● Covered by 1 source · 1 reportLow impact1 neutral
Automated Alt Text Checks Often Miss Poor Quality Descriptions, GitHub Intern Finds
Key points
- Over 25% of images on top home pages have missing or poor alt text.
- Automated tools check for alt text presence, not descriptive quality.
- Poor alt text includes generic terms, filenames, or duplicated descriptions.
- GitHub developed a plugin to improve alt text quality checks.
The Problem with Current Alt Text Checks
Taarik Ashenafi, a former software engineering intern on GitHub's accessibility team, identified a significant issue with alt text quality. According to WebAIM's 2026 WebAIM Million report, 16.2% of images on the top million home pages lack alt text entirely. Additionally, 10.8% of images with alt text use undescriptive attributes such as "image," raw filenames, or descriptions duplicated from adjacent images.
Limitations of Automated Tools
Automated accessibility checkers are effective at identifying missing alt text but struggle with assessing the quality of existing descriptions. These tools typically verify if an accessible name for an image exists, rather than evaluating if the alt text provides useful information. This design choice prevents false positives that might cause teams to disable the checks, but it allows poor descriptions like "IMG_2847.png" or repeated generic text to pass.
GitHub's Approach to Quality Improvement
GitHub developed an alt text plugin for its Accessibility Scanner to address this gap. The plugin distinguishes between what a checker can objectively prove and what it can only suspect. It focuses on deterministic rules that do not require AI models or network calls, such as identifying absent or whitespace-only attributes, filenames, placeholders like "TODO," generic terms like "image," and repeated alt text across adjacent images.
Impact on Web Accessibility
The prevalence of poor-quality alt text indicates a broader challenge in web accessibility. While the presence of alt text is a foundational step, its descriptive quality is crucial for users relying on screen readers. Improving automated checks to flag more nuanced issues with alt text can significantly enhance the accessibility of web content for a wider audience.
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Primary sources
GitHub en/get-started GitHub software-development/innersource GitHub github/accessibility-scanner-alt-text-plugin GitHub github/accessibility-scanner GitHub en/githubReporting from
A former GitHub software engineering intern found that over 25% of images on popular home pages have missing or poor-quality alt text, despite passing automated checks. Automated tools primarily verify the presence of alt text, not its descriptive quality, leading to issues like generic descriptions or duplicated content. This highlights a gap in current accessibility testing methods, where objective checks for existence are prioritized over subjective quality assessments.