Ensuring web accessibility is crucial for advancing social welfare, justice, and equality in digital spaces, yet the vast majority of website user interfaces remain non-compliant, due in part to the resource-intensive and unscalable nature of current auditing practices. While WCAG-EM offers a structured methodology for site-wise conformance evaluation, it involves great human efforts and lacks practical support for execution at scale. In this work, we present an auditing framework, AAA, which operationalizes WCAG-EM through a human-AI partnership model. AAA is anchored by two key innovations: GRASP, a graph-based multimodal sampling method that ensures representative page coverage via learned embeddings of visual, textual, and relational cues; and MaC, a multimodal large language model-based copilot that supports auditors through cross-modal reasoning and intelligent assistance in high-effort tasks. Together, these components enable scalable, end-to-end web accessibility auditing, empowering human auditors with AI-enhanced assistance for real-world impact. We further contribute four novel datasets designed for benchmarking core stages of the audit pipeline. Extensive experiments demonstrate the effectiveness of our methods, providing insights that small-scale language models can serve as capable experts when fine-tuned.

Web accessibility ensures that individuals with disabilities can access and interact with digital content without barriers, yet a significant majority of most used websites fail to meet accessibility standards. This study evaluates ChatGPT's (GPT-4o) ability to generate and improve web pages in line with Web Content Accessibility Guidelines (WCAG). While ChatGPT can effectively address accessibility issues when prompted, its default code often lacks compliance, reflecting limitations in its training data and prevailing inaccessible web practices. Automated and manual testing revealed strengths in resolving simple issues but challenges with complex tasks, requiring human oversight and additional iterations. Unlike prior studies, we incorporate manual evaluation, dynamic elements, and use the visual reasoning capability of ChatGPT along with the prompts to fix accessibility issues. Providing screenshots alongside prompts enhances the LLM's ability to address accessibility issues by allowing it to analyze surrounding components, such as determining appropriate contrast colors. We found that effective prompt engineering, such as providing concise, structured feedback and incorporating visual aids, significantly enhances ChatGPT's performance. These findings highlight the potential and limitations of large language models for accessible web development, offering practical guidance for developers to create more inclusive websites.

Accessibility is crucial for inclusive app usability, yet developers often struggle to identify and fix app accessibility issues due to a lack of awareness, expertise, and inadequate tools. Current accessibility testing tools can identify accessibility issues but may not always provide guidance on how to address them. We introduce FixAlly, an automated tool designed to suggest source code fixes for accessibility issues detected by automated accessibility scanners. FixAlly employs a multi-agent LLM architecture to generate fix strategies, localize issues within the source code, and propose code modification suggestions to fix the accessibility issue. Our empirical study demonstrates FixAlly's capability in suggesting fixes that resolve issues found by accessibility scanners -- with an effectiveness of 77% in generating plausible fix suggestions -- and our survey of 12 iOS developers finds they would be willing to accept 69.4% of evaluated fix suggestions.

With the increasing need for inclusive and user-friendly technology, web accessibility is crucial to ensuring equal access to online content for individuals with disabilities, including visual, auditory, cognitive, or motor impairments. Despite the existence of accessibility guidelines and standards such as Web Content Accessibility Guidelines (WCAG) and the Web Accessibility Initiative (W3C), over 90% of websites still fail to meet the necessary accessibility requirements. For web users with disabilities, there exists a need for a tool to automatically fix web page accessibility errors. While research has demonstrated methods to find and target accessibility errors, no research has focused on effectively correcting such violations. This paper presents a novel approach to correcting accessibility violations on the web by modifying the document object model (DOM) in real time with foundation models. Leveraging accessibility error information, large language models (LLMs), and prompt engineering techniques, we achieved greater than a 51% reduction in accessibility violation errors after corrections on our novel benchmark: ACCESS. Our work demonstrates a valuable approach toward the direction of inclusive web content, and provides directions for future research to explore advanced methods to automate web accessibility.

