Accessibility feedback often falls through the cracks when teams lack a structured process to capture, triage, and act on it. At GitHub, we turned chaos into a continuous inclusion engine by combining centralised organisation with AI-powered automation. This guide walks you through how to replicate that approach using GitHub Actions, Copilot, and Models—so every accessibility issue becomes a tracked, prioritised, and resolved item.
What You Need
- A GitHub account with repository access
- GitHub Actions enabled on your repository
- GitHub Copilot (with business/enterprise plan) and GitHub Models (or equivalent AI/ML capabilities)
- A cross-functional team that includes accessibility experts, developers, and product managers
- A commitment to human-in-the-loop review—AI handles repetition, humans make decisions
Step-by-Step Guide
Step 1: Centralise Your Accessibility Feedback Channels
Before AI can help, you need a single source of truth. Scattered emails, issue boards, and Slack messages won't cut it. Create a dedicated repository or a labelled area within an existing repository where all accessibility feedback—from users, customers, and internal testers—is funneled. Use a template issue form that captures critical details: user persona (screen reader, keyboard-only, low vision), affected component, and steps to reproduce.
Step 2: Design Standardised Issue Templates
Poorly structured feedback leads to lost context. Build issue templates using YAML frontmatter with fields for:
- Accessibility barrier type (e.g., missing alt text, keyboard trap, contrast failure)
- User environment (browser, assistive technology, version)
- Priority (critical, high, medium, low)
- Cross-team impact (e.g., shared component, authentication flow)
These templates ensure every report is machine-readable and ready for AI processing.
Step 3: Triage and Prioritise Your Existing Backlog
Don't start fresh—go through your current bug list and mark every accessibility-related issue with a consistent label (e.g., accessibility). Use GitHub's bulk operations to assign owners, set priorities, and add missing information. This foundational work makes the next steps possible. We spent weeks cleaning up backlog so that later automation could work cleanly.
Step 4: Build a GitHub Actions Workflow for Continuous Capture
Create a .github/workflows/accessibility-feedback.yml file that triggers on issue creation or comment. This workflow will:
- Parse the issue body using GitHub Copilot's AI to extract structured data (barrier type, affected component, user persona)
- Apply labels automatically (e.g., `screen-reader`, `keyboard`, `low-vision`)
- Route the issue to the correct team(s) using team-mention logic
- Add a comment confirming the issue is tracked and providing an expected SLA
Example action snippet (conceptual):
name: Accessibility Feedback Triage
on:
issues:
types: [opened, edited]
jobs:
triage:
runs-on: ubuntu-latest
steps:
- uses: actions/extract-issue-data@v1
with:
model: gpt-4
prompt: "Extract accessibility type, component, and urgency from the issue body."
- run: |
echo "Issue ${{ number }} routed to team based on component."
Step 5: Integrate AI Models for Deeper Analysis
Use GitHub Models to run natural language processing on incoming feedback. For example, when a user says “I couldn’t tab through the login form,” the model can detect a keyboard trap and suggest a fix (e.g., “Add focus management to the submit button”). The model can also catch duplicate issues by comparing similar descriptions. This reduces manual triage by up to 60% in our experience.
Step 6: Automate Follow-Ups and Timely Updates
Nothing kills trust like silence. Program your workflow to automatically:
- Post a weekly status reminder on open issues (e.g., “This issue has been triaged and is awaiting review by the platform team”)
- Flag issues that have not been updated in 30 days for escalation
- Notify the reporter when a fix is deployed or a new version is released
Use GitHub Actions cron jobs or issue comment triggers for these.
Step 7: Close the Loop with Continuous Improvement
After a fix, have your workflow add a public comment linking to the release notes and ask the original reporter to verify. This turns one-time feedback into a learning opportunity. Periodically review your AI model's accuracy—are false positives high? Are certain barriers underrepresented? Adjust your prompts and templates accordingly.
This living system, as we call it, ensures accessibility is not a phase but a continuous process woven into every sprint.
Tips for Success
- Keep humans in the loop. AI is great for classification and summarisation, but final decisions—like priority and ownership—need human judgment, especially for complex cross-team issues.
- Start small. Pilot the workflow with one product area (e.g., login flow) before rolling out globally. Measure time-to-first-triage before and after.
- Involve real users. Your AI model is only as good as the feedback it receives. Engage screen reader users, keyboard-only users, and low-vision users in testing your templates and workflows.
- Document and share. Make your workflow public (if possible) so the open-source community can learn from it. Transparency builds trust and invites collaboration.
- Celebrate wins. When a long-standing issue gets resolved, share it. Acknowledge the user who reported it and the team that fixed it. This reinforces the culture of inclusion.
By following these steps, you'll transform accessibility feedback from a black hole of lost reports into a dynamic engine for inclusion—one that scales with your growth and keeps real people at the centre of your software.