A Green Paper on AI, Data Governance, and Metadata Policies for Europe's Music Ecosystem

A Green Paper on AI, Data Governance, and Metadata Policies for Europe’s Music Ecosystem Practical Steps Towards a Decentralised and Open European Music Observatory Daniel Antal, CFA 2024-11-30 Table of contents Introduction 4 Glossary 9 Musicterms...................................... 9 Dataterms ...................................... 10 AI&SystemsTerms................................. 12 Dataprotectionterms ................................ 14 Data curation and collection terms . . . . . . . . . . . . . . . . . . . . . . . . . 14 Rightsmanagementterms.............................. 15 Statisticalterms ................................... 15 Registers, authorities, standards and identifiers . . . . . . . . . . . . . . . . . . 16 Organisations..................................... 19 Otherabbreviations ................................. 20 1 Policy context and problem map 21 1.1 Three structural pressures . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1.2 National and European pilots as anchors . . . . . . . . . . . . . . . . . . . 23 1.2.1 The Slovak Comprehensive Music Database (SKCMDb) . . . . . . 23 1.2.2 Unlabel ................................. 25 1.3 Questforefficiency............................... 26 1.4 Potentialsolutions ............................... 28 2 Fixing Music Data at the Source 31 2.1 Discussion.................................... 31 2.1.1 Structural fragmentation of data and value flows . . . . . . . . . . 31 2.1.2 Cost barriers in documentation and claims . . . . . . . . . . . . . . 34 2.1.3 Why one grand collection model will not work . . . . . . . . . . . . 34 2.1.4 Legacymetadata............................ 35 2.1.5 Named-entity resolution, attribution, and privacy . . . . . . . . . . 38 2.2 Policyproposals................................. 40 2.2.1 Reducing redundancy . . . . . . . . . . . . . . . . . . . . . . . . . 40 2.2.2 Reconciling attribution and privacy . . . . . . . . . . . . . . . . . . 41 2.2.3 Pragmatic metadata alignment . . . . . . . . . . . . . . . . . . . . 43 3 Open Music Observatory: Building a Shared Music Data Space 46 3.1 Discussion.................................... 47 3.1.1 Why centralisation is a futile model . . . . . . . . . . . . . . . . . . 47 3.1.2 Open Data Directive: right without means . . . . . . . . . . . . . . 50 2 3.1.3 Why voluntary workarounds do not scale . . . . . . . . . . . . . . . 50 3.1.4 Public infrastructures bypass music’s real data flows . . . . . . . . 51 3.1.5 Subsidiarity and infrastructures for scaling music data . . . . . . . 53 3.1.6 Economies of scale in metadata . . . . . . . . . . . . . . . . . . . . 54 3.2 PolicyProposals ................................ 55 3.2.1 Workflow playbooks and provenance trails . . . . . . . . . . . . . . 57 3.2.2 Federated infrastructure as a cost and governance solution . . . . . 57 3.2.3 Legal, standards, and funding levers . . . . . . . . . . . . . . . . . 59 3.2.4 Alignment with the European Open Science Cloud . . . . . . . . . 59 4 AI that Works for Music, Not Against It 61 4.1 Discussion.................................... 64 4.1.1 Structural problems for music businesses to apply AI . . . . . . . . 64 4.1.2 European regulation that misses the point . . . . . . . . . . . . . . 65 4.1.3 Policy issues at the intersection of AI, copyright, and GDPR . . . . 67 4.1.4 AI design without awareness of limits . . . . . . . . . . . . . . . . . 68 4.1.5 Unfreezing frozen assets . . . . . . . . . . . . . . . . . . . . . . . . 69 4.1.6 AI support for investment into new repertoire assets . . . . . . . . 70 4.2 Policy Proposals: Aligning AI with Governance and Value Creation . . . . 70 4.2.1 EU-Level Policy: Compass and Guardrails . . . . . . . . . . . . . . 71 4.2.2 Industry-Level Policy: Standards and Collaboration . . . . . . . . . 71 4.2.3 Organisational-Level Policy: Playbooks for CMOs, Publishers, Archives................................. 72 4.2.4 Curative AI and Reparative AI as a Remediation Solution . . . . . 72 4.2.5 Lowering Documentation Barriers . . . . . . . . . . . . . . . . . . . 73 4.2.6 Observatory: European = Open . . . . . . . . . . . . . . . . . . . . 73 4.2.7 The Open Music Observatory as a Collective Guardrail . . . . . . . 74 5 What Europe Should Do Next for Music Data & AI 76 Sources & Further Reading 78 3 Introduction There are musical works that are reinterpreted thousands of times across centuries. A symphony by Beethoven or a folk song from the Baltic coast can be heard again and again, each performance producing a new reading of something that never becomes “final.” The same is true of sound recordings. Some perennial recordings are rediscovered after sixty years, remastered, and brought into circulation for new audiences. Music assets, in other words, have an unusually long lifecycle. This is just as true of their documentation — the metadata that accompanies them from creation to archiving. Metadata does not freeze a work or recording in time. Instead, it evolves with it: from the moment of rights registration, through commercial distribution and playlisting, to preservation in a library or archive. Each new interpretation, remix, or reissue generates new metadata; and each new information system demands new connections and contexts. ĹWhy this Green Paper matters for music professionals? • Streaming has centralised power in platforms, but left rights-holders with microroyalties and huge admin burdens. • Metadata mistakes mean lost revenue — each unlinked ISRC or ISWC is money left on the table. • AI is already changing music — either it helps you fix documentation and get paid, or it floods the system with untracked works. • Europe needs federated, cooperative solutions so independents, CMOs, and archives can compete on fairer terms. 4 There is rarely a single moment when music metadata can be considered complete. Metadata, like music itself, is open to reinterpretation. A name can be reconciled with an identifier; a work can be linked to a new performance; a recording can be embedded in new file formats. Each act of documentation adds layers of meaning and makes the music informative in a new environment. This is not an invitation to reinvent the wheel. We can read Beethoven’s early prints as well as Iris Szeghy’s 21st-century scores because music notation — a standardised way of presenting the metadata of musical works — has remained remarkably stable for centuries. Notation shows that standardisation can endure, and that shared conventions make music legible across time, geography, and institutions. 5 The invention of the computer, and later the internet, introduced new ways to document and transmit music. These innovations brought powerful efficiencies: identifiers like the ISRC and ISWC, digital distribution pipelines, and networked catalogues have enabled the global circulation of music at unprecedented scale. But they also created new fragmentation. Standards proliferated, identifiers failed to interconnect, and workflows designed for one purpose often broke down in another. What was intended as progress sometimes left behind a mess of overlapping, incompatible, or incomplete metadata — a mess that now needs to be cleared up. ĹNote This Green Paper is an early-stage policy document, prepared in line with Open Policy Analysis and the Horizon Europe Data Management Guidelines. It has been released early to allow consultation, incorporate stakeholder input, and provide a transparent development process. This Green Paper extends the analysis developed in the first OpenMusE policy brief on music metadata mainstreaming and EU law (Deliverable D5.6), and its findings are condensed into the second policy brief (Deliverable D5.7), which incorporates wider stakeholder consultations.1 Transparency note: Following the principles of Open Policy Analysis, all related deliverables and technical documentation are publicly accessible to foster broad engagement and ensure a clear audit trail. Supporting documents for each chapter of this Green Paper are referenced in similar boxes. The current version (and future White Paper drafts) is available at https://zenodo.org/records/17075796. Standardised folders, figures, and bibliographies are available at https://github.com/dataobservatoryeu/open-music-data-white-paper. Please note that this document puts the Open Music Observatory, a prototype of a modern European Music Observatory developed by the OpenMusE consortium, which is being currently populated with economy, diversity, society, innovation data and has already three federated modules, can reviewed in the technical documentation (see versioned Zenodo DOIs), our viewn on the temporary landing page. Funding acknowledgement: This project has received funding from the European Union’s Horizon Europe programme under Grant Agreement No. 101095295. The views expressed are those of the authors only and do not necessarily reflect those of the European Commission or its agencies.2 Citation note: When citing this Green Paper, please use the latest versioned DOI available on Zenodo, and include the date of access if referring to material hosted on our GitHub repository.3This is an early version (0.9.0.) 6 Our document has been presented and discussed with industry specialists on the following forums: • Big Data Value Association, Gaia-X: Dataweek²�: Introducing a new European music dataspace4 • Echoes/ECCH: • Hungarian stakeholders interested in replication of the Slovak pilot versions 5 • CISAC: Protecting Creators’ Rights in the AI Era: OpenMusE at the European Committee Meeting, Vilnius, 29-30 April 6. • The Fair MusE - Prelude to a fairermusic industry Fair MusE project7 • IAMIC 8: The International Association of Music Information Centres and several key members of the organisation. • IAML: The International Association of Music Libraries, Archives and Documentation Centers and several national chapters and key members 9. 3The Policy Brief 1: Music Metadata Mainstreaming and EU Law (Senftleben et al. 2024) provides the legal and institutional framing for metadata mainstreaming in European copyright and data law. The present Green Paper builds on that foundation with a lifecycleand sovereignty-oriented conceptual framework, tested in pilots such as the Slovak Comprehensive Music Database. Its key recommendations are further condensed in OpenMusE Policy Brief 2: An Open, Scalable Data-to-Policy Pipeline for European Music Ecosystems (Deliverable D5.7, 2025) (Open Music Europe Consortium 2025), which integrates broader stakeholder consultations (CISAC, IAMIC, IAML, FairMusE, Music360, ECCCH forums, among others) and translates them into policy actions for EU institutions. 3This document has been prepared by Open Music Europe (OpenMusE) project partners as an account of work carried out within the framework of this contract. Any dissemination of results must indicate that it reflects only the author’s view and that the Commission Agency is not responsible for any use that may be made of the information it contains. Neither Project Coordinator, nor any signatory party of Open Music Europe (OpenMusE) Project Consortium Agreement, nor any person acting on behalf of any of them: (a) makes any warranty or representation whatsoever, express or implied, (i) with respect to the use of any information, apparatus, method, process, or similar item disclosed in this document, including merchantability and fitness for a particular purpose, or (ii) that such use does not infringe on or interfere with privately owned rights, including any party’s intellectual property, or (iii) that this document is suitable to any particular user’s circumstance; or (b) assumes responsibility for any damages or other liability whatsoever (including any consequential damages, even if advised of the possibility) resulting from your selection or use of this document or any information, apparatus, method, process, or similar item disclosed herein. 3Always use the latest versioned DOI when citing this Green Paper, available via Zenodo. If you rely on supporting material hosted in the GitHub repository, please add the date of access in your reference. The figures and charts can be found on FigShare and may be reused separately, citing their DOI and, for context, the Green Paper that contains them. 4Jun 5, 2024, Dataweek²�, Leuven, Belgium. 5Federation possibilities of the Slovak music data sharing space in Hungary (Antal 2024a) 6Protecting Creators’ Rights in the AI Era: OpenMusE at the European Committee Meeting, our presentation (Mikš 2025) 7We received useuful feedback for this Green Ppaer from the project and see further synergies in presenting our policy findings together. https://fairmuse.eu/about/ 8We presented and discussed these ideas at the International Association of Music Information Centres on the General Assembly and Annual Conference 2024 on November 21, 2024, at Music Austria, Vienna. See the presentation and its poster format (Antal 2024d). 9We presented and discussed these ideas at the International Association of Music Libraries, Archives and Documentation Centers on the General Assembly and Annual Conference 7th and 9th of July 2025 in Salzburg, Austria. See the presentation and its poster format (Antal 2025a, 2025b). 7 • Polifonia: In October 2023 Polifonia invited a few stakeholders - Podiumkunst.net, the Open Music Observatory, Uni Firenze, IC Fonseca School, Joséphine Simonnot/PRISM, Maria Luisa Onida/D’Istruzione Superiore Leonardo Da Vinci, Carnegie Hall Archive, Municipality of Bologna - for a work session, which gave us a great opportunity to strengthen the metadata framework of our policy recommendations and infrastructure planning. • Music Futures: the AHRC Creative Industries Cluster project MusicFutures in the United Kingdom. • Slovak national stakeholders interested in cultural data.10 • Wikimedia community and developers11. • European music industry stakeholders on LineCheck 2025 12 The CITF’s First Project Report (Ministry of Education and Culture, Finland, 2025) validates and extends the policy logic of this Green Paper. CITF formulates cross-sectoral requirements for trustworthy, machine-readable copyright infrastructures in the AI era — focusing on identifiers, rights management information, provenance, and federated governance. OpenMusE provides a concrete domain implementation of these ideas within the European music ecosystem, showing how interoperable identifiers, FAIR principles, and data spaces can work in practice for a highly fragmented cultural sector. Because of this complementarity, we have aligned all major sections of the Green Paper with CITF’s three-layer model. The Chapter 2reflects CITF’s foundational layer (authoritative identifiers and repairable rights metadata); the Chapter 3corresponds to the semantic and technical layers (federated registries, mappings, and interoperability profiles); and the Chapter 4translates CITF’s AI-era requirements into music-specific governance questions. This deliberate alignment is meant to make both documents usable in parallel: CITF as the horizontal framework for copyright data in Europe, and this Green Paper as a domainspecific blueprint for music that can be reused, extended, or replicated in other cultural sectors13. 10Based on a memorandum of understanding with a broad range of public and private stakeholders, (Ministerstvo kultúry SR and Open Music Europe 2023) we developed a model for renewing statistical production for better cultural and music statistics (Antal 2023). 11Our work was presented in the Technology session of the Wikimedia CEE Meeting 2024 in Istanbul, and the Wikimedia CEE Meeting 2025 in Thessaloniki, and the Wikidata Conf 2025 online; we have built relationships with various national chapters and the Wikidata and Abstract Wikipedia teams, and joined the Wikidata Ontology Cleanup Task Force and the Wikidata Mereology Task Force to help the coordiantion of our open source technology, data curation and dissemination efforts. (Antal 2024b, 2025c; Antal, Pigozne, and Federico 2025). 