Turning Accessibility Feedback into Action: A Step-by-Step Guide to Building an AI-Powered Inclusion Workflow

Introduction

For years, accessibility feedback at many organizations—including GitHub—had no clear home. Unlike typical product feedback that lands with a single team, accessibility issues cut across the entire ecosystem. A screen reader user might report a broken workflow spanning navigation, authentication, and settings. A keyboard-only user might hit a trap in a shared component used across dozens of pages. A low-vision user might flag a contrast problem affecting every surface with a shared design element. No single team owns these problems, yet every one blocks a real person.

This guide shows you how we transformed chaos into a system where every accessibility report is tracked, prioritized, and acted on—not eventually, but continuously. The key: a living methodology that combines automation, artificial intelligence, and human expertise. Instead of relying on static ticketing or one-time audits, we built a dynamic engine powered by GitHub Actions, GitHub Copilot, and GitHub Models. Here’s how you can do it too.

What You Need

• GitHub repository with admin access to set up Actions and workflows
• GitHub Copilot (optional but recommended) for AI-assisted issue analysis and drafting
• GitHub Models (or other LLM integration) for automated prioritization and categorization
• A central feedback collection point (e.g., a dedicated issue template, a form, or a label)
• Accessibility expertise in your team to review AI outputs and validate fixes
• Time to triage existing backlog – this foundation is critical before adding AI

Step-by-Step Guide

Step 1: Centralize Scattered Feedback

Before you can apply AI, you need a single source of truth. Map all current channels where accessibility feedback arrives: email, support tickets, direct messages, internal spreadsheets, or even hallway conversations. Create a dedicated GitHub issue form that captures key fields like type of barrier (screen reader, keyboard, low vision, motor), affected area, user environment (browser, assistive tech version), and expected vs. actual behavior. Use labels such as accessibility, feedback, and needs-triage to flag incoming issues. If you use a form, store submissions as issues automatically via a GitHub Actions workflow that listens to webhooks or form submissions.

Step 2: Create Standardized Templates and Labels

Consistency is key for AI to work well. Design issue templates that guide users to provide structured feedback. For example, a screen reader template might request the exact page URL, the steps taken, the unexpected behavior, and the severity (blocker, major, minor). Similarly, a keyboard-only template can ask about focus order and trapped elements. Use YAML frontmatter in your templates to pre-populate labels and assignees. This structure makes it easier for AI to parse and categorize issues without ambiguity.

Step 3: Triage Years of Backlog

Now, tackle existing backlog. Go through old accessibility-related issues, pull requests, and notes. Group them by component or workflow. If an issue is still relevant, migrate it to the new template format and apply labels. If it’s duplicate or outdated, close it with a helpful comment pointing to newer reports. This step is manual but essential: clean data in, clean insights out. After triage, you’ll have a curated list of items that your AI can learn from. Use this as a baseline to train models or fine-tune prompt templates.

Step 4: Integrate GitHub Actions and AI to Automate Triage

With clean, structured data flowing in, it’s time to build your automation. Create a GitHub Actions workflow that triggers on every new issue with the accessibility label. Use an action like actions/github-script to call GitHub Models (or an external LLM via API) to:

• Summarize the feedback into one sentence
• Predict the severity (critical, high, medium, low) based on keywords and context
• Suggest the most likely owning team or component (e.g., navigation, form, color token)
• Check for duplicates by comparing against open issues using semantic similarity

The AI should not replace human judgment—instead, it should add a comment with its analysis and set a status label like AI-triaged. A human accessibility champion can then review and either confirm or override the recommendation.

Step 5: Route Issues to the Right Teams

Once triaged, issues need owners. In your workflow, automatically assign the issue to the team identified by the AI (e.g., @github/design-systems for a color contrast problem). If the AI is uncertain, assign to a central accessibility team lead who will re-route. Use GitHub's team mention in the issue body to ensure notification. Also, create a project board (e.g., “Accessibility Improvements”) with columns like Needs Review, In Progress, Fixed – Needs User Test. Move issues automatically based on status labels—this creates a living system where feedback becomes tracked and prioritized.

Step 6: Enable Continuous Follow-Through

Automation doesn’t stop after assignment. Set up scheduled workflows (e.g., weekly) that check for stale issues—those in “Needs Review” for more than 30 days or “In Progress” for more than 14 days. The workflow can add a reminder comment, repost in Slack, or escalate to a manager. For issues marked as “Fixed”, notify the original reporter via a comment or email to close the loop. User testing is critical: invite the person who reported the barrier to verify the fix. This builds trust and ensures improvements actually work in real contexts, not just in test environments.

Tips for Success

• Listen to real people, not just scanners. The most important breakthroughs come from user feedback, not automated audits. Use AI to amplify human voices, not replace them.
• Iterate on your prompts. If the AI keeps misclassifying issues, refine the instructions and provide examples. Consider fine-tuning a small model on your historical triage data for better accuracy.
• Don’t skip the human-in-the-loop. AI can suggest ownership and severity, but a human accessibility expert should always review before action is taken. This prevents false positives and reduces bias.
• Celebrate wins publicly. When a user’s feedback leads to a meaningful fix, release notes or blog about it. This encourages more high-quality feedback and shows your commitment to inclusion.
• Keep the system dynamic. As your codebase evolves, update templates and labels. What works today might miss new components tomorrow. Schedule quarterly reviews of your workflow and backlog.
• Start small. You don’t need to handle all feedback at once. Pick one high-impact area (e.g., login flow or main navigation) and prove the workflow works before scaling.
• Align with broader initiatives. Tie your workflow to pledges like the Global Accessibility Awareness Day (GAAD) Pledge. It strengthens your commitment and provides a narrative for your team’s efforts.

Remember: The goal is not to eliminate human judgment but to free humans from repetitive work so they can focus on fixing the software. With this step-by-step guide, you can turn chaos into a continuous cycle of inclusion.