Publications

'Ohhh, he's the boss!': Unpacking Power Dynamics Among Developers, Designers, and End-Users in FLOSS Usability
Jazlyn Hellman
Itai Epstein
Jinghui Cheng
Jin L.C. Guo
Addressing usability in free, libre, and open-source software (FLOSS) is a challenging issue, particularly due to a long-existing ''by devel… (see more)oper, for developer'' mentality. Engaging designers and end-users to work with developers can help improve its usability, but unequal power dynamics among those stakeholder roles must be mitigated. To explore how the power of different FLOSS stakeholders manifests and can be mediated during collaboration, we conducted eight design workshops with different combinations of key FLOSS stakeholders (i.e., developers, designers, and end-users). Leveraging existing theories on Dimensions of Power, we revealed how participants navigate existing role-based power structures through resource utilization, knowledge gap management, and experience referencing. We also observed that participants exhibited diverse behaviors confirming and challenging the status quo of FLOSS usability. Overall, our results contribute to a comprehensive understanding of the power dynamics among FLOSS stakeholders, providing valuable insights into ways to balance their power to improve FLOSS usability. Our work also serves as an exemplar of using design workshops as a research method to study power dynamics during collaboration that are usually hidden in the field.
2025-10-15
Proceedings of the ACM on Human-Computer Interaction (published)
doi.org
arxiv.org
Predicting the Subhalo Mass Functions in Simulations from Galaxy Images
Andreas Filipp
Tri Nguyen
Laurence Perreault-Levasseur
J. Rose
Chris Lovell
Nicolas Payot
Francisco Villaescusa-navarro
Yashar Hezaveh
2025-10-15
ArXiv (preprint)
arxiv.org
arxiv.org
It Takes Two: Your GRPO Is Secretly DPO
Yihong Wu
Liheng Ma
Lei Ding
Muzhi Li
Xinyu Wang
Kejia Chen
Zhan Su
Zhanguang Zhang
Chenyang Huang
Yingxue Zhang
Mark J. Coates
Jian-Yun Nie
Group Relative Policy Optimization (GRPO) is a prominent reinforcement learning algorithm for post-training Large Language Models (LLMs). I… (see more)t is commonly believed that GRPO necessitates a large group size to ensure stable training via precise statistical estimation, which incurs substantial computational overhead. In this work, we challenge this assumption by reframing GRPO as a form of contrastive learning, which reveals a fundamental connection to Direct Preference Optimization (DPO). Motivated by DPO's empirical success, we investigate the minimal two-rollout case (2-GRPO)—a configuration previously deemed infeasible. We provide a rigorous theoretical analysis to validate 2-GRPO and demonstrate empirically that it achieves performance on par with 16-GRPO, despite using only
2025-10-15
NeurIPS.cc/2025/Workshop/ER (spotlight)
openreview.net
openreview.net
A Comprehensive Review of Transmission and Distribution Optimal Power Flow Problems for the Integration of Distributed Energy Resources
Samuel M. Muhindo
Hanane Dagdougui
Antoine Lesage-Landry
Hussein Suprême
This paper presents a comprehensive review of coordination methods for addressing large-scale transmission and distribution optimal power fl… (see more)ow (TDOPF) problems involving distributed energy resources. With distinct objectives, each transmission and distribution system operator (TSO/DSO) independently seeks to solve its own optimal power flow (OPF) instance. First, iterative methods are reviewed, in which the central OPF is solved recursively by decomposing the full problem into smaller, more manageable sub-problems or by replacing peripheral portions of the network within the central OPF with reduced equivalent grids. Generally, the convergence to an optimal solution of the full problem when all sub-OPFs are coordinated is not guaranteed as iterative methods repeat procedures until the changes in control variables of the central OPF are minimal. Second, sequential methods are reviewed, in which the central OPF is solved sequentially in a fixed, nonrepeating procedure by considering previous results. Achieving a fair balance between TSO and DSO interests in sequential methods might adversely affect the performance of a largescale central OPF. The advantages and the limitations of the two coordination methods are presented based on the operation mode of TSO-DSO network. Future research opportunities for coordination methods of TSO-DSO network are drawn using the Kron reduction method and mean-field games.
