@nadeenfathallah @wai If you are interested in the #ASSETS2025 conference, its website is: https://assets25.sigaccess.org
You can read about the WHO statistics about disability on https://www.who.int/news-room/fact-sheets/detail/disability-and-health
A week ago, @wai tell us (https://w3c.social/@wai/115411162522521110) that WCAG 2.2 becomes an ISO standard. According to the WHO, 1.3 billion people experience significant disabilities. This is thus a big step for making a #Web for all.
Today, Nadeen Fathallah (@nadeenfathallah) will present a paper at #ASSETS2025 on correcting violations to these #accessibility guidelines using #LLMs.
We’ve extended the deadline for our #ASSETS2025 online workshop to August 22, 2025!
SOI (15–20 min): https://forms.gle/XrTLxiypCMxgvmiF
More info: https://assets-recruitment-2025.github.io/
No need to register for the main conference to participate, and you can get early bird registration rates till 8/28!
Making the Web More Inclusive: Enter AccessGuru
Despite the availability of accessibility guidelines like #WCAG, most websites still present barriers for users with disabilities. This paper introduces AccessGuru, a system that leverages Large Language Models (#LLMs) to automatically detect and correct accessibility violations in HTML code.
AccessGuru is guided by a novel taxonomy of syntactic, semantic, and layout violations and combines rule-based tools with LLM reasoning over code and visuals.
It reduces violation scores by up to 84%, outperforming existing tools, and achieves 73% similarity to human-generated semantic corrections. A benchmark dataset of 3,500 real-world violations is also released to support future research.
This work demonstrates how LLMs can meaningfully automate accessibility efforts and foster a more inclusive Web.
Fathallah, N. (@nadeenfathallah), Hernández, D. (@daniel), & Staab, S. (2025). AccessGuru: Leveraging LLMs to detect and correct web accessibility violations in HTML code. The 27th International ACM SIGACCESS Conference on Computers and Accessibility #ASSETS2025. http://arxiv.org/abs/2507.19549.
#AI #AIResearch #LLMs #Accessibility #HTML #PromptEngineering
The vast majority of Web pages fail to comply with established Web accessibility guidelines, excluding a range of users with diverse abilities from interacting with their content. Making Web pages accessible to all users requires dedicated expertise and additional manual efforts from Web page providers. To lower their efforts and promote inclusiveness, we aim to automatically detect and correct Web accessibility violations in HTML code. While previous work has made progress in detecting certain types of accessibility violations, the problem of automatically detecting and correcting accessibility violations remains an open challenge that we address. We introduce a novel taxonomy classifying Web accessibility violations into three key categories - Syntactic, Semantic, and Layout. This taxonomy provides a structured foundation for developing our detection and correction method and redefining evaluation metrics. We propose a novel method, AccessGuru, which combines existing accessibility testing tools and Large Language Models (LLMs) to detect violations and applies taxonomy-driven prompting strategies to correct all three categories. To evaluate these capabilities, we develop a benchmark of real-world Web accessibility violations. Our benchmark quantifies syntactic and layout compliance and judges semantic accuracy through comparative analysis with human expert corrections. Evaluation against our benchmark shows that AccessGuru achieves up to 84% average violation score decrease, significantly outperforming prior methods that achieve at most 50%.
🎉 Researchers from our AI institute at the University of Stuttgart @Uni_Stuttgart will present two papers tackling real-world challenges with AI:
- "Making the Web More Inclusive: Enter AccessGuru" (#ASSETS2025).
- "MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learning" (#ICCV2025).
Daniel Hernández
Saad Hassan
Institute for AI