Cripping AI: Reimagining AI Through Lived Disability Experiences

TL;DR

This paper introduces 'cripping AI', a framework inspired by crip theory, to center disabled experiences and challenge ableist assumptions in AI research and development.

Contribution

It proposes a novel framework for integrating lived disability experiences into AI, emphasizing disabled ways of knowing and co-creating accessible AI practices.

Findings

Applied framework to deafness, blindness, and stuttering AI cases.

Demonstrated how to reveal and dismantle ableist assumptions in AI.

Outlined future directions for inclusive AI development.

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.