2025-10-14
2025 IEEE Electrical Power and Energy Conference (EPEC) (published)
doi.org
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
Fairness in Federated Learning: Fairness for Whom?
Afaf Taïk
Khaoula Chehbouni
Golnoosh Farnadi
Fairness in federated learning has emerged as a rapidly growing area of research, with numerous works proposing formal definitions and algor… (see more)ithmic interventions. Yet, despite this technical progress, fairness in FL is often defined and evaluated in ways that abstract away from the sociotechnical contexts in which these systems are deployed. In this paper, we argue that existing approaches tend to optimize narrow system level metrics, such as performance parity or contribution-based rewards, while overlooking how harms arise throughout the FL lifecycle and how they impact diverse stakeholders. We support this claim through a critical analysis of the literature, based on a systematic annotation of papers for their fairness definitions, design decisions, evaluation practices, and motivating use cases. Our analysis reveals five recurring pitfalls: 1) fairness framed solely through the lens of server client architecture, 2) a mismatch between simulations and motivating use-cases and contexts, 3) definitions that conflate protecting the system with protecting its users, 4) interventions that target isolated stages of the lifecycle while neglecting upstream and downstream effects, 5) and a lack of multi-stakeholder alignment where multiple fairness definitions can be relevant at once. Building on these insights, we propose a harm centered framework that links fairness definitions to concrete risks and stakeholder vulnerabilities. We conclude with recommendations for more holistic, context-aware, and accountable fairness research in FL.
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
Longitudinal intergenerational hyperscanning indexes changes in social connection
Ryssa Moffat
Guillaume Dumas
Emily S. Cross
Loneliness is globally acknowledged as a severe and burgeoning health risk, fuelling interest in helping people of all ages form meaningful … (see more)social connections. One promising approach consists of intergenerational social programs. While behavioural and qualitative evidence derived from such programs promise health and wellbeing benefits, the physiological consequences of repeated intergenerational encounters remain unknown. Insight into physiological changes will shed light on the mechanisms of social connection and can inform program design choices. We charted changes in interpersonal neural synchrony (INS) in 31 intergenerational (older/younger adult) and 30 same generation (younger adult) dyads across a six-session creative drawing program. At each session, dyads completed self-report measures, drew together and alone, and had their cortical activation recorded with fNIRS. In both groups, INS was greater while dyads drew together than alone. Across sessions, intergenerational dyads’ INS decreased and same generation dyads’ INS increased. INS in RIFG∼RTPJ and RIFG∼RIFG were predictive of loneliness levels and feelings of social closeness, respectively. The research reinforces the multi-faceted nature of INS dynamics as social connections are forged.
2025-10-14
bioRxiv (preprint)
doi.org
Measuring What Matters: Connecting AI Ethics Evaluations to System Attributes, Hazards, and Harms
Shalaleh Rismani
Renee Shelby
Leah Davis
Negar Rostamzadeh
AJung Moon
Over the past decade, an ecosystem of measures has emerged to evaluate the social and ethical implications of AI systems, largely shaped by … (see more)high-level ethics principles. These measures are developed and used in fragmented ways, without adequate attention to how they are situated in AI systems. In this paper, we examine how existing measures used in the computing literature map to AI system components, attributes, hazards, and harms. Our analysis draws on a scoping review resulting in nearly 800 measures corresponding to 11 AI ethics principles. We find that most measures focus on four principles – fairness, transparency, privacy, and trust – and primarily assess model or output system components. Few measures account for interactions across system elements, and only a narrow set of hazards is typically considered for each harm type. Many measures are disconnected from where harm is experienced and lack guidance for setting meaningful thresholds. These patterns reveal how current evaluation practices remain fragmented, measuring in pieces rather than capturing how harms emerge across systems. Framing measures with respect to system attributes, hazards, and harms can strengthen regulatory oversight, support actionable practices in industry, and ground future research in systems-level understanding.
