A New Text and Data Mining Exception Restrictive vs. Extensive Interpretations of Copyright Limitation

Muto, Gaia Sofia, A New Text and Data Mining Exception Restrictive vs. Extensive Interpretations of Copyright Limitation (September 13, 2017). MIPLC Master Thesis Series (2016/17). Available at SSRN: https://ssrn.com/abstract=3203296

In this data-driven world, where our perception of reality is indeed digital-mediated, Text and Data Mining (TDM) represents a very helpful tool. This umbrella term describes a wide range of researching techniques able to select and assess enormous quantities of sources, extracting from them new and valuable information. TDM applications are on the rise, and are spreading in many fields, e.g. biomedical research, social media, corporate governance, governmental organizations.

In many cases, TDM presupposes processing, copying and extracting of information that needs to be assessed. Those acts, according to current copyright law, fall within the author’s exclusive rights and therefore require prior authorization. On the contrary, unless an exception applies, unauthorized acts may amount to copyright infringements. As known, in Europe there is a closed list of exceptions, which, in the majority of cases, is optionally implemented.

Whether TDM may be adjusted by one of these exceptions has been discussed over the last decade and has recently led to a new Proposal from the European Commission, on a new digital single market, which among other exceptions, introduces a mandatory and specific exception for TDM.

This dissertation aims to explore copyright issues related to TDM, showing if and how TDM can fit within the current legal framework of rights and limitations. A three-step-test analysis will be provided, justifying the introduction of a more flexible, technology-neutral exception for TDM, in light of the public interest to free access to research information. Lastly the new legislative proposal will be assessed.

Lire la suite :  https://ssrn.com/abstract=3203296


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