# Accessibility Considerations in the Development of an AI Action Plan

arXiv: https://arxiv.org/abs/2503.14522 · HTML: https://mlsanity.com/paper/2503.14522/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 14 Mar 2025.

## Builds on (most cited first, 6 of 6)

- [Toward Fairness in AI for People with Disabilities: A Research Roadmap](https://mlsanity.com/paper/1907.02227/) (2019)
- [Misfitting With AI: How Blind People Verify and Contest AI Errors](https://mlsanity.com/paper/2408.06546/) (2024)
- ["It's Kind of Context Dependent": Understanding Blind and Low Vision People's Video Accessibility Preferences Across Viewing Scenarios](https://mlsanity.com/paper/2403.10792/) (2024)
- [Emerging Practices for Large Multimodal Model (LMM) Assistance for People with Visual Impairments: Implications for Design](https://mlsanity.com/paper/2407.08882/) (2024)
- [An Autoethnographic Case Study of Generative Artificial Intelligence's Utility for Accessibility](https://mlsanity.com/paper/2308.09924/) (2023)
- [Areas of Strategic Visibility: Disability Bias in Biometrics](https://mlsanity.com/paper/2208.04712/) (2022)

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

- [Surfacing Variations to Calibrate Perceived Reliability of MLLM-generated Image Descriptions](https://mlsanity.com/paper/2507.15692/) (2025)

## Abstract (arXiv)

We argue that there is a need for Accessibility to be represented in several important domains: - Capitalize on the new capabilities AI provides - Support for open source development of AI, which can allow disabled and disability focused professionals to contribute, including - Development of Accessibility Apps which help realise the promise of AI in accessibility domains - Open Source Model Development and Validation to ensure that accessibility concerns are addressed in these algorithms - Data Augmentation to include accessibility in data sets used to train models - Accessible Interfaces that allow disabled people to use any AI app, and to validate its outputs - Dedicated Functionality and Libraries that can make it easy to integrate AI support into a variety of settings and apps. - Data security and privacy and privacy risks including data collected by AI based accessibility technologies; and the possibility of disability disclosure. - Disability-specific AI risks and biases including both direct bias (during AI use by the disabled person) and indirect bias (when AI is used by someone else on data relating to a disabled person).