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• Shiny Stories, Hidden Struggles: Investigating the Representation of Disability Through the Lens of LLMs

  Marco Bombieri, Simone Paolo Ponzetto, Marco Rospocher · 2026 · ACM Transactions on Intelligent Systems and Technology

  This paper investigates how Large Language Models (LLMs) represent disability by comparing AI-generated social media posts with self-descriptions from real people with disabilities on Reddit. The study addresses a critical gap in bias research: while prior work has focused on…

  AI bias · large language models · disability representation · inspiration porn · toxic positivity

• Toward Enhancing Subtitle Features in Mobile Apps: Analyzing User Reviews for Accessibility and Usability Insights

  Wajdi M Aljedaani, Matheus Souza, Marcelo Medeiros Eler · 2026 · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI '26)

  This large-scale study analyses user reviews from the Google Play Store to understand how people experience subtitle and caption features in mobile applications. The authors extracted roughly 180 million reviews from the top 340 Android apps across diverse categories, then…

  subtitles · captioning · mobile applications · user reviews · app store analysis

• QuickQue: Enabling Quick Access to Information in User Reviews for Screen Reader Users

  Mohan Sunkara, Akshay Kolgar Nayak, Sandeep Kalari, Yash Prakash, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok · 2025 · Proceedings of the 22nd International Web for All Conference (W4A 2025)

  This paper presents QuickCue, a Google Chrome browser extension that helps blind screen reader users efficiently access online customer reviews by using LLM-powered aspect and sentiment classification to organize and summarize review content. The current experience of reading…

  screen readers · blind users · online reviews · large language models · browser extension

• Adapting Online Customer Reviews for Blind Users: A Case Study of Restaurant Reviews

  Mohan Sunkara, Akshay Kolgar Nayak, Sandeep Kalari, Yash Prakash, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok · 2025 · Proceedings of the 22nd International Web for All Conference (W4A)

  This paper addresses the significant usability challenges blind screen reader users face when navigating online customer reviews, using restaurant reviews on Google Maps as a case study. The research proceeds in two phases. First, an interview study with 30 blind screen reader…

  screen readers · blind users · online reviews · LLM accessibility · content summarization

• Unintended Machine Learning Biases as Social Barriers for Persons with Disabilities

  Ben Hutchinson, Vinodkumar Prabhakaran, Remi Denton, Kellie Webster, Yu Zhong, Stephen Denuyl · 2020 · SIGACCESS Accessibility and Computing

  This paper from Google's Ethical AI team provides concrete empirical evidence that widely deployed NLP models encode measurable biases against people with disabilities, creating social barriers through technology. The authors examine three layers of bias using a perturbation…

  machine learning bias · NLP · toxicity detection · sentiment analysis · disability representation

• Using data from social media websites to inspire the design of assistive technology

  Xing Yu · 2016 · Proceedings of the 13th International Web for All Conference (W4A)

  This doctoral consortium paper proposes using social media data as a low-cost, scalable method to inform the design of assistive technology, addressing limitations of traditional user research approaches. The author argues that designing assistive technology faces unique…

  assistive technology · social media · natural language processing · machine learning · prosthetics

6 results.