The Orchha Principles for AI and Copyright in the Digital Age [The Orchha AI Principles]

Respect for Intellectual Property: AI developers and end-users must uphold intellectual property rights by recognising creators' investments, honouring contractual terms, and respecting opt-out requests.

Fair Use and Model Integrity: While leveraging publicly available information under fair use principles, AI developers must implement transparent data provenance tracking, establish interoperable validation protocols to detect and mitigate cascading errors in synthetic data generation, and create standardised cross-model verification APIs to ensure the long-term reliability, diversity, and integrity of AI outputs.

Data Minimisation and Retention: AI developers must implement stringent data minimisation protocols that limit data collection, processing, and storage to what is strictly necessary for the intended AI service or function. This includes establishing clear data lifecycle policies, implementing automated data disposal mechanisms when the purpose of collection is fulfilled, and ensuring regular audits of data retention practices.

Privacy Rights and Protection: AI systems must be designed with privacy as a fundamental right, implementing robust safeguards for personal data protection throughout the AI lifecycle. This includes respecting individual privacy rights through clear consent mechanisms, implementing privacy-by-design principles in AI development, and ensuring that privacy considerations are prioritized in both training and deployment phases.

Licensing and Fair Compensation: Compulsory licensing measures, limited in scope but efficient in application, should enable AI training while ensuring fair compensation streams for content creators across the artificial intelligence value chain. These frameworks should balance intellectual property protection and support AI innovation while providing fair remuneration for creators.

Multi-Modal Synthetic Content Ecosystem: AI developers must responsibly manage the symbiotic relationship between multi-modal synthetic content generation and data pooling by implementing ethical sourcing practices, ensuring diverse representation, developing open APIs for seamless integration, and establishing safeguards against harmful feedback loops while fostering interoperability across AI systems.

Transparency and Attribution: AI systems must ensure transparency regarding the data, information, or content used in their training datasets. Robust forensic methods—both technical and human—should be employed & made open-source to attribute AI-generated outputs sensibly and accurately to their original sources.

Significant Human Input: Copyright protection should be reserved for AI-assisted works that reflect substantial human creativity and intellectual effort, ensuring that human authorship remains central to the creative process.