High-Stakes Scenarios

Situations where errors in AI-generated information could lead to significant safety, health, financial, or social consequences. In the context of visual access technology for BLV users, high-stakes scenarios include medication identification (where misreading a dosage could be dangerous), navigation (where misidentifying a street sign or obstacle could cause harm), healthcare documents (where incorrect information could affect treatment), and financial information (where misread numbers could lead to monetary loss). BLV users express the strongest interest in reliability tools like variation surfacing for high-stakes tasks, where the consequences of AI errors are most severe and the need for trust calibration is greatest.