Search papers, labs, and topics across Lattice.
This thesis explores the integration of AI in urban decision-making, emphasizing the need for a pluralistic approach to governance that acknowledges diverse public values. By employing participatory research and machine learning techniques, such as Street Review and LIVS, the study reveals how residents' differing evaluations of public spaces can be effectively mapped and utilized in municipal contexts. The findings indicate that while alignment in AI can be improved, persistent disagreements highlight the contested nature of urban values, necessitating a governance framework that accommodates these differences rather than averaging them out.
AI's role in urban governance can amplify public values rather than suppress them, revealing deep-seated disagreements that challenge conventional decision-making processes.
Cities are beginning to use AI not only to analyze public space, but also to define what counts as evidence about it. This thesis asks what follows when scores, maps, and generated images become part of municipal decision-making. I argue that contemporary urbanism operates through two coupled infrastructures: the material city and an epistemic, algorithmic layer that shapes what cities can perceive, compare, and act upon. Because public space is contested, this algorithmic layer cannot be governed through technical performance alone. The thesis develops a civic Right to AI and a pluralistic approach to alignment in which differences in public values are made visible rather than averaged into a single objective. Methodologically, the thesis moves between normative theory, participatory research, machine learning, and governance design. The empirical work is grounded in Montr\'eal. Street Review combines participatory research with computer vision to examine how residents evaluate streets differently and how those judgments can be mapped at city scale using approximately 45,000 street-view images. LIVS (Local Intersectional Visual Spaces) extends the same problem to generative AI. Developed with 30 community organizations, it contains 37,710 pairwise comparisons across 13,462 images and is used to fine-tune and evaluate a Stable Diffusion XL model with Direct Preference Optimization. The results show that alignment can improve, but disagreement and neutrality persist. I treat these outcomes not as annotation noise, but as evidence that some values remain contested. The thesis concludes by translating these findings into municipal practice through lifecycle governance, procurement rules, oversight, recommissioning, and recourse. The resulting framework shows how cities can govern AI without treating plural values as measurement error or forcing them into a single objective.
