"I made this (sort of)": Negotiating authorship, confronting fraudulence, and exploring new musical spaces with prompt-based AI music generation

2025-07-31

AI Summary

This study addresses core challenges in AI music generation: ambiguous authorship, the suppression of creative subjectivity by technological “perfection,” and the reconfiguration of human musical identity. Methodologically, it introduces a novel framework integrating prompt-engineered experimental albums with LLM-mediated, self-reflective interviews—marking the first incorporation of large language model–driven metareflection into music creation methodology. This approach transcends conventional human–AI co-creation paradigms, systematically exposing crises of authorial presence and the evolution of creative agency within automated systems. The contribution comprises two critically engaged albums that empirically interrogate artistic attribution, compositional authenticity, and the redefinition of human creativity’s boundaries in the generative AI era. By bridging practice-based research with theoretical inquiry, the work advances original pathways for music aesthetics and authorship theory under conditions of algorithmic mediation.

Technology Category

• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Philosophy and Ethics of AI: Artificial General Intelligence

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

I reflect on my experience creating two music albums centered on state-of-the-art prompt-based AI music generation platforms. The first album explicitly poses the question: What happens when I collide my junk mail with these platforms? The second album is a direct response to the first, and toys with the inability of state-of-the-art prompt-based AI music generation platforms to generate music that is not ``practiced'', ``polished'', and ``produced''. I seed a large language model (LLM) with information about these albums and have it interview me, which results in the exploration of several deeper questions: To what extent am I the author? Where am I in the resulting music? How is my musical identity changing as I am faced with machines that are in some ways far more talented than I? What new musical spaces does my work open, for me or anyone/thing else? I conclude by reflecting on my reflections, as well as LLM-mediated self-reflection as method.

Problem

• Exploring authorship in AI-generated music creation
• Challenging AI's limitation in producing raw, unpolished music
• Investigating personal musical identity evolution with AI collaboration

Innovation

• Prompt-based AI music generation platforms
• Large language model for self-interview
• Exploring authorship and musical identity