Problem
Research questions and friction points this paper is trying to address.
urban art accessibility
blind and low vision
public art
accessibility
multisensory engagement
Unseen City Canvases: Exploring Blind and Low Vision People's Perspectives on Urban and Public Art Accessibility
🤖 AI Summary
This study addresses the longstanding neglect of accessibility in urban public art for blind and low-vision individuals. Through semi-structured interviews and design probes integrated with AI-generated image descriptions and real-time interactive technologies, the research systematically investigates this population’s preferences, challenges, and multisensory needs when encountering and engaging with public art in urban environments. The work reveals, for the first time, their spontaneous exploratory behaviors and strong desire for social participation. It proposes a novel seven-dimensional inclusive design framework encompassing safety, perception, and interaction, among other factors, and underscores the critical role of accurate AI-generated descriptions in conveying cultural meaning. These findings offer an empirical foundation and innovative pathways for human–computer interaction–driven inclusive urban art design.
📝 Abstract
Public art can hold cultural, social, political, and aesthetic significance, enriching urban environments and promoting well-being. However, a majority of urban art is inaccessible to blind and low vision (BLV) people. Most art access research has focused on private and curated settings (e.g., museums, galleries) and most urban access work has centered on outdoor navigation, leaving urban and public art accessibility largely understudied. We conducted semi-structured interviews with 16 BLV participants, using design probes featuring AI-generated descriptions and real-time AI interactions to investigate preferences for both discovering and engaging with urban art. We found that BLV people valued spontaneous art exploration, multisensory (e.g., tactile, auditory, olfactory) engagement, and detailed descriptions of culturally significant artwork. Participants also highlighted challenges distinct to urban art contexts: safety took precedence over art exploration, multisensory access measures could be disruptive to others in the public space, and inaccurate AI descriptions could lead to cultural erasure. Our contributions include empirical insights on BLV preferences for urban art discovery and engagement, seven design dimensions for public art access solutions, and implications for expanding HCI urban accessibility research beyond navigation.
Innovation
Methods, ideas, or system contributions that make the work stand out.
AI-generated descriptions
real-time AI interaction
multisensory accessibility
urban public art
design probes
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