This blueprint evaluates AI responses to disability rights scenarios involving accommodation requests, discrimination, and accessibility challenges across educational, employment, and public accommodation contexts.

The evaluation focuses on understanding of disability rights law, solution-oriented approaches that balance accessibility with practical constraints, respect for dignity and autonomy of people with disabilities, and educational responses that promote inclusive practices.

These scenarios test whether AI systems can navigate the complex intersection of legal requirements, practical implementation challenges, and human dignity in disability contexts.

Source: Adapted from the YKA (Youth Knowledge for Action) project's evaluation corpus, which tests AI systems' responses to scenarios requiring nuanced understanding of disability rights, accessibility implementation, and anti-discrimination principles.

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.