---
title: "Identifying Accessibility Data Gaps in CodeGen Models :: Aaron Gustafson"
url: https://daily.dev/posts/identifying-accessibility-data-gaps-in-codegen-models-aaron-gustafson-xcy5len3b
source_url: https://www.aaron-gustafson.com/notebook/identifying-accessibility-data-gaps-in-codegen-models
type: article
source: "Aaron Gustafson"
published: 2026-07-05T07:21:33.613Z
updated: 2026-07-05T07:21:59.276Z
tags: ["llm", "html", "accessibility"]
reading_time: 9
upvotes: 0
comments: 0
language: en
---

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# Identifying Accessibility Data Gaps in CodeGen Models :: Aaron Gustafson

**[Aaron Gustafson](https://daily.dev/sources/aaron_gustafson)** · 9 min read · 0 upvotes · 0 comments

## Summary

A systematic study evaluating GPT-4's ability to generate accessible HTML code reveals consistent failures: missing form labels, redundant or misapplied ARIA attributes, incorrect use of `required` on checkbox groups, missing fieldset/legend structures, and color-only error states. The author built a Python testing framework that sent prompts to Azure OpenAI's GPT-4, generated 10 iterations per prompt at high temperature, and manually reviewed each output for accessibility compliance. A key finding is that including diff-based accessibility hints in prompts dramatically improved output quality, suggesting the root cause is insufficient accessible code in training data rather than fundamental model incapability. Proposed solutions include curating open-source accessible code datasets, accessibility-aware fine-tuning, integrating accessibility linting into IDE AI tools, and embedding accessibility guidance into default system prompts.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.aaron-gustafson.com/notebook/identifying-accessibility-data-gaps-in-codegen-models>

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Tags: [#llm](https://daily.dev/tags/llm), [#html](https://daily.dev/tags/html), [#accessibility](https://daily.dev/tags/accessibility)

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