Who Gets Left Behind? Auditing Disability Inclusivity in Large Language Models
This work addresses the problem of ensuring equitable accessibility guidance for diverse disability groups, highlighting actionable improvements for LLM developers, though it is incremental as it builds on existing auditing methods.
The paper tackled the problem of disability inclusivity gaps in Large Language Models (LLMs) used for accessibility guidance, revealing that models frequently address Vision, Hearing, and Mobility but underserve Speech, Genetic/Developmental, Sensory-Cognitive, and Mental Health categories, with depth similarly concentrated.
Large Language Models (LLMs) are increasingly used for accessibility guidance, yet many disability groups remain underserved by their advice. To address this gap, we present taxonomy aligned benchmark1 of human validated, general purpose accessibility questions, designed to systematically audit inclusivity across disabilities. Our benchmark evaluates models along three dimensions: Question-Level Coverage (breadth within answers), Disability-Level Coverage (balance across nine disability categories), and Depth (specificity of support). Applying this framework to 17 proprietary and open-weight models reveals persistent inclusivity gaps: Vision, Hearing, and Mobility are frequently addressed, while Speech, Genetic/Developmental, Sensory-Cognitive, and Mental Health remain under served. Depth is similarly concentrated in a few categories but sparse elsewhere. These findings reveal who gets left behind in current LLM accessibility guidance and highlight actionable levers: taxonomy-aware prompting/training and evaluations that jointly audit breadth, balance, and depth.