“Who is the author when the machine creates?”

And how does modern copyright law apply? Those are the questions at the center of J.-M. Deltorn, “Deep creations: Intellectual property and the automata,” Front. Digit. Humanit, vol. 4, no. 3, 2017. These are interesting questions that apply not only to deep learning, but the numerous other applications of machinery to human creation.

Deltorn identifies three “hurdles” bearing on the difficulty of answering these questions:

  1. clarity of the “causal chain”: it’s not very clear how a real-world creation comes to be with the use of such machines.
  2. “multiplicity of interactions”: it’s not easy to enumerate what contributes what to that creative machine.
  3. “new forms of interaction”: the model is operating together with an artist.

After a nice review of machine generation in a variety of arts, and a good review of the Berne Convention for the Protection of Literary and Artistic Works, Deltorn centers his discussion of the two questions above around two tasks: “style transfer” and “training data selection”.

“Style transfer” refers to separating “an artwork’s style from its subject-matter, and to subsequently transpose this style onto another object.” Here are many examples. Does an artist’s selection of source material constitute enough originality that the resulting work would be protected? Deltorn argues that simply transferring the “style” of one image to an image of a person’s choice is not likely to exceed the minimum level of originality that would protect the new work. However, as the number of choices made by the artist increases, e.g., taking and mixing styles of several sources, originality increases.

“Training data selection” refers to the role of the artist as “curator” of the material on which a model is trained. By the nature of training, the possibility exists that a computer-generated output will produce training material verbatim, and so the artist must remain vigilant against such overfitting. When overfitting is avoided, does the artist’s curation of training data constitute enough originality that the resulting work would be protected? Deltorn argues that such a role seems to be so small a contribution rewarding ownership to the endless amount of output the machine can produce that it would harm the function of copyright: to promote creativity: “Care should be taken, therefore, to limit copyright attribution to the creations that are indeed the locus of a ‘creative spark,’ a human one, that is, and not just the electric glint of a computational engine.”

Though the article focuses on work using a specific approach to statistical machine learning, it also nicely situates itself in the wider and richer history of machine generated art. Considering Deltorn comes from the domain of intellectual property and not engineering, he does very well in accurately relating principles and methods of machine learning (although at times I feel he oversells the power of these methods when it comes to creativity). The references assemble a variety of sources relevant to these issues as well. Here are some I want to explore:

  1. Bridy, A. (2012). Coding creativity: copyright and the artificially intelligent author. Stanford Technology Law Review 5:1–28.
  2. Jacobson, W.P. (2011). Robot’s record: protecting the value of intellectual property in music when automation drives the marginal costs of music production to zero. Loyola of Los Angeles Entertainment Law Review 32: 31–46.
  3. O’Hear, A. (1995). Art and technology: an old tension. Royal Institute of Philosophy Supplements 38: 143–58. doi:10.1017/S1358246100007335
  4. Zeilinger, M. (2016). Digital art as ‘Monetised Graphics’: enforcing intellectual property on the blockchain. Philosophy & Technology 1–27. doi:10.1007/ s13347-016-0243-1
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