12Open Access Music Dataspaces – Open Music Observatory presented on LineCheck 2025 (Mikš and Antal 2025) 13We are planning to give feedback to the (Partanen, Rixhon, Bandere, Ziediņš, Dutt, Bolšteins, Frosterus, Lehtinen, Miklūna-Žukeviča, Ozerskis, Pihlaja, Sauka, Sornova, and Uzu 2025) on 19 November, and we asked the authors of the report to comment on our Green Paper, too. 8 Glossary Music terms audio recording: fixation of sounds (ISO 2019a) creator: in the context of this policy paper, we use the broad term for the arranger, author,composer,lyricist; for individual definitions see ISWC standard (ISO 2022) DSP or digital streaming platform: Digital service providers (DSPs), or Digital Streaming Platforms are companies or organisations that provide access to services online. DSPs can provide access to music downloads, like Apple’s iTunes Store, or access to streaming music like Spotify, or even provide satellite-delivered content such as SiriusXM in the USA. expression: intellectual or artistic realisation of one and only one work Note: may take the form of a notation , sound, image, object, movement or text (ISO 2017b) manifestation: physical embodiment of an expression (ISO 2017b) movement: A principal division of a musical work. (ISO 2022) music video recording: fixation of sounds synchronized with pictures or moving pictures where (a) the fixed sounds are wholly or substantially a musical performance or (b) the recording is intended for viewing in association with a recording of a musical performance. This definition includes music videos and concert recordings, together with music-related interviews and documentaries, but does not extend to genera! audiovisual material, even if it includes music.(ISO 2019a) musical work: composed of a combination of sounds, with or without accompanying text (ISO 2022) original title: A title given to the work by its creator(s) shown in its original language. (ISO 2022) formal title: A standardized title in which the elements are arranged in a predetermined order, such as titles created for classical works. (ISO 2022) rights management (organisations): the function of managing the rights on behalf of rights owners. It can be companies whose sole purpose is to ensure that content that has been licensed has delivered royalties that are identified and accounted for. The role can be taken by collective management organisations or by private companies on behalf of songwriters, composers, performers, music publishers, or record labels. 9 code list: predefined list from which some statistical coded concepts take their values (ISO 2013) data pipeline: a method in which raw data is ingested from various data sources and then ported to data store. FAIR or FAIR Guiding Principles for scientific data management and stewardship: guidelines to improve the Findability, Accessibility, Interoperability, and Reuse of digital assets, emphasising machine-actionability (i.e., the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention.) indicator: the representation of statistical data for a specified time, place or any other relevant characteristic, corrected for at least one dimension (usually size) so as to allow for meaningful comparison. microdata: non‐aggregated observations or measurements of characteristics of individual units, without direct identifier. observation unit: an identifiable entity about which data can be obtained, it is also often called a statistical unit or data subject in case of a natural person. Open Policy Analysis Guidelines: a set of information management rules to make policy analysis more transparent. personal data: any information relating to an identified or identifiable natural person. pseudonymisation: processing of personal data in such a manner that the personal data can no longer be attributed to a specific data subject without the use of additional information. survey: a systematic examination and record of a physical or social area and its features so as to construct a map, plan, or description. In social sciences it usually refers to a well-structured questionnaire and answers given to its items by a target population. statistics: quantitative and qualitative, aggregated and representative information characterising a collective phenomenon in a considered population. visualisations: schematic charts, drawings, photographs, and their collages will as still image files that help to explain the relationship between information carriers, data points, or processes. Registers, authorities, standards and identifiers agent identifier: persistent identifier assigned to an author, performer, contributor, or other agent. CITF requires agent identifiers to be standardised, trustworthy, and interoperable. (Partanen, Rixhon, Bandere, Ziediņš, Dutt, Bolšteins, Frosterus, Lehtinen, Miklūna-Žukeviča, Ozerskis, Pihlaja, Sauka, Sornova, and Uzula 2025, 85–86) asset identifier: persistent identifier used for musical works, sound recordings, editions, audiovisual items, or other cultural objects. CITF requires asset identifiers to be resolvable 16 and interoperable across systems. (Partanen, Rixhon, Bandere, Ziediņš, Dutt, Bolšteins, Frosterus, Lehtinen, Miklūna-Žukeviča, Ozerskis, Pihlaja, Sauka, Sornova, and Uzula 2025, 86) IČO: The organisation identification number (IČO) is an identifier assigned to all types of legal entities, entrepreneurs and public authorities by the Statistical Office of the Slovak Republic. The Czech Republic’s organisation identifier is also called IČO. (→ agent identifier) OpenCorporates: a public corporation database which sources data from national business registries. (→ agent identifier) ISNI: an ISO certified global standard number for identifying the millions of contributors to creative works and those active in their distribution. (→ agent identifier) VIAF: The Virtual International Authority File (VIAF) is an international service that consolidates multiple name authority files into a single database. Their primary goal is to enhance the efficiency and usability of library authority files by linking and merging widely used authority records and making them accessible online. VIAF ID: The VIAF (Virtual International Authority File) combines multiple name authority files into a single OCLC-hosted name authority service. (→ agent identifier) ISRC: The International Standard Recording Code (ISRC) is a standard identifying code that can be used to identify sound recordings and music video recordings so that each such recording can be referred to uniquely and unambiguously. (→ asset identifier) ISWC: The purpose in creating an ISWC for musical works is to enable more efficient administration of rights to those works on a worldwide basis. The ISWC provides an efficient means of identifying musical works in computer databases and related documentation and for the exchange of information between rights societies, publishers, record companies and other interested parties on an international level. ISBN: the International Standard Book Number is an identification system for the publishing industry and its supply chains. (→ asset identifier) ISMN: The International standard music number (ISMN) was developed by, and for, the music publishing sector as a separate system to complement the International standard book number (ISBN). The existence of the ISMN as a separate identifier system makes it possible to identify printed and notated music as a distinct category of publication within the global supply chain and to develop trade directories and similar services for the specialized market for music publications. (→ asset identifier) ISCC: The International Standard Content Code (ISCC) is an identifier for numerous types of digital assets. (→ asset identifier) DOI: The Digital Object Identifier is a standardised unique number given to many (but not all) articles, papers and books, by some publishers, to identify a particular publication. ORCID: the Open Researcher and Contributor ID is a unique, persistent identifier free of charge to researchers. (→ agent identifier) 17 URI: A Uniform Resource Identifier (URI) is a string of characters used to identify a resource on the internet. This resource can be either abstract or physical, such as a website, an email address, or a file. URIs are essential for enabling interactions with resources over a network using specific protocols. DDI: The Data Documentation Initiative is originating for the world of social sciences data archives and more and more in use in statistical organisations for the documentation of microdata. Wikibase: Wikibase is a software system that help the collaborative management of knowledge in a central repository. It was originally developed for the management of Wikidata, but it is available now for the creation of private, or public-private partnership knowledge graphs. It is developed by Wikimedia Deutschland. SDMX: Statistical Data and Metadata eXchange (SDMX), is an international initiative that aims at standardising and modernising (“industrialising”) the mechanisms and processes for the exchange of statistical data and metadata among international organisations and their member countries. CIDOC-CRM: The conceptual model of CIDOC, the standard conceptualisation of collection management systems in heritage organisations. RiC:Records in Context is a new conceptual model that replaces the four most important international archiving standards. DCTERMS or DCMI: the Dublin Core Metadata Terms is a vocabulary of metadata terms developed and maintained by the Dublin Core Metadata Initiative (DCMI). These terms are used to describe various aspects of digital resources, such as web pages, documents, and other online content. They provide a standardized way to assign metadata to resources, making them easier to discover, manage, and exchange. RDFS: the Resource Description Framework Schema is an extension of the Resource Description Framework (RDF) that provides a vocabulary for describing classes and properties of resources within an RDF graph. EDM: the Europeana Data Model is a framework for collecting, connecting, and enriching cultural heritage metadata. It’s designed to facilitate the sharing and reuse of cultural heritage information by providing a standardized way to represent and link data. PROV-O: the Provenance ontology is a formal ontology developed by W3C to represent and interchange provenance information. MARC: MAchine-Readable Cataloging, is a standard digital format used by libraries to represent and exchange bibliographic information. DCAT: an RDF vocabulary designed to facilitate interoperability between data catalogues published on the Web. 18 Organisations AEPO-ARTIS: Organisation representing European artists-performers. Regroups most of the European CMO representing performers. ALOADED: is a company which distributes and exploits recordings. CISAC: The International Confederation of Societies of Authors and Composers is an international non-governmental, not-for-profit organisation that aims to protect the rights and promote the interests of creators worldwide. CNM (former CNV): the Centre National de la Musique is a public organisation managing a tax on concert tickets EMO: The European Music Observatory (EMO) is envisioned as a hub for collecting and analysing data on the music sector across Europe. Its primary aim is to address the current gaps and inconsistencies in music data collection, which have been a significant challenge for the sector. Europeana: a digital platform provided by the European Union that aggregates digitized cultural heritage from institutions across Europe. GESAC: The European Grouping of Societies of Authors and Composers (GESAC) comprises of 32 European authors’ societies in music, audiovisual, visual arts, literature and drama. IAML: International Association of Music Libraries, Archives and Documentation Centres IAMIC: International Association of Music Centres, an international network of organisations that collectively and collaboratively provides information and promotes the music of their countries or regions. ICMP: the global trade body representing the music publishing industry worldwide. SCAPR: International association for the development of the practical cooperation between performers’ collective management organisations (CMOs) SOZA: SOZA (Slovenský ochranný zväz autorský pre práva k hudobným dielam, Slovak Performing and Mechanical Rights Society) is a legal entity, non-profit civic association of authors and publishers of musical works, association of natural persons and legal entities. Hudobné Centrum: Music Centre Slovakia is a music organisation with a mission to promote Slovak contemporaly music. 19 Other abbreviations CEEMID: the Central European Music Industry Databases is a multi-country project that was a predecessor of Reprex’s Digital Music Observatory DSP: Digital service providers (DSPs), or Digital Streaming Platforms are companies or organisations that provide access to services online. EIF: The European Interoperability Framework (EIF) is a set of recommendations and guidelines that aims to facilitate communication and collaboration between public administrations, businesses, and citizens within the European Union and across national borders. ECCCH: The European Collaborative Cloud for Cultural Heritage is a European Union initiative for a digital infrastructure that will connect cultural heritage institutions and professionals across the EU. EOSC: The European Open Science Cloud (EOSC) aims to create a trusted, open, and multidisciplinary environment for researchers and innovators in Europe. PPP: A Public-Private Partnership (PPP) is a collaborative arrangement between government entities and private sector companies aimed at financing, designing, implementing, and operating projects or services traditionally provided by the public sector. RDM: Research Data Management refers to the suite of practices, policies, and processes used to handle data throughout the lifecycle of a research project. W3C: The World Wide Web Consortium (W3C) is an international community that develops standards for the World Wide Web. Their mission is to lead the Web to its full potential by creating technical specifications and guidelines that are designed to be open and royaltyfree. These standards include HTML, CSS, and other web technologies, which ensure that web content is accessible across different browsers and devices. Our glossary is harmonised with relevant music-sector specific standards and with the • ISO Information technology Vocabulary (ISO 2023b); Cloud computing — Taxonomy based data handling for cloud services (ISO 2020); Cloud computing — Interoperability and portability (ISO 2017a); Metadata registries (MDR) — 1. Framework (ISO 2023a) standards and the Information and documentation — Foundation and vocabulary (ISO 2017b) standard. • ISO Information technology Artificial intelligence — Concepts and terminology (ISO/IEC 2022) and Artificial intelligence — Management system and (ISO/IEC 2023) standard’s vocabulary. 