A Dataset of Alt Texts from HCI Publications: Analyses and Uses Towards Producing More Descriptive Alt Texts of Data Visualizations in Scientific Papers
Open accessAbstract
Figures in scientific publications contain important information and results, and alt text is needed for blind and low vision readers to engage with their content. We conduct a study to characterize the semantic content of alt text in HCI publications based on a framework introduced by Lundgard and Satyanarayan [30]. Our study focuses on alt text for graphs, charts, and plots extracted from HCI and accessibility publications; we focus on these communities due to the lack of alt text in papers published outside of these disciplines. We find that the capacity of author-written alt text to fulfill blind and low vision user needs is mixed; for example, only 50% of alt texts in our sample contain information about extrema or outliers, and only 31% contain information about major trends or comparisons conveyed by the graph. We release our collected dataset of author-written alt text, and outline possible ways that it can be used to develop tools and models to assist future authors in writing better alt text. Based on our findings, we also discuss recommendations that can be acted upon by publishers and authors to encourage inclusion of more types of semantic content in alt text.
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A recent audit of HCI and accessibility publications found that around 50% of figures with alt text, especially those involving complex visualizations, contain uninformative descriptions.
“A recent audit [14] of HCI and accessibility publications found that around 50% of figures with alt text, especially those involving complex visualizations, contain uninformative descriptions.”
From How University Disability Services Professionals Write Image Descriptions for HCI Figures Using Generative AI (2026) · cites this paper as [14]
The Introduction reports that only 50% of sampled alt texts for graphs, charts, and plots discuss extrema or outliers, and only 31% discuss trends or comparisons. The Discussion says many descriptions lack useful detail, but the paper does not classify around 50% of all figures with alt text as having uninformative descriptions, nor does it show that complex visualizations are especially affected. No page numbers are available in our copy. (LLM, conf=0.87)
Experimental — accuracy not yet measuredThey often fall short of meeting alt text accommodation needs for students.
“Therefore, they often fall short of meeting (alt text) accommodation needs for students [14, 67].”
From How University Disability Services Professionals Write Image Descriptions for HCI Figures Using Generative AI (2026) · cites this paper as [14]
The Discussion & Conclusion (Section 6; page unavailable) says many author-written alt texts for graphs and charts in the sampled HCI publications do not yet provide the basic information proposed for blind and low-vision readers. The paper does not assess students or their accommodation needs, so that population-specific part of the claim is unsupported. (LLM, conf=0.85)
Experimental — accuracy not yet measuredLLMs generate hallucinated content or omit essential visual details, sometimes producing ineffective alt text for science figures.
“While LLMs offer promise, they also generate hallucinated content or omit essential visual details; thus, sometimes producing ineffective alt text for science figures [14, 70].”
From How University Disability Services Professionals Write Image Descriptions for HCI Figures Using Generative AI (2026) · cites this paper as [14]
Section 2.4 reports that Office 365’s automatically generated alt text for scientific figures often gives only the chart type and omits other content, making it of limited use. That supports the concern about missing visual details, but the paper does not establish that LLMs produce these omissions or hallucinated content. (LLM, conf=0.84)
Experimental — accuracy not yet measuredChecking for semantic information can help assess coverage in alt text, such as by assessing length, structure, or specifics (e.g., outliers) of alt text.
“Checking for semantic information can help assess coverage in alt text, such as by assessing length, structure, or specifics (e.g., outliers) of alt text [14].”
From How University Disability Services Professionals Write Image Descriptions for HCI Figures Using Generative AI (2026) · cites this paper as [14]
The Introduction and §4.2 assess which semantic details figure alt text covers, including outliers. The RQ3 analysis examines length but cautions that alt texts of comparable length can contain different amounts of information; the cited passages do not establish alt-text structure as a way to assess coverage. No page numbers are available in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredMost published work lacks accurate alt text for visual content (e.g., scientific figures) and requires alt text remediation.
“Despite the widespread acknowledgment of these accessibility needs, most published work lacks accurate alt text for visual content (e.g., scientific figures) [14, 41] and requires alt text remediation [33].”
From How University Disability Services Professionals Write Image Descriptions for HCI Figures Using Generative AI (2026) · cites this paper as [14]
In HCI, only about 5% of publications have valid alt text, which is even worse in other fields like science, medicine, and engineering.
