FigurA11y: AI Assistance for Writing Scientific Alt Text
Open accessAbstract
High-quality alt text is crucial for making scientific figures accessible to blind and low-vision readers. Crafting complete, accurate alt text is challenging even for domain experts, as published figures often depict complex visual information and readers have varied informational needs. These challenges, along with high diversity in figure types and domain-specific details, also limit the usefulness of fully automated approaches. Consequently, the prevalence of high-quality alt text is very low in scientific papers today. We investigate whether and how human-AI collaborative editing systems can help address the difficulty of writing high-quality alt text for complex scientific figures. We present FigurA11y, an interactive system that generates draft alt text and provides suggestions for author revisions using a pipeline driven by extracted figure and paper metadata. We test two versions, motivated by prior work on visual accessibility and writing support. The base Draft+Revise version provides authors with an automatically generated draft description to revise, along with extracted figure metadata and figure-specific alt text guidelines to support the revision process. The full Interactive Assistance version further adds contextualized suggestions: text snippets to iteratively produce descriptions, and hypothetical user questions with possible answers to reveal potential ambiguities and resolutions. In a study of authors (N=14), we found the system assisted them in efficiently producing descriptive alt text. Generated drafts and interface elements enabled authors to quickly initiate and edit detailed descriptions. Additionally, interactive suggestions from the full system prompted more iteration and highlighted aspects for authors to consider, resulting in greater deviation from the drafts without increased average cognitive load or manual effort.
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Most research in this space has focused on content creators or domain experts who author their figures.
“Most research in this space has focused on content creators or domain experts who author their figures [4, 26, 48, 71].”
From How University Disability Services Professionals Write Image Descriptions for HCI Figures Using Generative AI (2026) · cites this paper as [71]
The Introduction and §3.2 Formative Study describe research with authors writing alt text for their own figures, supporting the claim that this is a focus of the cited paper. The paper does not establish that most research in the space focuses on such authors; its Related Work also discusses automated and crowdsourced approaches. (LLM, conf=0.82)
Experimental — accuracy not yet measuredExisting work shows the need for human subject matter validation for evaluating AI-augmented alt text quality to enhance the interpretation for screen reader users.
“Existing work also shows the need for human subject matter validation for evaluating AI-augmented alt text quality to enhance the interpretation for screen reader users [71, 87].”
From How University Disability Services Professionals Write Image Descriptions for HCI Figures Using Generative AI (2026) · cites this paper as [71]
The Introduction calls for authors to critically review and refine AI-generated descriptions because model errors can produce misleading alt text. Section 7.2 says evaluation of whether collaboratively written alt text improves blind and low-vision readers’ comprehension remains future work. The paper supports human review and the need to evaluate reader outcomes, but does not establish that subject-matter validation enhances screen-reader users’ interpretation. (LLM, conf=0.83)
Experimental — accuracy not yet measuredWhen LLMs are used as a collaborative tool, they show promise in assisting authors to write alt text.
“Nevertheless, when LLMs are used as a collaborative tool, they show promise in assisting authors to write alt text [3, 71].”
From How University Disability Services Professionals Write Image Descriptions for HCI Figures Using Generative AI (2026) · cites this paper as [71]
This concern is often attributed to authors' challenges in writing effective alt text for science figures.
“This concern is often attributed to authors' challenges in writing effective alt text for science figures [71, 79].”
From How University Disability Services Professionals Write Image Descriptions for HCI Figures Using Generative AI (2026) · cites this paper as [71]
Little is known about writing support for non-experts, such as DSO professionals, in figure accessibility.
“While previous research in figure accessibility has mostly focused on readers [2, 32, 66], or authors of papers [31, 71], little is known about the writing support for non-experts , such as DSO professionals.”
From How University Disability Services Professionals Write Image Descriptions for HCI Figures Using Generative AI (2026) · cites this paper as [71]
LLMs have demonstrated the ability to generate descriptions of images, including data charts and scientific diagrams.
“Specifically, LLMs have demonstrated the ability to generate descriptions of images, including data charts and scientific diagrams [53, 71, 91].”
From How University Disability Services Professionals Write Image Descriptions for HCI Figures Using Generative AI (2026) · cites this paper as [71]
Singh et al. demonstrated that human-AI collaboration can support alt text writing when designed with user needs in mind.
“Recently, Singh et al. [71] demonstrated that human-AI collaboration can support alt text writing when designed with user needs in mind.”
