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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

• Creating Disability Story Videos with Generative AI: Motivation, Expression, and Sharing

  Shuo Niu, Dylan Clements, Hyungsin Kim · 2026 · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI '26)

  Niu, Clements, and Kim worked with nine people with disabilities (PwDs) from a Massachusetts disability advocacy group (pseudonymized as 'Campaign') to study how novice users adopt generative AI for creating first-person disability storytelling videos. Participants had…

  generative AI · disability storytelling · video accessibility · disability advocacy · LLM

• Interface Support for Evaluating Disability Bias in AI-Generated Images

  Kelly Avery Mack, Lucy Jiang, Lotus Zhang, Leah Findlater · 2026 · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI '26)

  Mack and colleagues investigate whether interface-level interventions can help users of generative text-to-image (T2I) tools recognise and avoid disability stereotypes in AI-generated images. The authors frame the work around a gap in AI safety: while model-side debiasing is an…

  AI bias · generative AI · text-to-image · disability representation · disability stereotypes

• Understanding Human-AI Misalignment in LLM-Based Job-Seeking Support for Neurodivergent Users

  Kaely Hall, Marcus Ma, Xinyue Zhang, Vedant Das Swain, Jennifer G Kim · 2025 · ASSETS 2025: 27th International ACM SIGACCESS Conference on Computers and Accessibility

  This paper examines how misalignments manifest between neurodivergent job-seekers and a GPT-4-powered career support chatbot deployed by Mentra, a neuroinclusive employment platform with over 46,000 neurodivergent users. The researchers analysed 348 real-world chat logs from 271…

  neurodivergence · large language models · employment · AI alignment · autism

• Examining Age-Bias and Stereotypes of Aging in LLMs

  Sherwin Dewan, Ismail Shaikh, Connie Shaw, Abhilash Sahoo, Akshita Jha, Alisha Pradhan · 2025 · ASSETS 2025: 27th International ACM SIGACCESS Conference on Computers and Accessibility

  This paper investigates how large language models encode and reproduce age-related stereotypes about older adults. Using prompts from the Bias Benchmarking Questionnaire (BBQ), a well-established fairness dataset, the researchers administered 1,648 age-bias prompts to ChatGPT…

  ageism · AI bias · large language models · older adults · stereotypes

• "It's Complicated": Negotiating Accessibility and (Mis)Representation in Image Descriptions of Race, Gender, and Disability

  Cynthia L. Bennett, Cole Gleason, Morgan Klaus Scheuerman, Jeffrey P. Bigham, Anhong Guo, Alexandra To · 2021 · CHI Conference on Human Factors in Computing Systems

  This qualitative study investigates how screen reader users who are also Black, Indigenous, People of Color (BIPOC), non-binary, and/or transgender navigate the complex landscape of image descriptions, particularly regarding how appearance characteristics like race, gender, and…

  image descriptions · alt text · screen readers · visual impairments · race

• Sense and Accessibility: Understanding People with Physical Disabilities' Experiences with Sensing Systems

  Shaun K. Kane, Anhong Guo, Meredith Ringel Morris · 2020 · Proceedings of the 22nd International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS 2020)

  This paper examines how sensing systems — the increasingly pervasive technologies that mediate our interactions with the digital and physical world — create accessibility barriers for people with physical disabilities. Through an online survey of 40 adults with physical…

  physical disability · AI bias · AI fairness · sensors · ubiquitous computing

• Privacy Considerations of the Visually Impaired with Camera Based Assistive Technologies: Misrepresentation, Impropriety, and Fairness

  Taslima Akter, Tousif Ahmed, Apu Kapadia, Swami Manohar Swaminathan · 2020 · Proceedings of the 22nd International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS 2020)

  This paper investigates the privacy concerns of both visually impaired people (PVIs) and sighted bystanders regarding camera-based assistive technologies like smart glasses (Orcam, Aira, eSight) that can identify people and provide demographic and behavioral information about…

  visual accessibility · blindness and low vision · privacy · AI bias · AI fairness

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