AI ยท Feature
Automatically generated text describing an image is not necessarily a good accessible alternative. What matters is the information the image conveys in its particular context. AI can provide drafts and draw attention to overlooked details. For charts, complex illustrations and uncertain recognition, however, expert review and a description tailored to the context of use remain particularly important.
Not everything visible is equally important
An image description should not indiscriminately list every recognisable object. It should make the image's function within the particular content accessible. A photograph may be decorative, explain an action or show a particular product feature. Appropriate text alternatives differ accordingly. For complex images, the W3C Web Accessibility Initiative explains a two-part approach: a brief identification and a more detailed account of the essential information. For a chart, this may include values, relationships and trends. A multimodal model can help with the first draft, but does not automatically know the editorial intention. Without this context, it may describe colours and shapes at length while missing the actual message. A good task specification therefore states the target audience, the image's function and the information that must be retained.
Plausible image details can be invented
Multimodal models process images and text together, but they are not infallible observers. Research benchmarks such as MMMU test, among other things, tasks involving charts, tables and other image types requiring specialist knowledge. Such tests make clear that visual tasks can demand more than recognising familiar objects: small labels, spatial relationships and specialist knowledge must fit together. This is particularly relevant to accessible content. An invented number or a mixed-up legend changes the information for people who cannot check the image themselves. Automatically generated descriptions should therefore not present unsupported interpretations as visible facts. Where identity, intention or emotion cannot be clearly recognised, cautious wording that stays close to observation is better than a seemingly vivid but speculative story.
A workable editorial process
The first step is to establish whether the image needs its own text alternative at all and what message it should convey. The AI can then produce a draft. A responsible person checks this against the original and the surrounding content. For data graphics, the underlying table should be used wherever possible, rather than having values estimated from pixels. For complex illustrations, a detailed description accessible to all readers is recommended. Finally, it is worth testing use without looking at the image: does the relevant information remain understandable, and are no additional claims introduced? In this way, AI supports the creation of accessible content without shifting responsibility for quality. The standard is not a particularly vivid description, but equivalent access to the information actually conveyed.
Sources & further reading
- W3C Web Accessibility Initiative: Complex Imagesw3.org
- MMMU: Multimodal Understanding and Reasoning Benchmarkmmmu-benchmark.github.io
Written in our own words with AI assistance, based on the sources listed above. The text distinguishes research findings, technical prototypes and established knowledge. This feature is not a breaking-news report.