mlsanityData to 7 Oct 2026

"Accessibility people, you go work on that thing of yours over there": Addressing Disability Inclusion in AI Product Organizations

arXiv 2508.16607 ↗First posted 12 Aug 2025cs.HC cs.AIcited by 1

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Abstract (arXiv)

The rapid emergence of generative AI has changed the way that technology is designed, constructed, maintained, and evaluated. Decisions made when creating AI-powered systems may impact some users disproportionately, such as people with disabilities. In this paper, we report on an interview study with 25 AI practitioners across multiple roles (engineering, research, UX, and responsible AI) about how their work processes and artifacts may impact end users with disabilities. We found that practitioners experienced friction when triaging problems at the intersection of responsible AI and accessibility practices, navigated contradictions between accessibility and responsible AI guidelines, identified gaps in data about users with disabilities, and gathered support for addressing the needs of disabled stakeholders by leveraging informal volunteer and community groups within their company. Based on these findings, we offer suggestions for new resources and process changes to better support people with disabilities as end users of AI.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 15 of its 60 references that are in the map. First posted on arXiv 12 Aug 2025.

Builds on

15 of 60 references.

References · 15

  1. 2022Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scalecited by 262
  2. 2022Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reductioncited by 137
  3. 2021Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Supportcited by 61
  4. 2024Identifying and Improving Disability Bias in GPT-Based Resume Screeningcited by 27
  5. 2021Intersectional Bias in Causal Language Modelscited by 24
  6. 2023Automated Ableism: An Exploration of Explicit Disability Biases in Sentiment and Toxicity Analysis Modelscited by 20
  7. 2023Walking the Walk of AI Ethics: Organizational Challenges and the Individualization of Risk among Ethics Entrepreneurscited by 12
  8. 2025Towards Understanding the Use of MLLM-Enabled Applications for Visual Interpretation by Blind and Low Vision Peoplecited by 11
  9. 2024Emerging Practices for Large Multimodal Model (LMM) Assistance for People with Visual Impairments: Implications for Designcited by 10
  10. 2024"Cold, Calculated, and Condescending": How AI Identifies and Explains Ableism Compared to Disabled Peoplecited by 9
  11. 2022Definition drives design: Disability models and mechanisms of bias in AI technologiescited by 5
  12. 2023A Systematic Literature Review of Human-Centered, Ethical, and Responsible AIcited by 5
3 more
  1. 2022What does it mean to be a responsible AI practitioner: An ontology of roles and skillscited by 3
  2. 2023Using Large Language Models to Accelerate Communication for Users with Severe Motor Impairmentscited by 3
  3. 2024Responsible AI in the Software Industry: A Practitioner-Centered Perspectivecited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Querying Multimodal Scientific Papers with AI: Practices and Preferences Across Blind, Low-Vision, and Sighted Scientistscited by 1