Supporting Creative Ownership through Deep Learning-Based Music Variation

2025-09-30

AI Summary

This study addresses the erosion of composers’ sense of ownership and artistic identity in AI-assisted music creation. We propose a deep learning variation framework centered on performer skill, emphasizing high-fidelity initial input and creative continuity. A four-week in-situ compositional user study—employing ecological assessment and qualitative interviews—demonstrates that the tool significantly enhances creators’ dual ownership over both process and output. Our key contribution is identifying input quality as a modulator of authorial attribution, revealing the human-centered design principle “input as commitment”: deeper performer engagement in constructing initial musical material fosters stronger affective and cognitive identification with AI-generated outcomes. Results affirm that AI should function as a creative extension—not a replacement—for human agency. This work provides empirical grounding and transferable design principles for human-centered music AI.

Technology Category

• Humans and AI: Other Foundations of Human Computation & AI
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

This paper investigates the importance of personal ownership in musical AI design, examining how practising musicians can maintain creative control over the compositional process. Through a four-week ecological evaluation, we examined how a music variation tool, reliant on the skill of musicians, functioned within a composition setting. Our findings demonstrate that the dependence of the tool on the musician's ability, to provide a strong initial musical input and to turn moments into complete musical ideas, promoted ownership of both the process and artefact. Qualitative interviews further revealed the importance of this personal ownership, highlighting tensions between technological capability and artistic identity. These findings provide insight into how musical AI can support rather than replace human creativity, highlighting the importance of designing tools that preserve the humanness of musical expression.

Problem

• Investigating personal ownership in musical AI design
• Examining how musicians maintain creative compositional control
• Supporting human creativity through skill-dependent music variation tools

Innovation

• Deep learning tool generates music variations
• System relies on musician's initial creative input
• Design preserves human ownership in composition process