Opening Musical Creativity? Embedded Ideologies in Generative-AI Music Systems

2025-08-12

AI Summary

This paper critically examines the ideological underpinnings embedded in generative AI music systems (e.g., AIVA, Stable Audio, Suno, Udio) and their role in constructing—and obscuring—the “democratization” narrative of music creation. Employing a combined methodological approach of autoethnography and digital ethnography, the study conducts critical discourse analysis of developer promotional rhetoric and user practice feedback. It reveals that these systems deploy inclusivity as a rhetorical device while advancing an overarching ideology rooted in individualism, globalism, and techno-libertarianism—standardizing musical practice to align with generative logics, attenuating authorial agency and ethical accountability, and concealing risks of power centralization and cultural homogenization. The paper offers the first systematic exposition of the structural tension between the myth of technological neutrality and the democratic promise in AI music tools, providing a crucial theoretical lens for algorithmic culture studies and AI ethics.

Technology Category

• Philosophy and Ethics of AI: Artificial General Intelligence
• Machine Learning: Ethics, Bias, and Fairness
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

AI systems for music generation are increasingly common and easy to use, granting people without any musical background the ability to create music. Because of this, generative-AI has been marketed and celebrated as a means of democratizing music making. However, inclusivity often functions as marketable rhetoric rather than a genuine guiding principle in these industry settings. In this paper, we look at four generative-AI music making systems available to the public as of mid-2025 (AIVA, Stable Audio, Suno, and Udio) and track how they are rhetoricized by their developers, and received by users. Our aim is to investigate ideologies that are driving the early-stage development and adoption of generative-AI in music making, with a particular focus on democratization. A combination of autoethnography and digital ethnography is used to examine patterns and incongruities in rhetoric when positioned against product functionality. The results are then collated to develop a nuanced, contextual discussion. The shared ideology we map between producers and consumers is individualist, globalist, techno-liberal, and ethically evasive. It is a 'total ideology' which obfuscates individual responsibility, and through which the nature of music and musical practice is transfigured to suit generative outcomes.

Problem

• Examining ideologies in generative-AI music systems
• Assessing democratization rhetoric versus actual inclusivity
• Analyzing producer-consumer shared techno-liberal ideology

Innovation

• Combines autoethnography and digital ethnography methods
• Analyzes rhetoric versus functionality in AI music systems
• Maps shared producer-consumer ideology in generative AI