Keyword: Auteursrecht
EU copyright law roundup – second and third trimester of 2025 external link
Are the European TDM Exceptions Applicable to GenAI Training? Despite the Three-Step Test? external link
Angemessene Vergütung insbesondere im Bereich Streaming und Plattform-Ökonomie/Reform des Vergütungssystems für gesetzlich erlaubte Nutzungen im Urheberrecht download
An EU Copyright Framework for Research: Opinion of the European Copyright Society external link
Abstract
Research and academic freedom are at the core of the EU project. Yet, the relationship between EU copyright law and research is intricate. Research and education interests have traditionally been recognized within copyright law to some degree, however, the current EU copyright acquis is not really conducive to an effective research environment. This jeopardises the fulfilment of the EU’s ambitions in the field.
Building on the pillars of action of the European Research Area (ERA) Policy Agenda 2022-2024 and its follow-up, the ECS emphasises the need for a copyright framework that fosters research, and supports the call for immediate action on the EU copyright framework to address the most pressing challenges it raises for European researchers and their institutions.
This Opinion stresses the need to ensure a proper balance between IP rights, protected under Article 17(2) CFREU, and the freedom of art and science (Article 13 CFREU), coupled with the ‘right to research’, as enshrined in international legal instruments (UDHR and ICESCR), the objectives of the EU treaties, and the CFREU and ECHR. Various EU and national legal instruments are in place that facilitate access and reuse of scientific works, but these have several shortcomings. They weaken the effective balance between copyright, research policy needs, and the fulfilment of ERA policy goals, including the EU Open Science agenda.
This opinion focuses on the flaws in key provisions aimed at balancing copyright and research needs: the general InfoSoc Directive research exception, the text and data mining exception of the CDSM Directive and national secondary publication rights. It also briefly assesses the interface between copyright and (research) data regulation. We propose several policy interventions to address the identified shortcomings. These include the introduction of an EU-wide secondary publication right with specific characteristics; the amendment of text and data mining exceptions; the creation of a general mandatory research exception overcoming the challenges raised by Article 5(3)(d) InfoSoc; and a more careful legislative drafting to reduce legal complexity and ensure consistency across copyright and data legislation.
Copyright, european copyright society, research
RIS
Bibtex
More Information Law Series Volumes Freely Available external link
Editorial: GenAI and the Copyright Three-Step Test – Do TDM Exceptions for AI Training Conflict With a Work’s Normal Exploitation? external link
Abstract
Text and data mining (TDM) for AI training can be regarded as the starting point of a complex process that impacts the market for human literary and artistic creations in different ways. The machine is only capable of mimicking human content after it had the opportunity to derive patterns for its own productions from myriad human creations that served as training resources. Once AI training has been completed and a generative AI (GenAI) system is brought to the market, AI output may support fruitful human/machine collaboration. However, it may also kill demand for the same human creativity that empowered the AI system to become a competitor in the first place. In the terminology of the ubiquitous three-step test in international and European copyright law, this latter challenge raises the question whether copyright exceptions permitting TDM for AI training cause a conflict with a work’s normal exploitation.
A closer inspection of the normal exploitation test shows that the chances of demonstrating a relevant conflict are slim in the case of AI training. Rightsholders seeking compensation for displacement effects caused by GenAI systems must resort to the final criterion of the three-step test and argue that the use for AI development unreasonably prejudices their legitimate interests. In practice, this means that copyright holders can hardly employ the three-step test as a tool to erode TDM exemptions altogether. They can only insist on the introduction of appropriate remuneration schemes to avoid unreasonable prejudice in cases of commercial AI training.
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Copyright, exploitation, GenAI, Text and Data Mining (TDM), three-step test
RIS
Bibtex
Auteursrechtsectoren hebben de Covid‑19-pandemie goed doorstaan download
Abstract
Wat is de economische bijdrage van de sectoren in Nederland die direct of indirect afhankelijk zijn van het auteursrecht? Om dit in kaart te brengen, ontwikkelde de World Intellectual Property Organization (WIPO) in 2003 een gestandaardiseerde methodiek. De afgelopen twee decennia hebben meer dan vijftig landen in totaal ruim zeventig studies uitgebracht waarin zij hun auteursrechtsectoren langs de WIPO-meetlat leggen.
