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Evidence record 1193 · automatically gathered

Ethical and Technical Limits of Deepfake Speech Datasets

Claims about the robustness and fairness of deepfake speech detectors are only as credible as the datasets used to train and evaluate those systems. We present a dataset-level audit of the deepfake speech landscape. We compile and analyze 39 deepfake speech datasets, examining key attributes including accessibility, documentation, demographic and language coverage, dataset scale, and the underlying bona fide speech sources. Our audit reveals two important takeaways. Firstly, fairness assessment

Record details

Published: 9 June 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Misinformation · Transparency
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (9 June 2026), “Ethical and Technical Limits of Deepfake Speech Datasets,” evidence record 1193, https://ethics.ai/record/1193 (originally published by arXiv).

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