Developers and quality assurance testers often rely on manual testing to test accessibility features throughout the product lifecycle. Unfortunately, manual testing can be tedious, often has an overwhelming scope, and can be difficult to schedule amongst other development milestones. Recently, Large Language Models (LLMs) have been used for a variety of tasks including automation of UIs, however to our knowledge no one has yet explored their use in controlling assistive technologies for the purposes of supporting accessibility testing. In this paper, we explore the requirements of a natural language based accessibility testing workflow, starting with a formative study. From this we build a system that takes as input a manual accessibility test (e.g., ``Search for a show in VoiceOver'') and uses an LLM combined with pixel-based UI Understanding models to execute the test and produce a chaptered, navigable video. In each video, to help QA testers we apply heuristics to detect and flag accessibility issues (e.g., Text size not increasing with Large Text enabled, VoiceOver navigation loops). We evaluate this system through a 10 participant user study with accessibility QA professionals who indicated that the tool would be very useful in their current work and performed tests similarly to how they would manually test the features. The study also reveals insights for future work on using LLMs for accessibility testing.

October 2021 Dennis DeGray is paralyzed from the neck down, but a virtuoso at using his brain to control a computer mouse. For the last five years, he has participated in a series of clinical trials in which surgeons have inserted tiny silicon probes into the brains of more than 20 paralyzed people. Using these brain-computer interfaces, scientists have enabled those with the implants to grasp objects with robot arms and steer planes around in flight simulators. While such technology is therapeutic and restorative for people such as DeGray, entrepreneurs including Elon Musk are pouring investment into brain implant projects that are for elective enhancement, creating an ethical maze for medical researchers. Twins really are magical We may finally know how the annual Geminid meteor shower came to be.

AudioEye, Inc., the industry-leading digital accessibility provider, announced a joint effort with Localogy, the leading non-profit trade association supporting local businesses, to provide Localogy members with access to digital accessibility resources and learning opportunities through the association's knowledge center. "Over the years, Localogy has built a powerful network of organizations with a shared goal of helping local businesses navigate challenges and thrive. One of the key challenges for businesses today is making their websites accessible to people with diverse needs and being discoverable online," said Bill Dinan, President at Localogy. "We're excited to team up with AudioEye and provide our members, and the more than 30 million local businesses they serve, with opportunities to learn about AudioEye's digital accessibility solution and ways to ensure ongoing accessibility on their sites." Based on a recent analysis of 3,500 randomly websites across 22 industries, including healthcare, e-commerce, and employment, AudioEye found that 83% of e-commerce sites, 78% of healthcare sites, and 77% of jobs and career sites had accessibility issues that blocked a screen reader user's ability to complete key tasks, such as viewing product descriptions, completing a purchase, filling out an application, or booking an appointment.

The 2021 Swiss Robotics Day marked the beginning of NCCR Robotics's final year. The project, launched in 2010, is on track to meet all its scientific goals in the three areas of wearable, rescue and educational robotics, while continuing to focus on supporting spin-offs, advancing robotics education and improving equality of opportunities for all robotics researchers.

Some reasons why that's the case are tied to the sheer volume of digital content and the complexity of the internet. For businesses and content creators who want to reach the widest audiences possible and meet the expectations of all users, including those with disabilities, the dynamic nature of content poses an ongoing challenge. Consumers today expect personalized content, interactive features, and intuitive interfaces to find information, shop, get entertainment, etc. This level of personalization requires continuous changes in content based on user behavior, preferences, and other data. Unfortunately, every change comes with a risk of making content inaccessible for users with disabilities.

Navigation is a huge part of the value smartphones provide for the blind and visually impaired. Thanks to recent advances in technology, the blind and visually impaired are now able to lead more independent lives than ever. The WeWALK Smart Cane is a great example of what is now possible. The WeWALK looks similar to the cane that some blind and visually impaired people have used for decades to avoid obstacles while walking, but it incorporates a few modern twists. With a standard cane, you can still run into obstacles that are not immediately underfoot, like poles, tree branches, and barriers.