# Misfitting With AI: How Blind People Verify and Contest AI Errors

arXiv: https://arxiv.org/abs/2408.06546 · HTML: https://mlsanity.com/paper/2408.06546/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2024.

## Built on by (most cited first, 15 of 15)

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- [A Pilot Study on Curator-Guided Multilingual Art Description for Blind and Low-Vision Audiences with Small Vision-Language Models](https://mlsanity.com/paper/2605.31080/) (2026)
- [Bespoke Visual Assistance: What and How do Blind and Low-Vision People Create with Agentic Programming?](https://mlsanity.com/paper/2607.21760/) (2026)
- [Chart-Supported or Model-Supplied? Examining MLLM-Generated Claims for Accessible Visualization](https://mlsanity.com/paper/2607.25021/) (2026)
- [Navigation Alone Is Not Enough: Evaluating Explanatory Assistive UI Agents](https://mlsanity.com/paper/2608.09944/) (2026)
- [RoboFind: Multi-Agent Personalized Object Search for People Who Are Blind or Have Low Vision](https://mlsanity.com/paper/2609.20330/) (2026)
- [Touvigation: Embodied Adaptive Object Acquisition for Blind and Low-Vision Users in Unfamiliar Indoor Environments](https://mlsanity.com/paper/2609.21828/) (2026)

## Abstract (arXiv)

Blind people use artificial intelligence-enabled visual assistance technologies (AI VAT) to gain visual access in their everyday lives, but these technologies are embedded with errors that may be difficult to verify non-visually. Previous studies have primarily explored sighted users' understanding of AI output and created vision-dependent explainable AI (XAI) features. We extend this body of literature by conducting an in-depth qualitative study with 26 blind people to understand their verification experiences and preferences. We begin by describing errors blind people encounter, highlighting how AI VAT fails to support complex document layouts, diverse languages, and cultural artifacts. We then illuminate how blind people make sense of AI through experimenting with AI VAT, employing non-visual skills, strategically including sighted people, and cross-referencing with other devices. Participants provided detailed opportunities for designing accessible XAI, such as affordances to support contestation. Informed by disability studies framework of misfitting and fitting, we unpacked harmful assumptions with AI VAT, underscoring the importance of celebrating disabled ways of knowing. Lastly, we offer practical takeaways for Responsible AI practice to push the field of accessible XAI forward.