Communicating AI Uncertainty in Assistive Navigation for People with Visual Impairments

Abstract

Visual impairments affect upwards of 2.2 billion people worldwide. As AI systems increasingly support navigation for people with visual impairments, how uncertainty is communicated becomes critical. Prior work shows that communicating uncertainty can improve trust calibration and decision-making, yet it remains underexplored in assistive navigation. This project develops an uncertainty-aware assistive navigation architecture that integrates AI uncertainty into auditory guidance during real-time scene descriptions. Rather than using explicit confidence statements, the prototype embeds uncertainty into speech via variations in tone, pacing, and emphasis. The prototype combines a real-time collision-warning module with a semantic reasoning layer powered by a large language model (LLM). When generating scene descriptions, token-level uncertainty is mapped to auditory prosodic cues, enabling users to implicitly gauge the system’s confidence without disrupting navigational task flow. This work presents a high-fidelity prototype that treats AI confidence as an interaction design feature, illustrating how model uncertainty can be rendered perceptible in assistive navigation and reframed as a human-factors design parameter.