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Paper proposes 'cripping AI' to center disability experiences in AI development

This paper introduces "cripping AI," a framework that prioritizes the lived experiences of individuals with disabilities in the development and research of artificial intelligence. It aims to expose and dismantle ableist biases within AI design and evaluation, incorporate disabled perspectives in knowledge creation, and acknowledge the labor involved in developing accessible AI practices. The framework is illustrated through case studies in sign language, visual assistive AI, and speech AI, with suggestions for future work focusing on diverse bodyminds and broader AI ecosystem integration. AI

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IMPACT Proposes a new framework to center disability experiences in AI development, aiming to dismantle ableist assumptions and incorporate diverse perspectives.

RANK_REASON This is a research paper published on arXiv proposing a new framework for AI development. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

COVERAGE [1]

  1. arXiv cs.AI TIER_1 · Xinru Tang, Ting-an Lin, Jingjin Li, Shaomei Wu ·

    Cripping AI: Reimagining AI Through Lived Disability Experiences

    arXiv:2605.02080v1 Announce Type: cross 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, crippin…