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

Jacopo de Berardinis, Lorenzo Porcaro, Albert Meroño-Peñuela, Angelo Cangelosi, Tess Buckley

TL;DR

The paper addresses the ethical, legal, and societal challenges of generative AI in music and proposes a trustworthy-by-design framework grounded in the EU Ethics Guidelines for Trustworthy AI. It operationalizes the seven macro-requirements into 45 concrete features tailored to generative music, and outlines a practical roadmap for evaluating, implementing, and documenting these features across interdisciplinary teams. By integrating policy perspectives, data provenance, copyright considerations, transparency, and stakeholder participation, the work offers a concrete pathway for responsible innovation in music AI and provides a companion website to support adoption and evolution. The significance lies in offering an actionable, collaborative approach that balances artistic creativity with artist rights, accountability, and societal well-being, fostering trust and sustainable progress in AI-driven music creation.

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/.

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

TL;DR

The paper addresses the ethical, legal, and societal challenges of generative AI in music and proposes a trustworthy-by-design framework grounded in the EU Ethics Guidelines for Trustworthy AI. It operationalizes the seven macro-requirements into 45 concrete features tailored to generative music, and outlines a practical roadmap for evaluating, implementing, and documenting these features across interdisciplinary teams. By integrating policy perspectives, data provenance, copyright considerations, transparency, and stakeholder participation, the work offers a concrete pathway for responsible innovation in music AI and provides a companion website to support adoption and evolution. The significance lies in offering an actionable, collaborative approach that balances artistic creativity with artist rights, accountability, and societal well-being, fostering trust and sustainable progress in AI-driven music creation.

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/.

Paper Structure

This paper contains 51 sections, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Responsible AI initiatives and projects
  3. Our contribution
  4. Background
  5. Related Trustworthy AI frameworks
  6. Music models and systems
  7. Trustworthiness in Generative Music AI
  8. 1) Human Agency and Oversight
  9. Human agency
  10. Human oversight
  11. 2) Robustness and Safety
  12. Accuracy
  13. Reliability and reproducibility
  14. Resilience to attack and security
  15. Fallback plan and general safety
  16. ...and 36 more sections

Figures (2)

  • Figure 1: Overview of the Trustworthy AI macro-requirements (orange) along with their contextualisation in Generative Music AI (blue). Each features is described in Section \ref{['sec:framework']}2 with and a summary is provided in Table \ref{['tab:features-all-c']} (Appendix).
  • Figure 2: Screenshot of the companion website showcasing the 45 responsible features for generative AI music. The website presents each feature in an individual card format, simplifying navigation and promoting wider understanding of the framework.