Pluralistic-Alignment Urbanism: Operationalizing a Right to AI for Inclusive Public Space

Abstract

Municipal agencies increasingly use machine learning to inventory sidewalks, score streetscapes, and generate visualizations of public-space interventions. These systems produce outputs that enter budgeting, design iteration, and public justification, yet judgments about inclusion, safety, and belonging remain contested. This paper proposes Pluralistic-Alignment Urbanism (PAU), a procedural governance framework that treats public-space AI systems as civic infrastructure and formulates a procedural Right to AI for municipal uses of such systems. Drawing on two participatory case studies with community organizations in Montreal, Canada, the paper examines how disagreement, subgroup variation, bounded predictive scaling, and neutral preference judgments can inform municipal AI governance. Street Review elicits resident criteria for streetscape evaluation and trains a subgroup-aware scaling model for co-produced judgments, achieving an R2 of 0.89 on a held-out test set. LIVS, a Local Intersectional Visual Spaces dataset, constructs pluralistic preference data for aligning text-to-image models and treats neutral selections as evidence of indeterminacy. Across the cases, disagreement appears structured, deliberation changes what counts as evidence, scaling is feasible but limited by modality and coverage, and neutrality constrains what preference tuning can justify. PAU translates these constraints into a municipal governance architecture with disaggregated reporting, a versioned value register, standing deliberative cells, procurement clauses, and defined pause and rollback authority.