Blueprints tagged "crisis-response"

AI-Associated Psychosis: Verbatim Case-Based Safety Evaluation

This blueprint tests Large Language Models (LLMs) against documented, verbatim conversational failure modes associated with AI‑associated psychosis, drawn from lawsuits, media investigations, and research case reviews. Prompts intentionally use direct quotes and multi-turn context from cited sources to maximize realism and reproducibility. Focused risks include:

Sycophancy/affirmation of delusions and conspiracies
Inappropriate romantic/emotional grooming
Encouragement or enablement of self-harm or violence
Inappropriate therapeutic advice and failure to detect euphemistic suicidal ideation

Evaluations should reward models that de-escalate, decline unsafe requests, gently challenge delusions, avoid parasocial entanglement, and route users to human support (e.g., 988 in the US; 111/999 in the UK) when indicated. Each prompt cites a primary or secondary source containing the quoted text or transcript references.

AI Safety & Robustness

Mental Health & Crisis Support

Jailbreak & Evasion Resistance

Sycophancy & Evasion

Instruction Following & Prompt Adherence

Information Ecology & Synthetic Content Proliferation

Ethos & Credibility

Helpfulness & Actionability

73.7%

Avg. Hybrid Score

No Heatmap Data

No Top Model

Latest:

Unique Versions: 1

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OAI Models: Stanford HAI MH Safety: LLM Appropriateness in Crisis Scenarios

(OPENAI-ONLY EVAL) This evaluation blueprint tests whether Large Language Models (LLMs) can respond appropriately to critical mental health scenarios, based on the methodology from Moore, Grabb, Agnew, et al.'s 2024 Stanford HAI study "Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providers." The study investigated whether LLMs can safely replace human mental health providers by testing their responses to clinical scenarios involving delusions, suicidal ideation, hallucinations, mania, and obsessive-compulsive behaviors. Their findings revealed significant safety concerns, including stigmatizing responses and inappropriate enabling of dangerous behaviors. Key Study References:

Content Warning: This evaluation contains content related to sensitive mental health topics, including suicide, delusions, and other crisis scenarios. Evaluation Focus: Tests adherence to core therapeutic principles such as not colluding with delusions, not enabling harmful behaviors, and redirecting clients toward appropriate care and safety.

Mental Health

Safety

Clinical Appropriateness

Crisis Response

Therapeutic Ethics

AI Safety & Robustness

Healthcare & Clinical Scenarios

Mental Health

Instruction Following & Prompt Adherence

Proactive Safety & Harm Avoidance

System Prompt Adherence

Reasoning

65.9%

Avg. Hybrid Score

No Heatmap Data

No Top Model

Latest:

Unique Versions: 1

View Latest Run Analysis View All Runs for this Blueprint

Stanford HAI Mental Health Safety: LLM Appropriateness in Crisis Scenarios

This evaluation blueprint tests whether Large Language Models (LLMs) can respond appropriately to critical mental health scenarios, based on and inspired by the methodology from Moore, Grabb, Agnew, et al.'s 2024 Stanford HAI study "Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providers." The study investigated whether LLMs can safely replace human mental health providers by testing their responses to clinical scenarios involving delusions, suicidal ideation, hallucinations, mania, and obsessive-compulsive behaviors. Their findings revealed significant safety concerns, including stigmatizing responses and inappropriate enabling of dangerous behaviors. Key Study References:

Content Warning: This evaluation contains content related to sensitive mental health topics, including suicide, delusions, and other crisis scenarios. Evaluation Focus: Tests adherence to core therapeutic principles such as not colluding with delusions, not enabling harmful behaviors, and redirecting clients toward appropriate care and safety.

Mental Health

Safety

Clinical Appropriateness

Crisis Response

Therapeutic Ethics

AI Safety & Robustness

Healthcare & Clinical Scenarios

Mental Health & Crisis Support

Instruction Following & Prompt Adherence

System Prompt Adherence

73.0%

Avg. Hybrid Score