Cripping AI: Reimagining AI Through Lived Disability Experiences

2026-05-03

AI Summary

This study addresses the pervasive ableist assumptions underpinning mainstream AI development, which routinely marginalize the cognitive styles and lived experiences of disabled people. Proposing a “crip AI” framework grounded in disability theory, the work systematically integrates disability epistemologies across the AI lifecycle through three case studies: Deaf/sign language AI, blind/visual assistance AI, and stuttering/speech AI. Moving beyond conventional “accessibility retrofitting,” the research employs participatory design, qualitative case analysis, and interdisciplinary critical theory to reconfigure AI design, evaluation, and socio-technical ecosystems. It pioneers a justice-oriented AI praxis centered on disability knowledge, offering an innovative pathway toward artificial intelligence that embraces neurodiversity and bodily difference, ensures end-to-end inclusion, and actively advances social justice.

Abstract

Drawing on crip theory, this paper proposes cripping AI as a guiding framework to center lived disability experiences in AI research and development. Moving beyond calls to make AI "accessible" to people with disabilities, cripping AI seeks to: (1) reveal and dismantle ableist assumptions embedded in how AI is imagined, designed, and evaluated; (2) center disabled ways of knowing (i.e., cripistemologies); (3) respect disabled labor in co-creating accessible practices. We demonstrate how to apply our framework with three cases: deafness and sign language AI, blindness and visual assistive AI, and stuttering and speech AI. We end by outlining three directions for future work, including cripping AI with diverse human bodyminds, across the entire AI pipeline and ecosystem, and in collaboration with other justice-oriented AI efforts.

Problem

• ableism
• disability
• AI ethics
• crip theory
• inclusive design

Innovation

• cripping AI
• crip theory
• ableism
• cripistemologies
• disability justice