AI-generated alt text and auto-captioning have made it realistic to address accessibility gaps at a scale manual authoring never could. They’ve also made it easy to ship confidently wrong descriptions at that same scale, which is the part teams adopting these tools tend to discover later than they’d like.
Real-time captioning for live events and recordings, now broadly available through cloud provider…
AI-generated alt text describes what’s visually in an image; it doesn’t know why that…
A missing alt attribute is an obvious, visible gap — a screen reader announces the filename…
AI-generated alt text describes what’s visually in an image; it doesn’t know why that image is there or what a screen reader user actually needs to know from it. A product photo of a red shoe might get an accurate literal description, “a red athletic shoe on a white background,” while missing that the image is a size-chart reference and the useful information is the chart overlay text the model may not have parsed correctly at all. Context, purpose, and the specific information a user needs are judgment calls an image-captioning model doesn’t make reliably.
A missing alt attribute is an obvious, visible gap — a screen reader announces the filename or nothing at all, and it’s easy to flag as broken. An AI-generated alt text that’s subtly wrong, outdated after an image changes, or technically accurate but missing the actual point of the image is much harder to catch, because it looks like the accessibility requirement has already been satisfied. That makes unreviewed AI-generated content a different, arguably worse risk than the gap it’s replacing.
Expect tighter integration of automated testing and AI-authoring assistance directly into design and content tools, reducing the gap between content creation and accessibility review. That doesn’t remove the need for testing with real assistive technology users on anything that matters — it shifts where in the workflow the human review happens, from a separate audit pass to something closer to real-time, in-tool suggestion with human approval.
For decorative or genuinely low-stakes images, with periodic spot-checking, this is a reasonable trade-off at scale. For anything functionally important — navigation elements, charts, forms, product images tied to a purchase decision — unreviewed AI-generated alt text carries real risk of being confidently wrong in ways that are hard to catch later.
Automated captioning accuracy has improved substantially, but most accessibility standards and legal requirements don’t specify a particular generation method — they specify an accuracy bar. For high-stakes or legally required captioning (training content, public communications), a human accuracy review pass is still the safer standard until automated accuracy is consistently and verifiably at that bar.
It can do either, depending on review practice — AI tooling that increases genuine coverage (images that previously had no alt text at all) reduces exposure, while AI tooling deployed as an unreviewed substitute for testing with real assistive technology users can create a false sense of compliance that increases exposure if the gap surfaces in an audit or complaint.