8.7.5.4 - Accessibility Failures: Relying on AI for Image Alt-Text That Is Generic or Inaccurate (Difficulty: Advanced | Path: Scale)

In the rush to scale content production and automate catalog management, merchants have increasingly turned to Artificial Intelligence to handle the tedious task of writing alternative text (alt-text) for images. On the surface, this seems like the perfect use case for computer vision: an algorithm looks at a photo, identifies the objects, and writes a description. This promises to solve the massive backlog of untagged images that plagues nearly every growing e-commerce store, ostensibly checking the box for legal compliance with standards like the ADA (Americans with Disabilities Act) and the European Accessibility Act (EAA).

However, a dangerous gap exists between identifying an object and describing its purpose. Current AI models excel at surface-level detection—identifying that an image contains "shoes," "a person," or "a chart"—but they frequently fail to understand the contextual intent of the image. For a visually impaired user relying on a screen reader, hearing "Image of a product" or "Shoes on a white background" offers zero utility when they are trying to determine if the footwear is formal leather or athletic mesh. This is not just a user experience failure; it is a breakdown of trust. When a customer cannot verify the product's details through text, they do not buy.

The risk compounds when AI actively hallucinates or misinterprets complex visual data. We have seen automated tools describe a size guide chart simply as "Graph," rendering the sizing information completely inaccessible. We have witnessed AI tag a safety warning label on a chemical product as "bottle with text," omitting the critical safety instructions contained within the image. These are not merely inconveniences; in regulated industries or safety-critical niches, they are liability magnets. The rise of "accessibility overlays"—widgets that claim to use AI to fix code on the fly—has paradoxically led to an increase in lawsuits, as these tools often mask underlying issues rather than resolving them.