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

arXiv: https://arxiv.org/abs/2507.23365 · HTML: https://mlsanity.com/paper/2507.23365/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 61 references that are in the map.
- First posted on arXiv 31 Jul 2025.

## Builds on (most cited first, 15 of 15)

- [MindfulDiary: Harnessing Large Language Model to Support Psychiatric Patients' Journaling](https://mlsanity.com/paper/2310.05231/) (2023)
- [How to Prompt? Opportunities and Challenges of Zero- and Few-Shot Learning for Human-AI Interaction in Creative Applications of Generative Models](https://mlsanity.com/paper/2209.01390/) (2022)
- [Prompting AI Art: An Investigation into the Creative Skill of Prompt Engineering](https://mlsanity.com/paper/2303.13534/) (2023)
- [PromptCARE: Prompt Copyright Protection by Watermark Injection and Verification](https://mlsanity.com/paper/2308.02816/) (2023)
- [Deep Learning and Music Adversaries](https://mlsanity.com/paper/1507.04761/) (2015)
- [Supporting Self-Reflection at Scale with Large Language Models: Insights from Randomized Field Experiments in Classrooms](https://mlsanity.com/paper/2406.07571/) (2024)
- [Autonomy, Authenticity, Authorship and Intention in computer generated art](https://mlsanity.com/paper/1903.02166/) (2019)
- [Reliable Local Explanations for Machine Listening](https://mlsanity.com/paper/2005.07788/) (2020)
- [Robots in the Middle: Evaluating LLMs in Dispute Resolution](https://mlsanity.com/paper/2410.07053/) (2024)
- [The GTZAN dataset: Its contents, its faults, their effects on evaluation, and its future use](https://mlsanity.com/paper/1306.1461/) (2013)
- [GAN-based Generation and Automatic Selection of Explanations for Neural Networks](https://mlsanity.com/paper/1904.09533/) (2019)
- [ExploreSelf: Fostering User-driven Exploration and Reflection on Personal Challenges with Adaptive Guidance by Large Language Models](https://mlsanity.com/paper/2409.09662/) (2024)
- [Score-informed Networks for Music Performance Assessment](https://mlsanity.com/paper/2008.00203/) (2020)
- [The "Horse'' Inside: Seeking Causes Behind the Behaviours of Music Content Analysis Systems](https://mlsanity.com/paper/1606.03044/) (2016)
- [Rethinking ChatGPT's Success: Usability and Cognitive Behaviors Enabled by Auto-regressive LLMs' Prompting](https://mlsanity.com/paper/2405.10474/) (2024)

## Abstract (arXiv)

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.