Copyright law at regulatory crossroads

Opt-in licensing model with transparent consent mechanisms

Rather than a blanket mandatory license, the framework should adopt an opt-in mechanism that preserves creator agency while reducing transaction costs. This model aligns with technological consent standards (for example, web cookie consent).

Proposed mechanism:

(i) Copyright registry creation: The proposed Copyright Royalties Collective for AI Training (CRCAT) should maintain a digitised registry of works available for AI training, organised by:

–  Work category (literary, artistic, musical, cinematographic, computer-generated programme)

–  Jurisdiction of copyright owner

–  Copyright holder contact information

(ii) Creator participation: Copyright owners register works they permit for AI training, specifying:

– Permitted use modalities language model training, image synthesis, voice modeling)

– Territorial scope

– Compensation modality (fixed royalty, revenue-share percentage, hybrid)

– Attribution and labeling requirements

(iii) Machine-readable metadata: Implement technical standards (Dublin Core, ONIX, or custom schema) enabling automated compliance verification. AI developers integrate API calls to check the CRCAT registry before ingesting training data.

(iv) Default position: Works not affirmatively registered remain inaccessible for AI training absent individual licensing agreements.

This preserves creator autonomy, incentivises participation through choice and reduces litigation by establishing clear-cut rules. A robust, opt‑in licensing scheme for AI training would also eliminate any question of infringement in respect of licensed uses, since every work incorporated into the training datasets would be used pursuant to express authorisation from the relevant rightsholders.