Copyright Infringement by Large Language Models in the EU : Misalignment, Safeguards, and the Path Forward (2025)

Abstract: This position paper argues that European copyright law has struggled to keep pace with the development of large language models (LLMs), possibly creating a fundamental epistemic misalignment: copyright compliance relies on qualitative, context-dependent standards, while LLM development is governed by quantitative, proactive metrics. This gap means that technical safeguards, by themselves, may be insufficient to reliably demonstrate legal compliance. We identify several practical limitations in the existing EU legal frameworks, including ambiguous “lawful access” rules, fragmented opt-outs, and vague disclosure duties. We then discuss technical measures such as provenance-first data governance, machine unlearning for post-hoc removal, and synthetic data generation, showing their promise but also their limits.Finally, we propose a path forward grounded in legal-technical co-design, suggesting directions for standardising machine-readable opt-outs, disclosure templates, clarifying core legal terms, and developing legally-informed benchmarks and evidence standards. We conclude that such an integrated framework is essential to make compliance auditable, thus protected creators’ rights while enabling responsible AI innovation at scale.

## introduction in 2025, researchers demonstrated that meta's llama 3.1 could reproduce entire chapters of copyright-protected novels, including harry potter, almost verbatim @xcite . in germany, the case of kneschke v. laion exposed the fragility of text-and-data mining (tdm) safeguards when a photographer's images were scraped into a large-scale dataset without consent @xcite . these incidents are indicative of broader foreseeable risks emerging from how large language models (llms) are trained: by ingesting petabytes of text, images, and code from the open internet, much of it copyright-protected @xcite @xcite . the european union (eu) has responded with a layered legal framework, most prominently copyright in the digital single market (cdsm) @xcite directive and the ai act @xcite . rooted in the civil law tradition, this framework relies on defined exceptions and limitations, contrasting with the broader and more flexible "fair use" doctrine in common law jurisdictions such as the united states. the instruments establish the principles of lawful access, opt-out mechanisms, and transparency obligations @xcite . however, they were not designed to accommodate the technical realities of large-scale model development, including petabyte-scale data ingestion or probabilistic memorisation @xcite . whereas copyright law is structured to resolve human-scale disputes retrospectively through courts; llms operate at machine-scale, enabling the ingestion and reproduction of billions of works, with minimal prompting @xcite . the mismatch contributes to a gap between legal expectations and technical characteristics of generative ai (e.g., llms) systems. this practical mismatch reflects a deeper structural problem. copyright law relies on qualitative, context-dependent standards (originality, substantial similarity) adjudicated retrospectively by courts @xcite court of justice of the european @xcite , whilst llm development operates through quantitative, proactive metrics (loss functions, similarity thresholds) optimised by automated pipelines @xcite . these domains operate in different conceptual languages, making it impractical to demonstrate legal compliance through technical metrics alone. this creates a persistent risk of partial or structural non-compliance: developers cannot credibly prove copyright respect, rightsholders cannot meaningfully enforce their rights, and regulators lack enforceable technical standards @xcite . we suggest that effective copyright compliance in the context of llms requires interdisciplinary co-design, where law frameworks inform the development of technical metrics and technical methods support compliance verification at scale. to support this position, this paper looks into why current eu frameworks fall short when applied to llms, highlighting the mismatch between retrospective legal adjudication and proactive technical safeguards. we then analyse the structural misalignment between qualitative legal standards and quantitative metrics before surveying the promise and limits of current technical solutions. finally, we propose a path forward: a triad of provenance infrastructure, adversarially robust unlearning, and clean-chain synthetic data generation, all embedded within a framework of legally-informed evaluation. ## current legal and technical frameworks for generative ai the eu's legal response to generative ai is layered. the cdsm directive created tdm exceptions with opt-out rights for rightsholders, and the ai act added transparency and risk-management obligations for general-purpose ai (gpai) @xcite . taken separately, each instrument is internally coherent. taken together, they reveal practical limitations for large-scale model training and deployment. ## qualitative law vs. quantitative metrics at the core of the compliance challenge lies an epistemic mismatch. eu copyright law relies on qualitative standards interpreted contextually by human adjudicators. llm development relies on quantitative metrics optimised by automated pipelines. each side has a coherent internal logic. together, they currently seem to fail to interlock @xcite @xcite @xcite . ## technical mitigation: promise and limits of current safeguards technical safeguards for copyright compliance cluster around various pillars, such as provenancefirst data governance, post-training removal via machine unlearning, and synthetic data generation. each contributes to the reduction of risk. none, in its current form, appears sufficient, on its own, to establish enforceable compliance with eu legal standards. ## path forward: policy and research directions bridging the legal-technical gap requires codesigned standards that are machine-actionable upstream and legally meaningful downstream. the direction below outlines plausible pathways for policymakers, developers, and researchers to make copyright compliance verifiable at scale. they are proposed as guidance rather than prescriptive steps. ## conclusion this paper has suggested that a persistent epistemic gap exists between eu copyright law and the practical realities of llm development, creating a risk of structural non-compliance. we have shown that the law's qualitative, retrospective standards of originality and substantial similarity are fundamentally misaligned with the quantitative, proactive controls that govern machine-scale pipelines. consequently, existing legal instruments like the tdm exceptions and gpai transparency duties are ill-matched to the challenges of petabyte-scale training, while technical safeguards such as provenance-first governance, mu, and sdg remain insufficient without legallyspecified standards and meaningful evaluation. the path forward is not a single technical fix or legal decree, but the development of a coherent, auditable ecosystem built on legal-technical co-design. this requires translating legal duties into machine-actionable specifications upstream, while enabling technical evidence to be legally meaningful downstream. such an ecosystem would be founded on provenance-first data ingestion, lawfully expanded via clean-chain synthetic data, and protected by robust, verifiable corrective techniques, such as machine unlearning. this integration could make compliance testable, proactive controls auditable, and retrospective adjudication reliant on verifiable artefacts rather than unattainable certainties. achieving this vision, however, will require coordination between legal and technical communities that may prove difficult in practice, and our technical recommendations await empirical validation at scale, with implementation costs likely varying significantly across organisational contexts. ultimately, the eu faces a strategic choice: work toward enforceable compliance frameworks grounded in this integrated approach, or risk gradual erosion of copyright relevance in the age of generative ai. our analysis focuses specifically on eu copyright law and may not generalise to jurisdictions with fundamentally different frameworks, such as the us fair use doctrine. moreover, whilst we outline policy directions toward standardised schemas and registries, the political economy of implementation involves stakeholder interests beyond our scope. our focus on copyright compliance necessarily brackets other relevant legal frameworks such as data protection under gdpr, competition law, and sector-specific regulations that may interact with the proposed solutions in ways requiring further research. nevertheless, by fostering policy, development, and research that bridge the legaltechnical gap, and by remaining responsive to the rapid evolution of both legal developments and technical capabilities, the eu can set the global standard for responsible ai: protecting creators' rights while enabling innovation at machine-scale. broader impact. our proposed co-design framework aims to mitigate these harms by making copyright compliance technically tractable and legally meaningful. however, implementation of our recommendations could impose costs on ai developers and potentially limit access to certain training data, affecting model performance and innovation velocity. we argue these trade-offs may be necessary to preserve creator rights and maintain public trust in ai systems.