Authorship in the Age of Machine Learning and Artificial Intelligence

Jean-Marc Deltorn and Franck Macrez have posted Authorship in the Age of Machine Learning and Artificial Intelligence, to be published in Sean M. O’Connor (ed.), The Oxford Handbook of Music Law and Policy, Oxford University Press, 2019 (Forthcoming):

New generations of algorithmic tools have recently become available to artists. Based on the latest development in the field of machine learning – the theoretical framework driving the current surge in artificial intelligence applications -, and relying on access to unprecedented amounts of both computational power and data, these technological intermediaries are opening the way to unexpected forms of creation. Instead of depending on a set of man-made rules to produce novel artworks, generative processes can be automatically learnt from a corpus of training examples. Musical features can be extracted and encoded in a statistical model with no or minimal human input and be later used to produce original compositions, from baroque polyphony to jazz improvisations. The advent of such creative tools, and the corollary vanishing presence of the human in the creative pipeline, raises a number of fundamental questions in terms of copyright protection. Assuming AI generated compositions are protected by copyright, who is the author when the machine contributes to the creative process? And, what are the minimal requirements to be rewarded with authorship?

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