  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.
