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Hugo Berard

Postdoctorat - UdeM
Superviseur⋅e principal⋅e
Shin (Alexandre) Koseki

Publications

From Efficiency to Equity: Measuring Fairness in Preference Learning
S. Gowaikar
Hugo Berard
Rashid A. Mushkani
Shin (Alexandre) Koseki
As AI systems, particularly generative models, increasingly influence decision-making, ensuring that they are able to fairly represent diver… (voir plus)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 (publié)
doi.org
From Efficiency to Equity: Measuring Fairness in Preference Learning
S. Gowaikar
Hugo Berard
Rashid A. Mushkani
Shin (Alexandre) Koseki
2025-10-15
Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (publié)
doi.org
From Efficiency to Equity: Measuring Fairness in Preference Learning
S. Gowaikar
Hugo Berard
Rashid A. Mushkani
Shin (Alexandre) Koseki
As AI systems, particularly generative models, increasingly influence decision-making, ensuring that they are able to fairly represent diver… (voir plus)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 (publié)
doi.org
arxiv.org
LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces
Rashid A. Mushkani
Shravan Nayak
Hugo Berard
Allison Cohen
Shin (Alexandre) Koseki
Hadrien Bertrand
2025-10-06
Proceedings of the 42nd International Conference on Machine Learning (publié)
doi.org
arxiv.org
LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces
Rashid A. Mushkani
Shravan Nayak
Hugo Berard
Allison Cohen
Shin (Alexandre) Koseki
Hadrien Bertrand
We introduce the Local Intersectional Visual Spaces (LIVS) dataset, a benchmark for multi-criteria alignment, developed through a t… (voir plus)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 (publié)
proceedings.mlr.press
Position: The Right to AI
Rashid A. Mushkani
Hugo Berard
Allison Cohen
Shin (Alexandre) Koseki
This position paper proposes a “Right to AI,” which asserts that individuals and communities should meaningfully participate in the deve… (voir plus)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 (publié)
proceedings.mlr.press
Position: The Right to AI
Rashid A. Mushkani
Hugo Berard
Allison Cohen
Shin (Alexandre) Koseki
2025-10-06
Proceedings of the 42nd International Conference on Machine Learning (publié)
proceedings.mlr.press
WeDesign: Generative AI-Facilitated Community Consultations for Urban Public Space Design
Rashid A. Mushkani
Hugo Berard
Shin (Alexandre) Koseki
2025-08-13
ArXiv (prépublication)
doi.org
arxiv.org
WeDesign: Generative AI-Facilitated Community Consultations for Urban Public Space Design
Rashid A. Mushkani
Hugo Berard
Shin (Alexandre) Koseki
Community consultations are integral to urban planning processes intended to incorporate diverse stakeholder perspectives. However, limited … (voir plus)resources, visual and spoken language barriers, and uneven power dynamics frequently constrain inclusive decision-making. This paper examines how generative text-to-image methods, specifically Stable Diffusion XL integrated into a custom platform (WeDesign), may support equitable consultations. A half-day workshop in Montreal involved five focus groups, each consisting of architects, urban designers, AI specialists, and residents from varied demographic groups. Additional data was gathered through semi-structured interviews with six urban planning professionals. Participants indicated that immediate visual outputs facilitated creativity and dialogue, yet noted issues in visualizing specific needs of marginalized groups, such as participants with reduced mobility, accurately depicting local architectural elements, and accommodating bilingual prompts. Participants recommended the development of an open-source platform incorporating in-painting tools, multilingual support, image voting functionalities, and preference indicators. The results indicate that generative AI can broaden participation and enable iterative interactions but requires structured facilitation approaches. The findings contribute to discussions on generative AI's role and limitations in participatory urban design.
2025-08-13
ArXiv (prépublication)
doi.org
arxiv.org
Co-Producing AI: Toward an Augmented, Participatory Lifecycle
Rashid A. Mushkani
Hugo Berard
Toumadher Ammar
Cassandre Chatonnier
Shin (Alexandre) Koseki
Despite efforts to mitigate the inherent risks and biases of artificial intelligence (AI) algorithms, these algorithms can disproportionatel… (voir plus)y impact culturally marginalized groups. A range of approaches has been proposed to address or reduce these risks, including the development of ethical guidelines and principles for responsible AI, as well as technical solutions that promote algorithmic fairness. Drawing on design justice, expansive learning theory, and recent empirical work on participatory AI, we argue that mitigating these harms requires a fundamental re-architecture of the AI production pipeline. This re-design should center co-production, diversity, equity, inclusion (DEI), and multidisciplinary collaboration. We introduce an augmented AI lifecycle consisting of five interconnected phases: co-framing, co-design, co-implementation, co-deployment, and co-maintenance. The lifecycle is informed by four multidisciplinary workshops and grounded in themes of distributed authority and iterative knowledge exchange. Finally, we relate the proposed lifecycle to several leading ethical frameworks and outline key research questions that remain for scaling participatory governance.
2025-08-01
arXiv (publié)
doi.org
arxiv.org
WeDesign: Generative AI-Facilitated Community Consultations for Urban Public Space Design
Rashid A. Mushkani
Hugo Berard
Shin (Alexandre) Koseki
Community consultations are integral to urban planning processes intended to incorporate diverse stakeholder perspectives. However, limited … (voir plus)resources, visual and spoken language barriers, and uneven power dynamics frequently constrain inclusive decision-making. This paper examines how generative text-to-image methods, specifically Stable Diffusion XL integrated into a custom platform (WeDesign), may support equitable consultations. A half-day workshop in Montreal involved five focus groups, each consisting of architects, urban designers, AI specialists, and residents from varied demographic groups. Additional data was gathered through semi-structured interviews with six urban planning professionals. Participants indicated that immediate visual outputs facilitated creativity and dialogue, yet noted issues in visualizing specific needs of marginalized groups, such as participants with reduced mobility, accurately depicting local architectural elements, and accommodating bilingual prompts. Participants recommended the development of an open-source platform incorporating in-painting tools, multilingual support, image voting functionalities, and preference indicators. The results indicate that generative AI can broaden participation and enable iterative interactions but requires structured facilitation approaches. The findings contribute to discussions on generative AI's role and limitations in participatory urban design.
2025-08-01
arXiv (publié)
doi.org
Co-Producing AI: Toward an Augmented, Participatory Lifecycle
Rashid A. Mushkani
Hugo Berard
Toumadher Ammar
Cassandre Chatonnier
Shin (Alexandre) Koseki
Despite efforts to mitigate the inherent risks and biases of artificial intelligence (AI) algorithms, these algorithms can disproportionatel… (voir plus)y impact culturally marginalized groups. A range of approaches has been proposed to address or reduce these risks, including the development of ethical guidelines and principles for responsible AI, as well as technical solutions that promote algorithmic fairness. Drawing on design justice, expansive learning theory, and recent empirical work on participatory AI, we argue that mitigating these harms requires a fundamental re‑architecture of the AI production pipeline. This re‑design should center co‑production, diversity, equity, inclusion (DEI), and multidisciplinary collaboration. We introduce an augmented AI lifecycle consisting of five interconnected phases: co‑framing, co‑design, co‑implementation, co‑deployment, and co‑maintenance. The lifecycle is informed by four multidisciplinary workshops and grounded in themes of distributed authority and iterative knowledge exchange. Finally, we relate the proposed lifecycle to several leading ethical frameworks and outline key research questions that remain for scaling participatory governance.
2025-07-31
ArXiv (prépublication)
doi.org
arxiv.org