AI and accessibility
Abstract
A discussion of ethical considerations.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The literature on AI and accessibility suggests specific concerns regarding the use of AI and people with disabilities.
“More generally, the literature on AI and accessibility also suggests specific concerns regarding the use of AI and people with disabilities (Guo et al., 2020;Morris, 2020;Trewin, 2018).”
From Exploring the role of generative AI in higher education: Semi-structured interviews with students with disabilities (2025) · cites this paper as Morris, 2020
The marketing of AI performance often sets high expectations of what AI can do, which can be deceptive for people who depend on the technology.
“Fifth, the marketing of AI performance often sets high expectations of what AI can do, which can be deceptive for people who depend on the technology (Morris, 2020).”
From Exploring the role of generative AI in higher education: Semi-structured interviews with students with disabilities (2025) · cites this paper as Morris, 2020
Employing AI may be problematic for people with disabilities because they may be excluded from a service that does not work for them.
“First, employing AI may be problematic for people with disabilities because they may be excluded from a service that does not work for them (Morris, 2020).”
From Exploring the role of generative AI in higher education: Semi-structured interviews with students with disabilities (2025) · cites this paper as Morris, 2020
AI systems are still imperfect and can output errors that people with specific impairments may be unable to check.
“Fourth, AI systems are still imperfect and can output errors that people with specific impairments may be unable to check (Morris, 2020).”
From Exploring the role of generative AI in higher education: Semi-structured interviews with students with disabilities (2025) · cites this paper as Morris, 2020
The use of simulated data to train algorithms does not represent well the experience of people with disabilities.
“Sixth, Morris (2020) warns against the use of simulated data to train algorithms as it does not represent well the experience of people with disabilities.”
From Exploring the role of generative AI in higher education: Semi-structured interviews with students with disabilities (2025) · cites this paper as Morris, 2020
Privacy concerns are more significant for people with disabilities than those without disabilities because disability status might be inferred from data and unintentionally disclosed without consent.
“Third, privacy concerns are more significant for people with disabilities than those without disabilities because disability status might be inferred from data and unintentionally disclosed without consent (Morris, 2020).”
From Exploring the role of generative AI in higher education: Semi-structured interviews with students with disabilities (2025) · cites this paper as Morris, 2020
The risk of reidentification is higher for individuals with disabilities.
“the risk of reidentification is higher for individuals with disabilities (Morris, 2020).”
From Exploring the role of generative AI in higher education: Semi-structured interviews with students with disabilities (2025) · cites this paper as Morris, 2020
Individuals with disabilities are particularly reliant on accurate information.
“as individuals with disabilities are particularly reliant on accurate information (Morris, 2020).”
From Exploring the role of generative AI in higher education: Semi-structured interviews with students with disabilities (2025) · cites this paper as Morris, 2020
One student reported that ChatGPT could be talking about autism in a harmful and biased manner.
“One student reported that ChatGPT could be talking about autism in a harmful and biased manner, concurring with concerns about bias in research on AI ethics and disability (Guo et al., 2020;Morris, 2020;Trewin, 2018).”
From Exploring the role of generative AI in higher education: Semi-structured interviews with students with disabilities (2025) · cites this paper as Morris, 2020
Cites
Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé, H., & Crawford, K. (2018) · arXiv · doi:10.48550/arxiv.1803.09010 · Cited by 2,474
Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., III, H. D., & Crawford, K. (2021) · Communications of the ACM · doi:10.1145/3458723 · Cited by 2,469 · see citing works
VizWiz Grand Challenge: Answering Visual Questions from Blind People
Gurari, D., Li, Q., Stangl, A. J., Guo, A., Lin, C., Grauman, K., Luo, J., & Bigham, J. P. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00380 · Cited by 621 · cited 1× · see citing works
“Efforts to directly source data from under-represented user groups, such as the VizWiz data set [5], which contains thousands of images and related questions captured by people who are blind, are a step in the right direction.”
