arXiv AI

Explainable AI for Screening Abuse-Related Trauma in Bangladeshi Children: A Training-Free Multimodal Framework Evaluated on Noise-Aware Synthetic Data

arXiv:2607. 04010v1 Announce Type: new Abstract: Bangladesh has an estimated 1.

arXiv Machine Learning

Jun 30

CAREBench: A Child-Safety Risk Benchmark for Language Models

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?

By Kaavya Krishna-Kumar, Elaine Lau, Vaughn Robinson, Jay Caldwell, Sheriff Issaka, Skyler Wang, Francisco Guzm\'an, Steven Kelling, Jonas Mueller

llms benchmarks safety

arXiv AI

Jul 29

DynaBridge: Dynamic Summary-Guided Cross-Task Multimodal Fusion for DASS-Structured Mental Health Assessment

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.

By Shiyu Teng, Haichen Yu, Jiaqing Liu, Hao Sun, Yu Song, Shurong Chai, Ruibo Hou, Lanfen Lin, Yen-Wei Chen

llms multimodal

Hugging Face Trending Papers

Jun 29

CAREBench: A Child-Safety Risk Benchmark for Language Models

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.

llms benchmarks safety

arXiv Machine Learning

22h ago

Predicting Male Domestic Violence Using Explainable Ensemble Learning and Exploratory Data Analysis

arXiv:2403. 15594v4 Announce Type: replace-cross Abstract: Domestic violence is commonly viewed as a gendered issue that primarily affects women, which tends to leave male victims largely overlooked.

By Md Abrar Jahin, Saleh Akram Naife, Fatema Tuj Johora Lima, M. F. Mridha, Md. Jakir Hossen

safety

arXiv AI

Jul 20

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

arXiv:2604. 23786v2 Announce Type: replace Abstract: In recent years, the integration of multimodal machine learning in wellbeing assessment has offered transformative potential for monitoring mental health.

By Sophie Chiang, Tom Brennan, Fethiye Irmak Dogan, Jiaee Cheong, Hatice Gunes

llms multimodal safety

arXiv AI

Jun 2

Structured Visual Evidence Decomposition for Evidence-Grounded Multimodal Screening of Obstructive Sleep Apnea-Hypopnea Syndrome

arXiv:2606. 00087v1 Announce Type: cross Abstract: Effective pre-polysomnography screening for obstructive sleep apnea-hypopnea syndrome (OSAHS) requires combining clinical risk factors with visible craniofacial and neck cues.

By Chen Zhan, Yingchen Wei, Xiaoyu Tan, Jingjing Huang, Xihe Qiu

llms multimodal

arXiv AI

Jun 30

EMPATH: A Multilingual Auditor-Judge Benchmark for Safety Evaluation of Emotional-Support Chatbots

arXiv:2606. 30256v1 Announce Type: new Abstract: Safety benchmarks often buy scalability by fixing the prompt, the language, and the turn structure.

By Camilo Chac\'on Sartori

benchmarks safety

arXiv AI

Jul 9

AI Chatbot Suicide Risk Detection and Response: Human Validation Study of the Open-Source VERA-MH Safety Evaluation

arXiv:2602. 05088v4 Announce Type: replace Abstract: Millions of people now use generative AI chatbots for psychological support.

By Kate H. Bentley, Luca Belli, Adam M. Chekroud, Emily J. Ward, Emily R. Dworkin, Emily Van Ark, Kelly M. Johnston, Will Alexander, Millard Brown, Matt Hawrilenko

llms agents benchmarks safety

arXiv AI

Jul 1

Training Therapeutic Judges and Multi-Agent Systems for Human-Aligned Mental Health Support

arXiv:2606. 30887v1 Announce Type: cross Abstract: Large language models show promise for mental health support, yet therapeutic quality improves only when evaluation functions as an actionable control signal rather than a passive metric.

By Mizanur Rahman, Abeer Badawi, Elahe Rahimi, Laleh Seyyed-Kalantari, Frank Rudzicz, Enamul Hoque, Elham Dolatabadi

llms agents nlp safety

arXiv Machine Learning

Jun 16

Temporal Validation Changes the Apparent Public-Health Utility of Under-Five Mortality Prediction in Bangladesh: A Four-Round DHS Machine-Learning Study

arXiv:2602. 03957v2 Announce Type: replace Abstract: Background: Under-five mortality in Bangladesh remains uneven despite national progress.

By Md Muhtasim Munif Fahim, M. Monimul Huq, M. Sabiruzzaman, Md Rezaul Karim

arXiv Machine Learning

Aug 10

Neurai-VN Benchmark: Standardized Machine Learning Models for Multimodal Digital Phenotyping in Mental Health Classification

arXiv:2607. 25232v2 Announce Type: replace Abstract: Digital phenotyping (DP) using smartphones and wearable devices has shown considerable potential for mental health monitoring.

By Quoc-Cuong Pham, Hoang-Thuy-Duong Vu, Thi-Thanh-Huong Ha, Huy-Hieu Pham

multimodal benchmarks

arXiv AI

1d ago

Prompting is not enough: supervised baselines and leakage control for measuring shared decision-making with LLMs in pediatric encounters

arXiv:2608. 14792v1 Announce Type: cross Abstract: Objectives: To determine whether zero-shot prompting of a large language model (LLM) is sufficient to detect shared decision-making (SDM) behaviors in real clinical encounters, and whether supervised learning adds value under patient-grouped, nested evaluation.

By Bernardo Modenesi, Jody Lin, Kimberly Kaphingst, Angela Zhu, Maya Wheeler, Peilu Zhang, Angela Fagerlin

llms rag

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