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

arXiv: https://arxiv.org/abs/2508.16607 · HTML: https://mlsanity.com/paper/2508.16607/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 60 references that are in the map.
- First posted on arXiv 12 Aug 2025.

## Builds on (most cited first, 15 of 15)

- [Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale](https://mlsanity.com/paper/2211.03759/) (2022)
- [Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction](https://mlsanity.com/paper/2210.05791/) (2022)
- [Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Support](https://mlsanity.com/paper/2112.05675/) (2021)
- [Identifying and Improving Disability Bias in GPT-Based Resume Screening](https://mlsanity.com/paper/2402.01732/) (2024)
- [Intersectional Bias in Causal Language Models](https://mlsanity.com/paper/2107.07691/) (2021)
- [Automated Ableism: An Exploration of Explicit Disability Biases in Sentiment and Toxicity Analysis Models](https://mlsanity.com/paper/2307.09209/) (2023)
- [Walking the Walk of AI Ethics: Organizational Challenges and the Individualization of Risk among Ethics Entrepreneurs](https://mlsanity.com/paper/2305.09573/) (2023)
- [Towards Understanding the Use of MLLM-Enabled Applications for Visual Interpretation by Blind and Low Vision People](https://mlsanity.com/paper/2503.05899/) (2025)
- [Emerging Practices for Large Multimodal Model (LMM) Assistance for People with Visual Impairments: Implications for Design](https://mlsanity.com/paper/2407.08882/) (2024)
- ["Cold, Calculated, and Condescending": How AI Identifies and Explains Ableism Compared to Disabled People](https://mlsanity.com/paper/2410.03448/) (2024)
- [A Systematic Literature Review of Human-Centered, Ethical, and Responsible AI](https://mlsanity.com/paper/2302.05284/) (2023)
- [Definition drives design: Disability models and mechanisms of bias in AI technologies](https://mlsanity.com/paper/2206.08287/) (2022)
- [What does it mean to be a responsible AI practitioner: An ontology of roles and skills](https://mlsanity.com/paper/2205.03946/) (2022)
- [Using Large Language Models to Accelerate Communication for Users with Severe Motor Impairments](https://mlsanity.com/paper/2312.01532/) (2023)
- [Responsible AI in the Software Industry: A Practitioner-Centered Perspective](https://mlsanity.com/paper/2412.07620/) (2024)

## Built on by (most cited first, 1 of 1)

- [Querying Multimodal Scientific Papers with AI: Practices and Preferences Across Blind, Low-Vision, and Sighted Scientists](https://mlsanity.com/paper/2607.18514/) (2026)

## 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.