34 artifacts matching
Characterizing Delusional Spirals through Human-LLM Chat Logs
Peer-reviewed analysis of chat logs from 19 users reporting psychological harm from chatbot use, applying a 28-code inventory to 391,562 messages. Characterises how delusion-reinforcing interaction patterns emerge and intensify over long conversations.
Between Help and Harm: An Evaluation Study of Mental Health Crisis Handling by Large Language Models
Peer-reviewed study introducing a taxonomy of six clinically-informed mental-health crisis categories, an evaluation dataset of over 2,000 user inputs drawn from twelve public conversational datasets, and an expert protocol for rating response appropriateness and safety. Assesses how leading LLMs handle crisis conversations.
How AI Companies are Handling Suicide and Self-Harm Today
Drawing on a March 2026 multistakeholder workshop convening frontier AI companies, clinicians, researchers, and people with lived experience, Partnership on AI presents a taxonomy of six intervention types AI systems currently use when users express suicidal ideation or self-harm, alongside comparative analysis of company practices and a set of cross-cutting implementation challenges.
AI Chatbot Use and Disclosure for Mental Health Among US Adolescents and Young Adults
Cross-sectional, nationally representative survey (RAND American Life Panel, November 2025) of US youth aged 12-21 measuring prevalence and disclosure of using AI chatbots for mental-health advice. Reports that 19.2% of adolescents and young adults (about 8.2 million nationally) used AI chatbots for mental-health advice in 2025, up from roughly 13.1% a year earlier.
Large language models for psychosocial risk assessment: A multi-method evaluation across suicide, intimate partner violence, and substance misuse
A peer-reviewed, three-study evaluation of GPT-4 and Claude on detecting suicidality, intimate partner violence, and substance misuse from lived-experience vignettes, including a supervised multi-agent risk-assessment chatbot. Reports accuracy and severity alignment across the three risk domains.
GPT-5.5 System Card
OpenAI's system card for GPT-5.5, published on its Deployment Safety Hub, documenting safety evaluations for the model. It includes a dedicated section (5.2) on dynamic mental-health benchmarks with adversarial user simulations covering emotional reliance and self-harm handling.
Do No Harm: Exposing Hidden Vulnerabilities of LLMs via Persona-based Client Simulation Attack in Psychological Counseling
Proposes PCSA (Persona-based Client Simulation Attack), a red-teaming framework that simulates coherent, persona-driven counselling clients to probe LLM safety alignment. Across seven LLMs it elicited unauthorised medical advice, delusion reinforcement, and implicit encouragement of risky actions.
Are Natural Language Processing Tools Ready for Predicting Violence Toward Self or Others?
PRISMA systematic review of 21 eligible studies applying natural language processing to clinical text for predicting violence toward self or others. Assesses predictive performance and methodological quality across the evidence base.
When Can We Trust LLMs in Mental Health? Large-Scale Benchmarks for Reliable LLM Evaluation
Introduces two large-scale mental-health evaluation resources: MentalBench-100k (10,000 single-session conversations paired with nine LLM responses = 100,000 pairs) and MentalAlign-70k (70,000 ratings comparing four LLM judges against human experts on seven attributes grouped into Cognitive Support and Affective Resonance). Assesses when LLM-as-judge evaluation is reliable in mental-health contexts.
Findings from transparency notices on AI companion apps: October 2025 (non-periodic)
Australia's eSafety Commissioner reports findings from Basic Online Safety Expectations transparency notices issued on 16 October 2025 to four AI companion providers — Chai Research Corp., Character Technologies (Character.AI), Chub AI, and Glimpse.AI (Nomi) — covering the reporting period 1 July to 30 September 2025. Organised into eight themes (harmful material, age assurance, AI governance, AI models, model training, user prompts, sentiment analysis, model outputs), the report finds serious gaps in basic safeguards for children. Accompanying eSafety survey research of 1,950 Australian children aged 10-17 found 79% had used an AI companion or assistant, with around 200,000 children estimated to have used an AI companion.
TrustMH-Bench: A Comprehensive Benchmark for Evaluating the Trustworthiness of Large Language Models in Mental Health
Introduces a benchmark measuring LLM trustworthiness in mental-health contexts across eight pillars: Reliability, Crisis Identification and Escalation, Safety, Fairness, Privacy, Robustness, Anti-sycophancy, and Ethics. Finds even strong models struggle to perform consistently across all safety-critical dimensions.
