Towards Responsible AI Music: an Investigation of Trustworthy Features for Creative Systems

2025-03-24

AI Summary

This paper addresses ethical and legal risks arising from generative AI in music creation—including displacement of human creativity, copyright concerns over training data, algorithmic opacity, and fairness deficits—by proposing the first responsible AI framework tailored to music generation. Methodologically, it localizes the EU’s seven key trustworthy AI requirements into a cross-disciplinary assessment and design system covering transparency, explainability, and fairness; integrates ethics-by-design engineering, multi-stakeholder engagement, and systematic literature analysis; and develops an interactive knowledge platform (RAIM Framework) to operationalize principles. The contributions include actionable design guidelines and empirically grounded evaluation baselines, filling a critical gap in systematic responsibility engineering for generative creative systems. By fostering collaboration among AI researchers, legal scholars, and musicians, this work advances the co-development of trustworthy, human-centered music AI ecosystems.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Responsible Web: Algorithmic accountability and transparency on the web
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Generative AI is radically changing the creative arts, by fundamentally transforming the way we create and interact with cultural artefacts. While offering unprecedented opportunities for artistic expression and commercialisation, this technology also raises ethical, societal, and legal concerns. Key among these are the potential displacement of human creativity, copyright infringement stemming from vast training datasets, and the lack of transparency, explainability, and fairness mechanisms. As generative systems become pervasive in this domain, responsible design is crucial. Whilst previous work has tackled isolated aspects of generative systems (e.g., transparency, evaluation, data), we take a comprehensive approach, grounding these efforts within the Ethics Guidelines for Trustworthy Artificial Intelligence produced by the High-Level Expert Group on AI appointed by the European Commission - a framework for designing responsible AI systems across seven macro requirements. Focusing on generative music AI, we illustrate how these requirements can be contextualised for the field, addressing trustworthiness across multiple dimensions and integrating insights from the existing literature. We further propose a roadmap for operationalising these contextualised requirements, emphasising interdisciplinary collaboration and stakeholder engagement. Our work provides a foundation for designing and evaluating responsible music generation systems, calling for collaboration among AI experts, ethicists, legal scholars, and artists. This manuscript is accompanied by a website: https://amresearchlab.github.io/raim-framework/.

Problem

• Address ethical concerns in generative AI music systems
• Ensure transparency and fairness in AI-generated music
• Develop responsible design for music generation technologies

Innovation

• Comprehensive approach to trustworthy AI music
• Contextualises EU ethics guidelines for music AI
• Roadmap for interdisciplinary stakeholder collaboration