The Ethics of AI Training in Music: How Are Machines Trained to Create Songs?

From composing soundscapes to generating entire songs, AI transforms the way we think about music creation, and is becoming an integral part of the music landscape. However, as exciting as these developments are, they raise important ethical concerns, particularly as it pertains to learning. How exactly are these AI models trained to create music, and are these processes fair to the original artists? In today's short read, we'll explore the AI training practices of generative music models, highlighting the different processes involved while evaluating each from an ethical perspective.

How AI Training Works Within Music

AI models rely on large datasets to learn how to compose music effectively. These datasets include a range of elements:

1. Audio Files – Full songs or samples provide AI with a blueprint of how music sounds and is structured.

2. Metadata – Information like tempo, genre, and artist credits helps the AI learn stylistic patterns across different types of music.

3. Lyrics – Analyzing lyrics allows AI models to generate text with meaningful emotions, themes, and rhymes.

These datasets are processed through a variety of learning methods:

- Supervised Learning: AI is trained using labeled datasets (e.g., songs tagged by genre) to identify patterns and predict outcomes.

- Unsupervised Learning: AI sifts through raw data to discover hidden patterns on its own, such as trends across different musical styles.

- Reinforcement Learning: The model receives rewards for meeting specific creative goals, such as creating a song with a desired mood or tempo.

Ethical Concerns Around AI Training - Our Webinar with Byta

As the use of AI in music becomes mainstream, the risks of inadequate or non-transparent training practices are growing. Currently, there is a significant debate around the source dataset used by AI models when training, and whether this contains copyrighted material. A seismic recent lawsuit against generative AI music platforms Suno & Udio, filed by the RIAA and its major label members, highlighted this very issue, and its severity for intellectual property and the unfair compensation of creators.

To explore these critical issues, we're hosting a collaborative workshop with Byta next Wednesday, October 30, titled: "Harmonizing Data: Advanced AI Training Practices for the Music Industry."

In this webinar, we’ll delve into AI’s impact on the music sector, from its experimental beginnings to today’s wide adoption. We’ll address challenges such as transparency, copyright risks, and how emerging solutions can ensure AI enhances creativity without compromising the rights of artists. Whether you’re an artist, producer, or industry professional, this workshop will help you navigate the evolving relationship between AI and music creation.

You can register for the workshop here.

Conclusion

While AI is undoubtedly revolutionizing the music industry, it also brings complex ethical challenges—particularly around the way these systems are trained. Ensuring transparency and control moving forward is key and, as the lines between human and machine creativity blur, it’s essential to develop robust and transparent training practices.

Join us at our October 30th webinar to learn how we can harmonize the data behind AI and ensure it serves both artists and the future of music.