# An Autoethnographic Case Study of Generative Artificial Intelligence's Utility for Accessibility

arXiv: https://arxiv.org/abs/2308.09924 · HTML: https://mlsanity.com/paper/2308.09924/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2023.

## Built on by (most cited first, 7 of 7)

- [Detecting Multimedia Generated by Large AI Models: A Survey](https://mlsanity.com/paper/2402.00045/) (2024)
- [Identifying and Improving Disability Bias in GPT-Based Resume Screening](https://mlsanity.com/paper/2402.01732/) (2024)
- ["Cold, Calculated, and Condescending": How AI Identifies and Explains Ableism Compared to Disabled People](https://mlsanity.com/paper/2410.03448/) (2024)
- [Engaging with Children's Artwork in Mixed Visual-Ability Families](https://mlsanity.com/paper/2407.18874/) (2024)
- [Accessibility Considerations in the Development of an AI Action Plan](https://mlsanity.com/paper/2503.14522/) (2025)
- [Prompt injections as a tool for preserving identity in GAI image descriptions](https://mlsanity.com/paper/2510.16128/) (2025)
- [Creating Disability Story Videos with Generative AI: Motivation, Expression, and Sharing](https://mlsanity.com/paper/2601.12617/) (2026)

## Abstract (arXiv)

With the recent rapid rise in Generative Artificial Intelligence (GAI) tools, it is imperative that we understand their impact on people with disabilities, both positive and negative. However, although we know that AI in general poses both risks and opportunities for people with disabilities, little is known specifically about GAI in particular. To address this, we conducted a three-month autoethnography of our use of GAI to meet personal and professional needs as a team of researchers with and without disabilities. Our findings demonstrate a wide variety of potential accessibility-related uses for GAI while also highlighting concerns around verifiability, training data, ableism, and false promises.