Trustworthy AI-Supported Copyright Infrastructure
Presentation At Collective Management and AI: Challenges and Perspectives
Abstract
Presentation and discussion at the scientific conference ‘Collective Management and Artificial Intelligence: Challenges and Prospects’, focusing on trustworthy AI, interoperable metadata, and federated copyright infrastructures for collective management and cultural governance.
This presentation discusses how trustworthy AI and interoperable copyright metadata can strengthen collective management systems and support cultural diversity in Europe.
Using examples from the Open Music Observatory and the Slovak Comprehensive Music Database (SKCMDb), it argues that AI systems require federated, provenance-rich, and rights-aware infrastructures rather than isolated platform silos.
Key Ideas
Trustworthy AI: AI systems depend on reliable identifiers, provenance, and rights metadata to support lawful and transparent reuse. Federated infrastructures: copyright data spaces should connect CMOs, archives, platforms, and public institutions without requiring centralisation. Metadata governance: sustainable copyright infrastructures require coordinated workflows, lifecycle management, and institutional cooperation. Cultural diversity: better metadata and discovery systems can improve the visibility of local and minority repertoires. Examples
- The Open Music Observatory demonstrates how federated governance and interoperability can support evidence-based cultural policy.
- The SKCMDb experiment connects rights management, heritage documentation, and statistical infrastructures in Slovakia. Comparisons between streaming services, archives, and collective management systems illustrate how inconsistent metadata reduces both visibility and remuneration.
Next Steps
- Develop interoperable metadata profiles aligned with European copyright and AI regulation.
- Improve lifecycle-aware provenance tracking for rights and licensing information.
- Expand cooperation between collective management organisations, GLAM institutions, and public-interest data spaces.
- Support pooled AI services for metadata reconciliation, multilingual enrichment, and documentation repair.