From How University Disability Services Professionals Write Image Descriptions for HCI Figures Using Generative AI (2026) · cites this paper as [71]
Existing work with experts shows that LLM-based support enhances the alt text writing process.
“Existing work [53, 71] with experts (i.e., authors of published work) shows that LLM-based support enhances the alt text writing process.”
From How University Disability Services Professionals Write Image Descriptions for HCI Figures Using Generative AI (2026) · cites this paper as [71]
Our sample size is closer to what is reported in HCI studies for qualitative research and prior work exploring similar topics.
“However, our sample size is closer to what is reported in HCI studies for qualitative research [9] and prior work exploring similar topics [53, 71].”
From How University Disability Services Professionals Write Image Descriptions for HCI Figures Using Generative AI (2026) · cites this paper as [71]
In the General context, a recurrent theme in planning and translating support was the provision of interfaces that enabled rapid iteration and organization of idea and text generations.
“A recurrent theme in planning and translating support in this context was the provision of interfaces that enabled rapid iteration and organization of idea and text generations [79,104,123].”
From Co-Writing with AI, on Human Terms: Aligning Research with User Demands Across the Writing Process (2025) · cites this paper as [123]
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Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
Learning Transferable Visual Models From Natural Language Supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al. (2021) · arXiv · doi:10.48550/arxiv.2103.00020 · Cited by 15,293 · see citing works
Flamingo: a Visual Language Model for Few-Shot Learning
Alayrac, J.-B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., et al. (2022) · arXiv · doi:10.48550/arxiv.2204.14198 · Cited by 1,555 · see citing works
Every Picture Tells a Story: Generating Sentences from Images
Farhadi, A., Hejrati, M., Sadeghi, M. A., Young, P., Rashtchian, C., Hockenmaier, J., & Forsyth, D. A. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15561-1_2 · Cited by 1,240 · see citing works
Cilibrasi, R., & Vitanyi, P. M. B. (2005) · IEEE Transactions on Information Theory · doi:10.1109/tit.2005.844059 · Cited by 1,198 · see citing works
BabyTalk: Understanding and Generating Simple Image Descriptions
Kulkarni, G. A., Premraj, V., Ordóñez, V., Dhar, S., Li, S., Choi, Y., Berg, A. C., & Berg, T. L. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.162 · Cited by 839 · see citing works
VizWiz: nearly real-time answers to visual questions
Bigham, J. P., Jayant, C., Ji, H., Little, G., Miller, A., Miller, R. C., Miller, R., Tatarowicz, A., White, B., White, S., et al. (2010) · Proceedings of the 23nd annual ACM symposium on User interface software and technology · doi:10.1145/1866029.1866080 · Cited by 601 · see citing works
Wordcraft: Story Writing With Large Language Models
Yuan, A., Coenen, A., Reif, E., & Ippolito, D. (2022) · 27th International Conference on Intelligent User Interfaces · doi:10.1145/3490099.3511105 · Cited by 374 · see citing works
Lee, M., Liang, P., & Yang, Q. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3502030 · Cited by 345 · cited 1× · see citing works
“Large language models (LLMs) have recently shown promise in providing contextual suggestions for diverse writing and revision tasks [8, 10, 23].”
BackgroundKocielnik, R., Amershi, S., & Bennett, P. N. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300641 · Cited by 277 · see citing works
(2017) · doi:10.1145/2998181.2998364 · Cited by 258 · see citing works
Lopez, P. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04346-8_62 · Cited by 244 · see citing works
Mirowski, P., Mathewson, K. W., Pittman, J., & Evans, R. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581225 · Cited by 240 · cited 1× · see citing works
“A common application is to open-ended tasks such as creative writing, which allow for wide-ranging suggestions useful for inspiration [32, 42, 53].”
