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Hugo Berard

Collaborating Alumni - Université de Montréal
Supervisor
Shin (Alexandre) Koseki

Publications

Co-Producing AI: Toward an Augmented, Participatory Lifecycle
Rashid Mushkani
Hugo Berard
Toumadher Ammar
Cassandre Chatonnier
Shin Koseki
Despite efforts to mitigate the inherent risks and biases of artificial intelligence (AI) algorithms, these algorithms can disproportionatel… (see more)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.
2025-10-14
Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (published)
doi.org
arxiv.org
From Efficiency to Equity: Measuring Fairness in Preference Learning
S. Gowaikar
Hugo Berard
Rashid A. Mushkani
Shin Koseki
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.
2025-10-14
Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (published)
doi.org
arxiv.org
LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces
Rashid Mushkani
Shravan Nayak
Hugo Berard
Allison Cohen
Shin Koseki
Hadrien Bertrand
We introduce the Local Intersectional Visual Spaces (LIVS) dataset, a benchmark for multi-criteria alignment, developed through a two-year p… (see more)articipatory 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.
2025-10-05
Proceedings of the 42nd International Conference on Machine Learning (published)
doi.org
proceedings.mlr.press
Position: The Right to AI
Rashid Mushkani
Hugo Berard
Allison Cohen
Shin Koseki
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.
2025-10-05
Proceedings of the 42nd International Conference on Machine Learning (published)
proceedings.mlr.press
proceedings.mlr.press
WeDesign: Generative AI-Facilitated Community Consultations for Urban Public Space Design
Rashid A. Mushkani
Hugo Berard
Shin Koseki
2025-08-12
ArXiv (preprint)
doi.org
arxiv.org
Public perceptions of Montréal's streets: Implications for inclusive public space making and management
Rashid Mushkani
Hugo Berard
Toumadher Ammar
Shin Koseki
2025-06-30
Journal of Urban Management (published)
doi.org
Negotiative Alignment: Embracing Disagreement to Achieve Fairer Outcomes -- Insights from Urban Studies
Rashid A. Mushkani
Hugo Berard
Shin Koseki
2025-02-28
arXiv (published)
doi.org
arxiv.org
AI-EDI-SPACE: A Co-designed Dataset for Evaluating the Quality of Public Spaces
S. Gowaikar
Hugo Berard
Rashid A. Mushkani
Emmanuel Beaudry Marchand
Toumadher Ammar
Shin Koseki
Advancements in AI heavily rely on large-scale datasets meticulously curated and annotated for training. However, concerns persist regarding… (see more) the transparency and context of data collection methodologies, especially when sourced through crowdsourcing platforms. Crowdsourcing often employs low-wage workers with poor working conditions and lacks consideration for the representativeness of annotators, leading to algorithms that fail to represent diverse views and perpetuate biases against certain groups. To address these limitations, we propose a methodology involving a co-design model that actively engages stakeholders at key stages, integrating principles of Equity, Diversity, and Inclusion (EDI) to ensure diverse viewpoints. We apply this methodology to develop a dataset and AI model for evaluating public space quality using street view images, demonstrating its effectiveness in capturing diverse perspectives and fostering higher-quality data.
2024-10-31
arXiv (published)
doi.org
arxiv.org
Evaluation algorithmique inclusive de la qualité des espaces publics
Shin Koseki
Toumadher Ammar
Rashid Ahmad Mushkani
Hugo Berard
Sarah Tannir
2023-12-31
SHS Web of Conferences (published)
doi.org
Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity
Nicolas Loizou
Hugo Berard
Gauthier Gidel
Ioannis Mitliagkas
Simon Lacoste-Julien
Two of the most prominent algorithms for solving unconstrained smooth games are the classical stochastic gradient descent-ascent (SGDA) and … (see more)the recently introduced stochastic consensus optimization (SCO) [Mescheder et al., 2017]. SGDA is known to converge to a stationary point for specific classes of games, but current convergence analyses require a bounded variance assumption. SCO is used successfully for solving large-scale adversarial problems, but its convergence guarantees are limited to its deterministic variant. In this work, we introduce the expected co-coercivity condition, explain its benefits, and provide the first last-iterate convergence guarantees of SGDA and SCO under this condition for solving a class of stochastic variational inequality problems that are potentially non-monotone. We prove linear convergence of both methods to a neighborhood of the solution when they use constant step-size, and we propose insightful stepsize-switching rules to guarantee convergence to the exact solution. In addition, our convergence guarantees hold under the arbitrary sampling paradigm, and as such, we give insights into the complexity of minibatching.
2021-11-08
NeurIPS.cc/2021/Conference (poster)
doi.org
openreview.net
Stochastic Hamiltonian Gradient Methods for Smooth Games
Nicolas Loizou
Hugo Berard
Alexia Jolicoeur-Martineau
Pascal Vincent
Simon Lacoste-Julien
Ioannis Mitliagkas
The success of adversarial formulations in machine learning has brought renewed motivation for smooth games. In this work, we focus on the c… (see more)lass of stochastic Hamiltonian methods and provide the first convergence guarantees for certain classes of stochastic smooth games. We propose a novel unbiased estimator for the stochastic Hamiltonian gradient descent (SHGD) and highlight its benefits. Using tools from the optimization literature we show that SHGD converges linearly to the neighbourhood of a stationary point. To guarantee convergence to the exact solution, we analyze SHGD with a decreasing step-size and we also present the first stochastic variance reduced Hamiltonian method. Our results provide the first global non-asymptotic last-iterate convergence guarantees for the class of stochastic unconstrained bilinear games and for the more general class of stochastic games that satisfy a "sufficiently bilinear" condition, notably including some non-convex non-concave problems. We supplement our analysis with experiments on stochastic bilinear and sufficiently bilinear games, where our theory is shown to be tight, and on simple adversarial machine learning formulations.
2020-07-13
ICML (Accept)
doi.org
proceedings.mlr.press