LLM Accessibility

The study and practice of how large language models (LLMs) and generative AI tools handle digital accessibility in their outputs, particularly when generating code, user interfaces, or content. Research in this area examines whether LLM-generated code meets accessibility standards like WCAG, how training data biases affect accessibility outcomes, and what strategies can improve accessible output. A key concern is the self-reinforcing cycle where LLMs trained predominantly on inaccessible web content reproduce those same accessibility barriers, potentially scaling inaccessibility as AI-assisted development becomes more widespread.