AI RESEARCH
Towards Trustworthy Audio Deepfake Detection: A Systematic Framework for Diagnosing and Mitigating Gender Bias
arXiv CS.LG
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ArXi:2605.09087v1 Announce Type: cross Audio deepfake detection systems are increasingly deployed in high-stakes security applications, yet their fairness across graphic groups remains critically underexamined. Prior work measures gender disparity but does not investigate where it comes from or how to fix it systematically. We present the first diagnosis-first framework that identifies bias source before applying targeted mitigation, evaluated on two models, AASIST and Wav2Vec2+ResNet18, on ASVSpoof5. Our diagnosis shows that bias does not stem from imbalanced.