Who Gets Left Behind? Auditing Disability Inclusivity in Large Language Models
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912563157008384 |
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| author | Dash, Deepika Bangera, Yeshil Bangera, Mithil Vadithya, Gouthami Panda, Srikant |
| author_facet | Dash, Deepika Bangera, Yeshil Bangera, Mithil Vadithya, Gouthami Panda, Srikant |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_00963 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Who Gets Left Behind? Auditing Disability Inclusivity in Large Language Models Dash, Deepika Bangera, Yeshil Bangera, Mithil Vadithya, Gouthami Panda, Srikant Computers and Society Artificial Intelligence 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. |
| title | Who Gets Left Behind? Auditing Disability Inclusivity in Large Language Models |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2509.00963 |