Accessibility testing
Evaluation of digital accessibility through automated checks, knowledgeable manual assessment and research with people with different access needs. Conformance evaluation and usability research provide complementary evidence.
- Standard
- WCAG
- Evaluation method
- WCAG-EM 2.0, W3C Group Note, July 2026
W3C says no evaluation tool alone can determine whether a website meets accessibility standards. Automated checks find repeatable structural failures; manual evaluation and disabled-user research reveal different problems.
Evaluate complete tasks, including errors and recovery. User testing does not itself certify WCAG conformance, and a conformance report does not prove that every journey is usable.
Connected
- WCAG (Web Content Accessibility Guidelines)The W3C's testable accessibility standard for web content, organised around perceivable, operable, understandable and robust; version 2.2 is the current recommendation.
- ARIA live regionsPage areas marked with aria-live so screen readers announce their changes; the standard way to announce chat replies and status messages without moving focus.
- European Accessibility ActEU Directive 2019/882, applying from 28 June 2025, requiring listed products and services sold to EU consumers, including e-commerce, banking, e-books and transport booking, to be accessible.
- Agent evaluationRepeatable testing of an AI agent's complete task, tool use, permissions and final system state against explicit acceptance criteria. A successful demo is not a production reliability estimate.
Linked from
Field notes that cover Accessibility testing
More concepts & methods
- machine learningscientific study of algorithms and statistical models that computer systems use to perform tasks without explicit instructions
- deep learningspecialized form of machine learning that uses artificial neural networks to identify patterns in data
- genetic algorithmcompetitive algorithm for searching a problem space
- Altitude, depth and rangeFelix Rieseberg’s framework for ambition with AI models: speed repeats work faster, altitude targets outcomes, depth uses expert instruments and range combines disciplines.
- Outcome-based promptingAsking an AI agent to pursue a specified change for a user or system, with evidence and boundaries, instead of merely producing a named deliverable.
- AI outside judgeAn acceptance check outside an agent’s self-assessment, such as a deterministic test, held-out evaluation, measurement or separately briefed reviewer.