mlsanityData to 7 Oct 2026

Cripping AI: Reimagining AI Through Lived Disability Experiences

arXiv 2605.02080 ↗First posted 3 May 2026cs.HC cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 51 of its 284 references that are in the map. First posted on arXiv 3 May 2026.

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51 of 284 references.

References · 51

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38 more
  1. 2024WorldScribe: Towards Context-Aware Live Visual Descriptionscited by 26
  2. 2023FluentSpeech: Stutter-Oriented Automatic Speech Editing with Context-Aware Diffusion Modelscited by 23
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