Instead of fixing accessibility issues later, prevent them from the start. We're piloting an experimental general-purpose accessibility agent to improve GitHub for people who use and rely on assistive technology. So far, it's reviewed 3,535 pull requests, with a 68% resolution rate. Here's what we learned along the way. 💡 https://t.co/Sa9yRLqnOH
GitHub Pilots AI Agent to Proactively Fix Accessibility Issues in Pull Requests
GitHubGitHub is piloting an experimental general-purpose accessibility agent that has reviewed 3,535 pull requests and achieved a 68% resolution rate. This agent aims to prevent accessibility barriers from the start by automatically identifying and remediating issues in front-end code.
- Pull Requests Reviewed
- 3,535
- Resolution Rate
- 68%
- Top Issue Type 1
- Making structure and relationships clear to assistive technologies
- Top Issue Type 2
- Providing clear and concise names for interactive controls
- Top Issue Type 3
- Ensuring users are aware of important announcements
- Sub-agent Architecture
- Passive reviewer and researcher, Active implementer
Key learnings include the need for structured, manually cataloged accessibility issues to counteract LLM bias. It also emphasizes efficient token consumption through a sub-agent architecture, with a passive reviewer and an active implementer working sequentially. This ensures traceability and allows for escalation checkpoints for complex or high-risk code patterns.
The accessibility agent augments existing efforts, not replacing them, by routing users to human experts for complex scenarios. Periodic manual review of agent output is crucial for identifying areas needing better instruction, ensuring accuracy.
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