A deeper look into the EU text and data mining exceptions: harmonisation, data ownership, and the future of technology

Margoni, T. and Kretschmer, M. (2022) A deeper look into the EU text and data mining exceptions: harmonisation, data ownership, and the future of technology. GRUR International, 71(8), pp. 685-701. (doi: 10.1093/grurint/ikac054)

Abstract

This paper focuses on the two exceptions for text and data mining (TDM) introduced in the Directive on Copyright in the Digital Single Market (CDSM). While both are mandatory for Member States, Art. 3 is also imperative and finds application in cases of text and data mining for the purpose of scientific research by research and cultural institutions; Art. 4, on the other hand, permits text and data mining by anyone but with rightholders able to ‘contract-out’ (Art. 4). We trace the context of using the lever of copyright law to enable emerging technologies such as AI and the support innovation. Within the EU copyright intervention, elements that may underpin a transparent legal framework for AI are identified, such as the possibility of retention of permanent copies for further verification. On the other hand, we identify several pitfalls, including an excessively broad definition of TDM which makes the entire field of data-driven AI development dependent on an exception. We analyse the implications of limiting the scope of the exceptions to the right of reproduction; we argue that the limitation of Art. 3 to certain beneficiaries remains problematic; and that the requirement of lawful access is difficult to operationalize. In conclusion, we argue that there should be no need for a TDM exception for the act of extracting informational value from protected works. The EU’s CDSM provisions paradoxically may favour the development of biased AI systems due to price and accessibility conditions for training data that offer the wrong incentives. To avoid licensing, it may be economically attractive for EU-based developers to train their algorithms on older, less accurate, biased data, or import AI models already trained abroad on unverifiable data.

• Item Type: Articles
• Additional Information: This research has been supported by the European Union’s ‘Horizon 2020’ research and innovation programme under grant agreement No. 870626870626 (‘reCreating Europe: Rethinking digital copyright law for a culturally diverse, accessible, creative Europe’).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Margoni, Dr Thomas and Kretschmer, Professor Martin
• Authors: Margoni, T., and Kretschmer, M.
• College/School: College of Social Sciences > School of Law
• Journal Name: GRUR International
• Publisher: Oxford University Press
• ISSN: 2632-8623
• ISSN (Online): 2632-8550
• Published Online: 26 July 2022
• Copyright Holders: Copyright © 2022 GRUR e.V.
• First Published: First published in GRUR International 71(8): 685-701
• Publisher Policy: Reproduced under a Creative Commons License