From Feedback to Fixes: A Step-by-Step Guide to Building an AI-Powered Accessibility Workflow at GitHub

Introduction

Accessibility feedback often gets lost in the cracks of development. At GitHub, we found that reports from screen reader users, keyboard-only users, and low vision users were scattered across backlogs, unanswered, and unowned. The solution wasn't a single tool—it was a living methodology that combines automation, AI, and human judgment. This guide walks you through how we built a continuous workflow using GitHub Actions, GitHub Copilot, and GitHub Models to turn every piece of accessibility feedback into a tracked, prioritized, and resolved issue.

What You Need

• A GitHub organization or repository to host the workflow
• GitHub Actions – for automating issue creation and routing
• GitHub Copilot – for AI-assisted feedback clarification and labeling
• GitHub Models – for priority scoring and suggestion generation
• A template system – structured issue forms for accessibility reports
• Cross-team buy-in – commitment from product, design, engineering, and QA
• A triage process – initial categorization of existing backlog

Step-by-Step Process

Step 1: Centralize Scattered Feedback

Accessibility issues don’t belong to one team—they cut across navigation, authentication, and design components. Start by gathering all existing feedback from support tickets, scattered backlogs, and user emails. Use a dedicated GitHub repository or a label in your main repository to collect these reports. This central hub becomes the single source of truth. For example, a keyboard user might report a trap in a shared component used across 50 pages; a low vision user might flag a color contrast issue in a global style. Without centralization, nothing gets owned.

Step 2: Create Structured Templates and Labels

Design issue templates that capture essential details: user type (screen reader, keyboard-only, low vision), the exact barrier, affected surface, and desired outcome. Use YAML frontmatter to predefine labels like accessibility, screen-reader, keyboard-only. This ensures consistency. Then triage the backlog: go through years of old reports, assign cursory labels, and mark duplicates. This foundational work is boring but critical—AI can’t fix chaos; it can only amplify order.

Step 3: Automate Issue Routing with GitHub Actions

Build a GitHub Actions workflow that triggers on issue creation (or on a form submission via a feedback link). The workflow should automatically assign the issue to the relevant team based on labels (e.g., component:auth goes to the auth team). Use a matrix strategy for parallel routing. Also add an auto-response comment acknowledging receipt and setting expectations for next steps. This removes the human delay in initial handling.

Step 4: Integrate AI for Clarification and Prioritization

Leverage GitHub Copilot via Actions to analyze the report’s text. Copilot can suggest clarifying questions when details are missing (e.g., “What browser and version?”). Use GitHub Models (like a language model) to score priority based on severity (e.g., screen reader blocker vs. low contrast annoyance) and surface area. The model can output a priority label (P1, P2) and a short summary. Add these as comments or issue fields. For example: “This navigation issue blocks all screen reader users on the login page. Suggested priority: critical.”

Step 5: Ensure Continuous Tracking and Follow-Through

Set up recurring Actions on a schedule (daily or weekly) to check stalled issues that haven’t received updates for X days. Automatically add a comment nudge to the assignee and escalate to the team lead if unresolved for longer. Also, when a PR closes an issue tied to accessibility feedback, automatically notify the original reporter with a summary of the fix. This closes the loop and builds trust. Use a label like accessibility-feedback to track these.

Step 6: Build Human Review Loops

AI should augment, not replace, human judgment. After automation handles ingestion and triage, schedule weekly or bi-weekly reviews by an accessibility champion or cross-functional team. They can validate AI’s priority, merge duplicate reports, and kick off design exploration. This step ensures that edge cases and nuanced user experiences are respected. It also provides feedback to improve the AI models over time.

Tips for Success

• Start small, scale slowly. Don’t automate everything at once. Begin with one repository or one component (e.g., navigation) and prove the workflow before expanding.
• Keep humans in the loop. The most important breakthroughs come from listening to real people. AI is a force multiplier, not a decision-maker.
• Measure impact. Track metrics like time from report to first action, number of unresolved issues, and user satisfaction (e.g., survey after closure).
• Iterate on templates and labels. As new types of feedback emerge, update your forms and AI prompts. The system should evolve with your product.
• Communicate progress publicly. When a user’s feedback leads to a fix, thank them. This encourages more reporting and builds a community of testers.
• Align with accessibility pledges. If your organization commits to something like the GAAD pledge, use this workflow to demonstrate accountability.

Conclusion

Continuous AI for accessibility isn’t a one-time audit—it’s a living system. By centralizing feedback, automating routing, and using AI for clarification, you can turn a scattered backlog into a machine for inclusion. The result: every user’s voice gets heard, and every barrier gets a path to resolution. Start with these steps, adapt them to your context, and watch how feedback transforms from noise into continuous improvement.