrics.
  • Human-authored music and lyrics with AI-assisted arrangement.
  • AI-generated material subsequently transformed through substantial human creative editing.
  • AI-generated instrumental or vocal performances accompanying an independently authored composition.
  • Multiple human contributors using different AI systems throughout production.

These scenarios should not automatically receive identical authorship classifications. A registration system should be capable of documenting the relevant contributions while preserving the distinction between copyrightable expression, production activity, and administrative ownership claims.

3. Universal Human–AI Contribution Classification Model

The following classifications are proposed as optional documentation categories, not automatic legal writer or ownership designations.

A. Human Lyric Author

A natural person who creates original lyrical expression.

B. Human Music Composer

A natural person who creates original musical expression qualifying for authorship under applicable law.

C. Human Creative Director

A person responsible for artistic concepts, creative instructions, musical preferences, production supervision, or selection of generated results. This designation does not independently establish authorship of AI-generated expression.

D. Human Creative Editor or Arranger

A person who modifies, reorganizes, edits, or arranges musical material. Any claim of copyrightable authorship depends on the nature and originality of the human contribution and applicable law.

E. AI-Generated Musical Material

A disclosure indicating that identified musical elements were generated through an AI system. Such disclosure does not designate the AI system as a legal author.

F. AI-Generated Vocal Performance

A recording-level disclosure identifying synthetic or AI-generated vocals. Vocal production credits should be distinguished from authorship of the underlying musical work.

G. Human–AI Hybrid Contribution Record

A structured record identifying the relationship between human-authored expression, AI-generated material, and subsequent human modifications.

H. Rights Verification Pending

A temporary administrative review indicator for cases requiring clarification concerning authorship, ownership, publishing administration, or royalty entitlement. This status would not itself establish eligibility for royalties.

4. Proposed Registration Architecture

The framework recommends separating registration information into four distinct layers.

Layer 1 — Creative Contribution

Documents the actions and creative contributions of the people and technologies involved.

Layer 2 — Legal Authorship

Identifies legally recognized authorship, subject to applicable copyright law and authoritative determinations.

Layer 3 — Rights and Administration

Records verified ownership interests, publishing administration, agreements, and authorized royalty claims.

Layer 4 — Recording and Work Identification

Connects musical works and sound recordings using applicable identifiers, including ISWC, ISRC, IPI, and relevant catalog identifiers.

These layers should be interoperable but must not be treated as interchangeable.

  • Creative contribution ≠ Automatic copyright ownership.
  • AI disclosure ≠ AI legal authorship.
  • Registration metadata ≠ Independent evidence of royalty entitlement.

5. Proposed Improvements to Music Registration Systems

5.1 Optional Human–AI Contribution Disclosure

Provide a standardized mechanism through which registrants can identify AI-generated material and documented human contributions.

5.2 Accurate Writer-Role Guidance

Develop clear administrative instructions for registrations involving human-authored lyrics, human-composed music, AI-generated elements, and hybrid production methods. Where an existing mandatory writer-role field cannot accurately describe a work, provide a review or exception pathway rather than encouraging unsupported classifications.

5.3 Technology-Neutral AI Documentation

Permit the identification of AI tools and their functional contributions without making one provider’s technology the basis of a universal legal classification.

5.4 Rights Verification

Maintain mechanisms for reviewing rights claims, supporting documentation, conflicting registrations, and applicable licensing agreements.

5.5 Musical Work and Recording Matching

Support accurate relationships between musical works, recordings, and their identifiers while respecting the different legal rights associated with each.

5.6 Correction and Audit History

Allow properly authorized parties to correct inaccurate information while retaining a record of relevant changes and their provenance.

6. Proposed Standardized Documentation Record

A voluntary human–AI contribution record could include:

  • Musical work title and available identifiers.
  • Names and identifiers of human contributors.
  • Specific human-authored lyrical or musical elements.
  • AI-generated elements and their general functions.
  • Documented human modifications or arrangements.
  • Relevant production and recording credits.
  • Rights-holder and administrator information, where verified.
  • Supporting documentation references.
  • Applicable agreements or restrictions.
  • Review status and correction history.

Sensitive personal information, confidential prompts, unpublished recordings, and proprietary production materials should not automatically become public merely because a contribution disclosure exists.

