Building an AI-Powered Accessibility Audit Tool
The author built an AI-based accessibility auditing tool that goes beyond traditional rule-based tools like Lighthouse. It combines axe-core results, DOM snapshots, and screenshots to identify accessibility issues that may be missed by other tools.
Why it matters
This tool demonstrates how AI can be leveraged to improve accessibility testing and catch issues that are missed by rule-based tools, leading to more inclusive digital experiences.
Key Points
- 1Lighthouse accessibility audits can miss critical issues that impact usability for users with disabilities
- 2The author's tool runs as part of the CI pipeline, using a Claude-based AI agent to evaluate the axe-core results, DOM, and screenshots
- 3The AI agent is trained to act as an accessibility specialist, finding problems that rule-based tools cannot
- 4The tool has helped the author catch accessibility issues that were missed by Lighthouse
Details
The author was frustrated when a blind user reported accessibility issues on a landing page that had scored 97/100 on Lighthouse. This led them to build an AI-powered accessibility auditing tool that runs as part of the CI pipeline. The tool combines axe-core accessibility testing, full DOM snapshots, and screenshots to provide a more comprehensive evaluation. The core of the tool is a Claude-based AI agent that acts as an accessibility specialist, analyzing the inputs to identify issues that rule-based tools like Lighthouse may miss. This includes problems with landmark roles, heading structure, and contrast ratios that are technically compliant but still create usability challenges. The author believes this AI-powered approach is essential for ensuring true accessibility, going beyond the limitations of traditional automated testing.
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