Building an AI-Powered Accessibility Feedback System: A Step-by-Step Guide for GitHub Teams

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For years, accessibility feedback at GitHub had no clear destination. Unlike typical product feedback, accessibility issues cut across the entire ecosystem—from screen reader users encountering broken workflows that span navigation, authentication, and settings, to keyboard-only users trapped in shared components used across dozens of pages, or low vision users flagging color contrast problems affecting every surface built with a shared design element. No single team owned these problems, yet each blocked a real person. Reports were scattered, bugs lingered without owners, and users followed up into silence. Improvements were often promised for a mythical “phase two” that rarely came.

To solve this, GitHub built a continuous, AI-powered workflow that ensures every piece of accessibility feedback becomes a tracked, prioritized issue—not eventually, but continuously. This guide walks you through the steps we took, from laying the groundwork to leveraging AI for triage and routing, so you can create a similar system for your organization.

What You Need

Step-by-Step Guide

Step 1: Define the Problem and Collect Stakeholders

Before building anything, gather representatives from every team that could be affected by accessibility issues—product, engineering, design, QA, support, and community management. Hold a workshop to map the current state: Where does feedback currently land? (Emails, Slack, spreadsheets, personal DMs?) How often does it get lost? Who is responsible for triage? By clarifying the pain points, you create a shared understanding that change is necessary. At GitHub, we found that feedback was scattered across backlogs, bugs lingered without owners, and users followed up to silence. This alignment is your foundation.

Building an AI-Powered Accessibility Feedback System: A Step-by-Step Guide for GitHub Teams
Source: github.blog

Step 2: Centralize Feedback Channels

Create a single intake repository for all accessibility feedback. Set up a simple web form, a GitHub Discussion category, or a dedicated email address that automatically creates issues in this repo. The key is to give users one known place to report barriers. Use a clear naming convention and add instructions for what information reporters should include: environment details, steps to reproduce, and impact assessment. At GitHub, this centralization was the first critical step before any AI could help.

Step 3: Create Templates and Standardize Reporting

Design issue templates that capture structured data: type of barrier (screen reader, keyboard, low vision, motor, etc.), severity, affected URLs or components, and a description. Use markdown with placeholders so reporters can easily fill in essential fields. Templates reduce ambiguity and make later AI processing more reliable. For example, a screen reader issue template might include fields for “JAWS vs NVDA,” “browser,” and “expected vs actual behavior.”

Step 4: Triage the Historical Backlog

Before introducing automation, clean up existing issues. Label all accessibility-related bugs with a consistent tag (e.g., accessibility). Assign a severity level to each. Close duplicates. Note which issues cross multiple teams (e.g., a shared component used by 20 pages). This manual cleanup is tedious but essential—AI can’t fix a messy dataset. At GitHub, this step took weeks but gave us a clean baseline to build upon.

Step 5: Design an AI-Powered Workflow

Now build the core automation using GitHub Actions, GitHub Copilot, and GitHub Models. The workflow should do three things:

  1. Capture: When a new issue is created from the centralized channel, the action runs automatically.
  2. Classify: Using an LLM (via GitHub Models or Copilot’s API), extract key information: which product area is affected? What type of accessibility issue? What priority? The AI can also suggest potential owners based on past issue assignments.
  3. Route: Based on classification, automatically add labels, assign the issue to the appropriate team or person, and post a comment summarizing the AI’s analysis for human review.

This pattern ensures every piece of feedback gets an immediate, structured response—no more lost reports. GitHub’s internal workflow functions less like a static ticketing system and more like a dynamic engine clarifying, structuring, and tracking feedback.

Building an AI-Powered Accessibility Feedback System: A Step-by-Step Guide for GitHub Teams
Source: github.blog

Step 6: Define Ownership and Escalation Rules

Accessibility issues often don’t belong to a single team. Create a policy that designates a “primary owner” for each type of issue. For example, shared component issues go to the design system team; workflow issues go to the product team responsible for that flow; color contrast issues go to the design team. If no owner can be determined automatically, route the issue to an accessibility champion who will triage manually. Set a service-level agreement (SLA) for response time (e.g., 48 hours for acknowledgment, 2 weeks for a fix plan).

Step 7: Implement Continuous Monitoring and Follow-Up

Add a scheduled GitHub Action (e.g., running weekly) that scans all open accessibility issues. It can generate a report of issues that are past their SLA, have no activity in 30 days, or are unassigned. Use AI to suggest possible re-assignments or reminders. Also, automatically notify reporters when their issue status changes (e.g., “in progress,” “closed”). This closes the loop and shows users their feedback is valued. GitHub’s system ensures follow-through until each barrier is addressed.

Step 8: Incorporate Human Review and Iteration

AI is a tool, not a replacement for judgment. Regularly review the workflow’s output. Are issues being routed correctly? Are some types missed? Tweak the LLM prompts, templates, and classification rules. At GitHub, this continuous feedback loop made the system more accurate over time. Also, hold monthly retrospectives with accessibility advocates to discuss what’s working and what needs improvement.

Tips for Success

By following these steps, you can transform chaos into a living system where accessibility feedback is continuously captured, prioritized, and acted upon—just as GitHub did. The most important breakthroughs come from listening to real people, and technology amplifies those voices.

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