20 1 Policy context and problem map The European music ecosystem has undergone disruptive transformations in recent decades. In the 2010s, the arrival of agentic AI in streaming platforms radically reconfigured distribution and consumption. These systems centralised global sales, expanding the commercially available repertoire in a typical EU country from roughly 100,000 titles to over 100 million titles competing for attention. At the same time, the average transaction value collapsed from around €18 (in current prices) to less than €0.005. This shock hollowed out much of the traditional infrastructure — record stores, radios, and music television — and shifted value capture toward data-driven platforms able to control access through recommender algorithms. In the 2020s, the rise of generative AI further exacerbates this situation. Large-scale models can mass-produce new compositions and recordings, often imitating or plagiarising patterns of human creators. This inflates supply, undermines the position of professional authors and performers, and aggravates existing problems of remuneration and discoverability.1 EU-level studies and policy frameworks have recognised these dynamics and increasingly frame them as systemic challenges. The Feasibility Study for the Establishment of a European Music Observatory diagnosed the fragmented, scarce, and poorly harmonised nature of music data collection across Member States, calling it the fundamental reason for an EU-level observatory. The Music Ecosystem 2025 study reframes the sector as an interconnected ecosystem, where platformisation, market consolidation, and emerging technologies like AI interact with broader societal challenges such as precarity, gender inequality, and sustainability. The European Parliament, in its Resolution on cultural diversity and the conditions for authors in the European music streaming market, echoed these concerns with explicit calls for reform.2 1Music Ecosystem 2025: Study on the Music Ecosystem (Music Moves Europe 2024); it frames the sector as an adaptive, networked ecosystem, highlights AI’s ability to disrupt on pp. 6–7, and mentions it as an opportunity particularly on p. 23. Feasibility Study for the Establishment of a European Music Observatory (Commission et al. 2020); stresses the fragmented, scarce, and poorly harmonised nature of music data (pp. 9–10), the need for cooperation with rights organisations, statistical agencies, and industry stakeholders (p. 61), and introduces CEEMID as a best practice (pp. 147–148). CEEMID emerged from Budapest, Bratislava, and Zagreb as an early effort to address data poverty in Eastern EU Member States. 2European Parliament Resolution on cultural diversity and the conditions for authors in the European music streaming market (European Parliament 2024); it recognises streaming as the dominant global revenue source while leaving many authors with very low income (recitals F–H), stresses accurate metadata allocation at the time of creation using identifiers ISWC, ISRC, ISNI, IPI, and IPN (recital R, and 9.), highlights the lack of quality data to properly identify authors, performers, and rights holders (recital L), and warns that AI-generated tracks are flooding streaming platforms, aggravating discoverability and remuneration imbalances (recital O). 21 A third major contribution to this landscape is the 2025 CITF First Project Report, coordinated by the National Libraries of Finland and Latvia. CITF identifies open identifiers, machine-readable rights metadata, and national libraries as core components of a future copyright infrastructure. It introduces a three-layer model (foundational, semantic, technical) and provides lifecycle analysis of protected works in the AI era. Its findings complement the Music Ecosystem 2025 and EMO feasibility studies by foregrounding the role of cultural heritage institutions and the need for trustworthy, interoperable copyright registries3. Our policy brief positions itself within this landscape. It aims to support and extend the Music Moves Europe framework by highlighting six crucial dimensions: 1. Practical solutions, grounded in dialogue between research and industry, and inspired by concrete experiences with open, federated data-sharing approaches. 2. Potential pitfalls where well-meaning initiatives may clash with legacy systems, existing business practices, or contradictions in legislation. 3. Legal and operational conflicts, such as the tension between GDPR’s data protection regime and the Berne Convention’s requirement of author attribution. 4. Cooperation and workflow sharing, recognising that no single actor can bear the full burden of metadata documentation. 5. Technology, including automation, entity recognition, reconciliation, and persistent identifiers. 6. AI adaptation and cooperative infrastructures, since most stakeholders cannot attract or retain scarce AI expertise. By foregrounding these issues, the brief complements the calls of the Music Ecosystem 2025 study and the European Music Observatory feasibility study, while remaining attentive to the practical challenges of implementation across Europe’s diverse music and cultural landscapes. 1.1 Three structural pressures Three structural pressures frame today’s metadata challenges: 1. Extreme efficiency pressure. Music is now monetised in micro-transactions worth a fraction of a cent. Each metadata mistake means lost royalties, while big-tech platforms enjoy economies of scale that self-releasing artists, small labels, and national CMOs cannot match. National libraries in many countries already maintain massive copyright-protected collections and identifier systems, which could be cross-utilised with CMOs. 3Interoperable, trustworthy, and machine-readable copyright data in the AI era. Report of the CITF First Project (Partanen, Rixhon, Bandere, Ziediņš, Dutt, Bolšteins, Frosterus, Lehtinen, Miklūna-Žukeviča, Ozerskis, Pihlaja, Sauka, Sornova, and Uzula 2025) 22 2. AI-driven disruption. Agentic AI in streaming platforms has already displaced much of the traditional retail and promotion infrastructure. Both pre-deployment and post-deployment of AI affect reproduction, distribution, and attribution rights. Generative AI risks flooding platforms with derivative works and further destabilising discoverability and revenues. Yet AI tools could also support documentation and reconciliation — if governance frameworks can enable them. 3. Governance and incentive conflicts. Identifiers such as ISWC, ISRC, ISNI, and IPN are essential for attribution and royalty distribution, but are maintained under costly, largely private regimes. Public policy increasingly demands more open metadata, but sustaining investment in these registers remains a challenge. Optout rights in AI training and the need for harmonised opt-out registries4, further complicate governance and incentive structures. These pressures mean that improving metadata is not only a matter of technical interoperability. It is also a question of economic sustainability, legal coherence, and cultural policy. 1.2 National and European pilots as anchors From the outset, we draw on concrete pilots that illustrate both the problems and possible solutions. Two of them — the Slovak Comprehensive Music Database (SKCMDb) and Unlabel — will recur throughout this paper as reference points. Together, they anchor the three thematic chapters: curation (Chapter 2), observatory (Chapter 3), and AI (Chapter 4). 1.2.1 The Slovak Comprehensive Music Database (SKCMDb) SKCMDb is our national pilot for federated metadata governance. It links together data from collective management (SOZA), national and city libraries, and archives, while ensuring that works can also be discovered in the digital environments where people actually listen: Spotify, YouTube, Apple Classical, and others. A further layer reconciles this metadata with the Slovak Statistical Office via a Satellite Business Register, so that cultural production is visible in official economic data. The SKCMDb is anchored in the Memorandum of Understanding signed between collective management organisations (SOZA, SLOVGRAM), cultural institutions (Hudobné centrum, Slovak National Library, Hudobný fond), and Reprex. SKCMDb’s strategy of combining copyright data (SOZA), neighbouring rights (SLOVGRAM), and national library authority control directly reflects CITF’s observation that national libraries must be integrated into copyright infrastructure, not treated as purely heritage institutions. 4As emphasised by CITF on pp. 17, 23–24 (Partanen, Rixhon, Bandere, Ziediņš, Dutt, Bolšteins, Frosterus, Lehtinen, Miklūna-Žukeviča, Ozerskis, Pihlaja, Sauka, Sornova, and Uzula 2025, p17, pp. 2325). 23 This MoU formalises a federated governance model where: •Attribution (names of authors, performers, composers) is preserved as legally mandatory under copyright law. •Privacy is safeguarded by layered access: public data (names, works, identifiers) circulate broadly, while sensitive data (e.g., addresses, birth dates) remain restricted. •Interoperability is achieved by aligning with VIAF, ISNI, ISWC, ISRC, and Europeana. As such, the Memorandum provides the legal and institutional foundation for SKCMDb, turning a technical pilot into a national dataspace aligned with the EU Data Strategy. Our pilot also The SKCMDb in action The chart illustrates the biography and works of Slovak composer Iris Szeghy as an example: Figure 1.1: A slide taken from: SKCMDb: Interoperability of Music Libraries and Archives with Public and Private Music Services (presentation at the IAML 2025 conference in Salzburg) <https://zenodo.org/records/16634558> •Left side: reconciliation of her works across SOZA, the Slovak National Library, the Bratislava City Library, and archives. •Right side: linking to listening platforms (Spotify, YouTube, Apple Classical). •Bottom: reconciliation with the Slovak Statistical Office via the Satellite Business Register. 24 SKCMDb thus acts as a bridge between cultural memory institutions, rights management, digital distribution, and public policy. SKCMDb provides a pragmatic response to fragmentation and duplication. It anchors the discussion of preventive metadata strategies in Chapter 2. This challenge is not unique to Slovakia. A recent Horizon Europe policy brief has highlighted how inadequate metadata infrastructures and fragmented European initiatives risk leaving the field open to dominance by extra-European players (for example, the US Mechanical Licensing Collective).5 1.2.2 Unlabel If SKCMDb focuses on building preventive infrastructures, Unlabel demonstrates how to repair the past. It is a collaborative pipeline connecting archives, libraries, collective rights organisations, and distributors to bring under-documented repertoires into the global digital supply chain. A striking example is the case of Hilda Griva, a bilingual Livonian–Estonian artist active in the interwar Finno-Ugric revival. Her recordings were rediscovered in the Latvian Archives of Folklore but lacked the metadata required for circulation. Through Unlabel, we translated and enriched her records, reconciled them with international authorities, and extended them with DDEX catalogue transfer metadata, enabling release via Spotify, YouTube, and Apple Music. Our multi-layer model (DDEX, DCTERMS, RiC patterns, and rights metadata) aligns with CITF’s three-layer structure: DCTERMS in the foundational layer, RiC and DDEX conceptual mappings in the semantic layer, and DDEX catalogue-transfer formats in the technical layer. ĹNote Infobox: Unlabel and Hilda Griva • Metadata repair began with archival records in the Latvian Archives of Folklore. • Records were translated, enriched, and reconciled with Wikidata, MusicBrainz, and VIAF. • DDEX-compliant catalogue transfer metadata enabled digital distribution. • The enriched catalogue allowed Hilda Griva’s recordings to be released and discovered globally. 5See Policy Brief 1: Music Metadata Mainstreaming and EU Law (Senftleben et al. 2024) (Deliverable D5.6, OpenMusE project). That brief emphasises that without a European metadata infrastructure, EU repertoires may remain underexploited and culturally invisible, while foreign platforms consolidate hegemony. The present Green Paper extends on this line of argument by focusing on lifecycle-based interoperability and federated observatories as safeguards for European sovereignty.The Policy Brief 1 Annex references the Slovak Listen Local / SKCMDb project as a national pilot, underlining its relevance for EU-level policy design. The Green Paper complements this by situating the MoU as a replicable governance framework for federated metadata spaces. 25 distributed across many actors with differing mandates and data models, and that inconsistent identifier governance contributes to systemic opacity and recurring reconciliation costs [Partanen, Rixhon, Bandere, Ziediņš, Dutt, Bolšteins, Frosterus, Lehtinen, MiklūnaŽukeviča, Ozerskis, Pihlaja, Sauka, Sornova, and Uzula (2025), pp10–18; p23]. This fragmentation is not an anomaly but the normal condition of the sector: tens of thousands of micro-enterprises and NGOs in Europe each manage slivers of data about works, recordings, or performances. As the Feasibility Study for a European Music Observatory underlined, “the fragmented, scarce and poorly harmonised nature of the data collection landscape in the field of music has led to calls … for a European Music Observatory” (Commission et al. 2020, p9). Likewise, the Music Ecosystem 2025 study frames the sector as an ecosystem, where knowledge and value are distributed across many small actors, each with partial perspectives (Music Moves Europe 2024, pp6–7). The institutional anchoring of a future European Music Observatory is indeed a critical question. In our own feasibility planning we reviewed approximately 80 functional and discontinued data observatories, understood here as permanent institutions for ongoing data collection and dissemination. The majority in Europe were initiated by the European Commission and maintained under various public–private partnership (PPP) formats, rather than as heavy agencies or autonomous bodies. In this sense, Europeana offers a useful analogy: it coordinates metadata and access across hundreds of institutions without requiring the scale or mandate of entities such as the EUIPO or the European Audiovisual Observatory. In our interim report deliverable we suggested a similar creation path like that of the Europeana Foundation and its various layers of stakeholders (Antal 2024c). From this perspective, we believe the Observatory should follow the lighter, federated PPP model: anchored by the Commission to ensure continuity and legitimacy, but implemented through a distributed network of partners across the public, private, and research domains. This strikes a balance between stability and flexibility, while staying true to the cooperative, federated spirit that underpins our proposal. The CITF report reaches a similar conclusion: copyright data cannot be centralised at European scale and must instead be organised through layered, federated arrangements where national libraries, rights organisations, and cultural institutions each retain their roles while interoperating through open standards1. Recognising this scattered landscape is essential. It explains why reconciliation overheads are high, why identifier coverage is incomplete, and why “capture once, reuse many” pipelines are necessary. It also provides the foundation for the next chapter: explaining why attempts at centralisation are futile in such an ecosystem, and why sustainable solutions must build on federation and interoperability. Yet fragmentation is not only institutional — it is also economic. Classic value-chain analyses describe three main income streams — live performance, publishing, and recordings 1See (Partanen, Rixhon, Bandere, Ziediņš, Dutt, Bolšteins, Frosterus, Lehtinen, Miklūna-Žukeviča, Ozerskis, Pihlaja, Sauka, Sornova, and Uzula 2025, pp13–18). 