Risk
Persons with disabilities who rely heavily on AI tools for writing, communication, and decision-making may be more likely to have their perspectives shaped by the system’s default patterns and tone. Over time, this can lead to a form of “voice flattening,” where individual ways of expressing ideas, describing experiences, or conveying meaning are replaced with more standardized or generalized outputs. Important details—especially those tied to lived experience, identity, or context—may be simplified, omitted, or reframed in ways that do not fully reflect the person’s intent. Over time, this may also limit the diversity of perspectives that are visible in systems that learn from user-generated content, reinforcing more common patterns and making it harder for AI to reflect a wide range of lived experiences.
Mitigation
Tools should be designed to preserve user voice, as opposed to automatically rewriting style. Disability scholarship and lived-experience expertise should be cited and surfaced, as opposed to being absorbed into model outputs. AI-literacy education should emphasize when to push back on AI suggestions.
Illustrative Examples
Education
AI-supported writing that narrows student voice
Students who rely heavily on AI tools for writing may begin to use more standard or neutral language generated by the system. Over time, this may reduce the visibility of their own voice, including perspectives shaped by their lived experience with disability.
Employment
AI-generated communication shaping employee voice
When AI tools are used to draft emails, reports, or other workplace communication, they may suggest standard language and tone. Employees who rely on these tools may gradually adopt this style, which can reduce the visibility of their own voice and perspective. This may affect how their contributions are understood.
Healthcare
Patient experiences simplified in AI summaries
AI tools that summarize patient input may simplify,reshape, or change what a patient has said. Important details about disability-related experiences may be removed or softened in the process. Over time, this may give a reduced or oversimplified view of the patient’s situation to their care team, not capturing the full context of their experience.
Services
Lived experiences flattened in automated feedback
A government service may use AI tools to summarize or relay user feedback from people with disabilities, for example, comments about barriers in public transportation, healthcare access, or community services. The AI tool may reshape feedback into more general or neutral language. As a result, specific details about lived experience, such as how a barrier affects daily routines or safety, may be simplified or left out. Over time, the feedback received by the service may reflect more uniform patterns rather than the full range of needs, making it harder for decision-makers to understand and respond to real issues.