Summary 
In this episode Joe Devon, co-founder of Global Accessibility Awareness Day (GAAD), talks about how generative AI can both help and harm digital accessibility — and what it will take to tilt the balance toward inclusion. Joe shares his personal motivation for the work, real-world stakes for disabled users across web, mobile, and developer tooling, and compelling stories that illustrate why accessible design is a human-rights issue as much as a compliance checkbox. He digs into AI’s current and future roles: from improving caption quality and auto-generating audio descriptions to evaluating how well code-gen models produce accessible UI by default. Joe introduces AIMAC (AI Model Accessibility Checker), a new benchmark comparing top models on accessibility-minded code generation, what the results reveal, and how model providers and engineering teams can practically raise the bar with linters, training data, and cultural change. He closes with concrete guidance for leaders, why involving people with disabilities is non-negotiable, and how solving for edge cases makes AI—and products—better for everyone. 

Announcements 

• Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
• When ML teams try to run complex workflows through traditional orchestration tools, they hit walls. Cash App discovered this with their fraud detection models - they needed flexible compute, isolated environments, and seamless data exchange between workflows, but their existing tools couldn't deliver. That's why Cash App rely on Prefect. Now their ML workflows run on whatever infrastructure each model needs across Google Cloud, AWS, and Databricks. Custom packages stay isolated. Model outputs flow seamlessly between workflows. Companies like Whoop and 1Password also trust Prefect for their critical workflows. But Prefect didn't stop there. They just launched FastMCP - production-ready infrastructure for AI tools. You get Prefect's orchestration plus instant OAuth, serverless scaling, and blazing-fast Python execution. Deploy your AI tools once, connect to Claude, Cursor, or any MCP client. No more building auth flows or managing servers. Prefect orchestrates your ML pipeline. FastMCP handles your AI tool infrastructure. See what Prefect and Fast MCP can do for your AI workflows at aiengineeringpodcast.com/prefect today.
• Unlock the full potential of your AI workloads with a seamless and composable data infrastructure. Bruin is an open source framework that streamlines integration from the command line, allowing you to focus on what matters most - building intelligent systems. Write Python code for your business logic, and let Bruin handle the heavy lifting of data movement, lineage tracking, data quality monitoring, and governance enforcement. With native support for ML/AI workloads, Bruin empowers data teams to deliver faster, more reliable, and scalable AI solutions. Harness Bruin's connectors for hundreds of platforms, including popular machine learning frameworks like TensorFlow and PyTorch. Build end-to-end AI workflows that integrate seamlessly with your existing tech stack. Join the ranks of forward-thinking organizations that are revolutionizing their data engineering with Bruin. Get started today at aiengineeringpodcast.com/bruin, and for dbt Cloud customers, enjoy a $1,000 credit to migrate to Bruin Cloud.
• Your host is Tobias Macey and today I'm interviewing Joe Devon about opportunities for using generative AI to improve the accessibility of digital technologies

Interview
 

• Introduction
• How did you get involved in AI?
• Can you starty by giving an overview of what is included in the term "accessibility"?
• What are some of the major contributors to a lack of accessibility in digital experiences today?
• Beyond the web, what are some of the other platforms and interfaces that struggle with accessibility?
• What role does/can generative AI utilities play in improving the accessibility of applications?
• You recently helped create the AI Model Accessibility Checker (AIMAC) to benchmark which coding agents produce the most accessible code. What are the goals of that project and desired outcomes from its introduction?
  • What were the key findings from AIMAC's initial benchmarking results? Were there any surprises in terms of which models performed better or worse at generating accessible code?
• The automation offered by using agentic software development toolchains reduces the manual effort involved in building accessible interfaces. What are the opportunities for using generative AI utilities to act as an assistive mechanism for existing sites/technologies?
• Beyond code generation, what other aspects of the AI development lifecycle need accessibility considerations - training data, model outputs, user interfaces for AI tools themselves?
• You co-host the Accessibility and Gen AI Podcast. What are some of the common misconceptions you encounter about AI's role in accessibility, either from the AI community or the accessibility community?
• There's often tension between moving fast with AI adoption and ensuring inclusive design. How do you advise engineering teams to balance innovation speed with accessibility requirements?
• What specific accessibility issues are most amenable to AI solutions today, and which ones still require human judgment and expertise?
• As AI models become more capable at generating code and interfaces, what guardrails or validation processes should engineering teams implement to ensure accessibility standards are met?
• How do you see the role of accessibility specialists evolving as AI tools become more prevalent in the development workflow? Does AI augment their work or change it fundamentally?
• For engineering leaders building platform and data infrastructure, what accessibility considerations should be baked into foundational systems that AI applications will be built upon?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on acessibility awareness?

Contact Info
 

• LinkedIn

Parting Question
 

• From your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?

Closing Announcements
 

• Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
• Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
• If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.
• To help other people find the show please leave a review on iTunes and tell your friends and co-workers.

Links

• AIMAC 
  • GitHub
• Global Accessibility Awareness Day (GAAD)
• GAAD Foundation
• AltaVista
• Cursor
• Accessibility
• Braille Display
• Ben Ogilvie
  • State of Mobile App Accessibility Report
• VT-100
• Ghostty
• Warp Terminal
• LLM-as-a-Judge
• FFMPEG
• Aria Tags
• Axe-Core
• MiniMax M1
• Codex Mini
• Qwen
• Kimi
• Google Lighthouse
• GitHub Copilot
• Be-My-Eyes
  • Be-My-AI
• WebAIM
• XRAccess
• XR == Extended Reality
• Deque University
• Fable accessibility feedback organization

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0