BackgroundVisual Programming: Compositional visual reasoning without training
Gupta, T., & Kembhavi, A. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01436 · Cited by 230 · see citing works
Accessible Visualization via Natural Language Descriptions: A Four-Level Model of Semantic Content
Lundgard, A., & Satyanarayan, A. (2022) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3114770 · Cited by 155 · see citing works
Twitter A11y: A Browser Extension to Make Twitter Images Accessible
Gleason, C., Pavel, A., McCamey, E., Low, C., Carrington, P., Kitani, K., & Bigham, J. P. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376728 · Cited by 123 · see citing works
Where to Hide a Stolen Elephant: Leaps in Creative Writing with Multimodal Machine Intelligence
Singh, N., Bernal, G., Savchenko, D., & Glassman, E. L. (2023) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3511599 · Cited by 119 · see citing works
Self-citationClark, C., & Divvala, S. (2016) · doi:10.1145/2910896.2910904 · Cited by 118 · see citing works
Gleason, C., Carrington, P., Cassidy, C., Morris, M., Kitani, K. M., & Bigham, J. (2019) · The Web Conference · doi:10.1145/3308558.3313605 · Cited by 108 · see citing works
Beyond Text Generation: Supporting Writers with Continuous Automatic Text Summaries
Dang, H., Benharrak, K., Lehmann, F., & Buschek, D. (2022) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3526113.3545672 · Cited by 85 · see citing works
Guiding Novice Web Workers in Making Image Descriptions Using Templates
Morash, V. S., Siu, Y.-T., Miele, J. A., Hasty, L., & Landau, S. (2015) · ACM Transactions on Accessible Computing · doi:10.1145/2764916 · Cited by 69 · see citing works
Designing Tools for High-Quality Alt Text Authoring
Mack, K., Cutrell, E., Lee, B., & Morris, M. R. (2021) · Proceedings of the 23rd International ACM SIGACCESS Conference on Computers and Accessibility · doi:10.1145/3441852.3471207 · Cited by 58 · cited 1× · see citing works
“Mack et al. [30] observed that templates helped authors write better alt text compared with automatically generated options, which were brief and regarded by authors as a gold standard, leading to reduced final quality.”
BackgroundDocFigure: A Dataset for Scientific Document Figure Classification
Jobin, K. V., Mondal, A., & Jawahar, C. V. (2019) · International Conference on Document Analysis and Recognition Workshops (ICDARW) · doi:10.1109/icdarw.2019.00018 · Cited by 52 · see citing works
Collaborative Storytelling with Large-scale Neural Language Models
Nichols, E., Gao, L., & Gomez, R. (2020) · Motion, Interaction and Games · doi:10.1145/3424636.3426903 · Cited by 50 · see citing works
Chintalapati, S. S., Bragg, J., & Wang, L. L. (2022) · Proceedings of the 24th International ACM SIGACCESS Conference on Computers and Accessibility · doi:10.1145/3517428.3544796 · Cited by 31 · see citing works
Self-citationToward supporting quality alt text in computing publications
Williams, C., de Greef, L., Harris, E., Findlater, L., Pavel, A., & Bennett, C. (2022) · Proceedings of the 19th International Web for All Conference · doi:10.1145/3493612.3520449 · Cited by 26 · see citing works
Accessible Visualization via Natural Language Descriptions: A Four-Level Model of Semantic ContentPreprint
Lundgard, A., & Satyanarayan, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.04406 · Cited by 11 · see citing works
PatFig: Generating Short and Long Captions for Patent Figures
Aubakirova, D., Gerdes, K., & Liu, L. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00305 · Cited by 5 · see citing works
Flagged first, then by citations
Research works printed but not linked: 28
- Arman Cohan, Sergey Feldman, Iz Beltagy, Doug Downey, and Daniel S Weld. 2020. Specter: Document-level representation learning using citation-informed transformers. arXiv preprint arXiv:2004.07180 (2020). · arXiv
- Katy Ilonka Gero, Vivian Liu, and Lydia Chilton. 2022. Sparks: Inspiration for science writing using language models. In Designing interactive systems conference. 1002-1019.
- Tanya Goyal, Junyi Jessy Li, and Greg Durrett. 2022. News summarization and evaluation in the era of gpt-3. arXiv preprint arXiv:2209.12356 (2022). · arXiv
- Danna Gurari, Yinan Zhao, Meng Zhang, and Nilavra Bhattacharya. 2020. Cap- tioning images taken by people who are blind. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XVII 16. Springer, 417-434.
