arXiv:2607. 04010v1 Announce Type: new Abstract: Bangladesh has an estimated 1.
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Anian is a safety‑gated multimodal AI backend designed for perinatal mental‑health support and mindfulness‑intervention routing. It maps user input into a four‑layer hierarchical state representation—emotion, psychosocial constructs, safety risk, and intervention routes—then fuses local and external risk signals to decide whether to generate AI responses or provide fixed safety content. Prototype evaluation on large public corpora showed high classification performance and perfect high‑risk recall in a controlled stress test, though clinical validity remains unestablished.
The paper evaluates the safety of conversational AI therapy bots for Generation Alpha, revealing that while these models understand 76‑82% of youth‑specific vocabulary, they correctly assess clinical risk only 64‑72% of the time, creating a significant vocabulary‑comprehension gap. Six failure patterns—such as sarcasm masking, minimization acceptance, and semantic drift—were identified, with compounded errors leading to a 94% miss rate when three or more patterns co‑occur. The authors estimate 146,880 missed crises annually and recommend mandatory human‑in‑the‑loop systems, quarterly youth‑specific validation, transparent performance disclosure, and regulatory oversight for youth‑facing mental health AI.
arXiv:2606. 29685v1 Announce Type: new Abstract: How can we evaluate whether frontier AI systems recognize child-safety risks before they escalate into explicit harm?
arXiv:2607. 25679v1 Announce Type: cross Abstract: Multimodal behavioral analysis offers a scalable approach to assessing depression, anxiety, and stress, yet generic fusion models often ignore the psychometric structure of questionnaire labels.
How can we evaluate whether frontier AI systems recognize child-safety risks before they escalate into explicit harm? Existing child safety evaluations focus on child sexual abuse material, yet many child-safety failures begin earlier: in model assistance that helps adults manipulate, impersonate, profile, or isolate minors, and in model responses that deepen children's emotional dependence on AI systems rather than redirecting them toward human support.
The paper presents a data‑driven study of male domestic violence (MDV) in Bangladesh, using exploratory data analysis to uncover patterns such as verbal abuse prevalence and the influence of financial dependency. It evaluates 10 traditional ML models, 3 deep learning models, and 2 ensemble models, ultimately proposing a stacking ensemble with ANN and CatBoost base classifiers and Logistic Regression meta‑model that achieves 95% accuracy and 99.29% AUC. Explainable AI techniques (SHAP, LIME) and statistical validation confirm the model’s superior performance and highlight key features driving predictions.