scholarly journals Position Statement of the Max Planck Institute for Innovation and Competition on the Proposed Modernisation of European Copyright Rules Part B Exceptions and Limitations (Art. 3 Text and Data Mining)

2017 ◽  
Author(s):  
Reto Hilty ◽  
Heiko Richter
Keyword(s):  
Data Mining ◽  
Position Statement ◽  
Max Planck ◽  
Exceptions And Limitations ◽  
Text And Data Mining

A Taxonomy of Training Data

2021 ◽  
pp. 221-242
Author(s):  
Benjamin Sobel
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Routine Activities ◽  
Training Data ◽  
Positive Development ◽  
Machine Learning Applications ◽  
Exceptions And Limitations ◽  
Low Threshold ◽  
The Right ◽  
Text And Data Mining

Many machine learning applications depend on unauthorized uses of copyrighted training data. Scholars and lawmakers often articulate this problem as a deficiency in copyright’s exceptions and limitations. In fact, today’s predicament results not from inadequate exceptions to copyright, but rather from two systemic features of the regime—the absence of formalities and the low threshold of copyrightable originality—combined with technology that turns routine activities into acts of authorship. This chapter taxonomizes AI applications by their training data. Four categories emerge: (1) public-domain data, (2) licensed data, (3) market-encroaching uses of copyrighted data, and (4) non-market-encroaching uses of copyrighted data. Copyright can only regulate market-encroaching uses, but these uses comprise only a narrow subset of AI, and copyright’s remedies are ill-suited to address them. The chapter concludes with a discussion of solutions to the problems it identifies. It observes that the exception for Text and Data Mining in the European Union’s Directive on Copyright in the Digital Single Market represents a positive development because the exception addresses structural causes of AI’s copyright problems. The TDM provision styles itself as an exception, but it may be better understood as a formality: rights holders must affirmatively reserve the right to exclude materials from training datasets. Thus, the TDM exception addresses a cause of AI’s copyright dilemma. The next step for an equitable AI framework will be to transition towards rules that not only clarify that non-market-encroaching uses do not infringe copyright, but also facilitate remunerated uses of copyrighted works for market-encroaching purposes.

Article 4—Exception or Limitation for Text and Data Mining

2021 ◽  
pp. 60-92
Author(s):  
Eleonora Rosati
Keyword(s):  
Data Mining ◽  
Cultural Heritage ◽  
Subject Matter ◽  
Digital Technologies ◽  
Internal Market ◽  
Member States ◽  
Exceptions And Limitations ◽  
Fair Balance ◽  
Text And Data Mining ◽  
Copyright Directive

This chapter focuses on Article 4 of Directive 2019/790, the European copyright directive, which require Member States to provide for an exception or limitation for reproductions and extractions of works and other subject matter for the purposes of text and data mining. It talks about digital technologies that permit new types of uses that are not clearly covered by the existing Union rules on exceptions and limitations in the fields of research, innovation, education, and preservation of cultural heritage. It also describes the optional nature of exceptions and limitations that could negatively impact the functioning of the internal market. The chapter discusses the exceptions and limitations provided in Directive 2019/790 that seek to achieve a fair balance between the rights and interests of authors, other rightholders, and users. It clarifies that text and data mining can be carried out in relation to mere facts or data that are not protected by copyright.

Text and data mining of in-copyright works

2021 ◽  
Vol 64 (11) ◽  
pp. 20-22
Author(s):  
Pamela Samuelson
Keyword(s):  
Data Mining ◽  
Copyright Law ◽  
Text And Data Mining

How copyright law might be an impediment to text and data mining research.

scholarly journals Τhe Exception of Text and Data Mining from the Academic Libraries Standpoint

2021 ◽  
Vol 09 (05) ◽  
pp. 502-539
Author(s):  
Maria-Daphne Papadopoulou ◽  
Krystallenia Kolotourou ◽  
Maria Bottis
Keyword(s):  
Data Mining ◽  
Academic Libraries ◽  
Text And Data Mining

scholarly journals New mandates? No problem for The Rockefeller University Press

2013 ◽  
Vol 210 (4) ◽  
pp. 643-645
Author(s):  
Mike Rossner
Keyword(s):  
Data Mining ◽  
Cell Biology ◽  
Third Parties ◽  
Public Access ◽  
Experimental Medicine ◽  
Access Policy ◽  
General Physiology ◽  
New Policies ◽  
Research Councils ◽  
Text And Data Mining

The existing public access policy for our three journals—The Journal of Cell Biology, The Journal of Experimental Medicine, and The Journal of General Physiology—is fully compliant with new policies from the Research Councils UK (RCUK) and the Wellcome Trust. In addition to mandating public access, the new policies specify licensing terms for reuse of content by third parties, in particular for text and data mining. We question the need for these specific terms, and we have added a statement to our licensing policy stipulating that anyone, including commercial entities, is permitted to mine our published text and data.

Computational Legal Methods: Text and Data Mining in Intellectual Property Research

2021 ◽  
pp. 487-505
Author(s):  
Thomas Margoni
Keyword(s):  
Data Mining ◽  
Intellectual Property ◽  
Law School ◽  
Human Observer ◽  
Systematic Classification ◽  
The Arts ◽  
High Quality Information ◽  
Recent Project ◽  
Analytical Tools ◽  
Text And Data Mining

Text and Data Mining (TDM) can generally be defined as the process of deriving high-quality information from text and data by using digital analytical tools . The impact that TDM may have on science, humanities, and the arts is invaluable. This is because by identifying the correlations and patterns that are often concealed to the eye of a human observer TDM allows for the discovery of knowledge that would have otherwise remained hidden. After a brief introduction, Section II of this chapter illustrates the state of the art in the nascent field of TDM applied to intellectual property (IP) research. It formulates some proposals of systematic classification in an area that suffers from a degree of terminological vagueness. In particular, the chapter argues that TDM, together with other types of data-driven analytical tools, should be autonomously classified as ‘computational legal methods’. Section III of the chapter offers concrete examples of the application of these methods in IP research. This is achieved by discussing a recent project on TDM, which required the development of dedicated approaches in order to address certain problems that emerged during the project’s execution.. The discussion identifies some of the most promising advances in terms of automation and predictive analysis that the use of TDM in intellectual property research could enable. At the same time, the partial success of the experiment shows that there are a number of training and skill-related issues that legal researchers and practitioners interested in the use of TDM should consider. Accordingly, the second argument advanced in this chapter is that law school programmes should include mandatory courses in computational legal methods in order to equip future lawyers with the skillsets needed in the digital (legal) environment.

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