How to Build a Continuous AI-Powered Accessibility Feedback System

Introduction

Ensuring that accessibility feedback doesn't get lost in the cracks is a challenge many organizations face. At GitHub, we transformed a scattered, ownerless feedback process into a living, AI-powered system that guarantees every user report is tracked, prioritized, and acted upon—continuously. This guide walks you through the steps we took, from laying the groundwork to deploying a workflow that leverages GitHub Actions, Copilot, and Models. Whether you're an accessibility advocate, a product manager, or a developer, you'll learn how to turn chaos into inclusion, one step at a time.

What You Need

• A GitHub organization or repository where you manage issues and projects
• Access to GitHub Actions (available in GitHub Free, Pro, Team, or Enterprise plans)
• GitHub Copilot (optional but recommended for automated analysis)
• GitHub Models (or equivalent LLM API) for natural language processing
• A process for collecting accessibility feedback (e.g., dedicated email, form, or issue template)
• Buy-in from cross-functional teams (design, engineering, QA, product)
• A commitment to human-centered design—AI is a tool, not a replacement for judgment

Step-by-Step Instructions

Step 1: Centralize All Accessibility Feedback

Start by creating a single, dedicated repository or project board where every piece of accessibility feedback lands. Use GitHub Issues with a custom issue template that captures key fields: user type (screen reader, keyboard-only, low vision, etc.), affected component or URL, description of the barrier, and any workarounds. Make it easy for users to submit reports—provide a direct link in your app’s footer or a prominent “Report an Accessibility Issue” button. This centralizes scattered feedback that previously lived in emails, backlogs, or support tickets. For example, a screen reader user’s report about a broken workflow can now become a single issue in your repository, visible to all contributors.

Step 2: Create Standardized Issue Templates

Design issue templates that guide users to provide all necessary details. Use YAML frontmatter for labels (e.g., accessibility, bug, enhancement) and include sections for:

• User persona (e.g., screen reader, keyboard-only, low vision)
• Severity (blocker, major, minor)
• Affected components (navigation, authentication, shared design elements)
• Expected vs. actual behavior
• Environment (browser, OS, assistive technology version)

Templates reduce ambiguity and ensure every issue is actionable from the start. For instance, a keyboard-only user hitting a focus trap in a shared component should automatically be tagged with the component name, making it easier to route to the right team.

Step 3: Triage the Existing Backlog

Before introducing AI, manually review and triage all existing accessibility issues in your backlog. Assign owners based on component ownership, set priority, and close duplicates. This foundation prevents the AI from being overwhelmed by noise. We spent several weeks cleaning up years of backlog—labeling every issue, linking related reports, and creating a clear status workflow (e.g., triage → confirmed → in progress → resolved). Only once this foundation was solid did we move to automation.

Step 4: Design the AI-Powered Workflow

Plan a workflow that runs on every new accessibility issue. The workflow should:

1. Capture the user’s feedback as soon as it’s submitted.
2. Analyze the text using a language model (via GitHub Models or Copilot) to extract key information: affected areas, severity, and potential duplicate issues.
3. Structure the analysis into a standardized comment or set of labels.
4. Route the issue to the appropriate team(s) based on components mentioned.
5. Notify relevant teammates via Slack, email, or GitHub notifications.

Use GitHub Actions to orchestrate these steps. For example, the workflow might call a Python script that uses the GitHub API to read the issue body, then passes it to an LLM (like GPT-4 via GitHub Models) for classification, and finally updates the issue with labels and a comment summarizing the analysis.

Step 5: Implement the Automation with GitHub Actions

Create a .github/workflows/accessibility-triage.yml file in your repository. A minimal example:

name: Accessibility AI Triage
on:
  issues:
    types: [opened]

jobs:
  triage:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout
        uses: actions/checkout@v4
      - name: Parse issue body
        run: |
          echo "Issue body: ${{ github.event.issue.body }}"
      - name: Call AI model
        uses: github/models@v1
        with:
          model: gpt-4o
          prompt: "Extract accessibility info from this issue: ${{ github.event.issue.body }}. Return JSON with fields: affected_components, severity, and is_duplicate (true/false)."
      - name: Add labels
        run: |
          gh issue edit ${{ github.event.issue.number }} --add-label "accessibility, ${{ steps.ai.outputs.severity }}"

This is a simplified version. In practice, we use multiple steps for context, deduplication, and routing. The AI model can also suggest remedial actions or flag missing information. Test the workflow with dummy issues before going live.

Step 6: Route Feedback to the Right Teams

Accessibility issues are cross-cutting—no single team owns them all. Use the AI’s analysis to assign the issue to the correct team(s). For example, if the issue mentions “navigation” and “authentication,” create a team-specific label (e.g., team:frontend, team:auth) and use GitHub’s CODEOWNERS file to automatically request reviews from those teams. The workflow can also create a project card on a shared board for visibility. Ensure each issue has a defined owner within 24 hours; if not, escalate to a designated accessibility lead.

Step 7: Monitor and Iterate

Continuous improvement is key. Set up a monthly review of the workflow’s performance:

• Are issues being classified correctly? (Check a random sample.)
• Are duplicate issues being caught? (Review the AI’s false positives/negatives.)
• How fast are resolved issues from creation to closure?

Gather feedback from users who reported issues—did they feel heard? Use these insights to tweak the AI model’s prompts, update issue templates, or add new labels. Treat the workflow itself as an accessibility feature: ensure it’s usable by screen reader users (e.g., all actions are accessible via keyboard).

Step 8: Scale to a Living System

Once the workflow is stable, expand beyond individual feedback. Integrate your AI-powered system with automated accessibility scans (e.g., axe-core, Lighthouse) so that findings from scans also become issues in the same workflow. Connect user feedback with code changes: when a developer fixes an issue, the AI can automatically notify the original reporter and invite them to verify the fix. This closes the loop and builds trust. Remember the centralization and triage steps—they make scaling possible. Over time, your system becomes a dynamic engine that learns from each interaction, turning accessibility into a continuous, inclusive practice.

Tips for Success

• Start small: Don’t automate everything at once. First, manually process a dozen issues to understand patterns, then automate the most repetitive parts.
• Keep humans in the loop: AI can misclassify or miss context. Always allow a human to override labels, routing, or priority. Use the AI as an assistant, not a dictator.
• Document your templates: Share your issue template and workflow YAML with the open-source community. Transparency invites contributions and improves accessibility for everyone.
• Test with real users: Before launch, ask people with disabilities to submit test issues and give feedback on the process. Their input is invaluable.
• Celebrate wins: When a reported bug gets fixed, celebrate it publicly (e.g., in a changelog or a “Fixed by you” section). This encourages more feedback and shows that user voices matter.
• Budget for maintenance: AI models change, GitHub Actions updates, and issue templates need periodic revision. Assign a team member to own the workflow’s health.