Literature Reviews

Reviewed research papers, articles, and publications relevant to digital accessibility.

• Disability-First AI Dataset Annotation: Co-designing Stuttered Speech Annotation Guidelines with People Who Stutter

  Xinru Tang, Jingjin Li, Shaomei Wu (2026) — Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI '26)

  Tang, Li, and Wu present the first study to push the 'disability-first' principle beyond dataset collection and into the dataset annotation stage of the AI pipeline. Their case is stuttered speech: despite a growing number of stuttering datasets (Flu…

  • AI dataset annotation
  • stuttering
  • speech recognition
  • disability-first design
  • embodied knowledge
  • data labeling
  • accessibility
  • speech technology
  • co-design
  • epistemic injustice
  • AI ethics
  • AI fairness

• Rhetoric vs Responsibility: How Tech Companies Shape AI for Accessibility

  Aparajita S Marathe, Quan Zhou, Achi Mishra, Anne Marie Piper (2026) — Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI '26)

  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…

  • artificial intelligence
  • AI accessibility
  • critical discourse analysis
  • disability justice
  • critical disability studies
  • technoableism
  • corporate rhetoric
  • AI ethics
  • participatory design
  • disability representation
  • ethics washing

• Policy Maps: Tools for Guiding the Unbounded Space of LLM Behaviors

  Michelle S. Lam, Fred Hohman, Dominik Moritz, Jeffrey P. Bigham, Kenneth Holstein, Mary Beth Kery (2025) — Proceedings of the 38th Annual ACM Symposium on User Interface Software and Technology (UIST '25)

  This paper introduces "policy maps," an approach to AI policy design for large language models inspired by physical mapmaking. The core insight is that comprehensive policy coverage over an unbounded space of LLM inputs and outputs is impossible — ju…

  • AI safety
  • AI policy
  • large language models
  • AI ethics
  • model evaluation
  • content moderation
  • data visualization
  • human-AI interaction
  • AI governance

• Shared Privacy Concerns of the Visually Impaired and Sighted Bystanders with Camera-Based Assistive Technologies

  Taslima Akter, Tousif Ahmed, Apu Kapadia, Manohar Swaminathan (2022) — ACM Transactions on Accessible Computing

  This paper investigates the privacy and ethical concerns surrounding camera-based assistive technologies (like smart glasses) from two perspectives: people with visual impairments (PVIs) as technology wearers and sighted people as bystanders who migh…

  • privacy
  • visual impairment
  • camera-based assistive technology
  • AI ethics
  • algorithmic bias
  • smart glasses
  • augmented reality
  • bystander privacy
  • facial recognition

• Bridging the Gap: Towards Advancing Privacy and Accessibility

  Rahaf Alharbi, Robin N. Brewer, Gesu India, Lotus Zhang, Leah Findlater, Yixin Zou, Abigale Stangl (2023) — Proceedings of the 25th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS '23)

  This workshop proposal addresses a critical but understudied gap between the accessibility and privacy research communities. The authors argue that while all technology has privacy implications, accessibility tools carry particularly overlooked priva…

  • privacy
  • accessibility
  • intersectionality
  • AI ethics
  • blind and low vision
  • workshop
  • inclusive design
  • surveillance

• "Dump it, Destroy it, Send it to Data Heaven": Blind People's Expectations for Visual Privacy in Visual Assistance Technologies

  Abigale Stangl, Emma Sadjo, Pardis Emami-Naeini, Yang Wang, Danna Gurari, Leah Findlater (2023) — Proceedings of the 20th International Web for All Conference (W4A)

  This paper investigates the visual privacy expectations of 16 totally blind individuals who use visual assistance technologies (VATs) such as Aira, Be My Eyes, Seeing AI, and Envision AI. VATs provide blind users access to visual information by conne…

  • visual privacy
  • blind users
  • visual assistance technology
  • privacy expectations
  • data protection
  • AI ethics
  • camera-based assistive technology
  • obfuscation

• "Before, I Asked My Mom, Now I Ask ChatGPT": Visual Privacy Management with Generative AI for Blind and Low-Vision People

  Tanusree Sharma, Yu-Yun Tseng, Lotus Zhang, Ayae Ide, Kelly Avery Mack, Leah Findlater, Danna Gurari, Yang Wang (2025) — Proceedings of the 27th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS 2025)

  This paper presents the first in-depth empirical study of how blind and low vision (BLV) individuals use generative AI (GenAI) tools to manage visual privacy across diverse everyday contexts. The researchers conducted semi-structured interviews with …

  • blind and low vision
  • visual privacy
  • generative AI
  • privacy management
  • impression management
  • visual interpretation services
  • AI ethics

• Temp access: Reflecting on multimodal GAI as an accessibility technology for temporary disability

  Kate S. Glazko (2025) — ASSETS 2025: 27th International ACM SIGACCESS Conference on Computers and Accessibility

  This paper presents an autoethnographic account of using multimodal generative AI (GAI) tools as accessibility technology during a period of temporary disability. The author, an accessibility researcher, experienced an illness that simultaneously imp…

  • generative AI
  • temporary disability
  • assistive technology
  • autoethnography
  • multimodal AI
  • communication access
  • disability identity
  • technology adoption
  • AI ethics

• Exploring Collaboration to Center the Deaf Community in Sign Language AI

  Rie Kamikubo, Abraham Glasser, Alex X Lu, Hal Daumé III, Hernisa Kacorri, Danielle Bragg (2025) — ASSETS 2025: 27th International ACM SIGACCESS Conference on Computers and Accessibility

  This paper investigates the critical disconnect between machine learning (ML) practitioners and Deaf American Sign Language (ASL) signers in the development of sign language AI technologies. Despite sign language processing's promise for societal inc…

  • Deaf community
  • sign language
  • American Sign Language
  • machine learning
  • participatory AI
  • co-design
  • interdisciplinary collaboration
  • AI ethics
  • community-centered design
  • sign language recognition