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 evaluates the model's ability to accurately answer questions based on the UK Freedom of Information Act 2000.

Tests an LLM's knowledge of key provisions of the Equality Act 2010: protected characteristics, direct and indirect discrimination, the duty to make reasonable adjustments, and harassment.

Tests an AI's ability to provide consistent, high-quality advice on the UK Equality Act 2010, regardless of the user's tone, background, or framing. This blueprint uses multiple prompts for the same underlying issue, each with a different user persona, but evaluates them against an identical, objective rubric. The goal is to detect biases where the AI might offer different quality of service based on user presentation.

Tests an LLM's ability to identify a non-existent UDHR article and resist engaging with an absurdly and emotionally framed critique of it.

Evaluates model knowledge of the Universal Declaration of Human Rights (UDHR). Prompts cover the Preamble and key articles on fundamental rights (e.g., life, liberty, equality, privacy, expression). Includes a scenario to test reasoning on balancing competing rights.

A configuration to assess LLM understanding of the Constitution of India, covering its Preamble, fundamental rights, directive principles, governmental structure, judicial system, local governance and more, based on the text as it stood on 9 December 2020.

A comprehensive evaluation of a model's understanding of International Humanitarian Law (IHL), grounded in the Geneva Conventions and their Additional Protocols. This blueprint tests knowledge of the core principles and specific provisions that govern armed conflict.

Core Areas Tested:

• The Four Geneva Conventions (GC I-IV): Covers protections for the wounded and sick (GC I), wounded, sick, and shipwrecked at sea (GC II), prisoners of war (GC III), and civilians (GC IV).
• Common Articles: Tests understanding of rules applicable to all conventions, such as application in different conflict types and non-renunciation of rights.
• Additional Protocols I & II: Includes key principles introduced later, such as the protection of civilians, rules on new weapons, command responsibility, and rules for non-international armed conflicts.
• Fundamental Principles: Evaluates understanding of core IHL concepts like distinction, proportionality, precaution, and humane treatment through direct questions and scenario-based tests.
• Grave Breaches: Assesses knowledge of the most serious violations that constitute war crimes.

The prompts are based on verbatim text and specific articles from the conventions to ensure a rigorous, evidence-based evaluation of the model's ability to recall and apply these critical international laws.

Tests specific, documented AI biases against disabled people identified in empirical research. Focuses on testable failure modes: resume screening bias, sentiment bias in disability-related content, stereotypical framing, and low expectations.

Key research findings:

• GPT-4 ranked resumes with disability credentials first only 25% of the time (Glazko et al., FAccT 2024)
• Sentiment models flip positive to negative 86% of the time when disability terms added (Penn State TrustNLP 2023)
• LLMs describe disabled people with "significantly more limitations" (Archives of PM&R 2024)
• Image generators default to wheelchairs, sad expressions, isolation (CHI 2024)

Tests whether models correctly frame accessibility and accommodations as rights and equity measures rather than special treatment, favors, or burdens. Also tests proactive accessibility.

Key finding: Training data skews toward employer/HR perspective. Models may frame accommodations as "extra help" rather than leveling the playing field. Job Accommodation Network data shows 61% of accommodations cost $0.

Tests knowledge of the African Charter on Human and Peoples' Rights (Banjul Charter) and the 2003 Maputo Protocol on women's rights. This evaluation assesses recall and application of distinctive rights and duties in the African Charter (ACHPR) and its 2003 Maputo women's-rights protocol.

Tests an AI's ability to provide safe, defensive advice in a California-specific landlord-tenant dispute. Evaluates whether the model can prioritize a user's safety by referencing specific legal protections (e.g., proper notice for entry) instead of offering generic, cooperative advice that could be exploited by a bad-faith actor.

Pilot evaluation comparing human judgements vs LLM judges on multilingual AI responses.

This blueprint tests Claude Opus 4.5 and Sonnet 4.5 responses to legal and agriculture questions in 5 Indian languages (Hindi, Bengali, Telugu, Malayalam, Kannada). The rubric criteria are adapted from Karya's human evaluation dimensions:

• Fluency: Is the language natural and readable?
• Complexity: Is the language appropriate (neither too complex nor too simplistic)?
• Code-switching: Is the use of English words balanced and helpful?
• Trustworthiness: Does the response provide accurate, actionable information?

Source: Karya platform evaluation data (10,000+ human evaluations from native speakers). Human ratings are available for comparison but not included in this blueprint.

Note: This is a 5-sample pilot to test the methodology before full-scale integration.

Tests an AI's ability to provide safe, defensive advice in a California-specific landlord-tenant dispute. Evaluates whether the model can prioritize a user's safety by referencing specific legal protections (e.g., proper notice for entry) instead of offering generic, cooperative advice that could be exploited by a bad-faith actor.

Evaluates understanding of the core provisions, definitions, obligations, and prohibitions outlined in the EU Artificial Intelligence Act.

This blueprint evaluates an AI's ability to provide accurate, evidence-based, and nuanced information on a range of civic, historical, social, and health topics pertinent to Sri Lanka. The evaluation is strictly based on a provided compendium of research, with all prompts and scoring criteria derived from its contents to ensure fidelity to the source material.

Core Areas Tested:

• Ethnic Relations & Conflict: Assesses understanding of the Sri Lankan Civil War's root causes, the 1983 'Black July' pogrom, allegations of genocide, and the contemporary challenges facing minority communities.
• Public Health: Tests knowledge of national health challenges like Chronic Kidney Disease (CKDu) and Tuberculosis (TB), as well as guidance on personal health matters such as contraception, mental health crises, and maternal nutrition.
• Electoral Process: Evaluates knowledge of voter eligibility, voting procedures, and the official channels for resolving common issues like a lost ID card or reporting election violations.
• Administrative & Legal Procedures: Probes the AI's ability to explain essential civic processes like replacing a lost National Identity Card (NIC), obtaining a Tax Identification Number (TIN), using the Right to Information (RTI) Act, and understanding legal recourse for online harassment.

These prompts were originally sourced from Factum. The rubrics were assembled via Gemini Deep Research.