Creative Machines—Machine Learning Models, Copyright, and Computational Creativity

The use of machine learning models—also known as (a sub-area of) artificial intelligence—is not restricted to mere probability prediction and data analysis in statistical applications. Machine learning models are also used in generative—sometimes even creative contexts: Be it to produce short news articles, translate text or to come up with music specifically tailored to a certain scene in a movie. Or—as in the case of the infamous Edward de Belamy or the Next Rembrandt project, to “create” “works of art”. As technology advances, questions arise as to whether the produced “art works” could be copyright protected and, if so, who would be considered the author. The paper assumes that the creative process is heavily dependent upon (human) autonomous decision-making and that the degree to which decisions are “delegated” to a computer program heavily influences copyright protection of the output. It is also considered that, while machine learning models seemingly automate artwork generation, possibly rendering attribution to a human author difficult or unnecessary, machine learning models are the result of intellectual efforts and should rather be considered potent tools (which might in fact not hinder copyright protection) than autonomous machine artists.