{
  "id": 1193,
  "url": "https://arxiv.org/abs/2606.10911v1",
  "title": "Ethical and Technical Limits of Deepfake Speech Datasets",
  "summary": "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 ",
  "authors": "Vojtěch Staněk, Eva Trnovská, Kamil Malinka, Anton Firc",
  "category": "research",
  "topics": "bias-fairness,misinformation,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-09T14:20:55.000Z",
  "fetched_at": "2026-07-14T14:15:03.617Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/1193",
  "original_url": "https://arxiv.org/abs/2606.10911v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}