5 citations · 9 across the 8 of their papers we have counts for
8 papers
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…
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…
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…