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Ethical and Technical Limits of Deepfake Speech Datasets

paperactiveethical-and-technical-limits-of-deepfake-speech-datasets-a8752504·1 events·first seen Jun 10, 2026

Aliases: Ethical and Technical Limits of Deepfake Speech Datasets

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5arXiv · cs.AI·Jun 10, 2026·source ↗

Audit of 39 deepfake speech datasets reveals fairness and generalization gaps

A dataset-level audit of 39 deepfake speech datasets examines accessibility, documentation, demographic coverage, scale, and source corpora. The study finds that fairness assessment is largely infeasible due to missing demographic metadata, and that substantial overlap in underlying speech corpora across datasets undermines cross-dataset evaluation and inflates generalization claims. The findings challenge the credibility of robustness and fairness claims made for deepfake speech detectors.

Evaluation and Benchmarking AI Safety Research Ethical and Technical Limits of Deepfake Speech Datasets