1,721,073 research outputs found
När utgör ändrade förhållanden viktig grund för att omedelbart frånträda ett avtal i förtid?
No full textSkadestånd som sanktion på immaterialrättsområdet: ersättning för annat eller mer än den faktiska skadan?
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Exceptions, limitations and collective management of rights as vehicles for access to information
No full textCopyright law reflects a balance between the competing interests of protection and access. It is often held that this balance has been disturbed by recent technological and related legal developments. This article describes and compares two vehicles to enhance access to information in the online environment, while also taking into account the interest of protection: (i) the introduction of (broader) exceptions and limitations, and (ii) the encouragement of collective rights management
Extended Collective Licensing for Use of Copyrighted Works for Machine Learning
No full textThe fast development of generative artificial intelligence (“AI”) services—such as ChatGPT, Midjourney, Dall-E—have within a short period of time gained immense uptake and popularity. At the same time, such services have given rise to fundamental challenges from a copyright perspective. Court proceedings have been initiated in many jurisdictions on the compatibility of such services with copyright legislation.Some scholars see the development of AI as a gradual process, to be dealt with, like earlier technologies, through incremental adaptation of the copyright framework. For others, AI represents so fundamental an innovation—a disruptive technology, a game changer, an apocalypse—that it threatens to shake copyright law to its very foundations. The Economist has described the challenges as a “battle royal.”These technological and legal developments—and related economic consequences—have, in turn, raised political and scholarly interest in the issues at stake. For example, the World Intellectual Property Organization (“WIPO”) has dedicated studies and seminars to the topic, the Association Littéraire et Artistique Internationale (“ALAI”) 2023 Congress in Paris focused on AI and copyright, and several jurisdictions have or are considering specific provisions in copyright law of relevance to this emerging technology. Entire symposia, including this one—the Kernochan Center’s 2024 annual symposium The Past, Present and Future of Copyright Licensing—are dedicated to related copyright issues.A copyright-related question that has gained much attention is whether the output generated by generative AI services can obtain copyright protection, and if so, who the author is. Another question, which is the focus of this contribution, is whether the use of copyright protected content as part of the “training” of the AI—i.e., machine learning—constitutes copyright-relevant use, i.e., falls within the rights protected by copyright. And if so, whether the so-called extended collective licensing model could be a relevant vehicle (or mechanism) for clearing rights for such use. Related to aspects of extended collective licensing, issues have been raised around whether there are challenges associated with competition law that need to be taken into account.Against this backdrop, this Article is structured as follows. Section I, deals with machine learning and copyright, i.e., whether and to what extent the use of copyrightprotected content as part of the “training” of the AI (machine learning) constitutes copyright-relevant use. Section II describes and discusses whether the extended collective licensing model could be a relevant mechanism for such use. Section III focuses on some challenges from a competition law perspective, and also relates to some relevant provisions in the EU directive on collective rights management. Section IV sets out some concluding remarks
Extended Collective Licensing for Use of Copyrighted Works for Machine Learning
Get PDFThe fast development of generative artificial intelligence (“AI”) services—such as ChatGPT, Midjourney, Dall-E—have within a short period of time gained immense uptake and popularity. At the same time, such services have given rise to fundamental challenges from a copyright perspective. Court proceedings have been initiated in many jurisdictions on the compatibility of such services with copyright legislation.
Some scholars see the development of AI as a gradual process, to be dealt with, like earlier technologies, through incremental adaptation of the copyright framework. For others, AI represents so fundamental an innovation—a disruptive technology, a game changer, an apocalypse—that it threatens to shake copyright law to its very foundations. The Economist has described the challenges as a “battle royal.”
These technological and legal developments—and related economic consequences—have, in turn, raised political and scholarly interest in the issues at stake. For example, the World Intellectual Property Organization (“WIPO”) has dedicated studies and seminars to the topic, the Association Littéraire et Artistique Internationale (“ALAI”) 2023 Congress in Paris focused on AI and copyright, and several jurisdictions have or are considering specific provisions in copyright law of relevance to this emerging technology. Entire symposia, including this one—the Kernochan Center’s 2024 annual symposium The Past, Present and Future of Copyright Licensing—are dedicated to related copyright issues.
A copyright-related question that has gained much attention is whether the output generated by generative AI services can obtain copyright protection, and if so, who the author is. Another question, which is the focus of this contribution, is whether the use of copyright protected content as part of the “training” of the AI—i.e., machine learning—constitutes copyright-relevant use, i.e., falls within the rights protected by copyright. And if so, whether the so-called extended collective licensing model could be a relevant vehicle (or mechanism) for clearing rights for such use. Related to aspects of extended collective licensing, issues have been raised around whether there are challenges associated with competition law that need to be taken into account.
Against this backdrop, this Article is structured as follows. Section I, deals with machine learning and copyright, i.e., whether and to what extent the use of copyrightprotected content as part of the “training” of the AI (machine learning) constitutes copyright-relevant use. Section II describes and discusses whether the extended collective licensing model could be a relevant mechanism for such use. Section III focuses on some challenges from a competition law perspective, and also relates to some relevant provisions in the EU directive on collective rights management. Section IV sets out some concluding remarks
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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