5 citations · 9 across the 22 of their papers we have counts for
23 papers
The urban right to AI: Pluralistic co-design and governance of public space
Rashid Mushkani
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…
Evidence-Grounded Constraint Checking in Construction Documents
Rashid Mushkani, Hugo Berard, Shin Koseki
Professional-document review is a constraint-checking problem in which decisions depend on relations among text, geometry, pages, and document revisions. We present an evidence-gro…
AI Pluralism and the Worlds It Misses
Rashid Mushkani
AI pluralism is often framed as a problem of representing diverse values, preferences, users, or outputs. This paper argues that this framing is incomplete because AI systems also…
Prompts for Public-Sector LLMs Should Be Governed as Commons
Rashid Mushkani
This paper argues that prompts used to deploy large language models (LLMs) in public-sector settings should be treated as governed artefacts rather than private, transient inputs.…
Benchmarks for Vision-Language Models in Urban Perception Should Be Reliability-Aware and Negotiated
Rashid Mushkani
Vision-language models (VLMs) are increasingly used to generate structured descriptions of street-level imagery for tasks such as streetscape auditing, mapping, and public consulta…
Voices in the Loop: Mapping Participatory AI
Rashid Mushkani
Participatory approaches to artificial intelligence are increasingly documented across public, civic, and humanitarian settings, but evidence about how participation is organized r…
Pluralistic-Alignment Urbanism: Operationalizing a Right to AI for Inclusive Public Space
Rashid Mushkani
Municipal agencies increasingly use machine learning to inventory sidewalks, score streetscapes, and generate visualizations of public-space interventions. These systems produce ou…
Traceable, Enforceable, and Compensable Participation: A Participation Ledger for People-Centered AI Governance
Rashid Mushkani
Participatory approaches are widely invoked in AI governance, yet participation rarely translates into durable influence. In public sector and civic AI systems, community contribut…
Do Vision-Language Models See Urban Scenes as People Do? An Urban Perception Benchmark
Rashid Mushkani
Understanding how people read city scenes can inform design and planning. We introduce a small benchmark for testing vision-language models (VLMs) on urban perception using 100 Mon…
Measuring What Matters: The AI Pluralism Index
Rashid Mushkani
Artificial intelligence systems increasingly mediate knowledge, communication, and decision making. Development and governance remain concentrated within a small set of firms and s…
Right-to-Override for Critical Urban Control Systems: A Deliberative Audit Method for Buildings, Power, and Transport
Rashid Mushkani
Automation now steers building HVAC, distribution grids, and traffic signals, yet residents rarely have authority to pause or redirect these systems when they harm inclusivity, saf…
Prompt Commons: Collective Prompting as Governance for Urban AI
Rashid Mushkani
Large Language Models (LLMs) are entering urban governance, yet their outputs are highly sensitive to prompts that carry value judgments. We propose Prompt Commons - a versioned, c…
WeDesign: Generative AI-Facilitated Community Consultations for Urban Public Space Design
Rashid Mushkani, Hugo Berard, Shin Koseki
Community consultations are integral to urban planning processes intended to incorporate diverse stakeholder perspectives. However, limited resources, visual and spoken language ba…
Collective Recourse for Generative Urban Visualizations
Rashid Mushkani
Text-to-image diffusion models help visualize urban futures but can amplify group-level harms. We propose collective recourse: structured community "visual bug reports" that trigge…
Urban AI Governance Must Embed Legal Reasonableness for Democratic and Sustainable Cities
Rashid Mushkani
This position paper argues that embedding the legal "reasonable person" standard in municipal AI systems is essential for democratic and sustainable urban governance. As cities inc…
Intersectoral Knowledge in AI and Urban Studies: A Framework for Transdisciplinary Research
Rashid Mushkani
Transdisciplinary approaches are increasingly essential for addressing grand societal challenges, particularly in complex domains such as Artificial Intelligence (AI), urban planni…
Street Review: A Participatory AI-Based Framework for Assessing Streetscape Inclusivity
Rashid Mushkani, Shin Koseki
Urban centers undergo social, demographic, and cultural changes that shape public street use and require systematic evaluation of public spaces. This study presents Street Review,…
Co-Producing AI: Toward an Augmented, Participatory Lifecycle
Rashid Mushkani, Hugo Berard, Toumadher Ammar +2
Despite efforts to mitigate the inherent risks and biases of artificial intelligence (AI) algorithms, these algorithms can disproportionately impact culturally marginalized groups.…
Negotiative Alignment: Embracing Disagreement to Achieve Fairer Outcomes -- Insights from Urban Studies
Rashid Mushkani, Hugo Berard, Shin Koseki
Urban assessments often compress diverse needs into single scores, which can obscure minority perspectives. We present a community-centered study in Montreal (n=35; wheelchair user…
LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces
Rashid Mushkani, Shravan Nayak, Hugo Berard +3
We introduce the Local Intersectional Visual Spaces (LIVS) dataset, a benchmark for multi-criteria alignment, developed through a two-year participatory process with 30 community o…
The Right to AI
Rashid Mushkani, Hugo Berard, Allison Cohen +1
This paper proposes a Right to AI, which asserts that individuals and communities should meaningfully participate in the development and governance of the AI systems that shape the…
AI-EDI-SPACE: A Co-designed Dataset for Evaluating the Quality of Public Spaces
Shreeyash Gowaikar, Hugo Berard, Rashid Mushkani +3
Advancements in AI heavily rely on large-scale datasets meticulously curated and annotated for training. However, concerns persist regarding the transparency and context of data co…
From Efficiency to Equity: Measuring Fairness in Preference Learning
Shreeyash Gowaikar, Hugo Berard, Rashid Mushkani +1
As AI systems, particularly generative models, increasingly influence decision-making, ensuring that they are able to fairly represent diverse human preferences becomes crucial. Th…