Ethics Statements in AI Music Papers: The Effective and the Ineffective

TL;DR

The paper identifies a gap between the rapid advancement of AI-driven music research and systematic ethical reflection within the field. Through a targeted review of ISMIR, NIME, and prominent 2020s papers, it documents how ethics statements are implemented—often minimally or superficially—and catalogs prevalent harms such as copyright infringement, labor displacement, and data biases. It contrasts the optional ethics page in ISMIR with NIME's mandatory statements, showing how venue policies shape discourse, and finds that even where ethics are acknowledged, deeper engagement is infrequent. The authors propose concrete, actionable changes—including mandatory ethics pages with exemplar content, pre-conference ethics prompts, and a living, decision-tree guide—to foster substantive ethical reflection and mitigate harms in AI music research, with the aim of improving responsible research practices across the field.

Abstract

While research in AI methods for music generation and analysis has grown in scope and impact, AI researchers' engagement with the ethical consequences of this work has not kept pace. To encourage such engagement, many publication venues have introduced optional or required ethics statements for AI research papers. Though some authors use these ethics statements to critically engage with the broader implications of their research, we find that the majority of ethics statements in the AI music literature do not appear to be effectively utilized for this purpose. In this work, we conduct a review of ethics statements across ISMIR, NIME, and selected prominent works in AI music from the past five years. We then offer suggestions for both audio conferences and researchers for engaging with ethics statements in ways that foster meaningful reflection rather than formulaic compliance.