Fairness-Aware Few-Shot Learning for Audio-Visual Stress Detection
This addresses fairness in mental healthcare AI for equitable stress detection, though it is incremental as it builds on existing meta-learning and fairness methods.
The paper tackled gender bias in AI-driven stress detection in data-scarce scenarios by proposing FairM2S, a fairness-aware meta-learning framework, which achieved 78.1% accuracy and reduced Equal Opportunity to 0.06, demonstrating substantial fairness gains.
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.