5 citations · 9 across the 22 of their papers we have counts for
9 papers · 1 filter
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…
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.…
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…
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…
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…