Dit artikel bespreekt de meest recente Nederlandse studie in deze WIPO-onderzoeklijn, waarbij het ingaat op de ontwikkelingen ten opzichte van eerdere Nederlandse edities en andere landen en een verdiepende analyse geeft van de impact van de Covid-19-pandemie.
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Copyright, covid-19, economical aspects
RIS
Bibtex
TDM, GenAI and the Copyright Three-Step Test external link
Abstract
In the debate on copyright exceptions permitting text and data mining (“TDM”) for the development of generative AI systems, the so-called “three-step test” has become a centre of gravity. The test serves as a universal yardstick for assessing the compatibility of domestic copyright exceptions with international copyright law. However, it is doubtful whether the international three-step test is applicable at all. Arguably, TDM copies fall outside the scope of the international right of reproduction and go beyond the ambit of the test’s operation. Only if national or regional copyright legislation declares the test applicable, the question arises whether copyright exceptions supporting TDM for AI training constitute certain special cases that do not conflict with a work’s normal exploitation and do not unreasonably prejudice legitimate author or rightsholder interests. As the following analysis will show, rules permitting TDM for AI training can satisfy all test criteria. An opt-out opportunity for copyright owners bans the risk of a conflict with a work’s normal exploitation and an unreasonable prejudice from the outset. A clear focus on specific policy goals, such as the objective to support scientific research, adds conceptual contours that dispel concerns about incompliance. In the case of TDM provisions covering commercial AI development, equitable remuneration regimes can be introduced as a counterbalance to avoid an unreasonable prejudice.
Copyright, Generative AI, Text and Data Mining (TDM), three-step test
RIS
Bibtex
Win-Win: How to Remove Copyright Obstacles to AI Training While Ensuring Author Remuneration (and Why the AI Act Fails to do the Magic) external link
Abstract
In the debate on AI training and copyright, the focus is often on the use of protected works during the AI training phase (input perspective). To reconcile training objectives with authors' fair remuneration interest, however, it is advisable to adopt an output perspective and focus on literary and artistic productions generated by fully-trained AI systems that are offered in the marketplace. Implementing output-based remuneration systems, lawmakers can establish a legal framework that supports the development of unbiased, high quality AI models while, at the same time, ensuring that authors receive a fair remuneration for the use of literary and artistic works for AI training purposes – a fair remuneration that softens displacement effects in the market for literary and artistic creations where human authors face shrinking market share and loss of income. Instead of imposing payment obligations and administrative burdens on AI developers during the AI training phase, output-based remuneration systems offer the chance of giving AI trainers far-reaching freedom. Without exposing AI developers to heavy administrative and financial burdens, lawmakers can permit the use of the full spectrum of human literary and artistic resources. Once fully developed AI systems are brought to the market, however, providers of these systems are obliged to compensate authors for the unbridled freedom to use human creations during the AI training phase and displacement effects caused by AI systems that are capable of mimicking human literary and artistic works.
As the analysis shows, the input-based remuneration approach in the EU – with rights reservations and complex transparency rules blocking access to AI training resources – is likely to reduce the attractiveness of the EU as a region for AI development. Moreover, the regulatory barriers posed by EU copyright law and the AI Act may marginalize the messages and values conveyed by European cultural expressions in AI training datasets and AI output. Considering the legal and practical difficulties resulting from the EU approach, lawmakers in other regions should refrain from following the EU model. As an alternative, they should explore output-based remuneration mechanisms. In contrast to the burdensome EU system that requires the payment of remuneration for access to human AI training resources, an output-based approach does not weaken the position of the domestic high-tech sector: AI developers are free to use human creations as training material. Once fully developed AI systems are offered in the marketplace, all providers of AI systems capable of producing literary and artistic output are subject to the same payment obligation and remuneration scheme – regardless of whether they are local or foreign companies. The advantages of this alternative approach are evident. Offering broad freedom to use human creations for AI training, an output-based approach is conducive to AI development. It also bans the risk of marginalizing the messages and values conveyed by a country’s literary and artistic expressions.
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Artificial intelligence, Copyright, remuneration