Hylly
Henkilönnimi Margoni, Thomas, kirjoittaja.
Nimeke- ja vastuullisuusmerkintö A Deeper Look into the EU Text and Data Mining Exceptions : Harmonisation, Data Ownership, and the Future of Technology
Julkaistu Verlag C.H. Beck, Oxford : 2022.
Ulkoasutiedot
Sarjamerkintö ei-lisäkirjausmuodossa GRUR International, ISSN 2632-8623 ; 71(8)
Huomautus sisällöstä, tiivistelmä tms. 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.
Asiasana
Sarjalisäkirjaus - yhtenäistetty nimeke GRUR International, 2632-8623; 71(8)
Elektronisen aineiston sijainti ja käyttö (URI) https://doi.org/10.1093/grurint/ikac054 Linkki verkkoaineistoon
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*1001 $aMargoni, Thomas,$ekirjoittaja.
*24512$aA Deeper Look into the EU Text and Data Mining Exceptions :$bHarmonisation, Data Ownership, and the Future of Technology /$cThomas Margoni, Martin Kretschmer.
*264 1$aOxford :$bVerlag C.H. Beck,$c2022.
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*520 $aThis paper focuses on the two exceptions for text and data mining (TDM) introduced in the Directive on Copyright in the Digital Single Market (CDSM). While both are mandatory for Member States, Art. 3 is also imperative and finds application in cases of text and data mining for the purpose of scientific research by research and cultural institutions; Art. 4, on the other hand, permits text and data mining by anyone but with rightholders able to ‘contract-out’ (Art. 4). We trace the context of using the lever of copyright law to enable emerging technologies such as AI and the support innovation. Within the EU copyright intervention, elements that may underpin a transparent legal framework for AI are identified, such as the possibility of retention of permanent copies for further verification. On the other hand, we identify several pitfalls, including an excessively broad definition of TDM which makes the entire field of data-driven AI development dependent on an exception. We analyse the implications of limiting the scope of the exceptions to the right of reproduction; we argue that the limitation of Art. 3 to certain beneficiaries remains problematic; and that the requirement of lawful access is difficult to operationalize. In conclusion, we argue that there should be no need for a TDM exception for the act of extracting informational value from protected works. The EU’s CDSM provisions paradoxically may favour the development of biased AI systems due to price and accessibility conditions for training data that offer the wrong incentives. To avoid licensing, it may be economically attractive for EU-based developers to train their algorithms on older, less accurate, biased data, or import AI models already trained abroad on unverifiable data.
*650 7$atekijänoikeuslaki$2yso/fin$0http://www.yso.fi/onto/yso/p9817
*650 7$aEU-direktiivit$0http://www.yso.fi/onto/yso/p10768$2yso/fin
*650 7$aEU-oikeus $0http://www.yso.fi/onto/yso/p20733$2yso/fin
*650 7$atiedonlouhinta$0http://www.yso.fi/onto/yso/p5520$2yso/fin
*650 7$adata$0http://www.yso.fi/onto/yso/p27250$2yso/fin
*650 7$atekoäly$2yso/fin$0http://www.yso.fi/onto/yso/p2616
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*85640$uhttps://doi.org/10.1093/grurint/ikac054$yLinkki verkkoaineistoon
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