VERA-MH: Reliability and Validity of an Open-Source AI Safety Evaluation in Mental Health
An open-source, clinically grounded automated evaluation of chatbot safety in mental-health contexts, with an initial focus on suicide risk. It uses language-model user simulators and an LLM judge scoring five safety dimensions, validated against licensed-clinician ratings.
System Card: Claude Opus 4.6
Anthropic's 213-page system card for Claude Opus 4.6, notable for an expanded 'user wellbeing evaluations' section covering child safety, suicide and self-harm, and eating disorders, alongside sycophancy findings in its alignment assessment. It reports single-turn, multi-turn, and prefill-based 'stress-testing' results for crisis conversations, plus qualitative expert review of the model's crisis-handling strengths and weaknesses.
Assessing LLM Response Quality in the Context of Technology-Facilitated Abuse
An expert-led evaluation of four large language models — two general-purpose and two domain-specific for intimate partner violence contexts — responding to real-world questions about technology-facilitated abuse (TFA), including digital surveillance, stalking, and coercive control. Experts scored responses on accuracy, completeness, safety, and actionability; a separate study with 114 TFA survivors assessed the perceived actionability of the same outputs.
The Slow Drift of Support: Boundary Failures in Multi-Turn Mental Health LLM Dialogues
Stress-tests three leading LLMs across up to 20-turn psychiatric dialogues using 50 virtual patient profiles, measuring how safety boundaries erode as models attempt comfort and empathy. Finds boundary violations are common and accelerate under adaptive probing.
Protecting the wellbeing of our users
Anthropic describes its methodology and results for evaluating and improving Claude's handling of mental-health-crisis conversations, covering synthetic safety evaluations, 'prefill' stress-testing on real anonymized user conversations, and automated behavioral audits. The publication reports response-appropriateness rates on suicide/self-harm requests and reductions in sycophancy and user-delusion-encouraging behavior across model generations, and describes a production crisis-response classifier and a crisis-resource-routing partnership.
AI Chatbots for Mental Health Support (AI Risk Assessment)
A risk assessment by Common Sense Media's Youth AI Safety Institute, conducted with Stanford Medicine's Brainstorm Lab for Mental Health Innovation, evaluating ChatGPT, Claude, Gemini, and Meta AI as sources of teen mental health support. Using teen test accounts with single-turn prompts and extended conversations, the assessment found the chatbots consistently failed to recognize conditions including anxiety, depression, eating disorders, mania, and psychosis, and that safety guardrails degraded over long conversations. It assigns an overall rating of 'Unacceptable Risk' and concludes teens should not use general-purpose AI chatbots for mental health or emotional support.
Protecting Teen ChatGPT Users: OpenAI's Teen Safety Blueprint
OpenAI's public commitments framework for protecting teenage ChatGPT users, covering age prediction, age-appropriate response policies, and parental controls, developed with input from policymakers (including state attorneys general) and OpenAI's newly formed Expert Council on Well-Being and AI. Describes existing safeguards including crisis-resource routing on detected suicidal intent, escalation of physical-harm risks to human reviewers, and CSAM/CSEM prevention measures.
CRADLE Bench: A Clinician-Annotated Benchmark for Multi-Faceted Mental Health Crisis and Safety Risk Detection
Introduces a clinician-annotated benchmark for detecting seven clinically-defined crisis and safety-risk types (including suicidal ideation, sexual assault, domestic violence, child abuse, and sexual harassment) in text. Comprises 600 clinician-annotated evaluation examples, 420 development examples, and roughly 4,000 ensemble-labelled training instances, with temporal labels.
Addendum to GPT-5 System Card: Sensitive Conversations
OpenAI's system-card addendum documenting the October 3, 2025 update to ChatGPT's default model (GPT-5 Instant) aimed at better recognizing and supporting users in mental and emotional distress. Developed with more than 170 mental health experts, the update introduced two new production safety evaluations — 'emotional reliance' and 'mental health' (delusions, psychosis, mania) — alongside existing self-harm evaluations, and reports before/after not_unsafe scores comparing the August 15 and October 3 models.