D3.7 Final report on the role of EU copyright law in relation to training models for machine learning purposes.

There is global attention on new data analytic methods. Artificial Intelligence (AI) is seen as a critical
technology, often relying on machine learning (where an algorithm is trained on data to recognise and predict
patterns). Data scraping, the acquiring and structuring of information from online sources, is a typical first
step for machine learning.

The technologies of scraping, mining and learning are often conflated, as are the legal regimes under which
they are regulated. One regulatory lever under one legal regime will not deliver policy aims, such as
innovation, personal dignity, Open Science, or the currently popular ‘data sovereignty’. The legal issues
involved in the governance of data range from proprietary approaches (copyright, database rights) to privacy
and data protection.

In addition, there is a wide range of public law instruments, for example relating to public sector data
governance1, access to and use of user-facilitated data2 or the right to non-discrimination.3 Competition law
again (which may be both privately and publicly enforceable) increasingly prescribes conduct in relation to
data, such as in merger or acquisition cases, or in transparency provisions (Art. 17 CDSM; and centrally in the
proposed DMA and AI Regulation).

The scope of our enquiry in this report is within private law, specifically on the attempt to assert quasi-proprietary
control of information and data, or vice versa limit such attempts, for example by exempting
desired activities via copyright exceptions, such as the exception for text and data mining in Arts. 3 and 4
CDSM.

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