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arXiv · 2511.09039

Fairness-Aware Few-Shot Learning for Audio-Visual Stress Detection

Anushka Sanjay Shelke·Aditya Sneh·Arya Adyasha·Haroon R. Lone
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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.

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BibTeXRIS

Anushka Sanjay Shelke, Aditya Sneh, Arya Adyasha, Haroon R. Lone. 2025-11-12. Fairness-Aware Few-Shot Learning for Audio-Visual Stress Detection. https://arxiv.org/abs/2511.09039

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