Project Title Web Accessibility Using Machine Learning Tools
The World Wide Web has become a ubiquitous platform for all kinds of everyday activities such as retail, entertainment, communication work, and education. The usability and accessibility of the web impact users of these services to varying degrees causing irritation to some and outright exclusion to others. People with disabilities and users of Assistive ICT such as screen readers, Switch devices and alternative I/O are particularly impacted by inaccessible websites. Access to Banking, Government Services and other important content and services is severely restricted. The response is in implementing Accessibility Standards and Guidelines such as the Web Content Accessibility Guidelines 2.1 and European Standards which embrace these such as the harmonised European standard. These guidelines are organised around the principles of Perceivable, Operable Understandable and Robust content. These are referred to as the P.O.U.R principles. They specify requirements such as Alternative Text for images and captioning for videos. They also insist on operational requirements such as good navigation and the ability to turn off animations and blinking effects. Recent work has looked at the role of how AI and ML can help meet these guidelines. This has produced tools for creating alternative text for images and auto-captioning for videos. ML-driven processes such as text simplification and word prediction are useful utilities. Technologies such as Speech recognition can provide alternative input mechanisms. These techniques have been aggregated into intelligent overlays with varying degrees of success. There are, however, problems with current versions of these technologies. They don’t work or only partially work in many instances causing major frustrations. There are ML approaches in other applications that the Web uses that could play a role here. Of particular interest is how ML can optimise navigation through websites. Work done in games for example could inform this work here. The question that this project addresses is what useful role can ML and AI play in making websites more accessible. Where the ML tools can best be deployed in development life-cycles. How they can best be supported through strategies like co-design and active inclusion. Of particular insight to this work is how intelligent web analytics and data insights from other web disciplines such as SEO and behaviour analysis can feed into the improvement of ML and AI web accessibility technologies.