2025-10-14
Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (published)
doi.org
arxiv.org
PoissonNet: A Local-Global Approach for Learning on Surfaces
Arman Maesumi
Tanish Makadia
Thibault Groueix
Vladimir Kim
Daniel Ritchie
Noam Aigerman
Many network architectures exist for learning on meshes, yet their constructions entail delicate trade-offs between difficulty learning high… (see more)-frequency features, insufficient receptive field, sensitivity to discretization, and inefficient computational overhead. Drawing from classic local-global approaches in mesh processing, we introduce PoissonNet, a novel neural architecture that overcomes all of these deficiencies by formulating a local-global learning scheme, which uses Poisson's equation as the primary mechanism for feature propagation. Our core network block is simple; we apply learned local feature transformations in the gradient domain of the mesh, then solve a Poisson system to propagate scalar feature updates across the surface globally. Our local-global learning framework preserves the features's full frequency spectrum and provides a truly global receptive field, while remaining agnostic to mesh triangulation. Our construction is efficient, requiring far less compute overhead than comparable methods, which enables scalability -- both in the size of our datasets, and the size of individual training samples. These qualities are validated on various experiments where, compared to previous intrinsic architectures, we attain state-of-the-art performance on semantic segmentation and parameterizing highly-detailed animated surfaces. Finally, as a central application of PoissonNet, we show its ability to learn deformations, significantly outperforming state-of-the-art architectures that learn on surfaces.
2025-10-14
ArXiv (preprint)
doi.org
arxiv.org
Reframing AI-for-Good: Radical Questioning in AI for Human Trafficking Interventions
Pratheeksha Nair
Gabriel Lefebvre
Maryam Molamohammadi
Sophia Garrel
Reihaneh Rabbany
This paper introduces Radical Questioning (RQ), a structured, pre-design ethics framework developed to assess whether artificial intelligenc… (see more)e (AI) should be applied to complex social problems rather than merely how. While much of responsible AI development focuses on aligning systems with principles such as fairness, transparency, and accountability, it often begins after the decision to build has already been made, implicitly treating the deployment of AI as a given rather than a question in itself. In domains such as human trafficking, marked by contested definitions, systemic injustice, and deep stakeholder asymmetries, such assumptions can obscure foundational ethical concerns. RQ offers an upstream, deliberative process for surfacing these concerns before design begins. Drawing from critical theory, participatory ethics, and relational responsibility, RQ formalizes a five-step framework to interrogate problem framings, confront techno-solutionist tendencies, and reflect on the moral legitimacy of intervention. Developed through interdisciplinary collaboration and engagement with survivor-led organizations, RQ was piloted in the domain of human trafficking (HT) which is a particularly high-stakes and ethically entangled application area. Its use led to a fundamental design shift: away from automated detection tools and toward survivor-controlled, empowerment-based technologies. We argue that RQ's novelty lies in both its temporal position, i.e, prior to technical design, and its orientation toward domains where harm is structural and ethical clarity cannot be achieved through one-size-fits-all solutions. RQ thus addresses a critical gap between abstract principles of responsible AI and the lived ethical demands of real-world deployment.
2025-10-14
AAAI/ACM Conference on AI, Ethics, and Society (published)
doi.org
Simplicial Embeddings Improve Sample Efficiency in Actor-Critic Agents
Johan Samir Obando Ceron
Walter Mayor
Samuel Lavoie
Scott Fujimoto
Aaron Courville
Pablo Samuel Castro
Recent works have proposed accelerating the wall-clock training time of actor-critic methods via the use of large-scale environment parallel… (see more)ization; unfortunately, these can sometimes still require large number of environment interactions to achieve a desired level of performance. Noting that well-structured representations can improve the generalization and sample efficiency of deep reinforcement learning (RL) agents, we propose the use of simplicial embeddings: lightweight representation layers that constrain embeddings to simplicial structures. This geometric inductive bias results in sparse and discrete features that stabilize critic bootstrapping and strengthen policy gradients. When applied to FastTD3, FastSAC, and PPO, simplicial embeddings consistently improve sample efficiency and final performance across a variety of continuous- and discrete-control environments, without any loss in runtime speed.
2025-10-14
ArXiv (preprint)
doi.org
arxiv.org