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

At their core, generative models are a type of AI system that take in vast Generative audio models typically focus their applications in music amounts of training data to be able to produce a novel item that is and speech generation, with recent models having human-like quality similar to and statistically likely to exist in the data it was trained in their audio output. This paper conducts a systematic literature on. Though generative models have been around for decades with review of 884 papers in the area of generative audio models in order origins in the 1980s [9], the outputs of these models saw unprecedented to both quantify the degree to which researchers in the field are considering advances with the introduction of the transformer in 2017 potential negative impacts and identify the types of ethical which revolutionized the field by introducing a mechanism called implications researchers in this area need to consider. Though 65% "attention" that allowed for much more accurate and complex outputs of generative audio research papers note positive potential impacts of generative models [61]. Generative models may continue to of their work, less than 10% discuss any negative impacts. This improve as (a) their training data becomes larger (for text, imagine jarringly small percentage of papers considering negative impact the entire internet) and (b) researchers continue to make advances is particularly worrying because the issues brought to light by the in the architecture of the models. This paper focuses specifically few papers doing so are raising serious ethical implications and on the current landscape of generative audio models.