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Can a web accessibility checker be enhanced by the use of AI?

Can a web accessibility checker be enhanced by the use of AI?
Can a web accessibility checker be enhanced by the use of AI?
There has been a proliferation of automatic web accessibility checkers over the years designed to make it easier to assess the barriers faced by those with disabilities when using online interfaces and content. The checkers are often based on tests that can be made on the underlying website code to see whether it complies with the W3C Web Content Accessibility Guidelines (WCAG). However, as the type of code needed for the development of sophisticated interactive web services and online applications becomes more complex, so the guidelines have had to be updated with the adoption of new success criteria or additional revisions to older criteria. In some instances, this has led to questions being raised about the reliability of the automatic accessibility checks and whether the use of Artificial Intelligence (AI) could be helpful. This paper explores the need to find new ways of addressing the requirements embodied in the WCAG success criteria, so that those reviewing websites can feel reassured that their advice (regarding some of the ways to reduce barriers to access) is helpful and overcomes issues around false positive or negatives. The methods used include image recognition and natural language processing working alongside a visual appraisal system, built into a web accessibility checker and reviewing process that takes a functional approach.
Artificial intelligence, Automatic checkers, Digital accessibility, Disability
67-73
Wald, Michael
90577cfd-35ae-4e4a-9422-5acffecd89d5
Ding, Chaohai
f92dff41-8249-46b3-9a73-284a3bd286ac
Draffan, E.A.
021d4f4e-d269-4379-ba5a-7e2ffb73d2bf
Newman, Russell
1b717ea6-78d1-4403-a6ca-1e0751d138db
Wald, Michael
90577cfd-35ae-4e4a-9422-5acffecd89d5
Ding, Chaohai
f92dff41-8249-46b3-9a73-284a3bd286ac
Draffan, E.A.
021d4f4e-d269-4379-ba5a-7e2ffb73d2bf
Newman, Russell
1b717ea6-78d1-4403-a6ca-1e0751d138db

Wald, Michael, Ding, Chaohai, Draffan, E.A. and Newman, Russell (2020) Can a web accessibility checker be enhanced by the use of AI? Lecture Notes in Computer Science, 12376, 67-73. (doi:10.1007/978-3-030-58796-3_9).

Record type: Article

Abstract

There has been a proliferation of automatic web accessibility checkers over the years designed to make it easier to assess the barriers faced by those with disabilities when using online interfaces and content. The checkers are often based on tests that can be made on the underlying website code to see whether it complies with the W3C Web Content Accessibility Guidelines (WCAG). However, as the type of code needed for the development of sophisticated interactive web services and online applications becomes more complex, so the guidelines have had to be updated with the adoption of new success criteria or additional revisions to older criteria. In some instances, this has led to questions being raised about the reliability of the automatic accessibility checks and whether the use of Artificial Intelligence (AI) could be helpful. This paper explores the need to find new ways of addressing the requirements embodied in the WCAG success criteria, so that those reviewing websites can feel reassured that their advice (regarding some of the ways to reduce barriers to access) is helpful and overcomes issues around false positive or negatives. The methods used include image recognition and natural language processing working alongside a visual appraisal system, built into a web accessibility checker and reviewing process that takes a functional approach.

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Accessibility Checker and AI - Accepted Manuscript
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More information

Published date: 4 September 2020
Keywords: Artificial intelligence, Automatic checkers, Digital accessibility, Disability

Identifiers

Local EPrints ID: 445088
URI: http://eprints.soton.ac.uk/id/eprint/445088
PURE UUID: bcf3fcf7-3030-4f4c-b4b9-e4de4021a0f3
ORCID for E.A. Draffan: ORCID iD orcid.org/0000-0003-1590-7556

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Date deposited: 19 Nov 2020 17:31
Last modified: 06 Jun 2024 01:45

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Contributors

Author: Michael Wald
Author: Chaohai Ding
Author: E.A. Draffan ORCID iD
Author: Russell Newman

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