Cripping AI: Reimagining AI Through Lived Disability Experiences
For AI researchers and practitioners, this paper offers a critical framework to challenge ableism in AI, but it is primarily conceptual and does not present empirical results.
This paper proposes 'cripping AI' as a framework to center lived disability experiences in AI, revealing ableist assumptions and centering disabled epistemologies. It demonstrates the framework through three case studies (deafness, blindness, stuttering) and outlines future directions.
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.