32 — that still structure industry practice.2Digital distribution has blurred these categories without unifying the underlying infrastructures. Each handover in the lifecycle — authoring, performing, recording, distributing, streaming — generates both a financial flow and a data event. Business flows are continuous, but metadata flows are siloed. ISWCs do not connect seamlessly with ISRCs; ISRCs are rarely linked to ISNIs or VIAF authority files. The result is redundancy, inconsistency, and costly reconciliation work. Figure 2.2: Adoption of the value chain model of the European music ecosystem in the CEEMID report To address these challenges, we have adopted the value chain model of the European music ecosystem3. This approach is especially useful for designing data collection that measures 2This value-chain framing originates in Hull’s The Music Business and Recording Industry (Hull et al. 2011) and Leurdijk et al.’s Statistical, Ecosystem and Competitiveness Analysis of the Media and Content Industries (Adnra Leurdijk and Ottilie 2012), and was adapted in subsequent CEEMID reports Antal (2020). The CEEMID work was recognised as a best practice in the Feasibility Study for a European Music Observatory (Commission et al. 2020), which highlighted its role in linking fragmented data sources into a coherent economic analysis. 3For the standard American/global analytical breakup of the music industry is described in (Hull et al. 2011), its European adaptation in (Andra Leurdijk and Ottilie 2012); our more detailed Central European breakup and the figure can is described in (Antal 2020, 2021), for the reuse of the figure please refer to (Antal 2022). 33 cash flows, gross value added, and zero-price uses of music. It highlights both typical price points (e.g. averages or medians) and the interlocking metadata flows that accompany transactions. For policymakers, the model provides a way to trace how consumption — such as a consumer buying a recording through a shop, distributor, and label — translates into revenues for performers and composers. For data governance, it illustrates why capturing the metadata trail of cash flows is essential not only for valuation and cultural statistics but also for building an audit trail for fair remuneration. In the context of this policy brief, the value chain perspective therefore complements the current ecosystem analysis by clarifying which agents must be accounted for in conceptual models of data interoperability. 2.1.2 Cost barriers in documentation and claims For small publishers, labels, and self-publishing artists, the economics of documentation create a vicious circle. Most European repertoire is released by micro-enterprises that cannot afford dedicated staff for accounting or metadata. They save costs by using spreadsheets or freelance accountants, but this is efficient only in total terms — on a per-unit basis, the costs of documentation and claims are very high. Poor metadata then leads to poor discoverability on platforms, which in turn depresses revenues and leaves even less money for proper documentation. Capital investments (CAPEX) present the same dilemma. Enterprise IT systems or royalty accounting platforms may be cost-effective for catalogues with millions of assets, but are unsustainable for catalogues of a few thousand. As a result, many small actors are locked into obsolete systems that are costly to maintain but too expensive to replace. This structural imbalance means that metadata costs are proportionally higher for small entities than for large ones. Without a way to share infrastructure or reduce per-unit costs, small rightsholders remain stuck: they cannot spend more on documentation and claims than their total royalty income allows, yet under-documentation ensures that much of their income is never collected. These cost barriers are not isolated bookkeeping problems — they are structural features of music data curation. How a data sharing space can provide scale effects and relieve these constraints is discussed in Section 3.2.2. 2.1.3 Why one grand collection model will not work Every actor in music — a library, an archive, a label, or a rights society — has its own way of defining what counts as music, what is a sound recording, how to collect such things, and what belongs in a “collection.” These logics are shaped by their missions, legal obligations, and incentives. A library may collect under a national deposit law, a collective management organisation must register what its members submit, and a distributor includes whatever its clients release. None of these logics are wrong, but they are different. This is why attempts to force everything into one universal collection model have failed. 34 In abstract terms, there is no single “conceptualisation” of the world that can fit a rights management organisation, a library, and a music archive equally well. On a very abstract level, the same lesson was drawn in mathematics and philosophy: Gödel showed that no formal system can capture all truths within itself, and Quine argued that reference is always relative to a conceptual scheme. In computer and information science, we know this as the impossibility of a universal ontology that could serve all databases.4These limits are well understood, but recognising them is not an excuse for inaction. It means we should work pragmatically: accept that multiple logics exist, and focus on making them interoperable where possible. ĹWhy collections differ in databases •Libraries collect under legal deposit rules: every book or score published in a country must be included, regardless of popularity. •Archives follow provenance: they keep what an organisation or individual produced, not necessarily what is “important.” •Collective management organisations (CMOs) must register only what their members submit — the collection reflects contracts and repertoire, not cultural completeness. •Distributors take what their clients release: the “collection” is shaped by market demand and contracts. Each of these logics is valid, but none can be reduced to the others. This is why a single “grand ontology” for all collections is not achievable. The pragmatic task is to connect them through lightweight, modular patterns that allow data to flow across boundaries while respecting institutional differences. 2.1.4 Legacy metadata The European Parliament has emphasised that accurate and standardised metadata is essential for ensuring fair remuneration and proper attribution in the music streaming market. It calls for identifiers such as ISWC, ISRC, ISNI, IPI, and IPN to be allocated 4As information science shows, a collection is not a mathematical set but a socially and institutionally constructed grouping, shaped by curatorial or organisational logics. Attempts to create one “megaontology” for music metadata have consistently failed, because the sector is too heterogeneous — collective management organisations, libraries, archives, platforms, and distributors operate under different standards and governance models. At a more philosophical level, Quine reminds us that any ontology is relative to its conceptual scheme, and there is no absolute description of the world that can serve all purposes equally ((Quine 1968)). Gödel’s incompleteness results, likewise, show the inherent limits of formal systems, underscoring why computer science and database theory recognise that no single universal ontology can capture all possible cases. The CITF report likewise rejects any attempt to impose a single universal schema. Instead, it argues for a semantic interoperability layer that allows heterogeneous systems to exchange meaning without erasing institutional differences[^citf-patterns]. 35 at the moment of creation, and warns that the flood of AI-generated tracks will worsen discoverability and revenue imbalances if metadata remains incomplete or inconsistent.5 In practice, achieving this goal has proven very difficult. The registers that underpin music metadata are privately governed, require continuous investment, and cannot simply be rebuilt from scratch. Hundreds of millions of assets are already circulating, and billions of transactions are handled annually on the basis of this legacy infrastructure. Even the term metadata is ambiguous: in libraries and IT it means descriptive information (title, genre, provenance), but in the music industry it usually refers narrowly to administrative identifiers that drive royalty distribution. This gap in terminology adds to confusion and misplaced expectations. ĹForward-looking identifier pilots: PRS Nexus and Teosto ISNI Two recent initiatives show how the industry is moving towards better identifier coverage at source: •PRS for Music – Nexus. A new portal linking works (ISWC) and recordings (ISRC) at the moment of release. It already covers nearly 3 million works and offers APIs for rights-holders and DSPs (PRS for Music 2023; World Intellectual Property Organization (WIPO) 2023). By embedding ISWC allocation into distribution workflows, Nexus aims to accelerate royalty payments and reduce reconciliation delays that often last months or years. •Teosto – ISNI for authors. The Finnish CMO Teosto now assigns ISNIs to its members, giving authors and composers persistent identifiers that interlink with VIAF, ORCID, and Wikidata (Teosto 2024). This connects music rights data with library and research infrastructures and strengthens international interoperability. These projects simplify metadata at the point of creation and release, aligning with persistent identifier strategies in the research sector (Cruz and Tatum 2021). But they mainly address future repertoire. The much larger challenge lies in the hundreds of millions of legacy assets already circulating without complete identifier links — a problem that requires complementary solutions, discussed later in this chapter. Together, ISRC (recordings), ISWC (works), and ISMN (printed music) form the backbone of music identification. In theory they provide global coverage, but in practice they remain fragmented: many recordings never receive identifiers, links between identifiers are often missing, and uptake is uneven across registries. This fragility makes the European Parliament’s ambitions difficult to realise without new layers of interoperability, observability, and shared responsibility. The sheer growth in repertoire makes this gap impossible to close with manual workflows: by 2024, more music was released in a single day than in the entire year of 1989 (Abing 2024)6. This scale of legacy under-documentation cannot 5European Parliament resolution of 17 January 2024 on cultural diversity and the conditions for authors in the European music streaming market, recital 32 (European Parliament 2024). 6The International Standard Recording Code (ISRC) was introduced in 1986 as a 12-character identi36 realistically be resolved with manual workflows alone — it points directly to the need for curative AI approaches, which we return to in Section 4.2. Although metadata repair is indispensable, metadata is never neutral. Without corrected identifiers, reconciled names, and enriched annotations, works remain invisible in royalty and discovery systems. However, just as heritage data spaces show how repairing metadata can restore visibility while also reinforcing institutional logics, in music ecosystems the same repair practices can unexpectedly increase exposure to generative AI. By making works more legible to agentic applications, enriched metadata improves attribution but also sharpens the ability of AI systems to imitate and substitute. This paradox is most acute for small-scale repertoires and independent artists, whose economic position mirrors the epistemic vulnerability of minority heritage collections. CITF emphasises this duality. It notes that richer attribution and provenance metadata are essential for rights enforcement, yet these same signals can enhance the capacity of AI systems to generate derivative content. This makes trustworthy provenance, audit trails, and transparent RMI all the more important in AI-era infrastructures7. ĹCase Study: Metadata Repair — Heritage and Repertoire Repairing heritage metadata - In the Finno-Ugric Data Sharing Space we worked with the Latvian Archive of Folklore and regional museums to repair and enrich metadata around Livonian, Latvian, and Hungarian folk music. - In Hungary, together with the House of Music, we began repairing the lost documentation of recordings suppressed under Communist censorship. Here, repair is not only a matter of accuracy but also of restitution: without corrected metadata, these works remain locked behind outdated copyright classifications long after the state label monopoly has ended. - Original records in both contexts were shallow, monolingual, and shaped by institutional or censored taxonomies. By reconciling names, places, languages, and cultural terms, we enabled works to be rediscovered across Wikidata and Wikipedia. fier for recordings (ISO 3901) and is managed operationally by IFPI (ISO 2019a; International ISRC Registration Authority 2021). Persistent problems include retroactive assignment, inconsistent embedding, and weak interoperability with ISWC (Paskin 2006, p4). The International Standard Musical Work Code (ISWC) identifies compositions and lyrics (ISO 15707), managed by CISAC through the ISWC Agency (ISO 2022). Challenges include duplicate codes, mismatches with ISRC, and uneven adoption by CMOs (Paskin 2006, p7). The International Standard Music Number (ISMN, ISO 10957) identifies printed music publications (ISO 2021). It provides a bridge between bibliographic and rightsmanagement practices, but remains underused in digital workflows. CITF also highlights the fragility of legacy rights metadata, noting that many identifiers lack persistent links, that national and sectoral registries follow incompatible governance models, and that historical gaps in RMI complicate both attribution and AI-related provenance. It stresses the need for repairable metadata chains capable of supporting lifecycle analysis and AI-era compliance [Partanen, Rixhon, Bandere, Ziediņš, Dutt, Bolšteins, Frosterus, Lehtinen, Miklūna-Žukeviča, Ozerskis, Pihlaja, Sauka, Sornova, and Uzula (2025), pp12–18; pp28–33]. 7See [Partanen, Rixhon, Bandere, Ziediņš, Dutt, Bolšteins, Frosterus, Lehtinen, Miklūna-Žukeviča, Ozerskis, Pihlaja, Sauka, Sornova, and Uzula (2025), pp20; p31; pp101–102]. 37 - These are extreme cases of damaged metadata (through censorship, Soviet-type copyright, or minority language non-standardisation). Yet similar problems affect the long tail of European music heritage and today’s independent or self-releasing artists. - As our forthcoming academic paper shows, this is not a neutral “clean-up”: choices about vocabularies and identifiers determine what communities can see of themselves. Repair here means cultural repair — restoring epistemic visibility to communities, legal heirs, and cultural stewards. Repairing repertoire metadata - Through the Unlabel prototype, we apply similar practices to contemporary selfreleased music: enriching works with ISRC/ISWC codes, multilingual annotations, and library-standard metadata. - This makes previously “invisible” tracks legible to streaming platforms and collection societies, improving discoverability and royalty flows. - Again, repair is not neutral: the way identifiers and categories are assigned shapes how artists’ works are found, monetised, or sidelined. The paradox - These cases illustrate that metadata is never neutral. Repair empowers artists and communities, but it also encodes assumptions and makes works more legible to agentic applications. - In heritage, institutional schemas may flatten local epistemologies; in the market, generative AI may exploit enriched metadata to imitate and substitute — a problem we will discuss in Chapter 4. - In both contexts, metadata repair empowers and exposes — visibility and risk are two sides of the same process, which makes metadata governance a policy concern, not a purely technical one. Our approach - Our solution is to use decentralised systems like Wikidata and Wikibase together with strong ontological patterns. - Heavy-weight ontologies take up to a decade to develop, may introduce new biases through the non-neutral nature of metadata, and by the time they are created, they may not address new challenges — for example, providing guardrails against negative outcomes of agentic or generative AI. - As with the infrastructure in Chapter 3, we aim for decentralisation already at the metadata-definition level. An Open Music Observatory will allow metadata to be managed through flexible, open processes that create definitions and establish equivalences to existing standards. 