Cited by 2Ethics and Social Impacts of AIDisability Rights and RepresentationDigital Accessibility for Disabilities

The DOI matches its Crossref record; 218 of the 291 references it cites were found in a registry or an index, and 34 are not checked yet; 6 of the 318 sources its claims cite were checked for support.

Cripping AI: Reimagining AI Through Lived Disability Experiences

Open access
Tang, Xinru, Lin, Ting-an, Li, Jingjin, Wu, Shaomei·2026·Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency

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.

Verified 2026-10-25 · 34 references not checked yet

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This paper proposes "cripping AI" as a guiding framework that moves beyond making AI merely accessible, instead centering lived disability experiences in how AI is imagined, designed, and evaluated. It develops three tenets—exposing disability politics, honoring cripistemologies, and respecting crip labor—and illustrates them through three case studies on sign language AI, visual assistive AI, and stuttering/speech AI.

Key findings

  • The framework rests on three interconnected tenets: (1) *expose disability politics*—interrogate how AI practices construct certain bodyminds as "disabled" rather than treating disabled/non-disabled as fixed; (2) *honor cripistemologies*—center knowledge produced through disabled bodyminds and communities rather than treating able-bodied perception as ground truth; (3) *respect crip labor*—recognize accessibility as ongoing, friction-filled, collective work in which disabled people play a key role.
  • The paper identifies three ableist assumptions recurring in accessibility-oriented AI: disability viewed as a medical deficit, able-bodied people positioned as the authoritative source of knowledge, and disabled people treated as passive objects of protection.
  • Cited evidence includes: WHO figures that around 1.3 billion people worldwide experience significant disability (~16% of the global population); a survey of 10k+ deaf signers in China finding fewer than 10% can fully understand sign language interpretation on TV broadcasting; and a finding that PWS annotators disagreed with labels in more than 25% of clips in a highly cited English stuttered speech dataset.
  • Three cross-case insights: toward lived-experience-preserving pipelines (preserving diversity, ambiguity, and subjectivity instead of a single ground truth), lived experience as expertise, and design with lived experience in the loop.

Methods

  • Conceptual/theoretical paper: it draws on crip theory and critical disability studies to propose a framework, offered explicitly as "a generative framework rather than a prescriptive method."
  • Three case studies drawn from the authors' own prior and ongoing research: deafness and sign language AI, blindness and visual assistive AI, and stuttering and speech AI (the latter conducted at AImpower.org with people who stutter).
  • No new empirical data collection is reported for this paper; the cases synthesize prior work (e.g., sign language content creation, everyday uncertainty in BLV GenAI use, stuttered speech dataset annotation and benchmarking) to illustrate applying the three tenets.

Limitations

  • Stated by the authors: they acknowledge "the risk of abstracting disabled experiences in our theorization and when we describe disabilities as a group"; that ableism remains a pervasive reality and not all disabled individuals want to be called "crip"; and that claiming a crip identity and access to disability justice can itself reflect a form of privilege, so the framework "must remain situated and context-sensitive in practice."
  • Stated scope limitation: "Given the scope limitation, we did not cover some crucial aspects such as model training, architecture, APIs, and governance."
  • Evident from the positionality statement: none of the authors identify as deaf or blind, none are signers, and only the first author has basic knowledge of sign language—so the deafness and blindness cases are not authored from lived experience of those disabilities (the last author has lived experience of stuttering).

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1145/3805689.3806744

Cites

218 of 291 references verified0 citation errors · 39 not checkable

Statistics

Nothing to recompute.

Verification

Claims the paper makes

6 of 318 cited sources checked · 2 supported · 0 flagged · accuracy not yet measured

Show all 112 claims

  • It may state two separate claims as one. 60
  • Probably not a substantial claim. 37
89 of 89
  • The paper relies on this without support.

    Centering disabled people's lived experiences is necessary for AI to be truly supportive of disability.

    “To truly center disability in AI development, this paper takes one step further from making AI accessible toward cripping AI as a guiding framework.”
  • The paper relies on this without support.

    The three proposed tenets are a sufficient starting framework for systematically cripping AI.

    “We propose three interconnected tenets as a starting point for systematically cripping AI”
  • The paper relies on this without support.

    The authors' three case studies adequately demonstrate how to apply the cripping AI framework.

    “We present three cases from our own research to exemplify how to put our framework in practice.”
  • The paper relies on this without support.

    Sign language technologies aim to fully convert sign languages into other languages.

    “The richness of sign language practices also questions the goal of sign language technologies to fully convert them into other languages.”
  • The paper relies on this without support.

    Speech AI systems are optimized for fluency and speed.

    “Optimized for fluency and speed, speech AI systems including voice commands, speech-to-text tools, and automatic captioning often have great difficulties when interacting with PWS.”
  • The paper relies on this without support.

    Visual assistive systems typically use images provided by BLV individuals or sourced from the Internet for training, while relying on sighted crowdworkers to generate corresponding visual descriptions.

    “These systems typically use images provided by BLV individuals or sourced from the Internet for training, while relying on sighted crowdworkers to generate the corresponding visual descriptions.”
6 of 6

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    Unresolved
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    World Health Organization (2025)

    Unresolved

Flagged first, then by citations

72 of the 288 references couldn’t be matched to a known paper.

Cited by

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Metadata

Authors
Tang, X., Lin, T.-A., Li, J., & Wu, S.
Affiliations
  • University of California, Irvine (US)
  • University of Connecticut (US)
  • AImpower.org, Mountain View, California, USA
Venue
Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency
Publisher
ACM
Published
2026-06-25
Pages
3508-3536
OpenAlex
W7160416972
Cited by
2
References
288
Citations by year
2026: 2
FWCI
19.68
Type
conference paper
Open access
Locations
  • ACM Conference on Fairness, Accountability, and Transparency (FAccT) (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Funders
NSF (National Science Foundation) (2427710)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
  • Social Sciences › Sociology and Political Science › Disability Rights and Representation
  • Social Sciences › Human Factors and Ergonomics › Digital Accessibility for Disabilities
Keywords
crip theory, disability studies, ableism, cripistemology

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

A language model drew the claims from the paper on 2026-10-25, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).

A second model labelled each claim on 2026-10-25 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).

Cited sources are checked with a language model’s help (gpt-6-sol).

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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