[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117010-en":3,"doc-seo-117010-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":20,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},117010,962084925636,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Using Machine Learning for Web Accessibility","Machine learning has enabled approaches for web accessibility such as generating image alternative text, auto-captioning, text simplification, overlays, and speech recognition. Yet many solutions are driven by technology rather than by end-user needs, leading to incomplete performance or unintended harms. A core issue is insufficient stakeholder involvement. The study examines how machine learning can be improved through effective stakeholder co-design, combining focus groups and semi-structured interviews to guide development.","| Using Machine Learning for Web Accessibility |  |  |\n| --- | --- | --- |\n|  | T. Makati*, J. Gilligan*, E. Murphy*\u003Cbr>*Technological University Dublin, School of Computer Science [Email: ](Email: D21124389@mytudublin.ie)[D21124389@mytudublin.ie](Email: D21124389@mytudublin.ie)\u003Cbr>SFI Centre for Research Training in Machine Learning\u003Cbr>[https://www.ml-labs.ie/](https://www.ml-labs.ie/) |  |\n\n• The use of machine learning (ML) in web accessibility has produced algorithms for creating alternative text for images, auto-captioning for videos, text simplification, overlays, speech recognition and more.  \n• However, these technologies are often technology-driven rather than focusing on the needs of end users. As a result, they often partially work or do not work or cause more problems than they were meant to fix.  \n• One major problem is the lack of stakeholder involvement.  \n• These ML technologies have highlighted a need for greater stakeholder involvement and proper techniques and strategies to enhance the outcomes.  \n• This work explores how machine learning can be used to enhance web accessibility.  \n• It examines how stakeholders might be effectively brought into the development processes to produce useful and beneficial ML-driven technologies for digital  \nBackground   \naccessibility.  \nMethodology  \nCo-Create & Co-Design  \n• Our initial trial will involve a co-creation approach.  \n• We will run focus groups with digital accessibility experts and users to explore the potential of machine learning in accessible web navigation.  \n• We plan to adopt an iterative methodology as shown in figure 1 :  \nFigure 1  \nStructure  \nWe aim to develop and run a number of separate focus groups with approximately 5-7 digital accessibility experts. Following each focus group, a subset of participants will be invited to take part in an individual interview. A semi-structured interview script will be created with open-ended questions based on the following themes:  \n• Understanding the web accessibility process.  \n• What are the barriers to accessible website design?  \n• Experience of the auditing and remedial process for web accessibility.  \n• Potential for machine learning to enhance or support these processes.  \n://[www.w3.org/WAI/fundamentals/accessibility-intro/](www.w3.org/WAI/fundamentals/accessibility-intro/)  \n2. Makati, T. (2022) . Machine learning for accessible web navigation.  \n[https://doi.org/10.1145/3493612.3520463](https://doi.org/10.1145/3493612.3520463)  \n3. Power, C. , Petrie, H. , Swallow, D. , Murphy, E. , Gallagher, B. , & Velasco, C. (2013) . Navigating, Discovering and Exploring the Web: Strategies Used by People with Print Disabilities on Interactive Websites. Lecture Notes in Computer Science, 667–684. [https://doi.org/10.1007/978-3-642-40483-2_47](https://doi.org/10.1007/978-3-642-40483-2_47)  \n4. Savage, N. (2021 , March 31) . Irish consumer trends post-COVID. KPMG.  \n[https://kpmg.com/ie/en/home/media/press-releases/2021/03/retail-consumer-trends-covid.html](https://kpmg.com/ie/en/home/media/press-releases/2021/03/retail-consumer-trends-covid.html)  \n[5](5). Web Accessibility Directive—Standards and harmonisation. (2023 , January 23) . Shaping Europe’s Digital Future.  [https://digital-strategy.ec.europa.eu/en/policies/web-accessibi](https://digital-strategy.ec.europa.eu/en/policies/web-accessibi)lity-directive-standards-andharmonisation  \n6. Web Content Accessibility Guidelines (WCAG) 2. 1 . (2018 , June 5) . W3C.  \n[https://www.w3.org/TR/WCAG21/](https://www.w3.org/TR/WCAG21/)  \nReferences  \n1. Henry, S. L. H. (Ed. ) . (2022 , March) . Introduction to Web Accessibility. Web Accessibility Initiative (WAI) .  \n[https](https)  \n1. Report on existing machine learning algorithms and tools used by digital accessibility experts: Participants will be involved in focus group discussions on existing applications of machine learning in web accessibility, auditing websites and remedial processes involved in digital accessibil","cbCaif2MUD72oDL2","https://ap.wps.com/l/cbCaif2MUD72oDL2","pdf",273617,1,"English","en",105,"# Methodology\n## Co-Create & Co-Design\n## Structure of Focus Groups and Interviews\n## Expected Deliverables\n# Conclusions\n## Acknowledgements","[{\"question\":\"Why do existing machine learning accessibility technologies often underperform?\",\"answer\":\"They are frequently technology-driven instead of centered on end-user needs, which can result in partial failure or problems beyond the intended fixes.\"},{\"question\":\"What major gap does the study identify?\",\"answer\":\"The study highlights a lack of stakeholder involvement in developing and applying ML-driven web accessibility technologies.\"},{\"question\":\"How does the research plan to incorporate stakeholders into development?\",\"answer\":\"It plans co-creation via focus groups with digital accessibility experts and users, followed by semi-structured individual interviews to generate actionable insights.\"}]","Using Machine Learning for Web Accessibility | 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