2.1.5 Named-entity resolution, attribution, and privacy Attribution is not optional in music: the names of authors, performers, and producers are structurally necessary for copyright, royalties, and cultural record-keeping. Yet under GDPR, these names count as personal data, creating a contradiction at the very foundations of metadata curation. What is mandatory under copyright law becomes a liability under data protection law. In practice, private actors face repeated balancing tests, 38 inconsistent interpretations, and the risk of complaints even when attribution is legally required. The CITF report explicitly identifies this contradiction. It notes that names and attribution data constitute rights-management information protected under Article 7 of the InfoSoc Directive, yet they are also personal data under GDPR. CITF therefore calls for trustworthy, machine-readable RMI governance that distinguishes public-interest attribution data from restricted personal information and supports layered access models8. This contradiction drives up costs and discourages investment in better metadata. Small publishers and self-releasing artists already face disproportionately high OPEX (documentation, bookkeeping) and CAPEX (IT systems). Without affordable, legally secure ways to resolve named entities, their works perform badly on platforms and royalties are lost. Policy communities in Europe recognise these issues. The Big Data Value Association (BDVA) has long argued that trust frameworks and governance pillars are essential for data sharing, while the Federation Working Group stresses that federation — not centralisation — is the only realistic model for connecting Europe’s fragmented data ecosystems (Big Data Value Association 2019; BDVA/DAIRO 2023; BDVA/DAIRO Federation Working Group 2023). These principles apply equally in music. But given the sector’s extreme fragmentation and micro-enterprise structure, implementing them here is especially difficult. How these structural problems can be addressed at systemic level is the subject of Section 3.1.3, where we show how data sharing spaces provide a way forward. 8See [Partanen, Rixhon, Bandere, Ziediņš, Dutt, Bolšteins, Frosterus, Lehtinen, Miklūna-Žukeviča, Ozerskis, Pihlaja, Sauka, Sornova, and Uzula (2025), pp12–18; p84]. 39 2.2 Policy proposals Figure 2.3: Explanation 2.2.1 Reducing redundancy The European Parliament has rightly highlighted that fragmented and unreliable metadata remains a major obstacle in the music sector. We agree with this diagnosis, but stress that the root cause lies partly in the need for backward compatibility with hundreds of millions of legacy assets, and in the costly redundancy of today’s practices: the same information must be repeatedly entered into separate systems such as ISNI, ISWC, ISRC, VIAF, or local authority files. This duplication creates errors, increases costs, and discourages accurate registration. CITF frames this redundancy as a foundational problem of copyright infrastructure. Its proposed foundational layer centres on open, authoritative identifiers for agents and assets, issued by trusted institutions and supported by interoperable mappings9. Our policy solution is to support redundancy-free registration by aligning the workflows of those who already maintain authoritative data. Instead of duplicating efforts, registration steps can be coordinated once and reused many times. We demonstrate this approach with our Open Music Registers pilot: a federated infrastructure that interconnects persistent identifiers (ISWC, ISRC, ISNI, VIAF) and, where relevant, links them 9Aligning workflows around these identifiers directly addresses the structural issues described in the report [Partanen, Rixhon, Bandere, Ziediņš, Dutt, Bolšteins, Frosterus, Lehtinen, Miklūna-Žukeviča, Ozerskis, Pihlaja, Sauka, Sornova, and Uzula (2025), pp23–28; pp85–87]. 40 to business and statistical identifiers (e.g. OpenCorporates, NACE, ISCO). This allows music creators and organisations to benefit from smoother workflows, while downstream users gain more reliable data for royalty distribution, cultural visibility, and AI-driven discovery. The Open Music Registers deliberately avoid centralisation. Each registrar — collective management organisations, libraries, archives, or statistical offices — retains ownership of its data but contributes to a shared semantic framework.10 By connecting rather than merging registers, redundancy is reduced while subsidiarity, accountability, and trust are safeguarded across public and private actors. This distributed model directly answers European Parliament’s call for metadata systems that are reliable, inclusive, and supportive of creators.11 2.2.2 Reconciling attribution and privacy The problem of reconciling copyright attribution with GDPR obligations cannot be solved by ignoring either side: both are binding legal requirements. Our approach, tested in the Slovak Comprehensive Music Database (SkCMDb), shows that progress is possible through layered governance and careful balancing. Academic institutions and libraries, with their cultural and research mandates, can lawfully handle personal data under derogations for public-interest processing. Collective management organisations (CMOs) and private actors, by contrast, must rely on legitimate interest tests, supported by transparent documentation, notification to rightsholders, and opt-out mechanisms where possible. ĹInteroperability is a means, not a goal Our Slovak pilot, the Slovak Comprehensive Music Database (SKCMDb), links libraries, rights management, streaming services, and the statistical office. This is not “interoperability for its own sake.” Ontologies and crosswalks are valuable only insofar as they enable better services: •For audiences: making music findable and accessible across cultural and commercial platforms. •For rightsholders: ensuring that attribution, identifiers, and royalty flows are correct. 10Technically, this corresponds to a provenance-oriented modelling approach such as the W3C PROV-O standard (W3C 2013b, 2013a), which connects actors, activities, and entities in chains of attribution (“a composer authors a work, a performer interprets it, a producer records it…”). These chains can be expressed in the layered terms of the European Interoperability Framework (EIF), ensuring legal, organisational, semantic, and technical interoperability (Commission and Digital Services 2017). 11The Data Spaces Support Centre (DSSC) Blueprint v2.0 underlines that identifiers and rulebooks are the foundation of any common European data space (Data Spaces Support Centre 2025b). In the music sector, however, attribution identifiers themselves are caught in the GDPR contradiction (see Section 2.1.5), which underscores the importance of redundancy-free but legally robust registration practices. 41 enterprises, NGOs, collective management organisations, and heritage institutions — each operating under distinct legal frameworks — this assumption is untenable. CITF reaches the same conclusion. It notes that copyright data is inherently distributed across many custodians with incompatible mandates and governance models, and that no single centralised registry can meet the legal, operational, and semantic requirements of modern copyright workflows. Instead, it argues that future-proof infrastructures must rely on federated, lifecycle-aware registries capable of exchanging trustworthy provenance and rights metadata while preserving institutional autonomy. ĹLessons from the Global Repertoire Database Between 2008 and 2014, European and global stakeholders pursued the Global Repertoire Database (GRD) as a solution to the chronic fragmentation of musical works data. Backed by collective management organisations (CMOs), major publishers, and digital service providers, the GRD aimed to establish a single, authoritative global database of musical works and rightsholders. Its promise was that licensees—especially online platforms—could obtain reliable rights information from one source, reducing duplication and disputes. However, the GRD ultimately collapsed before launch, despite several years of investment and the establishment of a London-based operating company. A similar project, the International Music Registry project, which was backed by the World Intellectual Property Organization, ended with similar results2. Post-mortems identified several reasons: - Governance conflicts: disagreements between major publishers, CMOs, and other stakeholders over who would control and fund the database. •High costs and unclear incentives: the project’s projected maintenance costs exceeded what many participants—particularly smaller CMOs—were willing or able to sustain. •Asymmetries of power: large publishers and CMOs were reluctant to share sensitive commercial data on equal terms with competitors. •Lack of trust: concerns over who would “own” the data and how revenues would be redistributed undermined cooperation. The failure of the GRD is now widely cited in policy and industry discussions as evidence of the limits of centralised, “single-database” solutions in the music sector. Similar initiatives even failed on national levels. We can also add that centralisation, even if it was possible, would pose a new risk of creating monopolistic gatekeepers to the music ecosystem. The predecessor of the Open Music Europe project, CEEMID, was based on the lessons of the following problems and on the insights of a decentralised, dataspace like approach (Antal 2020). In such federated, interoperable approaches—where data remains with its custodians but can be linked through shared identifiers, standards, and protocols—have proven more viable. CISAC’s CIS-Net, Europeana in the heritage 48 field, and emerging European data space initiatives exemplify this more distributed model of governance. CITF’s analysis reinforces these lessons. It identifies governance opacity, unclear mandates, and incompatible identifier regimes as recurring causes of failure in large-scale copyright registries. It stresses that unless registries adopt transparent governance, open identifiers, and shared semantic profiles, centralised projects inevitably collapse under conflicting incentives3. EU infrastructure initiatives have already moved beyond this logic. Since the 2000s, projects such as Europeana, the European Open Science Cloud (EOSC), the European Collaborative Cloud for Cultural Heritage (ECCCH), and DARIAH have all adopted federated architectures, linking distributed collections through shared standards and interoperability frameworks rather than consolidating them into one database. The Audiovisual Observatory, established in 1993 as a centralised reporting body, represents an earlier institutional logic that is now being phased out in favour of federation. The heritage sector, including music heritage, has consistently stressed the need for open, federated models. Libraries, archives, and museums use authority files and collaborative platforms (e.g. VIAF,Wikidata,Wikibase) to enable interoperability while preserving institutional autonomy. Commercial infrastructures do the same: the ISRC system, managed by IFPI, is inherently decentralised, while CISAC’s CIS-Net gives access to rights data without centralising ownership. Even the Mint initiative, launched by CISAC and Armonia Online, shows how shared infrastructure can deliver economies of scale for identifier allocation and metadata management while avoiding dependence on a single repository.4 Even official governmental statistics, often seen as centralised, are in reality decentralised. The ESSnet-Culture project, coordinated under Eurostat, produced the first comprehensive framework for cultural statistics in 2012, adapted from the UNESCO model, and remains a “basic reference” for the field. More broadly, national statistical offices, labour force surveys, and administrative registers each collect partial data, which are harmonised at EU level for comparability. Increasingly, surveys and administrative datasets are complemented by flows from platforms, rights management organisations, and other industry actors. Indicators therefore emerge from hybrid constellations of public and private data sources, confirming that decentralisation is a structural feature of European evidence creation.5 3See for example Goldenfein and Hunter (n.d.); Milosic (2015). 3See (Partanen, Rixhon, Bandere, Ziediņš, Dutt, Bolšteins, Frosterus, Lehtinen, Miklūna-Žukeviča, Ozerskis, Pihlaja, Sauka, Sornova, and Uzula 2025, pp15–18). 4On heritage practices, see (Bianchini, Bargioni, and Pellizzari di San Girolamo 2021, p210) and (Sardo and Bianchini 2022, p297), which describe how VIAF, Wikidata, and Wikibase function as authority tools in libraries and archives. On identifiers, the ISRC Handbook (International ISRC Registration Authority 2021, p5) explains the decentralised structure of the ISRC system, while CISAC’s Mint Digital Services (CISAC/SUISA/SESAC 2017) illustrates how federated allocation works in practice. Together, these examples show how distributed stewardship and shared standards underpin global metadata infrastructures. 5The ESSnet-Culture framework (Commission et al. 2020, p9) demonstrates how cultural statistics are built on national contributions harmonised at EU level, not on central databases. A Slovak pilot (Antal 2023) further illustrates how decentralisation works in practice, integrating public and private sources 49 3.1.2 Open Data Directive: right without means The Open Data Directive grants a right of reuse for public-sector information and requires that certain “high-value datasets” be made freely available across Europe (Directive (EU) 2019/1024 of the European Parliament and of the Council of 20 June 2019 on Open Data and the Re-Use of Public Sector Information 2019). This includes cultural heritage institutions such as libraries, museums, and archives. However, the Directive stops short of providing the means to ensure that such data is actually usable. Studies consistently show that open data often remains more of a promise than a reality. In practice, much open data is poorly documented, lacks common identifiers, and is released in unstandardised formats. While it may be free of charge or available at marginal cost, making it interoperable and trustworthy for cross-border use requires significant additional effort. The burden of curation, harmonisation, and enrichment falls on downstream users, which can be prohibitively expensive for smaller organisations. As the CEDAR project put it, “Public authorities are only required to make existing data available, not to create new data or improve existing systems. This leads to significant disparities in usability and accessibility” (Project 2023). A recent EU-wide usability study adds that “many open data portals remain difficult to navigate, poorly documented, and inconsistent in their metadata quality, limiting actual reuse” (Jachimczyk and Nowak 2024). These structural weaknesses of open data provision set the stage for the Observatory’s role in providing workflow playbooks and redundancy-free registration, discussed in Section 3.2.6 3.1.3 Why voluntary workarounds do not scale The Slovak pilot shows that voluntary workarounds for attribution under GDPR are possible (see Section 2.1.5), but they do not scale. Even with strong communication and opt-in procedures, fewer than 1.3% of authors responded. Every new dataset requires fresh balancing tests, repeated notifications, and continued exposure to legal risk. For observatories and data spaces, this is untenable. Interoperability requires clarity and legal certainty across borders and institutions. Without guidance from a Data Protection Authority or the European Commission, every national or sectoral initiative risks being into coherent cultural indicators. 