解决音视频压力检测中的性别偏见，实现公平且少样本的精准识别。
Fairness-Aware Few-Shot Learning for Audio-Visual Stress Detection
- 通过对抗梯度掩码与公平约束更新，双阶段抑制模型偏见。
- 在少样本下达到78.1%准确率，机会均等性偏差降至0.06。
- 适用于心理健康AI中资源有限但需公平性的实际场景。
AI驱动的压力检测在心理健康领域至关重要，但现有模型常存在性别偏见，尤其在数据稀缺情况下。为此，我们提出FairM2S——一种基于音视频数据的公平性感知元学习框架。该框架在元训练和适应阶段均引入相等机会约束，采用对抗梯度掩码与公平性约束的元更新机制，有效缓解偏见。在五种先进基线对比中，FairM2S实现78.1%准确率，同时将平等机会差距降至0.06，显著提升公平性。我们还发布了SAVSD——一个由智能手机采集、含性别标注的数据集，旨在支持低资源、真实场景下的公平性研究。本文成果可公开获取，为可扩展、公平的少样本压力检测提供新范式。
原文摘要 · Abstract (English)
Fairness in AI-driven stress detection is critical for equitable mental healthcare, yet existing models frequently exhibit gender bias, particularly in data-scarce scenarios. To address this, we propose FairM2S, a fairness-aware meta-learning framework for stress detection leveraging audio-visual data. FairM2S integrates Equalized Odds constraints during both meta-training and adaptation phases, employing adversarial gradient masking and fairness-constrained meta-updates to effectively mitigate bias. Evaluated against five state-of-the-art baselines, FairM2S achieves 78.1% accuracy while reducing the Equal Opportunity to 0.06, demonstrating substantial fairness gains. We also release SAVSD, a smartphone-captured dataset with gender annotations, designed to support fairness research in low-resource, real-world contexts. Together, these contributions position FairM2S as a state-of-the-art approach for equitable and scalable few-shot stress detection in mental health AI. We release our dataset and FairM2S publicly with this paper.
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