AI and Music Digital Streaming Platforms: The Effectiveness of Implementing an Ethical Identifier to Highlight AI Compositions

This research paper investigates the possible efficacy of implementing an ethical badge to identify Artificial Intelligence compositions on digital music streaming platforms. The concept was explored by interviewing music industry experts and conducting two separate experiments involving the general public. AI’s relationship with the music industry is developing rapidly, and an ethical solution is needed to put musicians ahead of AI. Laws alone cannot provide this because they are ununified globally and slow to change to implement the necessary protections. Placing an emphasis on this area should be justified to protect the career path of musicianship in the future.

The objective of this thesis was to form an answer to the research question: Can integrating an ethical identifier that highlights AI compositions on digital music streaming platforms positively impact the music ecosystem? The different methodologies utilised in the research process aided in concluding an answer to this question. Interviewing experts actively working to traverse this problem offered interesting insights from researchers, musician copyright managers and artist perspectives. As examination in this area is still in the early development stages, it will provide a firm basis for others to advance this research.

The research also employed two listening experiments and a survey questionnaire to gather feedback on participant’s perceptions of AI-composed works compared to human works. The experiment aimed to highlight the diminishing likelihood of distinguishing AI and human works and demonstrate a form of skill and luck required when using AI-powered composition software. The songs in experiment A were created far more equally to ensure difficulty distinguishing between the two works. In contrast, the AI-generated piece in experiment B was noticeably sub-par due to changes in compositional technique. The study participants encompassed a broad demographic to highlight that this is a problem for everyone.

Overall, both the interviews and the experiments provided valuable insight to allow an answer to the research question to be formed. Most participants in experiment A incorrectly identified the AI composition as human because this experiment was more challenging, while correctly identifying it in experiment B. This highlights both the increasingly indistinguishable nature of AI and human compositions, while also demonstrating that tracks generated by AI can be sub-par and inconsistent in terms of quality. These compositions require an element of skill and luck in the creation process when using AI music generation software. Along with the interviews, the results gathered from the surveys provided enough detail on how an ethical identifier can exist on digital streaming platforms. While the majority of participants were in favour of placing a badge to identify AI works on streaming platforms, the interviews conducted presented a more realistic approach. They concluded it is too difficult to place an ethical identifier composition created with AI due to too many variables where AI can be involved in the creative process.

To conclude, this research paper was a positive step in implementing ethical practices regarding AI in the music industry. Although an ethical identifier precisely placed on AI compositions may not be feasible, similar solutions may be the way forward. Creating an ethical badge that sets standards that entities in the music industry must adhere to receive this badge is a possible route. Further research in this area will aid in finding a solution to this rapidly growing problem.