Lipton, Z. C., & Steinhardt, J. (2019) · Queue · doi:10.1145/3317287.3328534 · Cited by 231 · see citing works
Toward fairness in AI for people with disabilities SBG@a research roadmap
Guo, A., Kamar, E., Vaughan, J. W., Wallach, H., & Morris, M. R. (2020) · ACM SIGACCESS Accessibility and Computing · doi:10.1145/3386296.3386298 · Cited by 128 · see citing works
Self-citationAI Fairness for People with Disabilities: Point of View
Trewin, S. (2018) · arXiv · doi:10.48550/arxiv.1811.10670 · Cited by 38 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Multimodal datasets: misogyny, pornography, and malignant stereotypes
Birhane, A., Prabhu, V. U., & Kahembwe, E. (2021) · arXiv · doi:10.48550/arxiv.2110.01963 · Cited by 160 · see citing works
AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications
Chen, P., Wu, L., & Wang, L. (2023) · Applied Sciences · doi:10.3390/app131810258 · Cited by 155 · see citing works
Harnessing AI to Foster Equity in Education: Opportunities, Challenges, and Emerging Strategies
Roshanaei, M., Olivares, H., & Lopez, R. R. (2023) · Journal of Intelligent Learning Systems and Applications · doi:10.4236/jilsa.2023.154009 · Cited by 147 · see citing works
Stangl, A., Verma, N., Fleischmann, K. R., Morris, M. R., & Gurari, D. (2021) · Proceedings of the 23rd International ACM SIGACCESS Conference on Computers and Accessibility · doi:10.1145/3441852.3471233 · Cited by 81 · see citing works
Self-citationLaMPost: Design and Evaluation of an AI-assisted Email Writing Prototype for Adults with Dyslexia
Goodman, S. M., Buehler, E., Clary, P., Coenen, A., Donsbach, A., Horne, T. N., Lahav, M., MacDonald, R., Michaels, R. B., Narayanan, A., et al. (2022) · Proceedings of the 24th International ACM SIGACCESS Conference on Computers and Accessibility · doi:10.1145/3517428.3544819 · Cited by 71 · see citing works
Self-citation“They only care to show us the wheelchair”: disability representation in text-to-image AI models
Mack, K. A., Qadri, R., Denton, R., Kane, S. K., & Bennett, C. L. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642166 · Cited by 67 · see citing works
The FATE Landscape of Sign Language AI Datasets: An Interdisciplinary Perspective
Bragg, D., Caselli, N., Hochgesang, J. A., Huenerfauth, M., Katz-Hernandez, L., Koller, O., Kushalnagar, R., Vogler, C., & Ladner, R. E. (2021) · ACM Transactions on Accessible Computing · doi:10.1145/3436996 · Cited by 55 · see citing works
Pierrès, O., Darvishy, A., & Christen, M. (2025) · Education and Information Technologies · doi:10.1007/s10639-024-13134-8 · Cited by 48 · see citing works
Theodorou, L., Massiceti, D., Zintgraf, L., Stumpf, S., Morrison, C., Cutrell, E., Harris, M. T., & Hofmann, K. (2021) · Proceedings of the 23rd International ACM SIGACCESS Conference on Computers and Accessibility · doi:10.1145/3441852.3471225 · Cited by 42 · see citing works
Blind Users Accessing Their Training Images in Teachable Object Recognizers
Hong, J., Gandhi, J., Mensah, E. E., Zeraati, F. Z., Jarjue, E., Lee, K., & Kacorri, H. (2022) · Proceedings of the 24th International ACM SIGACCESS Conference on Computers and Accessibility · doi:10.1145/3517428.3544824 · Cited by 22 · see citing works
- Authors
- Morris, M. R.
- Affiliations
- Microsoft (United States) (US)
- Corresponding author
- Morris, Meredith Ringel
- Venue
- Communications of the ACM
- Published
- 2020-05-21
- Volume
- 63
- Issue
- 6
- Pages
- 35-37
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W3123973939
- Cited by
- 114
- References
- 6
- Citations by year
- 2026: 21 · 2025: 30 · 2024: 24 · 2023: 12 · 2022: 13 · 2021: 12 · 2020: 2
- FWCI
- 13.29
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Communications of the ACM (publishedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Human Factors and Ergonomics › Digital Accessibility for Disabilities
- Health Sciences › Health Professions › Occupational Therapy › Assistive Technology in Communication and Mobility
- Keywords
- artificial intelligence, accessibility, AI ethics, assistive technology
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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