Sound Check: Auditing Audio Datasets
This addresses ethical issues in generative audio datasets, such as bias and copyright, for researchers and artists, though it is incremental as it extends prior work from visual/textual domains to audio.
The study audited seven prominent audio datasets for generative models and found they are biased against women, contain toxic stereotypes about marginalized communities, and include significant copyrighted work, with a web tool developed for artists to check dataset inclusion.
Generative audio models are rapidly advancing in both capabilities and public utilization -- several powerful generative audio models have readily available open weights, and some tech companies have released high quality generative audio products. Yet, while prior work has enumerated many ethical issues stemming from the data on which generative visual and textual models have been trained, we have little understanding of similar issues with generative audio datasets, including those related to bias, toxicity, and intellectual property. To bridge this gap, we conducted a literature review of hundreds of audio datasets and selected seven of the most prominent to audit in more detail. We found that these datasets are biased against women, contain toxic stereotypes about marginalized communities, and contain significant amounts of copyrighted work. To enable artists to see if they are in popular audio datasets and facilitate exploration of the contents of these datasets, we developed a web tool audio datasets exploration tool at https://audio-audit.vercel.app.