Is Transformative Use Eating the World?

Asay, Clark D.; Sloan, Arielle; Sobczak, Dean

doi:10.2139/ssrn.3332682

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…The economic character has to be seen considering that this exception is fair only for purposes like research, and so without a real economic character; however, the previously done distinction between market-encroaching uses and nonmarket-encroaching uses acquires significance in dividing between an (almost) sure fair use and a dubious case. Finally, when analyzing the purpose of the use (and its fairness), one needs to consider if it is transformative, the most common reason to assess fair use (Asay et al, 2020): if it adds something new, with a different character, which does not substitute for the original use of the work, the use is more likely to be considered fair 4 . In particular, the key question to determine a fair use is nowadays if the work is used for a different expressive purpose from that for which the work was created (Netanel, 2011).…”

Section: Storage Of Protected Work and Use For Trainingmentioning

confidence: 99%

Copyright in generative deep learning

Giorgio

Musolesi

2022

Data & Policy

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Machine-generated artworks are now part of the contemporary art scene: they are attracting significant investments and they are presented in exhibitions together with those created by human artists. These artworks are mainly based on generative deep learning (GDL) techniques, which have seen a formidable development and remarkable refinement in the very recent years. Given the inherent characteristics of these techniques, a series of novel legal problems arise. In this article, we consider a set of key questions in the area of GDL for the arts, including the following: is it possible to use copyrighted works as training set for generative models? How do we legally store their copies in order to perform the training process? Who (if someone) will own the copyright on the generated data? We try to answer these questions considering the law in force in both the United States and the European Union, and potential future alternatives. We then extend our analysis to code generation, which is an emerging area of GDL. Finally, we also formulate a set of practical guidelines for artists and developers working on deep learning generated art, as well as some policy suggestions for policymakers.

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Section: Storage Of Protected Work and Use For Trainingmentioning

confidence: 99%

Copyright in generative deep learning

Giorgio

Musolesi

2022

Data & Policy

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Cracking the Copyright Dilemma in Software Preservation

Butler

Aufderheide

Jaszi

et al. 2019

JCEL

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Copyright problems may inhibit the crucially important work of preserving legacy software. Such software is worthy of study in its own right because it is critical to accessing digital culture and expression. Preservation work is essential for communicating across boundaries of the past and present in a digital era. Software preservationists in the United States have addressed their copyright problems by developing a code of best practices in employing fair use. Their work is an example of how collective action by users of law changes the norms and beliefs about law, which can in turn change the law itself insofar as the law takes account of community norms and practices. The work of creating the code involved facilitators who are communication, information sciences, and legal scholars and practitioners. Thus, the creation of the code is also an example of crossing the boundaries between technology and policy research.

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Opportunity, not Welfare: How Behavioral Insights Should Lead to a Reorientation of the Normative Foundation in Law and Economics

van Emmerick,

Dold

2022

SCHM

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Is Transformative Use Eating the World?

Cited by 3 publications

References 17 publications

Copyright in generative deep learning

Copyright in generative deep learning

Cracking the Copyright Dilemma in Software Preservation

Opportunity, not Welfare: How Behavioral Insights Should Lead to a Reorientation of the Normative Foundation in Law and Economics

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