Authenticity, Ethics, and Public Acceptance in AI-Generated Music: An Integrative Review

Abstract

Background: The rapid advancement of artificial intelligence (AI) in music creation has intensified debates regarding artistic authenticity, ethical risks, and the legitimacy of algorithmically generated works within the global creative ecosystem.

Objective: This study aims to map how current literature conceptualizes authenticity in AI-generated music, identify key ethical risks, and examine the interrelationship between authenticity perceptions, ethical legitimacy, and public acceptance.

Methods: An integrative literature review was conducted following Snyder guidelines, analyzing publications from 2020–2025 sourced from Scopus, Google Scholar, and ScienceDirect. A total of 20 primary sources were selected based on relevance to three predetermined themes: authenticity, ethical risks, and public acceptance. Thematic analysis was applied to synthesize arguments across these themes.

Results: Findings reveal that authenticity concerns center on emotional depth, creative originality, and listener perception bias against AI-labeled works. The main ethical risks include copyright infringement through unauthorized dataset use, ambiguous authorship attribution, unfair compensation structures, disruption of creative labor markets, cultural bias in training datasets, and insufficient transparency in generative processes. These issues directly affect how audiences and industry stakeholders evaluate the legitimacy and acceptance of AI-generated music.

Conclusion: Public acceptance of AI-generated music depends on addressing authenticity challenges and ensuring ethical, accountable practices. This study contributes a conceptual synthesis linking perceived authenticity, ethical legitimacy, and public acceptance, emphasizing the need for regulatory frameworks that enable AI to complement rather than displace human creativity.