6Early modelling stressed the economic potential of open data but also identified major obstacles in practice: lack of availability, uneven quality, and poor usability (Carrara et al. 2015, p7; Huyer and van Knippenberg 2020, p14). Comparative studies show that simply granting a right to reuse rarely produces machine-actionable datasets. In complex domains like music, where attribution depends on precise identifiers, these shortcomings become particularly costly. Cross-sector reviews underline persistent fragmentation: heterogeneous formats and divergent practices across Member States (Buttow and Meijer 2024, p12); variability even in high-value geospatial datasets (Kević, Kuveždić Divjak, and Welle Donker 2023, p3); and sectoral case studies (e.g. mineral intelligence) repeatedly call for shared profiles beyond legal openness (Simoni, Aasly, and Schjøth 2021, p5). Additional evidence shows that preparing legacy administrative data for reuse requires cleansing and enrichment that impose real costs, even when the data are nominally “open” (EuroSDR 2021, p9; Schnurr 2021, p14; Nakos and Tsoulos 2022, p6). 50 challenged. The result is paralysis: public infrastructures cannot fully attribute works, and private actors refrain from sharing metadata for fear of liability. In effect, Europe’s music data infrastructures remain locked in uncertainty — unable to guarantee attribution, diversity monitoring, or local content compliance. This makes a purely local or voluntary approach insufficient. The solution must be systemic: a federated data sharing space, supported by common specifications and clear governance frameworks, so that attribution and interoperability can scale. How such systemic solutions can be embedded into the Observatory’s conformance and legal levers is developed in Section 3.2. These unresolved attribution issues ultimately undermine not only observatories but also AI fairness and governance (see Section 4.1.3)7. 3.1.4 Public infrastructures bypass music’s real data flows Europe has invested heavily in cultural and research data infrastructures such as Europeana, the European Collaborative Cloud for Cultural Heritage (ECCCH), and the European Open Science Cloud (EOSC). Yet these initiatives remain poorly aligned with how music metadata is generated and maintained in practice — mostly by private actors such as labels, distributors, and collective management organisations. Unlike archives, museums, or libraries, where digitisation was largely funded with public money, in music and film the bulk of digitisation has been carried out by industry. Public infrastructures therefore miss the systems where music’s real data flows originate. The Europeana Data Model (EDM) was designed for library holdings and is well suited to printed works, but it cannot capture the attribution needs of recorded music, which must identify at least three groups of rightsholders: authors, producers, and performers.8 The ECCCH report likewise overlooked music entirely, focusing instead on monuments, archaeology, textiles, and museums.9Its first projects — such as AUTOMATA,TEXTaiLES,HERITALISE, and ECHOES — developed advanced tools for other heritage assets, but none addressed music directly. Our own attempts to include music datasets in ECHOES’ cascading grants illustrate the problem: proposals were screened out early, despite the clear need for music representation. CITF highlights the same structural blind spot. It observes that national libraries already curate large volumes of copyright-protected material and maintain authoritative identifiers, yet they remain largely decoupled from rights metadata workflows. This is exactly 7The Slovak pilot demonstrated that even with careful communication and GDPR balancing tests, participation was below 1.3%, showing the practical limits of voluntary attribution workarounds. Without EU-level guidance, every dataset requires fresh legal reasoning, making scale impossible. Comparable findings in other cultural domains underline the risk: voluntary consent-based models tend to collapse under low response rates and high compliance costs. See also discussions of attribution and AI fairness in (Commission et al. 2020) and (Music Moves Europe 2024). 8The EDM builds on DCTERMS, which works well for printed music but not for recordings. It fails to capture neighbouring rights such as those of producers and performers (Europeana 2017). 9Ex–ante impact assessment on the European Collaborative Cloud for Cultural Heritage (Commission et al. 2022). The first ECCCH pilots (AUTOMATA, TEXTaiLES, HERITALISE, ECHOES) focused on archaeology, textiles, and monuments, leaving music out. 51 the workflow we tested out in Slovakia, and are going to introduce in our Hungary replication. CITF therefore recommends treating national libraries and cultural institutions as copyright-infrastructure actors, not only heritage custodians, and integrating their registries into federated rights environments, which is exactly what we did in our Slovak national federated module, and what we aim to replicate in Hungary. Other initiatives show the same bias. The Polifonia project created modular ontologies, but it was “blind” to rights management and did not align with ISWC and ISRC identifiers used by industry. As a result, public knowledge graphs and registries do not interoperate smoothly with private-sector identifiers. The result is duplication, costly reconciliation, and under-use of culturally significant catalogues. EOSC, intended as Europe’s backbone for research data, is also relevant. Its federated model provides long-term preservation and persistent identifiers (via Zenodo and OpenAIRE), and music datasets deposited there already attract visibility. But EOSC has no dedicated workflow for music, and industry uptake remains minimal. As with ECCCH, music is underrepresented and rights-aware curation pathways are absent. CITF confirms that lifecycle-based metadata and provenance are prerequisites for integrating cultural and commercial systems10. The European Interoperability Framework (EIF) helps explain why these gaps persist. Interoperability depends not only on formats but also on legal, organisational, semantic, and technical alignment. Without shared governance and profiles, public and private systems diverge. The principle of subsidiarity adds another layer: stewardship over cultural data is distributed across national and regional authorities, as well as private actors. Centralisation is therefore both impractical and politically illegitimate. The challenge is not whether decentralisation should exist, but how to make decentralised contributions work together.11 This challenge directly motivates the Observatory’s bridging role with EOSC, Europeana, and ECCCH, elaborated in Section 3.212. 10EOSC provides federated access and persistence through Zenodo and OpenAIRE, but music workflows remain marginal. On EOSC’s role, see the European Strategy for Data (European Commission 2020). CITF notes that without harmonised identifiers, provenance chains, and semantic profiles, public infrastructures cannot interoperate with private-sector rights workflows, especially in AI contexts where reproduction and transformation rights depend on reliable metadata [Partanen, Rixhon, Bandere, Ziediņš, Dutt, Bolšteins, Frosterus, Lehtinen, Miklūna-Žukeviča, Ozerskis, Pihlaja, Sauka, Sornova, and Uzula (2025), pp20; pp28–33]. 11The EIF defines layered interoperability (legal, organisational, semantic, technical) (Commission and Digital Services 2017). The European Strategy for Data frames subsidiarity as compatible with federation (European Commission 2020). BDVA and the Federation Working Group emphasise that interoperability frameworks are needed to operationalise federation (BDVA/DAIRO 2023; BDVA/DAIRO Federation Working Group 2023). 12The EIF defines layered interoperability (legal, organisational, semantic, technical) (Commission and Digital Services 2017). The European Strategy for Data frames subsidiarity as compatible with federation (European Commission 2020). BDVA and the Federation Working Group emphasise that interoperability frameworks are needed to operationalise federation (BDVA/DAIRO 2023; BDVA/DAIRO Federation Working Group 2023). 52 3.1.5 Subsidiarity and infrastructures for scaling music data The European principle of subsidiarity requires that decisions be taken as closely as possible to the citizens they affect. In cultural policy, this means that responsibilities are distributed across multiple levels: in some Member States, culture is managed regionally or provincially; in others, nationally. Beyond public administrations, many important datasets are held by private actors — collective management organisations, platforms, or archives. Any attempt to centralise music data governance would therefore risk losing both legitimacy and local relevance. Instead, subsidiarity must be built into the design of the Observatory. The European Interoperability Framework (EIF) provides a layered model — legal, organisational, semantic, and technical — for reconciling governance across institutions. The Data Governance Act (DGA) codifies the same principle: Member States retain stewardship over sensitive datasets, but EU-level standards ensure they can circulate securely and comparably across borders. The Data Space Support Centre (DSSC) extends this approach into practice, developing blueprints and building blocks that allow decentralised initiatives to scale. Together, these frameworks show how subsidiarity and federation are not barriers but design principles for data spaces. CITF’s three-layer model aligns directly with this reasoning13. At the technical level, Wikidata and Wikibase provide a proven backbone for collaborative metadata management. They are already embedded in EU infrastructures such as the official EU Knowledge Graph and in national projects like MetaBelgica in Belgium. In Flanders, the performing arts field has gone further: since 2017, Kunstenpunt and meemoo have published decades of performing arts data on Wikidata, showing how enrichment happens automatically once data becomes part of a wider ecosystem. These pilots illustrate how subsidiarity and federation can work in practice, with decentralised actors maintaining control of their own data while contributing to a shared framework.14 The problem of scale makes such infrastructures essential. Large platforms and labels can manage millions of assets cheaply, but small actors cannot. Without shared systems, independent and community-based repertoires remain undocumented because the cost of proper registration exceeds likely revenue. Federated tools — strengthened by automation and AI — are the only realistic way to close this gap. 13On subsidiarity and federation: the Data Governance Act (European Parliament and Council 2022) and the European Strategy for Data (European Commission 2020). On technical frameworks: DSSC’s blueprints (Data Spaces Support Centre 2025b, 2025a). On governance: BDVA (BDVA/DAIRO 2023) and the Federation Working Group (BDVA/DAIRO Federation Working Group 2023). CITF’s foundational layer concerns authoritative identifiers and registries, its semantic layer provides shared meaning across heterogeneous models, and its technical layer covers APIs, mappings, and resolution services. Together these layers provide a structured approach for embedding subsidiarity into copyright data governance without forcing schema or organisational unification (Partanen, Rixhon, Bandere, Ziediņš, Dutt, Bolšteins, Frosterus, Lehtinen, Miklūna-Žukeviča, Ozerskis, Pihlaja, Sauka, Sornova, and Uzula 2025, pp23–33). 14On official adoption: EU Knowledge Graph (Diefenbach, De Wilde, and Alipio 2021); SEMIC guidelines (SEMIC Support Centre 2023). On Belgian pilots: MetaBelgica (Stallmann et al. 2023) and Flemish performing arts enrichment (Magnus and Van D’huynslager 2021). 53 ĹFinno-Ugric Data Sharing Space Our pilot with the Finno-Ugric Data Sharing Space illustrates subsidiarity in practice (see: https://finnougric.net/). By collaborating with regional NGOs and national archives, we curated and repaired datasets that would have remained invisible in a central repository. The project showed that decentralised actors are best placed to manage their own data, but that interoperability frameworks and shared observability layers can connect them effectively 15. International comparison confirms this. In the United States, the Mechanical Licensing Collective (MLC) was created in 2021 to administer a blanket mechanical license for streaming and downloads. It inherited more than $424 million in unmatched royalties and developed large-scale reconciliation systems to allocate them. By 2022, it had already distributed nearly $700 million. The MLC shows what can be achieved when identifiers such as ISWC and ISRC are used systematically and backed by law. But it also highlights the limits of centralisation: creators must still claim and maintain their records, education gaps persist, and disputes between platforms and rights bodies continue.16 ĹThe U.S. Mechanical Licensing Collective (MLC) The Mechanical Licensing Collective was created under the U.S. Music Modernization Act (2018) to administer a blanket mechanical license for streaming and downloads. It inherited more than $424 million in unmatched royalties from digital services and developed large-scale reconciliation systems to allocate them. By late 2022, it had distributed nearly $700 million. The MLC shows what can be achieved when identifiers (ISWC, ISRC) are captured systematically and backed by legislation. But it also highlights the limits of centralisation: creators must still claim and maintain their records, education gaps persist, and disputes between platforms and rights bodies continue. For Europe, the lesson is clear: scaling metadata infrastructure is possible, but it must respect subsidiarity and federation rather than rely on a single central clearinghouse (Mechanical Licensing Collective 2021; Varghese 2024). 3.1.6 Economies of scale in metadata Large platforms and major labels can document millions of tracks at very low per-unit cost, because they manage everything in bulk. Smaller actors — independent labels, nonprofits, or community archives — face the opposite situation: the cost of registering and maintaining each track is often higher than the revenue it will ever generate. This imbalance explains why so many “frozen” assets remain unregistered and invisible in today’s digital ecosystem. 15See (Antal et al. 2025; Antal, Pigozne, and Federico 2025). 16On the MLC’s establishment and operations: (Mechanical Licensing Collective 2021); on contested governance and disputes with platforms: (Varghese 2024). 