Ethical and Technical Limits of Deepfake Speech Datasets
A new audit of 39 deepfake speech datasets reveals significant limitations in their ethical and technical aspects. Researchers found that most datasets lack crucial demographic metadata, making fairness assessments nearly impossible and preventing subgroup analysis. Additionally, a substantial overlap in the source audio corpora used across these datasets could lead to inflated claims of generalization and undermine cross-dataset evaluation. AI
IMPACT Highlights critical data limitations that could hinder the development and fair evaluation of AI-powered speech technologies.