FAIR_XAI: Improving Multimodal Foundation Model Fairness via Explainability for Wellbeing Assessment
In recent years, the integration of multimodal machine learning in wellbeing assessment has offered transformative potential for monitoring mental health. However, with the rapid advancement of Vision-Language Models (VLMs), their deployment in clinical settings has raised concerns due to their lack of transparency and potential for bias. While previous research has explored the intersection of fairness and Explainable AI (XAI), its application to VLMs for wellbeing assessment and depression pre
Record details
Published: 26 April 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Healthcare · Transparency
Retrieved: 14 July 2026
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How to cite this record
ethics.ai (26 April 2026), “FAIR_XAI: Improving Multimodal Foundation Model Fairness via Explainability for Wellbeing Assessment,” evidence record 5342, https://ethics.ai/record/5342 (originally published by arXiv).
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