54 Without a way to share infrastructure, small actors remain stuck. They cannot afford the per-track cost of full documentation, yet under-documentation ensures their work remains undiscovered. This is not just an accounting issue, but a structural barrier to diversity in music data flows. A federated approach, as outlined in Section 3.2.2, is essential to rebalance these inequalities and enable small actors to benefit from the same efficiencies as global players; CITF frames this imbalance as a foundational infrastructure issue.17 3.2 Policy Proposals ÁEditing reminder • Open Music Observatory as the convening + conformance + observability layer (not a single database). • Workflow playbooks: rights→distribution→charting→preservation; changepropagation patterns; provenance trails that survive system boundaries. • Legal/standards/public investment inline: GDPR legal bases per flow; recommended codes of conduct; lightweight policy for data fitness/quality; funding hooks (ECCCH pilots, national ministries). ĹPublic–private reconciliation in practice Reconciling public and private infrastructures: The ALOADED pilot in Latvia The Unlabel workflow was tested with Latvian archives and the distributor ALOADED, showing how public heritage metadata can be reconciled with private supply chains. • Archival recordings (Hilda Griva’s songs and Latvian/Latgalian midsummer songs) were located in the Latvian Archives of Folklore. • Metadata was translated, enriched, and aligned with international authority files. 17Comparative research shows that costs per asset decrease sharply with catalogue size, creating scale advantages for majors and global platforms. Without shared infrastructures, small actors are disproportionately disadvantaged. The Feasibility Study for a European Music Observatory emphasised this imbalance as a structural barrier (Commission et al. 2020, p9), while the Music Ecosystem 2025 study highlighted how fragmentation and duplication reinforce these scale inequalities (Music Moves Europe 2024). CITF frames this imbalance as a foundational infrastructure issue: without open, authoritative identifiers and interoperable registries, small actors face disproportionately high documentation costs and cannot benefit from economies of scale. It therefore recommends strengthening the foundational identifier layer as a precondition for fair and efficient copyright ecosystems (Partanen, Rixhon, Bandere, Ziediņš, Dutt, Bolšteins, Frosterus, Lehtinen, Miklūna-Žukeviča, Ozerskis, Pihlaja, Sauka, Sornova, and Uzula 2025, pp23–28). 55 • ALOADED extended this metadata with DDEX-compliant catalogue transfer and ingested it into Spotify and other platforms. This demonstrated that reconciliation between public infrastructures (archives) and private infrastructures (distributors and platforms) is both technically and institutionally feasible, reconnecting suppressed or marginalised repertoires with contemporary audiences. See a more technical description of what we did here. Conformance and observability rules in the Open Music Observatory should be designed in line with the European Interoperability Framework (EIF) and the FAIR data principles. This ensures compatibility with wider European data space initiatives and reduces integration costs for institutions already adapting to these standards (Commission et al. 2020, p9). Figure 3.2: The Open Music Observatory sits where open science, public sector information reuse, and music industry workflows overlap. By aligning with the European Interoperability Framework, it creates a shared space where libraries, rights managers, publishers, and researchers can collaborate. This positioning highlights OMO’s role as a bridge between cultural heritage, commercial distribution, and open knowledge. DOI: [10.6084/m9.figshare.30073267.v1](10.6084/m9.figshare.30073267.v1) 56 3.2.1 Workflow playbooks and provenance trails The Observatory should not only harmonise data formats but also document workflow playbooks that capture how metadata flows across the music lifecycle: • from rights registration, • to distribution and royalty attribution, • to charting and visibility, • to long-term preservation. Each step should include change-propagation rules: if a correction is made in one register, it should ripple through to others. Provenance trails must survive system boundaries, using standards such as PROV-O to show who did what, when, and under what authority. This makes corrections auditable, supports cross-border comparability, and prevents “data death” when an asset leaves its original system. 3.2.2 Federated infrastructure as a cost and governance solution The imbalance described in Section 3.1.6 makes one thing clear: small actors cannot compete on metadata without shared infrastructures. Federation, not centralisation, is the only viable way forward. Adata sharing space provides the framework. Instead of forcing everyone into a single metadata schema or legal agreement, it allows organisations to share and reuse data on an “as-needed” or “as-permitted” basis, while keeping full control of their own assets. For music — where rights, identifiers, and content are dispersed across hundreds of micro-actors and institutions — this model avoids both duplication and dependency. Crucially, it also avoids creating a new single gatekeeper: centralisation risks not only technical brittleness but also the emergence of a monopolistic intermediary able to close access or impose conditions on others18. Music is one of the most demanding test cases for European data governance. Attribution rules interact with privacy law, identifiers are used unevenly across the sector, and most music enterprises are too small to build their own compliance or documentation infrastructure. If a federated model can function in this environment, it can function anywhere. But decentralisation brings its own challenges: organisations with stronger infrastructures may prefer to protect competitive advantages by withholding data. Effective governance 18Our definition here is an extended paraphrase of (Curry 2020) and reflects that a “data [sharing] space is an ecosystem of exchange, processing, sharing and provision of data between trusted partners, for a fee or not” from (EBU and Gaia-X 2022, p16). CITF complements this argument by emphasising that trustworthy provenance, machine-readable rights metadata, and auditable RMI are essential components of a federated copyright infrastructure. It stresses that lifecycle-based provenance chains are necessary not only for attribution but also for AI-era compliance, where training and generation both trigger rights that depend on reliable metadata [Partanen, Rixhon, Bandere, Ziediņš, Dutt, Bolšteins, Frosterus, Lehtinen, Miklūna-Žukeviča, Ozerskis, Pihlaja, Sauka, Sornova, and Uzula (2025), p31; pp101–102]. 57 4.1 Discussion 4.1.1 Structural problems for music businesses to apply AI 1. AI benefits are unevenly distributed. Music businesses operate in value chains where platforms and large intermediaries already use agentic, generative, and even inference AI. Most platforms already rely on agentic workflows (matching, recognition, playlisting, claim resolution). These actors reap most of the benefits, while smaller publishers, labels, and managers may not even be aware that AI is shaping outcomes in discoverability, rights management, and revenue flows. 2. AI impacts the bottom line in multiple ways. •Operating costs (OPEX): Most European music is released by self-publishers or very small labels who cannot afford dedicated staff for documentation or accounting. They save costs by using Excel or freelance accountants, but per unit this is very expensive and leads to poor metadata. As a result, their works perform badly on agentic platforms where poor documentation means poor sales. AI could sharply reduce documentation and claims costs — but deploying it is not easy. •Capital costs (CAPEX): Investing in proper IT or ERP systems is rarely viable at small scale. A system that pays off when managing a million works is wasteful when managing 3,000. Curative AI could extend the life of outdated IT and reduce the need for costly replacements. •Working capital: Many rights holders experience late or missing royalty payouts, even for well-known artists, because the cost of claiming is high compared to the low value of claims. This ties up cash between payment periods. AI could accelerate claims processing and improve matching, smoothing liquidity. •Sales: While dedicated “sales AI” projects are often prone to failure, in music most transactions already run through agentic AI on platforms like Spotify, YouTube, TikTok, and Apple Music. Simply providing these agents with better documented music can improve sales outcomes without the need for standalone sales AI. 3. Generative AI is only part of the problem. Public debate often focuses on generative AI flooding the market with unlimited non-copyrighted music, which can devalue existing repertoires. This is a real issue, but it is not the only one. Agentic AI in distribution platforms has been shaping the market for at least 14 years, determining who gets discovered, listened to, and paid — long before generative AI became a concern3. 3Surveys and management research confirm these patterns. PwC’s Global CEO Survey shows how quickly generative AI rose from a marginal issue in 2023 to a central boardroom concern by 2024–25, though most executives expressed only “bounded optimism” (PwC 2024). Bloomberg and BCG’s CEO Radar tracked quarterly earnings calls in 2025, reporting a 100% increase in references to AI and machine learning, but also rising caution about productivity claims (Bloomberg and Boston Consulting Group 2025). MIT’s Project NANDA concluded in August 2025 that 95% of enterprise generative AI initiatives 64 4. Severe talent shortages. Recruiting and integrating digital expertise is difficult across industries, but especially in music where most enterprises are micro-enterprises. A Chief Data Officer (CDO) is often recommended, yet unrealistic for most publishers, labels, or agencies. Even Fortune 500 companies — far larger than Europe’s 50,000 “large” enterprises — report persistent difficulties in filling CDO and AI leadership roles. With 23 million SMEs in Europe, and several hundred thousand music entities, usually with less than 2 people in full-time positions, this AI and data talent shortage cannot be solved on an individual business level4. 4.1.2 European regulation that misses the point Europe prides itself on having some of the world’s strictest AI rules. Compared to the United States and China, the EU has adopted a risk-based framework in the AI Act, with strong obligations for high-risk systems (such as self-driving cars) and lighter rules for low-risk ones. But this framework is poorly suited to music. Music was classified as “low-risk” on the assumption that nobody is harmed by being offered a bad song. This framing ignores how agentic AI governs the marketplace itself. If recommendation systems consistently fail to show music created by women, small nations, or minorities, they devalue those repertoires to zero by depriving them of discoverability. Copyright value is based on the present value of expected royalty flows; if works are never recommended, those flows vanish, and with them the rights protected under EU law and international treaties. In other words: Europe regulates AI strictly where physical safety is at stake, but does not protect cultural diversity, women’s authorship, or the economic rights of creators. What is framed as “low-risk” can in practice be systemically high-risk for the music ecosystem. This problem is then reflected in the actual design of commercial or institutional AI systems. The problem with this categorisation is even more problematic with the rise of large language models (LLMs) and their applications like ChatGPT, Gemini, Llama. The “Human Artistry Campaign” was initiated by a coalition of 150+ organisations, including major music industry bodies (IFPI, RIAA, BPI) and artist representative groups (AIM, Featured Artists Coalition, Impala), establishing a collaborative effort to advocate for responsible AI development within the creative sector. failed to deliver measurable value, with back-office automation offering the clearest returns (MIT Sloan School of Management 2025). These findings mirror evidence from talent studies: Gartner’s CDO Survey reports persistent shortages in chief data officer and AI leadership roles, even among Fortune 500 companies (Gartner, Inc. 2024), while PwC’s Digital IQ survey highlights the difficulties of capturing ROI on digital transformation and AI investments (PwC 2023). 4According to Eurostat’s Culture statistics — 2023 edition, cultural and creative industry (CCI) enterprises in the EU are overwhelmingly micro-enterprises. More than 95% employ fewer than 10 people, and the average enterprise size across the sector is below two employees (Eurostat 2023). This structural feature explains why most music publishers, labels, and agencies lack in-house IT, accounting, documentation, or HR functions — and why recruiting specialised AI or data talent is unrealistic without shared infrastructures. 65 However, recent research questions if the EU’s AI Act is even practically applicable as a legal framework to Generative AI. The Act’s risk-based categorisation may struggle to capture the emergent behaviour of LLMs and their potential for misuse, and it is highly questionable that human oversight or human control is possible with LLM alone5. ĹMetadata repair may increase generative AI risks Last, but not least, we want to highlight that efforts at metadata repair and publication — as we propose in earlier chapters — also increase the risk of generative AI misuse. A prompt like create me an ABBA-like disco hit is likely to combine: •Core musical learning from audio/MIDI (raising clear copyright and GDPR risks), and •Metadata signals that guide the model’s interpretation. Core musical learning (from audio/MIDI): From training on ABBA’s catalogue (and related artists), a model learns to reproduce: - Harmony → diatonic progressions (e.g. I–V–vi–IV), bright major keys. - Melody → catchy, stepwise motifs (e.g. Mamma Mia hooks). - Rhythm & texture → steady 4/4 grooves, piano/guitar foundations, layered vocals. - Structure → verse–chorus–bridge arcs with memorable refrains. - Production cues → lush vocal overdubs, polished pop arrangements, disco influences. How metadata sharpens the imitation: -Artist metadata (“ABBA”) → links to Swedish pop, Eurovision history, chart success. Tags like “Europop,” “disco-pop,” “vocal harmony group” cue specific stylistic markers. Models may also trace producers and collaborators to expand training. -Genre/award metadata (“disco-pop hit”) → narrows toward 1970s–80s tropes: syncopated basslines, string pads, tambourine. -Chart/award metadata → biases output toward catchy, chorus-driven songs resembling global hits. The paradox: - Metadata repair makes ABBA’s legacy more discoverable across platforms (archives, streaming, Wikidata, Wikipedia). - But richer metadata also helps AI pinpoint and reproduce their exact style, making prompts like “ABBA-style Eurovision anthem” feasible. Metadata empowers and exposes: it restores visibility for heritage and repertoire, but also creates new pathways for substitution by generative AI. For this reason, metadata governance is a policy concern, not just a technical task6. 5 Towards Responsible AI Music: an Investigation of Trustworthy Features for Creative Systems is an excellent review of the theoretical or practical applicability of the EU’s trustworthy AI paradigm for generative AI (Berardinis et al. 2025). For more information on the Human Artistry Campaign, see https://www.humanartistrycampaign.com/. 6CITF frames this as a core AI-era requirement. It argues that lifecycle-based provenance and machinereadable RMI are essential for assessing lawful uses in training and generation, and that without 66 4.1.3 Policy issues at the intersection of AI, copyright, and GDPR AI in music does not operate in a legal vacuum. It interacts with existing European law on intellectual property, author’s rights, moral rights, and data protection. In practice, this creates tensions and unresolved policy gaps that directly undermine cultural policy goals. This governance problem builds directly on the interoperability failures described in Section 3.1.3. 