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arxiv: 2605.02080 · v1 · submitted 2026-05-03 · 💻 cs.HC · cs.AI

Recognition: 2 theorem links

· Lean Theorem

Cripping AI: Reimagining AI Through Lived Disability Experiences

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

Pith reviewed 2026-05-08 18:55 UTC · model grok-4.3

classification 💻 cs.HC cs.AI
keywords cripping AIcrip theorydisability studiesAI ethicsaccessible technologylived experienceableisminclusive AI design
0
0 comments X

The pith

Cripping AI centers lived disability experiences to dismantle ableist assumptions in AI development and evaluation.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper proposes cripping AI as a framework drawn from crip theory to place the real experiences of disabled people at the center of how artificial intelligence is researched, designed, and judged. Rather than treating disability as a limitation to patch with accessibility add-ons, the approach works to expose and remove the hidden assumptions that treat typical bodies and minds as the default. The authors apply the framework to three cases involving sign language AI, visual assistive technologies, and speech systems for stuttering to show how disabled knowledge can reshape what counts as a good AI outcome. A reader would care because the shift could make disabled people active co-creators instead of after-the-fact users, leading to systems that fit a wider range of human variation from the start.

Core claim

Drawing on crip theory, the 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 reveal and dismantle ableist assumptions embedded in how AI is imagined, designed, and evaluated; center disabled ways of knowing known as cripistemologies; and respect disabled labor in co-creating accessible practices. The framework is demonstrated through three illustrative cases of deafness and sign language AI, blindness and visual assistive AI, and stuttering and speech AI, followed by directions for extending the work to diverse human bodyminds, to

What carries the argument

The cripping AI framework, which applies crip theory to identify ableist assumptions, value cripistemologies, and honor disabled labor across AI design and use.

If this is right

  • AI evaluation would expand beyond accuracy or speed to include whether systems challenge or reinforce norms that exclude disabled bodyminds.
  • Disabled people would participate throughout the full AI pipeline as co-creators rather than as end users or test subjects.
  • The framework would apply across the entire AI ecosystem, from data collection to deployment and ongoing maintenance.
  • AI efforts would routinely connect with other justice-oriented work to address overlapping forms of marginalization.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same approach could surface parallel biases in non-AI technologies such as medical devices or workplace software.
  • New success measures for AI might emerge that track user empowerment across varied bodies and minds rather than standardization.
  • Industry adoption would require concrete changes in hiring, data practices, and decision-making structures to make the framework operational.
  • Links to intersectional disability justice could extend the framework's reach to combined experiences of race, gender, and disability.

Load-bearing premise

That applying the cripping AI framework to AI projects will reliably reveal and dismantle ableist assumptions and yield meaningfully different outcomes, despite the absence of empirical testing or comparisons.

What would settle it

Future AI systems built under the cripping AI framework that show no change in ableist features or exclusionary designs compared with conventional development methods, or that fail to incorporate disabled knowledge in observable ways.

read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes 'cripping AI' as a guiding framework drawn from crip theory to center lived disability experiences in AI research and development. It articulates three goals—revealing and dismantling ableist assumptions in AI imagination, design, and evaluation; centering disabled ways of knowing (cripistemologies); and respecting disabled labor in co-creating accessible practices—and illustrates the framework through three narrative case studies on sign language AI for deafness, visual assistive AI for blindness, and speech AI for stuttering, concluding with directions for future work.

Significance. If the framework can be shown to produce distinct design choices and outcomes, it would offer a valuable conceptual contribution to HCI and AI ethics by moving beyond accessibility-focused approaches toward a more transformative critique grounded in disability experience. The paper's explicit articulation of the three goals and its grounding in established crip theory provide a clear starting point for such work.

