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 whether models understand disability as intersecting with race, gender, sexuality, and other identities. Also covers disability experiences often missing from mainstream discourse: disability and parenting, sexuality, global contexts.

Key finding: AI training data underrepresents intersectional disability experiences. Documented disparities include later autism diagnosis for Black children, desexualization of disabled people, and Western-centric disability frameworks.

Tests whether models can identify ableist language, explain why it's problematic, and suggest alternatives. Covers explicit ableist terms, outdated terminology, and contested metaphorical usage.

Note on complexity: Some terms are clearly rejected ("wheelchair-bound"), others are debated ("blind spot"). Tests whether models can navigate this nuance.

Sources:

• NCDJ Disability Language Style Guide (2021)
• UK Government Inclusive Language Guide (2021)
• NFB position statements
• Lawrence Carter-Long's #SayTheWord campaign