{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T23:32:48Z","timestamp":1788910368604,"version":"build-2803163510"},"reference-count":44,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T00:00:00Z","timestamp":1772064000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:p>As artificial intelligence (AI) systems continue to remain prevalent in society, ensuring their accessibility for all users, including those with disabilities, is of great importance. This paper presents a comprehensive framework for AI Testing, Evaluation, Verification and Validation (TEVV) focused on accessibility. The proposed methodology incorporates methods for red teaming, model testing, and field testing with a particular emphasis on usability testing for accessibility. The results demonstrate, through detailed case studies, that systematically evaluating AI systems for accessibility barriers and biases improves the inclusivity and effectiveness of AI technologies for diverse user populations. The findings suggest that this accessibility-focused TEVV framework provides a structured approach for developing more equitable and universally usable AI systems that benefit all members of society.<\/jats:p>","DOI":"10.3389\/fdgth.2025.1679603","type":"journal-article","created":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T11:47:50Z","timestamp":1772106470000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["AI testing, evaluation, verification and validation for accessibility: a comprehensive framework"],"prefix":"10.3389","volume":"7","author":[{"given":"Gabriella","family":"Waters","sequence":"first","affiliation":[{"name":"Cognitive and Neurodiversity AI Lab, Center for Responsible AI, Virginia State University","place":["Baltimore, VA, United States"]},{"name":"Center for Equitable AI and Machine Learning Systems, Morgan State University","place":["Baltimore, VA, United 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