1. Attribution vs GDPR. The Treaty on the Functioning of the European Union enshrines protection of intellectual property. European copyright law gives authors moral rights, including attribution. Yet GDPR may prohibit storing or publishing the same identifying data needed to respect these rights. In the absence of jurisprudence from the Court of Justice of the EU or guidance from competent data protection authorities, actors who try to give proper attribution risk GDPR penalties. This legal uncertainty has direct implications for AI: • If attribution is blocked, it becomes impossible to test whether AI systems treat authors fairly. • More broadly, GDPR makes it difficult to safeguard against algorithmic discrimination if information on gender, nationality, or other attributes cannot legally be used7, and the current announced revision of GDPR by the Commission is the best moment to address this problem. 2. Local content protection gaps. In broadcasting, local content quotas were established in line with WTO rules to safeguard cultural diversity (e.g. Slovak private radios playing at least 25% Slovak music). Similar obligations now exist in audiovisual streaming. But in music streaming there are no binding European diversity or local content rules. This creates two problems: • AI-driven distribution platforms can crowd out local repertoire with global catalogues, depriving smaller nations of audiences. • Even where voluntary quotas exist, compliance depends on knowing the origin of repertoire. If we cannot know whether a work is Slovak, French, or by a young author, quotas or diversity targets cannot be implemented. 3. Voluntary compliance is impractical. Current practice relies on voluntary measures by radio editors, festival curators, or platform users to include local or diverse content. But without accessible data, this becomes unworkable. Our own experiments with GDPR balancing tests and opt-ins show the futility of this approach. interoperable identifiers, neither attribution nor AI governance can scale [Partanen, Rixhon, Bandere, Ziediņš, Dutt, Bolšteins, Frosterus, Lehtinen, Miklūna-Žukeviča, Ozerskis, Pihlaja, Sauka, Sornova, and Uzula (2025), pp28–33; pp101–102]. This tension is also recognised in recent policy research. 7CITF identifies this same contradiction. It notes that attribution data is simultaneously necessary for copyright, required for RMI, and treated as personal data under GDPR, creating legal uncertainty that directly undermines AI governance, lifecycle compliance, and fairness testing (Partanen, Rixhon, Bandere, Ziediņš, Dutt, Bolšteins, Frosterus, Lehtinen, Miklūna-Žukeviča, Ozerskis, Pihlaja, Sauka, Sornova, and Uzula 2025, pp12–18). 67 Fewer than 1% of artists responded to requests to consent to attribution data — even prominent Slovak artists, puzzled at being asked to consent to rights they already legally hold. In short, AI cannot be made trustworthy for music without resolving these legislative and policy blocks. The AI Act currently misplaces risk, treating music as “low-risk” while ignoring systemic harms. GDPR, in practice, blocks data use that would enable fairness testing. And the absence of local content rules in streaming removes a cornerstone of cultural policy. AI in music will remain misaligned with European policy goals unless these conflicts are addressed. 4.1.4 AI design without awareness of limits AI systems are not usually designed with an awareness of their own conceptual limits. •Agentic AI systems (recommenders, playlist builders, rights-management bots) operate without recognising the biases or incompleteness of the datasets they learn from. Because European legislation deems the agentic use of AI in music “low risk”, currently there are no real expectations to address this problem. •Generative AI produces synthetic material without constraints, and its training processes seldom acknowledge gaps or skew in the underlying data. This problem touches upon various issues that we discussed earlier in this paper: author’s rights and performer rights are assigned to natural persons (and their heirs), as well as sometimes producer’s rights, too. GDPR currently appears to conflict both with designing safer AI systems and with providing proper attribution without legal risk to creators of protected work. We could technically guardrail generative AI to not produce plagiarism, but not without giving it access to whose work is forbidden. • Even Inference AI, which is supposed to reason from formal rules, can miss the point: ontological relativity and incompleteness are structural limits and not optional refinements. We discussed in the Chapter 2, and just as well as database designers must be aware that that no ontology or schema is ever complete, AI engineers must realise that they train algorithms that cannot capture all perspectives. Without this awareness, AI will silently reproduce exclusions — whether of women, minorities, or smaller repertoires — while appearing “intelligent.” This is a design issue: the guardrails must be built in from the start, not bolted on afterwards. We see a lot of promise in building Inference AI tools, perhaps in a publicprivate partnership, that can actually provide help for human-in-control principles for the use of agentic and generative AI. 68 4.1.5 Unfreezing frozen assets Many music assets remain “frozen” because their documentation costs exceed their current commercial value. This applies to non-commercial repertoires, small-label releases, and culturally valuable but low-market recordings. Without affordable workflows, these works cannot enter modern distribution systems, regardless of their cultural or artistic significance. The Unlabel pilot illustrates this problem: by treating catalogue transfers and documentation as high-cost, high-friction processes, valuable repertoires remain locked away. AIassisted metadata repair and DDEX-compliant catalogue transfer workflows provide a pathway to lower costs and bring neglected repertoires back into circulation. ĹNote Example: Old SQL Database in a Cultural Institution • A label or archive has a recording stored in a 20–30 year-old SQL database, built on a schema that was never fully documented. The system’s author is retired (or no longer alive). • The institution wants to re-release the recording, but to distribute it today, the metadata must be expressed in DDEX Catalogue Transfer messages — a completely different schema, designed decades later. Curative AI • Acts at the system level: it can “read” the old database structure, infer undocumented field meanings, and patch outputs so the legacy database can still talk to modern pipelines. • Instead of rebuilding or migrating the old database (expensive, risky), curative AI extends its lifespan by making its outputs usable. Reparative AI • Acts at the metadata/epistemic level: it can detect inconsistencies or missing fields (e.g., composer names stored in free-text notes, titles in mixed languages) and reformat or enrich them into structured DDEX-compliant fields. • This not only enables distribution but also restores visibility for works that might otherwise remain trapped in inaccessible formats. The policy point • Without curative/reparative AI, such recordings risk becoming “frozen assets”: legally owned but practically undistributable because the metadata cannot be transformed. • By investing in these AI uses, Europe can preserve access to cultural heritage, reduce IT churn, and ensure that both heritage archives and independent labels can connect to modern digital value chains. 69 Unlike U.S.-style copyright, Europe’s author’s rights regime contains a moral component. Authors (and, for a period, their heirs) retain certain rights over how their works are used, even after economic rights expire. This recognises that works are part of a creator’s moral and cultural heritage, not only economic assets. Various legal norms, for example, local content guidelines, also gave tool earlier to national or ethnic communities to provide some guardrails to the use of their shared heritage, even this means community stewardship and not inheritance in legal terms. Metadata repair and publication strengthen visibility, but also create risks that generative AI will use these works in ways that undermine moral rights, where heirs object to uses they see as distorting or trivialising an author’s legacy and community stewardship norms, where groups perceive their folk or minority heritage as being misappropriated, even when no legal infringement occurs. While we do not identify these challenges at this point as similarly actionable public policy challenges as the problems of GDRP and the creation of trustworthy music AI, regulators do face political risk if ethical expectations of communities around cultural stewardship are not addressed. Even if no author’s rights or other legal norms are breached, the ability to create “fake” Livonian, Latvian or Basque folk songs may strongly conflict with the expectation of communities on the ethical use of AI. 4.1.6 AI support for investment into new repertoire assets While generative AI that disregards human repertoires can undermine cultural value, AI also has constructive roles. Just as photographers benefit from embedded AI in tools like Photoshop or GIMP, musicians and producers can use AI to reduce the costs of composition, recording, and documentation. In practice, this means that creating new works and registering them with identifiers can become less burdensome and more accessible. This perspective aligns with the European Parliament’s call for “metadata from birth” (European Parliament 2024), but it goes further. AI can not only generate metadata automatically at the moment of creation, but also support sound recording, scoring, and archiving processes directly, ensuring that new assets enter circulation with complete, interoperable metadata. 4.2 Policy Proposals: Aligning AI with Governance and Value Creation Generative, agentic, and inference AI are now woven into the global creative economy. But value is not created by algorithms alone — it comes from governance, curated data, and institutions that ensure trust. Policy interventions are needed on three levels: EU,industry, and organisational. 70 Our focus is the metadata and data needs of the music ecosystem — labels, distributors, publishers, managers, CMOs, archives — not the creative act of composing music itself. 4.2.1 EU-Level Policy: Compass and Guardrails •Embed cultural sectors in the EU AI Act & Data Spaces so music and cultural industries are not treated as “low risk.” •Subsidise shared AI utilities for identifier reconciliation, metadata repair, and fraud/plagiarism detection. •Adopt “metadata from birth” principles: embed ISNI/ISWC/ISRC identifiers at the point of creation. •Tax incentives for onboarding frozen assets, supporting digitisation and enrichment of under-documented catalogues. •Resolve attribution vs GDPR conflicts through legal clarification or jurisprudence, enabling fairness testing and copyright compliance. 4.2.2 Industry-Level Policy: Standards and Collaboration •Codes of conduct for AI in music, modelled on GDPR codes. •Identifier crosswalks across ISRC, ISWC, ISNI, VIAF, etc. •Federated AI services for claims, reconciliation, multilingual enrichment. •Training and reskilling to close the AI/data talent gap. •Working capital optimisation through AI-assisted claims and faster distributions. These principles do not stand in isolation: they echo and extend ongoing work such as the Responsible AI Music framework, ensuring that sector-specific practices in Europe are consistent with emerging international standards.8 8The Responsible AI Music framework (RAIM) sets out principles for transparency, fairness, sustainability, and accountability in the use of AI in music (Herremans, Sturm, et al. 2025). Several of the codes of conduct proposed here — such as clarity around data provenance, safeguards for attribution, and limits on exploitative recommendation practices — align closely with RAIM’s recommendations. Where RAIM defines broad principles, this Green Paper provides concrete mechanisms for their operationalisation within European music data spaces and observatories. 71 4.2.3 Organisational-Level Policy: Playbooks for CMOs, Publishers, Archives •Embed AI in workflows so metadata is generated and validated during creation/distribution. •Capture once, reuse many times, reducing redundant re-entry. •Invest in knowledge capital, not IT churn (ontologies, vocabularies, multilingual enrichment). •Subscribe to shared AI utilities instead of bespoke in-house builds. •Develop internal AI governance — even small actors can appoint an “AI steward.” 4.2.4 Curative AI and Reparative AI as a Remediation Solution While data spaces establish rules for new data flows, they do not address the legacy backlog of poorly formatted or incomplete open data. Here, curative AI provides a complementary solution. AI-assisted services can detect duplicates, infer missing identifiers, reconcile heterogeneous formats, and enrich metadata with multilingual descriptions. In effect, they transform datasets that are legally open but practically unusable into resources that can circulate across the ecosystem. ĹNote Curative AI as regeneration, not replacement Figure 4.1: ���� (Ise Grand Shrine): a wooden sanctuary in continuous use for 1,600 years thanks to regeneration practices handed down through generations. 72 The Ise Grand Shrine in Japan has been in continuous use for 1,600 years — not because its wooden beams never rotted, but because the knowledge of renewal was embedded and transmitted across generations. The true asset was the embedded know-how of regeneration, not any single plank of wood. Curative AI can play the same role in the digital domain: - Extend the life of legacy systems by fixing patchy outputs from old ERPs, catalogues, or distributor software. - Preserve the methods of repair: how to reconcile corrupted records, reshape data for new systems, and upgrade databases while remaining compatible with older formats. - Transform investment logic: instead of constant capex for new IT systems, shared data infrastructures with curative AI reduce costs, smooth opex, and deliver futureproof and past-proof services. Our pilots — such as Unlabel and SKCMDb — show that new value can be created without additional IT investment or system upgrades by the participating companies, libraries, and rights management agencies. Thus, governance and remediation are two sides of the same coin: -Data sharing spaces ensure that new data is created in interoperable ways. -Curative AI repairs the inherited stock of legacy and low-quality datasets. Together, they close the gap between the right of reuse (granted by the Open Data Directive) and the means of reuse required for music, culture, and AI-driven innovation. 4.2.5 Lowering Documentation Barriers We propose to adapt Unlabel’s approach as a model for unfreezing frozen assets. By leveraging AI-assisted metadata repair and DDEX-compliant catalogue transfer workflows, documentation costs can be reduced enough to enable non-profits, small labels, and community archives to register and redistribute neglected repertoires. Public support should subsidise onboarding costs, create standardised pipelines, and incentivise low-friction reuse of metadata across systems. 4.2.6 Observatory: European = Open When we call for a European Music Observatory, the adjective “European” should not be read as a cultural filter that limits scope to European repertoires. Music is, and always has been, global. 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