- Ting-Yao Hsu, C Lee Giles, and Ting-Hao'Kenneth' Huang. 2021. SciCap: Gener- ating captions for scientific figures. arXiv preprint arXiv:2110.11624 (2021). · arXiv
- Kung-Hsiang Huang, Mingyang Zhou, Hou Pong Chan, Yi R Fung, Zhenhailong Wang, Lingyu Zhang, Shih-Fu Chang, and Heng Ji. 2023. Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioning. arXiv preprint arXiv:2312.10160 (2023). · arXiv
- Alyssa Hwang, Andrew Head, and Chris Callison-Burch. 2023. Grounded Intuition of GPT-Vision's Abilities with Scientific Images. arXiv preprint arXiv:2311.02069 (2023). · arXiv
- Vladimir I Levenshtein et al. 1966. Binary codes capable of correcting deletions, insertions, and reversals. In Soviet physics doklady, Vol. 10. Soviet Union, 707-710.
- Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. 2023. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. arXiv preprint arXiv:2301.12597 (2023). · arXiv
- Fangyu Liu, Julian Martin Eisenschlos, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Wenhu Chen, Nigel Collier, and Yasemin Altun. 2022. DePlot: One-shot visual language reasoning by plot-to-table transla- tion. arXiv preprint arXiv:2212.10505 (2022). · arXiv
- Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. 2023. Visual instruc- tion tuning. arXiv preprint arXiv:2304.08485 (2023). · arXiv
- Ahmed Masry, Parsa Kavehzadeh, Xuan Long Do, Enamul Hoque, and Shafiq Joty. 2023. UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoning. arXiv preprint arXiv:2305.14761 (2023). · arXiv
- Mickey Nguyen, Matthew Crane, John Romley, and Yannis M Paulus. 2023. Acces- sibility of Figures in Leading Biomedical and Ophthalmology Journals: Analysis of Alternative Text Use. Investigative Ophthalmology & Visual Science 64, 8 (2023), 2806-2806.
- OpenAI. 2023. GPT-4 Technical Report. arXiv:2303.08774 [cs.CL] · arXiv
- Nils Reimers and Iryna Gurevych. 2019. Sentence-bert: Sentence embeddings using siamese bert-networks. arXiv preprint arXiv:1908.10084 (2019). · arXiv
- Jacob Schreiber, Jeffrey Bilmes, and William Stafford Noble. 2020. apricot: Sub- modular selection for data summarization in Python. The Journal of Machine Learning Research 21, 1 (2020), 6474-6479.
- Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, and Yueting Zhuang. 2023. Hugginggpt: Solving ai tasks with chatgpt and its friends in huggingface. arXiv preprint arXiv:2303.17580 (2023). · arXiv
- Zejiang Shen, Tal August, Pao Siangliulue, Kyle Lo, Jonathan Bragg, Jeff Ham- merbacher, Doug Downey, Joseph Chee Chang, and David Sontag. 2023. Beyond Summarization: Designing AI Support for Real-World Expository Writing Tasks. arXiv preprint arXiv:2304.02623 (2023). · arXiv
- Dídac Surís, Sachit Menon, and Carl Vondrick. 2023. Vipergpt: Visual inference via python execution for reasoning. arXiv preprint arXiv:2303.08128 (2023). · arXiv
- Benny J Tang, Angie Boggust, and Arvind Satyanarayan. 2023. VisText: A Bench- mark for Semantically Rich Chart Captioning. arXiv preprint arXiv:2307.05356 (2023). · arXiv
- Teng Wang, Jinrui Zhang, Junjie Fei, Yixiao Ge, Hao Zheng, Yunlong Tang, Zhe Li, Mingqi Gao, Shanshan Zhao, Ying Shan, et al. 2023. Caption anything: Interactive image description with diverse multimodal controls. arXiv preprint arXiv:2305.02677 (2023). · arXiv
- Chenfei Wu, Shengming Yin, Weizhen Qi, Xiaodong Wang, Zecheng Tang, and Nan Duan. 2023. Visual chatgpt: Talking, drawing and editing with visual foun- dation models. arXiv preprint arXiv:2303.04671 (2023). · arXiv
- Xianjun Yang, Yan Li, Xinlu Zhang, Haifeng Chen, and Wei Cheng. 2023. Explor- ing the limits of chatgpt for query or aspect-based text summarization. arXiv preprint arXiv:2302.08081 (2023). · arXiv
- Zhishen Yang, Raj Dabre, Hideki Tanaka, and Naoaki Okazaki. 2023. SciCap+: A Knowledge Augmented Dataset to Study the Challenges of Scientific Figure Captioning. arXiv preprint arXiv:2306.03491 (2023). · arXiv
- Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Ehsan Azarnasab, Faisal Ahmed, Zicheng Liu, Ce Liu, Michael Zeng, and Lijuan Wang. 2023. Mm- react: Prompting chatgpt for multimodal reasoning and action. arXiv preprint arXiv:2303.11381 (2023). · arXiv
