Portrait of Shin (Alexandre) Koseki

Shin (Alexandre) Koseki

Affiliate Member
Assistant Professor, Université de Montréal, School of Urban Planning and Landscape Architecture
Research Topics
Data Mining

Biography

Shin Koseki is an assistant professor at the School of Urban Planning and Landscape Architecture of the Faculty of Environmental Design, Université de Montréal. He is also the director and chairholder of the UNESCO Chair in Urban Landscape. Trained in architecture and urban planning in Canada and Switzerland, Koseki is interested in the integration of new technologies in planning practices, the contribution of interactive democracy to the sustainable development of territories, and the role of public space in the acquisition of knowledge and skills. His research interests include the application of AI systems in urban design and new processes of environmental and technological governance.

In 2022, Koseki co-authored the white paper produced jointly by Mila – Quebec Artificial Intelligence Institute and UN-Habitat entitled “AI & Cities: Risks, Applications and Governance.”

His research on codesigning responsible AI systems in cities is supported by the New Frontiers in Research Fund and the Quebec Ministry of Economy, Innovation and Energy. Koseki has conducted research at the Swiss Federal Institute of Technology (Lausanne and Zurich), University of Oxford, National University of Singapore, Massachusetts Institute of Technology (MIT), University of Zurich, and Max Planck Institute for Art and Architecture History (Bibliotheca Hertziana). Back in his home city of Montréal, he works with his students on projects to revitalize and renaturalize the St. Lawrence River, as well as improve the quality of life of communities living along its shores.

Current Students

Postdoctorate - Université de Montréal
PhD - Université de Montréal
PhD - Université de Montréal
Master's Research - Université de Montréal

Publications

Intersecting perspectives: A participatory street review framework for urban inclusivity
Rashid A. Mushkani
Intersecting perspectives: A participatory street review framework for urban inclusivity
Rashid A. Mushkani
Intersecting perspectives: A participatory street review framework for urban inclusivity
Rashid A. Mushkani
Street Review: A Participatory AI-Based Framework for Assessing Streetscape Inclusivity
Rashid A. Mushkani
WeDesign: Generative AI-Facilitated Community Consultations for Urban Public Space Design
WeDesign: Generative AI-Facilitated Community Consultations for Urban Public Space Design
Community consultations are integral to urban planning processes intended to incorporate diverse stakeholder perspectives. However, limited … (see more)resources, visual and spoken language barriers, and uneven power dynamics frequently constrain inclusive decision-making. This paper examines how generative text-to-image methods, specifically Stable Diffusion XL integrated into a custom platform (WeDesign), may support equitable consultations. A half-day workshop in Montreal involved five focus groups, each consisting of architects, urban designers, AI specialists, and residents from varied demographic groups. Additional data was gathered through semi-structured interviews with six urban planning professionals. Participants indicated that immediate visual outputs facilitated creativity and dialogue, yet noted issues in visualizing specific needs of marginalized groups, such as participants with reduced mobility, accurately depicting local architectural elements, and accommodating bilingual prompts. Participants recommended the development of an open-source platform incorporating in-painting tools, multilingual support, image voting functionalities, and preference indicators. The results indicate that generative AI can broaden participation and enable iterative interactions but requires structured facilitation approaches. The findings contribute to discussions on generative AI's role and limitations in participatory urban design.
Co-Producing AI: Toward an Augmented, Participatory Lifecycle
Rashid A. Mushkani
Toumadher Ammar
Cassandre Chatonnier
Despite efforts to mitigate the inherent risks and biases of artificial intelligence (AI) algorithms, these algorithms can disproportionatel… (see more)y impact culturally marginalized groups. A range of approaches has been proposed to address or reduce these risks, including the development of ethical guidelines and principles for responsible AI, as well as technical solutions that promote algorithmic fairness. Drawing on design justice, expansive learning theory, and recent empirical work on participatory AI, we argue that mitigating these harms requires a fundamental re-architecture of the AI production pipeline. This re-design should center co-production, diversity, equity, inclusion (DEI), and multidisciplinary collaboration. We introduce an augmented AI lifecycle consisting of five interconnected phases: co-framing, co-design, co-implementation, co-deployment, and co-maintenance. The lifecycle is informed by four multidisciplinary workshops and grounded in themes of distributed authority and iterative knowledge exchange. Finally, we relate the proposed lifecycle to several leading ethical frameworks and outline key research questions that remain for scaling participatory governance.
WeDesign: Generative AI-Facilitated Community Consultations for Urban Public Space Design
Community consultations are integral to urban planning processes intended to incorporate diverse stakeholder perspectives. However, limited … (see more)resources, visual and spoken language barriers, and uneven power dynamics frequently constrain inclusive decision-making. This paper examines how generative text-to-image methods, specifically Stable Diffusion XL integrated into a custom platform (WeDesign), may support equitable consultations. A half-day workshop in Montreal involved five focus groups, each consisting of architects, urban designers, AI specialists, and residents from varied demographic groups. Additional data was gathered through semi-structured interviews with six urban planning professionals. Participants indicated that immediate visual outputs facilitated creativity and dialogue, yet noted issues in visualizing specific needs of marginalized groups, such as participants with reduced mobility, accurately depicting local architectural elements, and accommodating bilingual prompts. Participants recommended the development of an open-source platform incorporating in-painting tools, multilingual support, image voting functionalities, and preference indicators. The results indicate that generative AI can broaden participation and enable iterative interactions but requires structured facilitation approaches. The findings contribute to discussions on generative AI's role and limitations in participatory urban design.
Public perceptions of Montréal's streets: Implications for inclusive public space making and management
Rashid A. Mushkani
Toumadher Ammar
Negotiative Alignment: Embracing Disagreement to Achieve Fairer Outcomes -- Insights from Urban Studies
LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces
Negotiative Alignment: Embracing Disagreement to Achieve Fairer Outcomes -- Insights from Urban Studies