“Recent work [5, 14, 41] shows that in Human-Computer Interaction (HCI), where accessibility research is advocated, only about 5% of publications have valid alt text, which is even worse in other fields like science, medicine, and engineering [51, 54].”
From How University Disability Services Professionals Write Image Descriptions for HCI Figures Using Generative AI (2026) · cites this paper as [14]
Several existing studies emphasize the importance of assessing alt text, particularly for academic and technical content.
“Several existing studies emphasize the importance of assessing alt text, particularly for academic and technical content [14, 47, 79].”
From How University Disability Services Professionals Write Image Descriptions for HCI Figures Using Generative AI (2026) · cites this paper as [14]
Chintalapati et al. conducted a systematic analysis of alt text from published work, identifying substantial semantic gaps in written alt text.
“For instance, Chintalapati et al. [14] conducted a systematic analysis of alt text from published work, identifying substantial semantic gaps in written alt text.”
From How University Disability Services Professionals Write Image Descriptions for HCI Figures Using Generative AI (2026) · cites this paper as [14]
There is a high prevalence of inaccessible figures in published work.
“There is a high prevalence of inaccessible figures in published work [14, 41, 54]”
From How University Disability Services Professionals Write Image Descriptions for HCI Figures Using Generative AI (2026) · cites this paper as [14]
We created the alt text rubric criteria and evaluation measures by analyzing multiple sources.
“Similarly, we created the alt text rubric criteria and evaluation measures by analyzing multiple sources [11, 14, 40, 47, 75, 76].”
From How University Disability Services Professionals Write Image Descriptions for HCI Figures Using Generative AI (2026) · cites this paper as [14]
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Kosova, Y., Redkokosh, K. I., & Mikheyev, P. O. (2024) · Open Education · doi:10.21686/1818-4243-2024-1-9-20 · Cited by 1 · see citing works
Enhancing Accessibility in Publishing: Leveraging GAI for Effective Alt-Text Solutions
Morelli, C. (2025) · Technology for Inclusion and Participation for All: Recent Achievements and Future Directions · doi:10.1007/978-3-032-01632-4_48
MCM-DPO : Multifaceted Cross-Modal Direct Preference Optimization for Alt-text Generation
Fu, J., Huangfu, S., Fei, H., Huang, Y., Shen, X., Qiu, X., & Ng, S.-K. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3754805
Составление альтернативного текста к изображениям с использованием больших языковых моделей
Kosova, E. A., & Redkokosh, K. I. (2024) · Sovremennye informacionnye tehnologii i IT-obrazovanie. · doi:10.25559/sitito.020.202403.699-714
Götzelmann, T., Sommer, S., & Karg, P. (2026) · Electronics · doi:10.3390/electronics15143223
Architecting Agentic AI Systems for Inclusive and Accessible Library Ecosystems
Khoso, R. H., Dunputh, V. M., & Monter, A. C. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7262-4.ch012
Optimizing Alt Text Design through Human-LLM Collaboration
Kosova, Y., & Redkokosh, K. I. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-27834-0_24
Turkarslan, A., Thakkar, V., Tekle, R., Chen*, T., Martinez, J. J., & Mankoff, J. C. (2026) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3797867.3829048
Hassan, S., Khalaila, A., Cashman, D., Sharif, A., & Faust, R. (2026) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3797867.3829030
- Authors
- Chintalapati, S. S., Bragg, J., & Wang, L. L.
- Affiliations
- University of Washington (US)
- Allen Institute for Artificial Intelligence (US)
- Venue
- Proceedings of the 24th International ACM SIGACCESS Conference on Computers and Accessibility
- Publisher
- ACM
- Published
- 2022-10-22
- Pages
- 1-12
- OpenAlex
- W4298051348
- Cited by
- 31
- References
- 51
- Citations by year
- 2026: 6 · 2025: 11 · 2024: 12 · 2023: 1 · 2022: 1
- FWCI
- 9.11
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- Allen Institute for Artificial Intelligence
- Topics
- Social Sciences › Human Factors and Ergonomics › Digital Accessibility for Disabilities
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Mathematics, Computing, and Information Processing
- Keywords
- alt text, accessibility, blind and visually impaired users, charts and graphs, semantic content
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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