major comments (2)
  1. [demonstration of the framework with three cases] In the demonstration of the framework with three cases (sign language AI, visual assistive AI, and speech AI for stuttering), the applications are presented as narrative illustrations of the three goals without any before/after comparison, alternative baseline, altered technical choices (e.g., training data, loss functions, or evaluation criteria), or outcome measures showing that the cripped approach reveals or dismantles ableist assumptions differently from conventional accessibility methods. This is load-bearing for the central claim that cripping AI achieves its stated goals.
  2. [proposal of the cripping AI framework] The paper states that cripping AI moves beyond calls to make AI 'accessible' and will 'reveal and dismantle ableist assumptions,' yet provides no systematic criteria, operational steps, or success metrics for applying the framework or verifying its effects, leaving the proposal at the level of re-labeling participatory design practices without demonstrated differentiation.
minor comments (2)
  1. [abstract and introduction] The abstract and introduction would benefit from additional citations to recent work in disability studies and critical AI to more sharply distinguish the contribution from existing inclusive design literature.
  2. [future work] The future work section outlines broad directions but could specify concrete next steps, such as pilot studies or collaboration protocols, to guide readers.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback, which identifies key areas for strengthening the demonstration and operationalization of the cripping AI framework. We address each major comment below and will incorporate revisions to clarify the paper's conceptual contributions while acknowledging its illustrative rather than empirical scope.

read point-by-point responses

  1. Referee: In the demonstration of the framework with three cases (sign language AI, visual assistive AI, and speech AI for stuttering), the applications are presented as narrative illustrations of the three goals without any before/after comparison, alternative baseline, altered technical choices (e.g., training data, loss functions, or evaluation criteria), or outcome measures showing that the cripped approach reveals or dismantles ableist assumptions differently from conventional accessibility methods. This is load-bearing for the central claim that cripping AI achieves its stated goals.

    Authors: We agree that stronger contrasts would better support the framework's claims. The case studies are narrative illustrations drawn from existing literature and practice to exemplify application of the three goals, rather than new empirical evaluations. In revision, we will expand each case to include explicit before/after-style contrasts with conventional accessibility approaches, highlighting differences in design choices (e.g., evaluation criteria incorporating cripistemologies) and potential outcomes based on cited prior work. This will be added without new data collection, as the paper remains conceptual. revision: yes

  2. Referee: The paper states that cripping AI moves beyond calls to make AI 'accessible' and will 'reveal and dismantle ableist assumptions,' yet provides no systematic criteria, operational steps, or success metrics for applying the framework or verifying its effects, leaving the proposal at the level of re-labeling participatory design practices without demonstrated differentiation.

    Authors: We acknowledge the value of more explicit guidance. The manuscript articulates the three goals as a conceptual framework grounded in crip theory, with differentiation shown through the case illustrations. To address this, we will add a new subsection proposing preliminary operational steps and example criteria (e.g., reflective questions for revealing ableist assumptions in AI pipelines and metrics for assessing centering of disabled ways of knowing). These will be positioned as initial proposals for future empirical testing, building on the existing future work section. revision: yes

Circularity Check

0 steps flagged

Conceptual framework proposal shows no circularity in derivation

full rationale

The paper proposes cripping AI as a guiding framework drawn from external crip theory and illustrates its application through three descriptive cases (sign language AI, visual assistive AI, speech AI for stuttering). No equations, fitted parameters, predictions, or self-citations appear in the provided text that reduce any central claim to its own inputs by construction. The derivation is a conceptual application of prior independent theory to new domains rather than a self-referential loop or renamed fit. This is a standard non-circular proposal in HCI literature.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the domain assumption that crip theory supplies valid and transferable ways of knowing for AI design; the framework itself is introduced as a novel organizing structure without independent empirical grounding or new measurable entities.

axioms (1)
  • domain assumption Crip theory provides legitimate and useful ways of knowing (cripistemologies) that can be centered in AI research
    Invoked as the foundation for the entire framework without derivation or external validation within the paper.
invented entities (1)
  • cripping AI framework no independent evidence
    purpose: To serve as a guiding structure that reveals ableist assumptions and centers disability experiences in AI
    New conceptual entity proposed by the authors; no independent falsifiable evidence or external validation supplied.

pith-pipeline@v0.9.0 · 5442 in / 1478 out tokens · 40866 ms · 2026-05-08T18:55:00.118003+00:00 · methodology

discussion (0)

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