Abstract Background Machine learning and natural language processing have demonstrated significant potential for mental health assessment: describing your mental health in your own words can offer a more ecologically valid approach than traditional rating scales. However, most models focus on specific diagnoses, condit...
Clara Wiebel, Veerle C. Eijsbroek, Vasudha Varadarajan et al.· JMIR Mental Health· 0 citations
Analysis of reasoning errors in vision language models and how they impact user trust and the ability to detect errors highlights how CoT explanations can simultaneously clarify and mislead, underscoring the need for NLP systems to provide explanations that encourage scrutiny and critical thinking rather than blind tru...
Eunkyu Park, Wesley Hanwen Deng, Vasudha Varadarajan et al.· arXiv.org· 6 citations
A large-scale audit of sociodemographic error disparities in both lexical- and transformer-based models of county-level health outcomes, over a dataset cover billions of community-mapped messages highlights a critical accuracy–fairness trade-off in community-level models for public health tasks.
A. Marker, Salvatore Giorgi, Adithya V Ganesan et al.· Proceedings of the Thirty-Fi...· 0 citations
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