7. Copyright and External Authority Safeguards

Any implementation should respect applicable copyright statutes, regulations, judicial decisions, contractual arrangements, and other binding legal requirements.

The framework should not presume that prompting an AI system automatically constitutes copyrightable authorship of the resulting musical expression. Likewise, the use of AI assistance should not automatically negate copyright protection for independently created human expression.

Copyrightability, ownership, licensing authority, and royalty entitlement require their own appropriate legal and factual analysis. Neither an AI provider’s commercial-use terms nor an administrative registration field should independently be treated as a conclusive determination of copyright ownership.

8. Industry-Wide Benefits

Adoption of a transparent contribution framework could support:

  • More accurate recognition of human songwriters and composers.
  • Clearer documentation of AI-generated musical material.
  • Fewer inaccurate writer-role selections.
  • Improved consistency across publishers and rights administrators.
  • Better musical-work and recording identification.
  • More reliable documentation for future licensing reviews.
  • Greater accountability in automated music metadata systems.
  • Stronger preservation of music-production history.

These are proposed benefits requiring practical evaluation rather than guaranteed outcomes.

9. Recommended Pilot Program

I respectfully propose that The MLC consider exploring a limited, voluntary research pilot with relevant industry participants. The pilot could examine:

  1. Representative categories of human–AI musical works.
  2. Existing writer-role limitations.
  3. Optional contribution-disclosure fields.
  4. Administrative review procedures for uncertain cases.
  5. Work-to-recording matching and metadata consistency.
  6. Privacy, accuracy, correction, and audit safeguards.
  7. The boundaries of The MLC’s statutory and operational responsibilities.

Participation should not automatically confer registration approval, ownership recognition, or mechanical royalty entitlement. The pilot’s findings could help determine whether changes to registration guidance, metadata standards, or coordination with other music-industry organizations would be appropriate.

10. Questions for Policy and Industry Consultation

I respectfully submit the following questions for consideration:

  1. How should registration systems distinguish human-authored expression from AI-generated musical material?
  2. What administrative pathway should be available when existing mandatory writer-role fields do not accurately describe a work?
  3. What information is necessary to document human–AI contributions without overcollecting sensitive or proprietary materials?
  4. How can publishing and mechanical royalty claims remain grounded in verified legal rights?
  5. What interoperability standards could improve consistency between rights organizations, publishers, distributors, and recording databases?
  6. What responsibilities fall within The MLC’s existing authority, and which require broader industry or regulatory coordination?
  7. How should uncertain classifications be reviewed, corrected, and documented over time?

11. Universal Application and Public Interest

This framework is intended for the benefit of the broader music ecosystem. It is not limited to a particular artist, recording, catalog, music genre, AI platform, or commercial organization.

The proposed classifications are non-exclusive and should remain open to revision through research, professional consultation, technical testing, and legal developments. The objective is not to establish ownership over an industry-wide classification system but to encourage transparent, practical, and responsible standards.

12. Invitation to Collaboration

I respectfully invite The MLC’s Policy and Member Services Teams to consider this proposal as a contribution to ongoing music registration and rights-administration discussions.

I would welcome opportunities to provide further documentation, participate in constructive research, or assist in evaluating the feasibility of an optional human–AI contribution model.

The proposal is offered for discussion, not as an assertion that The MLC has adopted or endorsed the proposed framework.

As artificial intelligence becomes more deeply integrated into musical creation, accurate documentation will be increasingly important to creators, publishers, administrators, researchers, and future generations.

Closing Principle: Human creativity deserves accurate recognition. AI-generated material deserves transparent documentation. Rights and royalties must remain grounded in verified legal authority.

Thank you for your consideration.

Respectfully,
Tony Ramos Torres
Recording Artist: RAPTIMETVTONY
Songwriter | Executive Producer | Cultural Archivist
Founder, RapTimeTV Cultural DNA Archive™
Website: https://raptimetvtony.com
Email: raptimetvtony@yahoo.com

Document Classification: Universal Policy Research Proposal | Version: 1.0 | Status: Proposed — Not Adopted by The MLC | Scope: International Research Relevance; Implementation Subject to Applicable Jurisdiction and Authority | Individual Artist Case Studies: Separate Supporting Records, Not Part of the Universal Framework