Work

Cripping AI: Reimagining AI Through Lived Disability Experiences

Year: 2026

Type: conference-paper

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.

Source: ACM Conference on Fairness, Accountability, and Transparency (FAccT)

DOI: 10.1145/3805689.3806744

Authors: Xinru Tang, Ting-an Lin, Jingjin Li, Shaomei Wu

Institutions: University of California, Irvine, University of Connecticut

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Reduced inequalities

Open Access status: hybrid

Funder: National Science Foundation