Portrait de Shin (Alexandre) Koseki

Shin (Alexandre) Koseki

Membre affilié
Professeur adjoint, Université de Montréal, École d'urbanisme et d'architecture de paysage
Sujets de recherche
Exploration des données

Biographie

Shin Koseki est professeur adjoint à l’École d'urbanisme et d'architecture de paysage de la Faculté de l’aménagement de l'Université de Montréal ainsi que directeur et titulaire de la Chaire UNESCO en paysage urbain. Formé en architecture et en urbanisme au Canada et en Suisse, il s'intéresse à l'intégration de nouvelles technologies dans les pratiques de planification, à la contribution de la démocratie interactive au développement durable des territoires, ainsi qu'au rôle de l'espace public dans l'acquisition de connaissances et de compétences. Ses domaines de recherche incluent l'application de systèmes d'intelligence artificielle dans la conception urbaine et les nouveaux processus de gouvernance environnementale et technologique.

En 2022, Shin Koseki a coécrit le livre blanc Mila–UN Habitat AI & Cities: Risks, Applications and Governance. Il bénéficie d’un financement des Fonds Nouvelles frontières en recherche et du ministère de l'Économie, de l'Innovation et de l'Énergie du Québec pour travailler sur la coconception de systèmes d'intelligence artificielle responsables dans les villes.

Shin Koseki a mené des recherches à l'École polytechnique fédérale de Lausanne (EPFL) et à l'École polytechnique fédérale de Zurich (ETH Zurich), à l'Université d'Oxford (Oxon.), à l'Université nationale de Singapour (NUS), au Massachusetts Institute of Technology (MIT), à l'Université de Zurich (UZH) et à l'Institut Max-Planck pour l'histoire de l'art et de l'architecture (Bibliotheca Hertziana). De retour à Montréal, sa ville natale, il travaille avec ses étudiant·e·s sur la revitalisation et la renaturalisation du fleuve Saint-Laurent ainsi que sur l'amélioration de la qualité de vie des communautés riveraines.

Étudiants actuels

Postdoctorat - UdeM
Maîtrise recherche - UdeM

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 … (voir plus)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… (voir plus)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 … (voir plus)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