The music sector is regularly confronted with disruptive technological innovations that impact the industry, the public, and the artists. Certain historical changes perceived today as progress could, at the time, destabilise the performers’ jobs, weaken their income, and profoundly modify their way of working. This was the case with radio, the LP, the minicassette, the CD, and computer music, more recently with downloading and streaming, and now, artificial intelligence.
The evolution of the normative framework can help balance these effects for the different parties concerned. For music performers, the Rome Convention and the WPPT have provided welcome solutions regarding broadcasting and communication to the public. Unfortunately, these instruments have failed to regulate download and streaming effectively, with Article 10 of the WPPT, as currently implemented, not allowing artists to benefit from a fair share of the revenue generated by the online exploitation of their recordings.
Recent advances in generative artificial intelligence and the techniques implemented for machine learning suggest an analogy with human learning mechanisms. However, this analogy quickly reaches its limits. Indeed, the volume of data ingested and the speed at which the machine collects and assimilates these data are incommensurate with what the human mind is capable of. Machine learning consists of appropriating all the creations of the human mind currently accessible and encoding them into algorithms to generate new content based on the knowledge acquired. This new paradigm radically differs from the slow and gradual knowledge acquisition process at work in humans.
The AI services recently made available to the public translate into a competitive and fast-growing market with strategic implications and considerable profit prospects. However, this new ecosystem is not regulated by any adequate normative framework protecting the community of creators whose work and talent are exploited in proportions beyond comprehension.
AN INADEQUATE COPYRIGHT FRAMEWORK
The existing copyright and neighbouring rights normative frameworks were not designed to address the particular problems posed today by generative AI, whether for incoming or outgoing data. One should, therefore, not assume that the transfer to a producer of a performer’s exclusive rights covers the right to authorise or prohibit the use by AI of that performer’s recorded performances, irrespective of whether such use includes an act of reproduction.
The performers’ moral right introduced by the WPPT in 1996 does not help. It is limited to “the right to claim to be identified as the performer of his performances, except where omission is dictated by the manner of the use of the performance and to object to any distortion, mutilation or other modification of his performances that would be prejudicial to his reputation”. In the AI environment, performers need and deserve a more robust moral right, broad enough to allow them to individually oppose the use of their works, sounds, voices, images, likenesses or styles for either TDM Purposes or the generation of audio products by AI (or with its assistance), including deep fakes.
The copyrightability of content produced by AI (or with its assistance) is a new and complex question that gives rise to discordant decisions depending on the country. At this stage, deciding firmly between the copyrighting of AI-generated content (or with its assistance) and its classification as public domain remains challenging.
Read more: https://www.fim-musicians.org/fim-statement-on-ai-in-music/?utm_source=FIM+News+%28EN%29&utm_campaign=8e65b4dc8e-RSS_EMAIL_CAMPAIGN&utm_medium=email&utm_term=0_c7643b1e81-8e65b4dc8e-1411820136