Copyright, compensation, and commons in the music AI industry

Abstract

Since 2015 a number of startups has emerged seeking to commercialise music AI. Two types of firm stand out. One markets services directly to consumers, in the form of adaptive music that responds to contextual and/or activity-related cues; another group markets AI-generated music to cultural producers, in the form of algorithmically-generated, royalty-free production music. Initiatives like these have generated debate among legal scholars about notions of copyright and authorship. But until recently discussion has focused on who (or what) should be awarded rights over the products of so-called ‘expressive AI’: Its programmers? Its users? Or the AI itself? Largely overlooked in such debates is the status of another repertoire: not the music put out by an AI, but that which is put into it, the music that constitutes the training set necessary for machine learners to learn. Given the massive datasets mobilised to train machine learners, existing copyright regimes prove inadequate in the face of the questions of distributive justice that such commercial systems raise. Specifically, commercial practices premised on the extraction of value from a special kind of common-pool resource – the shared knowledge of a given music community – demand remedies grounded not in the methodological individualism of copyright law, but commons-based responses instead. As such, the article sketches a couple of alternative models (levy-based trust funds, ownership funds) that could provide a more equ…