As generative AI tools become integrated into design workflows, students increasingly engage with these tools not just as aids, but as collaborators.This study analyzes reflections from 33 student teams in an HCI design course to examine the kinds of judgments students make when using AI tools.We found both established forms of design judgment (e.g., instrumental, appreciative, quality) and emergent types: agency-distribution judgment and reliability judgment.These new forms capture how students negotiate creative responsibility with AI and assess the trustworthiness of its outputs.Our findings suggest that generative AI introduces new layers of complexity into design reasoning, prompting students to reflect not only on what AI produces, but also on how and when to rely on it.By foregrounding these judgments, we offer a conceptual lens for understanding how students engage in co-creative sensemaking with AI in design contexts.

Task allocation in multi-human multi-robot (MHMR) teams presents significant challenges due to the inherent heterogeneity of team members, the dynamics of task execution, and the information uncertainty of operational states. Existing approaches often fail to address these challenges simultaneously, resulting in suboptimal performance. To tackle this, we propose ATA-HRL, an adaptive task allocation framework using hierarchical reinforcement learning (HRL), which incorporates initial task allocation (ITA) that leverages team heterogeneity and conditional task reallocation in response to dynamic operational states. Additionally, we introduce an auxiliary state representation learning task to manage information uncertainty and enhance task execution. Through an extensive case study in large-scale environmental monitoring tasks, we demonstrate the benefits of our approach. More details are available on our website: https://sites.google.com/view/ata-hrl.

Trust is essential in human-robot collaboration, particularly in multi-human, multi-robot (MH-MR) teams, where it plays a crucial role in maintaining team cohesion in complex operational environments. Despite its importance, trust is rarely incorporated into task allocation and reallocation algorithms for MH-MR collaboration. While prior research in single-human, single-robot interactions has shown that integrating trust significantly enhances both performance outcomes and user experience, its role in MH-MR task allocation remains underexplored. In this paper, we introduce the Expectation Confirmation Trust (ECT) Model, a novel framework for modeling trust dynamics in MH-MR teams. We evaluate the ECT model against five existing trust models and a no-trust baseline to assess its impact on task allocation outcomes across different team configurations (2H-2R, 5H-5R, and 10H-10R). Our results show that the ECT model improves task success rate, reduces mean completion time, and lowers task error rates. These findings highlight the complexities of trust-based task allocation in MH-MR teams. We discuss the implications of incorporating trust into task allocation algorithms and propose future research directions for adaptive trust mechanisms that balance efficiency and performance in dynamic, multi-agent environments.

As deceptive design practices proliferate on technology platforms and increasingly threaten user agency and well-being, concerned online communities are using social media platforms to discuss and challenge these unethical practices. In this paper, we conducted a case study analysis of two subreddits, r/privacy and r/assholedesign, to investigate the kinds of ethical concerns expressed within both subreddits, the strategies employed to express those concerns, the goals participants hoped to achieve through participation, and the community infrastructure these communities created to support their collective action against technology manipulation. Our findings show that posts on these subreddits employ different strategies to discuss ethical and value-related issues, revealing instances where community members engage in ethics ''by other means''-raising attention to problematic practices, identifying workarounds, and encouraging activism. The findings also showed that members of both communities transformed individual frustrations with technology manipulation into collective ethical action, involving value contestation and interaction criticism of problematic technology artifacts mediated by the socio-technical community infrastructure they designed to support these objectives. We conclude by highlighting opportunities for CSCW scholars to further encourage user engagement with technology ethics concepts by considering the role of community infrastructure and the different rhetoric of ethics that express different combinations of values and desired outcomes.

Design and technology practitioners are increasingly aware of the ethical impact of their work practices, desiring tools to support their ethical awareness across a range of contexts. In this paper, we report on findings from a series of six co-creation workshops with 26 technology and design practitioners that supported their creation of a bespoke ethics-focused action plan. Using a qualitative content analysis and thematic analysis approach, we identified a range of roles and process moves that practitioners and design students with professional experience employed and illustrate the interplay of these elements that impacted the creation of their action plan and revealed aspects of their ethical design complexity. We conclude with implications for supporting ethics in socio-technical practice and opportunities for the further development of methods that support ethical engagement and are resonant with the realities of practice.

LLMs are increasingly fine-tuned using RLHF datasets to align them with human preferences and values. However, very limited research has investigated which specific human values are operationalized through these datasets. In this paper, we introduce Value Imprint, a framework for auditing and classifying the human values embedded within RLHF datasets. To investigate the viability of this framework, we conducted three case study experiments by auditing the Anthropic/hh-rlhf, OpenAI WebGPT Comparisons, and Alpaca GPT-4-LLM datasets to examine the human values embedded within them. Our analysis involved a two-phase process. During the first phase, we developed a taxonomy of human values through an integrated review of prior works from philosophy, axiology, and ethics. Then, we applied this taxonomy to annotate 6,501 RLHF preferences. During the second phase, we employed the labels generated from the annotation as ground truth data for training a transformer-based machine learning model to audit and classify the three RLHF datasets. Through this approach, we discovered that information-utility values, including Wisdom/Knowledge and Information Seeking, were the most dominant human values within all three RLHF datasets. In contrast, prosocial and democratic values, including Well-being, Justice, and Human/Animal Rights, were the least represented human values. These findings have significant implications for developing language models that align with societal values and norms. We contribute our datasets to support further research in this area. https://github.com/hv-rsrch/valueimprint

Pandemic-tracking apps may form a future infrastructure for public health surveillance. Yet, there has been relatively little exploration of the potential societal implications of such an infrastructure. In semi-structured interviews with 23 participants from India, the Middle East and North Africa (MENA), and the United States, we discussed attitudes and preferences regarding the deployment of apps that support contact tracing to contain the spread of COVID-19. Through interpretive analysis, we examined the relationship between persistent discomfort and vulnerability when using such apps. Such an examination yielded three temporal forms of vulnerability: real, anticipatory, and speculative. By identifying and defining the temporalities of vulnerability through an analysis of people's pandemic-related thoughts and experiences, we develop the overlapping discourses of humanistic infrastructure studies and infrastructural speculation. In doing so, we explore the concept of vulnerability itself and present implications for the study of vulnerability in Human-Computer Interaction (HCI) and for the oversight of app-based public health surveillance.