Keyword: Text and Data Mining (TDM)
LAION Round 2: Machine-Readable but Still Not Actionable — The Lack of Progress on TDM Opt-Outs – Part 1 external link
Article 3: The Untapped Legal Basis for Europe’s Public AI Ambitions external link
Are the European TDM Exceptions Applicable to GenAI Training? Despite the Three-Step Test? 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
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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
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Bibtex
Towards a European Research Freedom Act: A Reform Agenda for Research Exceptions in the EU Copyright Acquis external link
Abstract
This article explores the impact of EU copyright law on the use of protected knowledge resources in scientific research contexts. Surveying the current copyright/research interface, it becomes apparent that the existing legal framework fails to offer adequate balancing tools for the reconciliation of divergent interests of copyright holders and researchers. The analysis identifies structural deficiencies, such as fragmented and overly restrictive research exceptions, opaque lawful access provisions, outdated non-commercial use requirements, legal uncertainty arising from the three-step test in the EU copyright acquis, obstacles posed by the protection of paywalls and other technological measures, and exposure to contracts that override statutory research freedoms. Empirical data confirm that access barriers, use restrictions and the absence of harmonised rules for transnational research collaborations impede the work of researchers. Against this background, we advance proposals for legislative reform, in particular the introduction of a mandatory, open-ended research exemption that offers reliable breathing space for scientific research across EU Member States, the clarification of lawful access criteria, a more flexible approach to public-private partnerships, and additional rules that support modern research methods, such as text and data mining.
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Copyright, open science, research exceptions, right to research, technological protection measures, Text and Data Mining (TDM), three-step test
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Bibtex
The TDM Opt-Out in the EU – Five Problems, One Solution external link
Editorial: What Is a ‘Research Organisation’ and Why It Matters: From Text and Data Mining to AI Research
The paradox of lawful text and data mining? Some experiences from the research sector and where we (should) go from here external link
Abstract
Scientific research can be tricky business. This paper critically explores the 'lawful access' requirement in European copyright law which applies to text and data mining (TDM) carried out for the purpose of scientific research. Whereas TDM is essential for data analysis, artificial intelligence (AI) and innovation, the paper argues that the 'lawful access' requirement in Article 3 CDSM Directive may actually restrict research by complicating the applicability of the TDM provision or even rendering it inoperable. Although the requirement is intended to ensure that researchers act in good faith before deploying TMD tools for purposes such as machine learning, it forces them to ask for permission to access data, for example by taking out a subscription to a service, and for that reason provides the opportunity for copyright holders to apply all sorts of commercial strategies to set the legal and technological parameters of access and potentially even circumvent the mandatory character of the provision. The paper concludes by drawing on insights from the recent European Commission study 'Improving access to and reuse of research results, publications and data for scientific purposes' that offer essential perspectives for the future of TDM, and by suggesting a number of paths forward that EU Member States can take already now in order to support a more predictable and reliable legal regime for scientific TDM and potentially code mining to foster innovation.
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Artificial intelligence, CDSM Directive, Copyright, Text and Data Mining (TDM)