Copyright and the Use of AI in Music
Written by GPT-4 using the ChatGPT GUI and edited by Dr. Ifan Payne. For information about AI and the opera, click here to read Bernadette Snider’s blog article, “A New Odyssey.”
This is the second of two articles on Artificial Intelligence in music which were written by Artificial Intelligence software GPT-4 , a Large Learning Model (LLM) using the ChatGPT bot and edited by me. Can you tell what was written by the AI bot and what was written by me?
The accompanying illustrations were selected from images that I generated from the AI programs, DreamStudio, Midjourney and Dall-E.
The use of Artificial Intelligence (AI) in music raises several copyright implications that need careful consideration. Listed below are some of the key points at issue regarding copyright and the use of AI.
1. Ownership of AI-generated music: When AI systems generate music, the question arises as to who owns the copyright to that music. In traditional music creation, the copyright typically belongs to the human composer or songwriter. However, with AI-generated compositions, the lines become blurred. Some argue that the copyright should belong to the person who trained or programmed the AI system, while others argue that it should belong to the AI system itself. This issue is still being debated and may require legal clarification in different jurisdictions.
2. Originality and infringement: Copyright law protects original works of authorship. In the context of AI-generated music, questions may arise regarding the originality of the compositions. If an AI system is trained on existing copyrighted music and produces works that are similar or derivative, it could potentially infringe upon the copyright of the original compositions. It is crucial for creators and developers to ensure that their AI systems do not produce infringing works.
3. Sampling and fair use: AI algorithms can be trained on vast music databases, including copyrighted material. The use of copyrighted samples in AI-generated music could raise issues of fair use. Fair use provisions vary across jurisdictions, but they generally allow for limited use of copyrighted material for purposes such as criticism, commentary, or parody. It is important for AI music creators to understand the applicable fair use provisions and ensure that their usage falls within the legal boundaries.
4. Attribution and moral rights: Copyright law often includes provisions for attribution and moral rights. In the context of AI-generated music, it is essential to attribute the contributions of both the human creators and the AI system itself. Additionally, if an AI system is used to modify or remix existing music, the moral rights of the original creators must be respected. Ensuring proper attribution and respecting moral rights helps maintain ethical standards and promotes a fair and transparent music ecosystem.
5. Licensing and royalties: When AI-generated music is commercially released, the issue of licensing and royalty payments becomes significant. If AI systems are trained on copyrighted music and produce commercially viable works, proper licenses and royalty payments may be required to compensate the original copyright holders. This is particularly relevant in cases where AI-generated music samples or incorporates copyrighted material.
It is important to note that copyright laws can vary across jurisdictions, and the application of these laws to AI-generated music is still evolving. As the technology progresses, it is crucial for lawmakers, industry professionals, and creators to work together to address these copyright implications and ensure a fair and balanced approach to AI's role in music creation and distribution.
CONCLUSION
By the time that you will have read these articles, whether it is a week or only 10 minutes after publication, such is the speed of the exponential growth of AI that it will already have spawned more code, more programs and burrowed deeper into the foundation of our lives. The ubiquitous ChatGPT has already been overtaken by more powerful bots accessing Large Learning Models which are themselves being overtaken by foundation models which use self-supervised and transfer learning.
This OperaBlog was contributed by Ifan Payne, Member At-Large, Santa Fe Opera Guild Board of Directors, June 1, 2023.