- Haoxuan You, Rui Sun, Zhecan Wang, Long Chen, Gengyu Wang, Hammad A Ayyubi, Kai-Wei Chang, and Shih-Fu Chang. 2023. IdealGPT: Iteratively De- composing Vision and Language Reasoning via Large Language Models. arXiv preprint arXiv:2305.14985 (2023). · arXiv
- Tianyi Zhang, Faisal Ladhak, Esin Durmus, Percy Liang, Kathleen McKeown, and Tatsunori B Hashimoto. 2023. Benchmarking large language models for news summarization. arXiv preprint arXiv:2301.13848 (2023). · arXiv
- Deyao Zhu, Jun Chen, Kilichbek Haydarov, Xiaoqian Shen, Wenxuan Zhang, and Mohamed Elhoseiny. 2023. Chatgpt asks, blip-2 answers: Automatic questioning towards enriched visual descriptions. arXiv preprint arXiv:2303.06594 (2023). · arXiv
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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
Nunes, E. H. D. C., de Castro, K. V. G., de Lima, V. S., Morais, A. J. A., & Monteiro, I. T. (2025) · Simpósio Brasileiro de Fatores Humanos em Sistemas Computacionais (IHC) · doi:10.5753/ihc.2025.10829
Sacramento, C., Ferreira, S. B. L., Braz, P., dos Remedios, S. E. L., & Marques, J. M. D. S. (2025) · Simpósio Brasileiro de Fatores Humanos em Sistemas Computacionais (IHC) · doi:10.5753/ihc.2025.10802
Labeling the Spectrum of AI Involvement: New Tag Proposals for Wikipedia and Commons
AHAMED, A., Wang, M., & Matthews, J. (2026) · ACM Web Science Conference (WebSci) · doi:10.1145/3795513.3810451
Ju, J., Seo, J., Jang, J., Lee, D., & Park, H. (2026) · IEEE Pacific Visualization Conference (PacificVis) · doi:10.1109/pacificvis68791.2026.00019
Gatto, S., & Gaggi, O. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-29372-5_24
Multi-sensory Learning of Data Structures for Blind Students Using 3D Printing
Brown, L., Zingaro, D., Petersen, A., Serafin, M., & Zhu, T. (2026) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3803400.3809305
Kimogol, S. S., Nachtnebel, D. A., Safari, M. C., & Sanderson, N. C. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-29653-5_17
Accessible Alt-Text Generation of 2D Graphs with Multimodal LLMs: Comparison and Evaluation
Cardia, M., Poggianti, C., Leporini, B., Pelagatti, S., & Chessa, S. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38407-2_59
Söderholm, E., Mylonopoulou, V., & Ferwerda, B. (2026) · Nordic Conference on Human-Computer Interaction · doi:10.1145/3829807.3829850
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
Song, H., Shin, M., Choi, J., Kim, Y., Jang, K., Suh, B., & Jung, H. (2026) · Proceedings of the 2026 28th International ACM SIGACCESS Conference on Computers and Accessibility · doi:10.1145/3797867.3828985
Liu, S., Zhang, J., Fayed, M. E. M. K., Zhao, Y., Premkumar, P., Adams, A. T., & Starner, T. E. (2026) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3797867.3841231
Wei, T., Piccardi, T., Реди, М., & Bernstein, M. S. (2026) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3797867.3841219
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
- Singh, N., Wang, L. L., & Bragg, J.
- Affiliations
- MIT Media Lab (US)
- University of Washington (US)
- Allen Institute for Artificial Intelligence (US)
- Venue
- Proceedings of the 29th International Conference on Intelligent User Interfaces
- Publisher
- ACM
- Published
- 2024-03-18
- Pages
- 886-906
- OpenAlex
- W4393970562
- Cited by
- 42
- References
- 27
- Citations by year
- 2026: 17 · 2025: 21 · 2024: 4
- FWCI
- 22.94
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM International Conference on Intelligent User Interfaces (IUI) (publishedVersion, cc-by)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Topics
- Social Sciences › Human Factors and Ergonomics › Digital Accessibility for Disabilities
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Academic Writing and Publishing
- Keywords
- alt text, scientific figures, accessibility, visual accessibility, human-AI collaboration, AI-assisted writing, cognitive load
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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