The Ethical Implications of Generative Audio Models: A Systematic Literature Review

TL;DR

This systematic review of 884 papers reveals that while generative audio models are advancing rapidly, very few studies address their ethical implications, raising concerns about misuse like deep-fakes and copyright issues.

Contribution

The paper quantifies the limited consideration of ethical impacts in generative audio research and highlights key areas of potential harm needing attention.

Findings

65% of papers note positive impacts

Less than 10% discuss negative impacts

Ethical concerns include fraud, deep-fakes, copyright infringement

Abstract

Generative audio models typically focus their applications in music and speech generation, with recent models having human-like quality in their audio output. This paper conducts a systematic literature review of 884 papers in the area of generative audio models in order to both quantify the degree to which researchers in the field are considering potential negative impacts and identify the types of ethical implications researchers in this area need to consider. Though 65% of generative audio research papers note positive potential impacts of their work, less than 10% discuss any negative impacts. This jarringly small percentage of papers considering negative impact is particularly worrying because the issues brought to light by the few papers doing so are raising serious ethical implications and concerns relevant to the broader field such as the potential for fraud, deep-fakes, and copyright infringement. By quantifying this lack of ethical consideration in generative audio research and identifying key areas of potential harm, this paper lays the groundwork for future work in the field at a critical point in time in order to guide more conscientious research as this field progresses.