Thirty-Six Views of Copyright Authorship, By Jackson Pollock

Humans have long used a variety of tools to convey artistic expression. Perhaps the most recent and mysterious artistic tools are machine learning or “artificially intelligent” (AI) computer systems that have captured popular attention. When taken in isolation, these devices seem to operate autonomously, giving the illusion that there is no author behind their output. In fact, there is a rich web of human effort and support behind any AI undertaking. When we pull aside the AI curtain, it becomes apparent that the attribution of authorship for AI-enabled creations is largely an exercise in tracing legal causation. Indeed, the concept of original expression, which is required for copyright authorship, implies a causal chain tracing the origin of fixed expression. In this presentation I will argue that concepts of causation, volition, and intention that are familiar from other areas of law also inform copyright authorship, and that the machine learning revolution affords us the opportunity to reveal previously hidden assumptions about copyright authorship. While I will begin by illustrating these concepts with examples from the graphic arts, the same principles are readily applied to other authorial works in other media