When AI Reveals the City's Forgotten People

Urban spaces, while appearing neutral, often present hidden barriers for many residents. A simple curb can become an insurmountable obstacle for someone with reduced mobility, while confusing signage can turn an evening stroll into a source of anxiety for an elderly person. The absence of welcoming cues can lead to subtle forms of exclusion, demonstrating that cities are not experienced equally by everyone.

In Montreal, Shin Koseki, an associate professor at the University of Montreal's School of Urban Planning, is using artificial intelligence to uncover these disparities. His research, supported by his role as holder of the UNESCO Chair in Urban Landscape and as an affiliate member of Mila, the Quebec AI Institute, bridges urbanism, design, social sciences, and algorithmic modeling.

Koseki's work, informed by his background at EPFL, not only explores the potential of AI in urban analysis but also questions fundamental assumptions in territorial planning. He asks: what is the value of our current tools if they consistently leave behind a portion of the population who live in and interact with the city? His research aims to make urban environments more inclusive by highlighting overlooked challenges through data-driven insights.