Risk
Training data often does not include enough information about persons with disabilities, so AI systems are less likely to adequately represent them in their outputs. For example, image generators frequently omit assistive devices and disability-relevant contexts even when explicitly prompted to depict disability (Manzoor, 2025). The issue of underrepresentation in training data is particularly detrimental for low- and middle-income countries (LMICs), where most large language models, text-to-speech systems, and assistive AI tools are trained on a handful of dominant languages. As a result, this can exclude users of minority languages such as under-resourced sign languages (i.e., Mexican Sign Language, Jamaican Sign Language, etc.) and minority spoken languages from accessing functional tools in their own language.
Mitigation
Disability representation benchmarks should be required for generative AI, and minority-language and sign-language coverage should be a condition of procurement. Funding low- and middle-income country-led model development for under-resourced languages (particularly sign-language AI in regions where interpreter shortages are most severe) is essential in addressing this gap.
Illustrative Examples
Education
Disability representation missing from classroom materials
When educators use image generation tools to create teaching materials, the outputs may rarely include assistive devices or disability-relevant situations, even when prompted. This can lead to classroom content that does not reflect a diverse range of learners.
Employment
Non-linear careers not reflected in “standard” profiles
Generative tools used to create résumés or portfolios may suggest removing or rewording gaps, part-time work, or non-traditional roles. As a result, disability-related experiences such as medical leave or flexible work may be omitted, creating a more “standard” profile that does not reflect the person’s full background.
Healthcare
Health information that excludes diverse languages and modes
Patient-information chatbots in many settings may default to content in dominant languages. This can leave people who use other languages, including ASL speakers, without clear or accessible health information. As a result, they may miss important guidance or receive less useful support.
Services
Service guidance that doesn’t address complex realities
A government agency may introduce a chatbot to help people understand eligibility for a service. Because the system’s training data may include limited information about disability-related situations, it may provide incomplete or incorrect answers when users ask about complex needs, such as how fluctuating conditions affect eligibility or what documentation is required for specific accommodations. As a result, people with disabilities may be given guidance that does not reflect their situation, leading to incorrect applications, delays in receiving support, or missed access to services they are eligible for.