Sebastian Stein

Evidence shows that Text-to-Image (T2I) models disproportionately reflect Western cultural norms, amplifying misrepresentation and harms to minority groups. However, evaluating cultural sensitivity is inherently complex due to its fluid and multifaceted nature. This paper proposes a participatory and community-based evaluation approach to assess cultural sensitivity in T2I models, developed and validated through workshops involving 59 individuals from 19 different countries, thus embracing first-person perspectives. We describe our systematic six-step evaluation approach consisting of a reflection of evaluators’ positionality, definition of topics to be evaluated, initial reflections and expectations, image generation, quantitative and qualitative image evaluation, and final reflections. Quantitative scores and qualitative responses in our evaluation expose convergence and disagreement within and across communities, illuminate the downstream consequences of misrepresentation, and trace how training data shaped by unequal power relations distort visual depictions. The paper provides recommendations for stakeholders, highlighting pathways to investigate the sources, mechanisms, and impacts of cultural (mis)representation in T2I models.

This workshop explores value alignment for participatory AI, focusing on interfaces and tools that bridge citizen participation and technical development. As AI systems increasingly impact society, meaningful and actionable citizen input in their development becomes critical. However, current participatory approaches often fail to influence actual AI systems, with citizen values becoming trivialized during translation to technical requirements. The workshop brings together researchers and practitioners from HCI, AI, and social sciences to address challenges such as risk articulation, value evolution, democratic legitimacy, and the translation gap between community input and system implementation. Topics covered include interactive demonstrations of interfaces (or other ‘boundary objects’) for value elicitation within different communities, critical analysis of failed participatory attempts, methods for capturing evolving community values, and approaches for making citizen concerns actionable for developers.

As mobile applications grow in complexity, there is an increasing need to perform computationally intensive tasks. However, User Devices (UDs), such as tablets and smartphones, have limited capacity to carry out the required computations. Task offloading in Mobile Edge Computing (MEC) is a strategy that meets this demand by distributing tasks between UDs and servers. Deep Reinforcement Learning (DRL) is a promising solution for this strategy because it can adapt to dynamic changes and minimize online computational complexity. However, the combination of continuous-valued soft constraints and discrete-valued hard constraints on UDs and MEC servers poses significant challenges for designing efficient DRL algorithms. Existing DRL-based task-offloading algorithms focus on the constraints of the UDs, assuming the availability of enough resources on the server. Moreover, existing Multiagent DRL (MADRL)-based task-offloading algorithms are homogeneous agents and consider homogeneous constraints as a penalty in their reward function. We propose a novel Client–Master MADRL (CMMADRL) algorithm for task offloading in MEC that uses client agents at the UDs to decide on their resource requirements and a master agent at the server to make a combinatorial action selection based on the decision of the UDs. CMMADRL is shown to achieve up to 59% improvement in performance over existing benchmark and heuristic algorithms.

This workshop paper examines challenges in designing agentic AI systems from a citizen-centric perspective. Drawing on three participatory workshops conducted in 2025 with members of the general public and cross-sector stakeholders, we explore how societal values and expectations shape visions of future AI agents. Using constructive design research methods, participants engaged in storytelling and lo-fi prototyping to reflect on potential community impacts. We identify three key challenges: enabling meaningful and sustained public engagement, establishing a shared language between experts and lay participants, and translating speculative participant input into implementable systems. We argue that reflexive, long-term participation is essential for responsible and actionable citizen-centric AI development.