Towards Trustworthy Audio Deepfake Detection: A Systematic Framework for Diagnosing and Mitigating Gender Bias

TL;DR

This paper introduces a systematic framework for diagnosing and mitigating gender bias in audio deepfake detection systems, emphasizing the importance of identifying bias sources before applying targeted fixes.

Contribution

It presents the first diagnosis-first approach that identifies bias origins and evaluates novel mitigation strategies, including a new fairness regularisation method, on two models and a benchmark dataset.

Findings

Bias does not originate from training data imbalance but from acoustic and feature leakage.

Adjusting decision thresholds per gender significantly reduces unfairness without harming accuracy.

A new epoch-level fairness regularisation outperforms existing batch-level methods.

Abstract

Audio deepfake detection systems are increasingly deployed in high-stakes security applications, yet their fairness across demographic 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 training data but from acoustic representation differences, gender leakage in learned features, and structural evaluation asymmetry. We test mitigation strategies across in-processing, post-processing and combined families, including novel methods introduced in this work. Adjusting the decision threshold separately per gender reduces unfairness by 54% to 75% at no cost to detection accuracy, and our new epoch-level fairness regularisation method outperforms existing per-batch approaches. Adversarial debiasing succeeds only when gender leakage is localised, and fails when it is diffuse, an outcome correctly predicted by our diagnosis before training. No single method fully closes the fairness gap, confirming that bias sources must be identified before fixes are applied and that fairer benchmark design is equally important