arXiv subjects

Howard Ziyu Han

Publications and source records attributed to Howard Ziyu Han.

4 recordsLinked to original sources

arXiv2026

Enacting Constructive Conflicts with AI Agents to Enhance Reconsideration among Novice Interaction Designers

Generative AI agents are increasingly used in interaction design to facilitate ideation and offer critique, often following their own internal reasoning. These interactions tend to add design ideas and expand the design space. Our work explores an antagonistic role for design agents, prompting designers to engage with stakeholder tension. We built an AI agent inspired by adversarial design theory that enacts constructive conflict. We examine the agent's influence in a between-subjects experiment with 45 design students across three conditions: Self Reflection (unsupported review of the design proposal), Stepwise Guidance (written prompts that walk designers through a constructive-conflict framework), and Interactive Engagement (an AI agent that enacts the constructive-conflict framework interactively by synthesizing stakeholder pushback). The latter two conditions share the framework but differ in whether it is self-enacted or agent-enacted. Results show that, compared with Self Reflection, both the Stepwise Guidance and Interactive Engagement groups reported significantly higher self-reconsideration and made more improvements to their design proposals. Compared with Stepwise Guidance, the antagonistic agent introduced more conflictual perspectives, and participants in the Interactive Engagement condition generated and discarded more ideas. These findings suggest that agent-enacted constructive conflict can turn reconsideration into concrete design actions and deepen engagement with divergent stakeholder perspectives.

cs.HC↗

arXiv2026

ACME: A Multi-Cultural, Multi-Embodiment Social-Navigation Dataset

Understanding how robots and humans move in shared spaces is essential for designing effective social robot navigation policies and predicting human behavior. However, existing datasets often lack the diversity needed to capture differences in culture, geography, and human-robot interaction-factors that strongly shape appropriate social behavior. To address this gap, we introduce ACME: A Cross-cultural, Multi-Embodiment dataset for social navigation. A large-scale data collection effort across 8 sites in 5 countries, using 7 robot embodiments, ACME is a large and diverse multi-modal dataset aimed at advancing social navigation research, providing 29.35 hours of onboard robot data and 43.5 hours of overhead pedestrian tracking data. Unlike prior datasets, it focuses on capturing goal-driven social navigation behavior in complex social scenarios with explicit robot-crowd interaction through robot speech. To facilitate learning navigation policies and predicting pedestrian trajectories, ACME provides 3D and 2D scene features, odometry, interaction information, and human-annotated pedestrian trajectory labels. We make ACME easy to use by providing both human-readable data for each sensor modality as well as raw binary data. Our qualitative and quantitative analyses show that our dataset captures more challenging scenarios and a broader distribution of pedestrian behavior than previous datasets.

cs.RO↗

arXiv2025

WeAudit: Scaffolding User Auditors and AI Practitioners in Auditing Generative AI

There has been growing interest from both practitioners and researchers in engaging end users in AI auditing, to draw upon users' unique knowledge and lived experiences. However, we know little about how to effectively scaffold end users in auditing in ways that can generate actionable insights for AI practitioners. Through formative studies with both users and AI practitioners, we first identified a set of design goals to support user-engaged AI auditing. We then developed WeAudit, a workflow and system that supports end users in auditing AI both individually and collectively. We evaluated WeAudit through a three-week user study with user auditors and interviews with industry Generative AI practitioners. Our findings offer insights into how WeAudit supports users in noticing and reflecting upon potential AI harms and in articulating their findings in ways that industry practitioners can act upon. Based on our observations and feedback from both users and practitioners, we identify several opportunities to better support user engagement in AI auditing processes. We discuss implications for future research to support effective and responsible user engagement in AI auditing and red-teaming.

cs.HC↗

arXiv2024

Co-design Accessible Public Robots: Insights from People with Mobility Disability, Robotic Practitioners and Their Collaborations

Sidewalk robots are increasingly common across the globe. Yet, their operation on public paths poses challenges for people with mobility disabilities (PwMD) who face barriers to accessibility, such as insufficient curb cuts. We interviewed 15 PwMD to understand how they perceive sidewalk robots. Findings indicated that PwMD feel they have to compete for space on the sidewalk when robots are introduced. We next interviewed eight robotics practitioners to learn about their attitudes towards accessibility. Practitioners described how issues often stem from robotic companies addressing accessibility only after problems arise. Both interview groups underscored the importance of integrating accessibility from the outset. Building on this finding, we held four co-design workshops with PwMD and practitioners in pairs. These convenings brought to bear accessibility needs around robots operating in public spaces and in the public interest. Our study aims to set the stage for a more inclusive future around public service robots.

cs.HC↗