Artificial intelligence is transforming how we live and work, but for people with disabilities, it has the potential to go even further. AI can bridge communication gaps, support independence and open up opportunities that were once out of reach. And yet, most conversations about AI focus on speed, automation and business impact. What’s missing is a deeper conversation about equity and human dignity.
4 Tips for Designing Accessible AI
- Start with real-world outcomes.
- Involve people with disabilities early.
- Treat accessibility as a core requirement.
- Watch for bias in the training sets.
Without inclusive design, AI risks reinforcing barriers for people who are already marginalized. With it, AI can become a powerful tool for accessibility, independence, and inclusion.
As a senior software engineer with experience building cloud-based systems and integrating AI into real-world projects, I have seen how even well-developed solutions can unintentionally leave people out. This article shares lessons I have learned from the field on how to build AI systems that are not just powerful, but truly inclusive.
3 Lessons on How to Build Accessible AI
1. Voice Recognition and Speech Diversity
In one client project, we built a voice interface using a standard speech-to-text API. During testing, everything looked fine until users with speech impairments or strong accents tried it. Accuracy dropped dramatically.
We replaced the engine with Voiceitt, a tool built specifically for non-standard speech. With only a short training phase, users with conditions like ALS and cerebral palsy achieved much higher transcription accuracy.
Lesson: Don’t assume mainstream AI models will work for everyone. Test with diverse users, and don’t hesitate to explore specialized accessibility tools.
2. Computer Vision for the Visually Impaired
Microsoft’s Seeing AI app shows how powerful computer vision can be for blind and low-vision users: reading text, recognizing people, and describing surroundings through a smartphone.
At a hackathon, I built a smaller tool with Azure Cognitive Services that read handwritten notes aloud. It worked, but accuracy was inconsistent, affected by lighting, camera angle and handwriting style. When we added simple pre-processing steps like contrast enhancement and cropping, accuracy improved by more than 30 percent.
Lesson: Real-world conditions matter. AI systems need to be optimized for the environments users actually live and work in.
3. Neurodiversity and Adaptive Text
Dense technical content can be overwhelming for neurodiverse users, particularly people with ADHD or dyslexia. To help with this, I built a Chrome extension using Hugging Face’s T5 model to simplify and summarise web pages in real time.
The most valuable part wasn’t just the summarization, it was personalization. Users could adjust reading level, tone and summary length. Feedback showed that flexibility mattered more than accuracy.
Lesson: Give users control. Adaptive AI works best when people can shape the experience to fit their needs.
4 Tips for Designing Accessible AI
- Start with real-world outcomes: Ask who benefits from your system and how; center the needs of users who are often excluded.
- Involve people with disabilities early: Co-design and testing with diverse users will uncover assumptions your team might miss.
- Treat accessibility as a core requirement: Like performance and security, it should be built into your testing, deployment, and reviews.
- Watch for bias in training data: Many models underperform for underrepresented groups. Use more inclusive datasets or augment your own.
Advantages to Building Accessible AI
Accessibility isn’t just a nice to have or a compliance checkbox, it’s a competitive advantage.
In my work, designing for edge cases has consistently made systems more resilient and scalable. Features that support people with disabilities often improve the experience for everyone. For example, text simplification helps people with dyslexia but also makes information clearer for general audiences.
Empathetic AI isn’t just charity, it’s good engineering. Whether you’re building a chatbot, a voice assistant or a content tool, there are always opportunities to make technology more inclusive to people with disabilities.
It’s time to stop treating people with disabilities as an afterthought. Instead, let’s design with them from the beginning. When we do, we don’t just build better products we build a better future for all.
