Shin (Alexandre) Koseki

Leandry Junior JIEUTSA NKUIDJE Jieutsa

Postdoctorate - Université de Montréal

PhD - Université de Montréal

Github

Rashid Mushkani

PhD - Université de Montréal

Master's Research - Université de Montréal

Publications

From Efficiency to Equity: Measuring Fairness in Preference Learning

S. Gowaikar

Rashid A. Mushkani

As AI systems, particularly generative models, increasingly influence decision-making, ensuring that they are able to fairly represent diver… (see more)se human preferences becomes crucial. This paper introduces a novel framework for evaluating epistemic fairness in preference learning models inspired by economic theories of inequality and Rawlsian justice. We propose metrics adapted from the Gini Coefficient, Atkinson Index, and Kuznets Ratio to quantify fairness in these models. We validate our approach using two datasets: a custom visual preference dataset (AI-EDI-Space) and the Jester Jokes dataset. Our analysis reveals variations in model performance across users, highlighting potential epistemic injustices. We explore pre-processing and in-processing techniques to mitigate these inequalities, demonstrating a complex relationship between model efficiency and fairness. This work contributes to AI ethics by providing a framework for evaluating and improving epistemic fairness in preference learning models, offering insights for developing more inclusive AI systems in contexts where diverse human preferences are crucial.

2025-10-15

Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (published)

From Efficiency to Equity: Measuring Fairness in Preference Learning

S. Gowaikar

Rashid A. Mushkani

As AI systems, particularly generative models, increasingly influence decision-making, ensuring that they are able to fairly represent diver… (see more)se human preferences becomes crucial. This paper introduces a novel framework for evaluating epistemic fairness in preference learning models inspired by economic theories of inequality and Rawlsian justice. We propose metrics adapted from the Gini Coefficient, Atkinson Index, and Kuznets Ratio to quantify fairness in these models. We validate our approach using a diverse collection of datasets, covering both visual preferences and textual content. Our analysis reveals variations in model performance across users, highlighting potential epistemic injustices. We explore pre-processing and in-processing techniques to mitigate these inequalities, demonstrating a complex relationship between model efficiency and fairness. This work contributes to AI ethics by providing a framework for evaluating and improving epistemic fairness in preference learning models, offering insights for developing more inclusive AI systems in contexts where diverse human preferences are crucial.

2025-10-15

Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (published)

From Efficiency to Equity: Measuring Fairness in Preference Learning

S. Gowaikar

Rashid A. Mushkani

2025-10-15

Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (published)

LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces

Rashid A. Mushkani

Shravan Nayak

Allison Cohen

Hadrien Bertrand

We introduce the Local Intersectional Visual Spaces (LIVS) dataset, a benchmark for multi-criteria alignment, developed through a t… (see more)wo-year participatory process with 30 community organizations to support the pluralistic alignment of text-to-image (T2I) models in inclusive urban planning. The dataset encodes 37,710 pairwise comparisons across 13,462 images, structured along six criteria—Accessibility, Safety, Comfort, Invitingness, Inclusivity, and Diversity—derived from 634 community-defined concepts. Using Direct Preference Optimization (DPO), we fine-tune Stable Diffusion XL to reflect multi-criteria spatial preferences and evaluate the LIVS dataset and the fine-tuned model through four case studies: (1) DPO increases alignment with annotated preferences, particularly when annotation volume is high; (2) preference patterns vary across participant identities, underscoring the need for intersectional data; (3) human-authored prompts generate more distinctive visual outputs than LLM-generated ones, influencing annotation decisiveness; and (4) intersectional groups assign systematically different ratings across criteria, revealing the limitations of single-objective alignment. While DPO improves alignment under specific conditions, the prevalence of neutral ratings indicates that community values are heterogeneous and often ambiguous. LIVS provides a benchmark for developing T2I models that incorporate local, stakeholder-driven preferences, offering a foundation for context-aware alignment in spatial design.

2025-10-06

Proceedings of the 42nd International Conference on Machine Learning (published)

proceedings.mlr.press

LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces

Rashid A. Mushkani

Shravan Nayak

Allison Cohen

Hadrien Bertrand

2025-10-06

Proceedings of the 42nd International Conference on Machine Learning (published)

Position: The Right to AI

Rashid A. Mushkani

Allison Cohen

This position paper proposes a “Right to AI,” which asserts that individuals and communities should meaningfully participate in the deve… (see more)lopment and governance of the AI systems that shape their lives. Motivated by the increasing deployment of AI in critical domains and inspired by Henri Lefebvre’s concept of the “Right to the City,” we reconceptualize AI as a societal infrastructure, rather than merely a product of expert design. In this paper, we critically evaluate how generative agents, large-scale data extraction, and diverse cultural values bring new complexities to AI oversight. The paper proposes that grassroots participatory methodologies can mitigate biased outcomes and enhance social responsiveness. It asserts that data is socially produced and should be managed and owned collectively. Drawing on Sherry Arnstein’s Ladder of Citizen Participation and analyzing nine case studies, the paper develops a four-tier model for the Right to AI that situates the current paradigm and envisions an aspirational future. It proposes recommendations for inclusive data ownership, transparent design processes, and stakeholder-driven oversight. We also discuss market-led and state-centric alternatives and argue that participatory approaches offer a better balance between technical efficiency and democratic legitimacy.

2025-10-06

Proceedings of the 42nd International Conference on Machine Learning (published)

proceedings.mlr.press

Position: The Right to AI

Rashid A. Mushkani

Allison Cohen

2025-10-06

Proceedings of the 42nd International Conference on Machine Learning (published)

proceedings.mlr.press

Intersecting perspectives: A participatory street review framework for urban inclusivity

Rashid A. Mushkani

2025-10-01

Habitat International (published)

Intersecting perspectives: A participatory street review framework for urban inclusivity

Rashid A. Mushkani

2025-10-01

Habitat International (published)

Intersecting perspectives: A participatory street review framework for urban inclusivity

Rashid A. Mushkani

2025-10-01

Habitat International (published)

Street Review: A Participatory AI-Based Framework for Assessing Streetscape Inclusivity

Rashid A. Mushkani

2025-08-14

ArXiv (preprint)

Street Review: A Participatory AI-Based Framework for Assessing Streetscape Inclusivity

Rashid A. Mushkani

Urban centers undergo social, demographic, and cultural changes that shape public street use and require systematic evaluation of public spa… (see more)ces. This study presents Street Review, a mixed-methods approach that combines participatory research with AI-based analysis to assess streetscape inclusivity. In Montr\'eal, Canada, 28 residents participated in semi-directed interviews and image evaluations, supported by the analysis of approximately 45,000 street-view images from Mapillary. The approach produced visual analytics, such as heatmaps, to correlate subjective user ratings with physical attributes like sidewalk, maintenance, greenery, and seating. Findings reveal variations in perceptions of inclusivity and accessibility across demographic groups, demonstrating that incorporating diverse user feedback can enhance machine learning models through careful data-labeling and co-production strategies. The Street Review framework offers a systematic method for urban planners and policy analysts to inform planning, policy development, and management of public streets.

2025-08-14

ArXiv (preprint)