FAIR_XAI: Improving Multimodal Foundation Model Fairness via Explainability for Wellbeing Assessment

ArXi:2604.23786v1 Announce Type: cross 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 prediction remains under-explored.