The European Parliament’s study is a crucial contribution that avoids techno-utopianism and legal inertia alike.

Generative AI and Copyright – Addressing the Legal Faultlines in the EU Framework

by ChatGPT-4o

The European Parliament-commissioned study “Generative AI and Copyright: Training, Creation, Regulation” (PE 774.095, July 2025) provides a landmark analysis of how generative artificial intelligence (GenAI) disrupts the core architecture of EU copyright law. As GenAI systems accelerate in scale and sophistication, their impact is shaking the foundations of authorship, remuneration, enforcement, and cultural sustainability. The report, authored by Professor Nicola Lucchi, identifies deep misalignments in the current legal regime and offers a roadmap for reform that emphasizes legal clarity, fairness, and innovation. This essay summarizes the study’s key findings, offers reflections on its implications, and concludes with strategic recommendations for stakeholders.

I. Key Challenges Identified

1. Input-Side Frictions: Copyright and Training Data

The study argues that the legal basis for using copyrighted content in AI training—currently resting on the text and data mining (TDM) exception in Article 4 of the CDSM Directive—is structurally inadequate for large-scale GenAI use. The “opt-out” model is fragmented, lacks standardization, and is technically ineffective. Current tagging protocols (e.g., metadata or robots.txt) cannot prevent duplication or unauthorized ingestion at scale. Moreover, this loophole creates a “reverse licensing” dynamic, where rights holders are forced to act defensively rather than proactively.

The report anticipates that the CJEU’s upcoming ruling in Case C-250/25 may provide judicial guidance on the scope and limits of TDM. In the meantime, many GenAI developers—faced with legal uncertainty—are shifting toward direct licensing deals with publishers and image libraries, reflecting a pragmatic move toward negotiated legality.

2. Output-Side Ambiguities: Authorship and Protection

The second major challenge concerns the legal status of GenAI outputs. Under current EU law, copyright requires a “personal intellectual creation,” which excludes fully machine-generated content. However, most AI-assisted outputs are hybrid in nature, created through iterative human-AI collaboration. The lack of harmonized criteria for determining whether such content is eligible for protection has resulted in legal fragmentation across Member States.

Compounding this, AI-generated content threatens traditional content markets by flooding them with low-cost synthetic material, thereby devaluing human-made work and consolidating revenue streams in the hands of a few platform monopolies. The absence of a clear framework for attribution and moral rights enforcement further exacerbates these risks.

3. The Value Gap: Remuneration and Market Fairness

Perhaps the most pressing concern is the lack of any statutory remuneration mechanism for creators whose works are scraped or ingested into training corpora. The study warns that the economic engine of copyright—which depends on incentives to create—is being quietly dismantled. Without compensation frameworks, creators are contributing unknowingly to models that may eventually replace or undercut them.

II. Evaluation and Reflections

This report is a principled, timely, and well-structured intervention in the AI policy debate. Crucially, it does not advocate for rewriting copyright law to accommodate AI, but rather for reinforcing foundational principles in light of new technological challenges. The emphasis on maintaining authorship, originality, and fair remuneration as guiding tenets is essential to preserving the integrity of Europe’s creative economy.

Several points are especially worthy of emphasis:

• Preserving legal coherence over improvisational reform: The report rightly resists calls to introduce sui generis rights for AI-generated content, which would risk destabilizing the doctrinal unity of EU copyright law.

• Opt-out versus opt-in: A compelling argument is made for transitioning from an opt-out to an opt-in regime for TDM involving generative AI. This would reverse the current burden on rights holders and restore meaningful control over their content.

• Three-Pillar Accountability Test: The proposed framework—based on epistemic (transparency), normative (fairness), and systemic (institutional) accountability—is a strong analytical tool for assessing future policy options.

• Differentiation in regulation: The recommendation to provide “yellow-label relief” for non-profit and open-source AI projects—exempting them from the full force of regulation up to certain thresholds—is a pragmatic step to support innovation outside the commercial mainstream.

Nonetheless, the study could further explore enforcement mechanisms, particularly regarding real-time monitoring and redress mechanisms for small creators. Also, the intersection of copyright with data protection and personality rights remains underexplored—an omission given that AI training often blurs the line between public content and personal data.

III. Recommendations for Stakeholders

For the European Commission and Parliament

• Reform the CDSM Directive: Move toward a permission-based system for generative AI training, supported by a unified machine-readable registry under the EUIPO.

• Codify criteria for AI-assisted authorship: Harmonize rules across Member States for recognizing and protecting hybrid AI-human works.

• Establish a statutory remuneration mechanism: Implement collective licensing, levies on GenAI outputs, or corpus-based compensation frameworks—possibly modeled after existing systems for private copying or digital lending.

For AI Developers and Tech Companies

• Adopt robust transparency protocols: Maintain logs of training datasets, ensure traceability through watermarking/fingerprinting, and implement corpus audit mechanisms.

• Engage in fair licensing: Proactively license content from publishers and creators to ensure legal certainty and ethical training practices.

• Support compliance through infrastructure: Integrate content recognition and rights-respecting APIs into model pipelines.

For Creators and Content Owners

• Assert rights through collective management: Leverage CMOs to negotiate AI training licenses and advocate for statutory remuneration systems.

• Utilize technical protection tools: Embed opt-out metadata or machine-readable signals to signal reservation of rights where possible.

• Document use cases and harms: Track evidence of displacement, substitution, or reputational damage to support policy reform and legal claims.

For Regulators and Civil Society

• Strengthen oversight: Create a permanent AI & Copyright Unit within the AI Office to coordinate with the EUIPO, Parliament, and CMOs.

• Ensure proportionality and inclusivity: Exempt smaller actors and non-commercial projects from onerous obligations, while targeting dominant platforms.

• Monitor cultural impacts: Develop indicators to assess whether GenAI contributes to or undermines Europe’s creative and linguistic diversity.

Conclusion

The EU faces a watershed moment in aligning AI development with the values of authorship, fairness, and cultural plurality. The European Parliament’s study is a crucial contribution that avoids techno-utopianism and legal inertia alike. By reaffirming the human-centric foundations of copyright, introducing proportionate safeguards, and proposing a path toward statutory remuneration, the report offers a balanced and actionable vision. The window for reform is rapidly closing—decisive action today will shape the creative future of tomorrow.

Europe has the opportunity to lead the world in designing an ethical, rights-respecting GenAI ecosystem. It must seize that chance—before the foundations of creative labor are irreversibly eroded.