Your alt text passes automated checks. That doesn't mean it's any good.

WebAIM’s 2026 Million report found that 16.2% of images on the web’s top million home pages lack alt text and another 10.8% have vague, filename-based, placeholder, or duplicated descriptions, highlighting a gap that conventional accessibility tools often detect but cannot meaningfully assess. GitHub’s alt-text plugin for its Accessibility Scanner addresses objectively verifiable issues through five default deterministic checks, while offering an opt-in vision-model check for contextual judgments that require seeing an image and nearby page content. The plugin avoids flagging intentionally decorative images with empty alt attributes, uses strict curated rules to minimize false positives, and evaluates repeated descriptions based on visual proximity rather than DOM order. Its model-assisted feature considers headings, captions, links, prose, and image content, but was redesigned to act as a structured reviewer rather than continually suggesting stylistic improvements. Because model analysis introduces privacy, security, and cost concerns, it is disabled by default, redacts portions of URLs and markup sent to the model, and may be better suited to scheduled scans than every commit. The authors emphasize that automated checks remain limited, may miss authenticated images or non-image elements such as SVGs and canvas, and cannot replace human review or testing with assistive-technology users; their central recommendation is to distinguish between checks that can prove a problem and those that can only suggest one.

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