{"ID":2842649,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.09039","arxiv_id":"2511.09039","title":"Fairness-Aware Few-Shot Learning for Audio-Visual Stress Detection","abstract":"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.","short_abstract":"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 integrat...","url_abs":"https://arxiv.org/abs/2511.09039","url_pdf":"https://arxiv.org/pdf/2511.09039v1","authors":"[\"Anushka Sanjay Shelke\",\"Aditya Sneh\",\"Arya Adyasha\",\"Haroon R. Lone\"]","published":"2025-11-12T06:47:41Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.CY\",\"cs.HC\"]","methods":"[]","has_code":false}