Literature Reviews
Reviewed research papers, articles, and publications relevant to digital accessibility.
15 results found tagged AI accessibility.
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Silence is a Feature, Not a Bug: A Deaf Developer’s Autoethnography on Agency and Local AI
This CHI 2026 Extended Abstract is a three-page autoethnographic provocation by a Deaf computer science graduate student who uses a MED-EL cochlear implant. The author refuses the medical-model framing of deafness as deficit and instead argues that t…
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The Perceptual Gap: Why We Need Accessible XAI for Assistive Technologies
Choudhury (University of Maryland, Baltimore County) presents a position paper and targeted literature review arguing that explainable AI (XAI) — the body of methods that help users understand why a black-box model produced a particular output — is f…
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"It's trained by non-disabled people": Evaluating How Image Quality Affects Product Captioning with Vision-Language Models
Garg and colleagues investigate how well Vision-Language Models (VLMs) caption product images taken by blind and low-vision (BLV) people — a high-stakes everyday task that increasingly depends on tools like Be My AI, Microsoft Seeing AI, and general-…
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Towards LLM-powered Assistive Drone for Blind and Low Vision Users
Wei and colleagues built and evaluated a voice-based assistive drone prototype for blind and low-vision (BLV) users that leverages GPT-4o in two stages: generating step-by-step Python drone-control code from natural-language commands, and interpretin…
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Reimagining Sign Language Technologies: Analyzing Translation Work of Chinese Deaf Online Content Creators
Tang and Piper investigate the translation practices of thirteen deaf Chinese online content creators who produce sign language videos for Kuaishou, Bilibili, Douyin, WeChat, and Xiaohongshu, reaching audiences that range from thousands to nearly a q…
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"I Don't Trust Any Professional Research Tool": A Re-Imagination of Knowledge Production Workflows by, with, and for Blind and Low-Vision Researchers
This CHI 2026 paper is an autoethnographically-grounded, mixed-methods study of how blind and low-vision (BLV) researchers actually do research inside an ecosystem of tools built with sighted workflows in mind. Written by two BLV researchers (one tot…
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I'm Always a Little Skeptical of It: Verification Practices of Blind Users When Working with Generative AI in Spreadsheets
This CHI 2026 paper reports a remote study with 12 blind screen reader users (11 totally blind, 1 legally blind) examining how they verify outputs produced by Generative AI tools when working on accuracy-critical spreadsheet tasks. Spreadsheets are p…
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Beyond Technical Metrics: Understanding the Gap Between AI Performance and Deaf User Experience in Chinese Natural Sign Language Generation
This CHI 2026 paper investigates the disconnect between technical performance metrics and actual Deaf user experience in AI-generated Chinese Natural Sign Language (CNSL). The authors argue that existing sign language generation research, dominated b…
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Rhetoric vs Responsibility: How Tech Companies Shape AI for Accessibility
Marathe, Zhou, Mishra, and Piper conduct a critical discourse analysis (CDA) of 126 public-facing blog posts and news articles published between 2016 and 2025 by 11 leading U.S.-based AI companies — Adobe, Amazon, Apple, Eleven Labs, Google, Meta, Mi…
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Enabling meaningful use of AI-infused educational technologies for children with blindness: Learnings from the development and piloting of the PeopleLens curriculum
This paper presents the development and pilot evaluation of a curriculum designed to support the meaningful use of PeopleLens, an AI-powered augmented reality system that helps children born blind develop social attention skills. PeopleLens uses a he…