Recent advances in AI are integrating AI into the fabric of human social life, creating transformative, co-shaping relationships between humans and AI. This trend makes it urgent to investigate how these systems, in turn, shape their users. We conducted a three-phase design study with 24 participants to explore this dynamic. Our findings reveal critical tensions: (1) social AI often exacerbates the very interpersonal problems it is designed to mitigate; (2) it introduces nuanced privacy harms for secondary users inadvertently involved in AI-mediated social interactions; and (3) it can threaten the primary user’s personal agency and identity. We argue these tensions expose a problematic tendency in the user-centered paradigm, which often prioritizes immediate user experience at the expense of core human values like interpersonal ethics and self-efficacy. We call for a paradigm shift toward a more provocative and relational design perspective that foregrounds long-term social and personal consequences.

Centering humans in explainable artificial intelligence (XAI) research has primarily focused on AI model development and highstake scenarios.However, as AI becomes increasingly integrated into everyday applications in often opaque ways, the need for explainability tailored to end-users has grown more urgent.To address this gap, we explore end-users' perspectives on embedding XAI into daily AI application scenarios.Our findings reveal that XAI is not naturally accepted by end-users in their daily lives.When users seek explanations, they envision XAI design that promotes contextualized understanding, empowers adoption and adaption to AI systems, and considers multistakeholders' values.We further discuss supporting users' agency in XAI non-use and alternatives to XAI for managing ambiguity in AI interactions.Additionally, we provide design implications for XAI design at personal and societal levels.These include understanding users through a computational rationality lens, adaptive design that coevolves with users, and advancing the "society-in-the-loop" vision with everyday XAI.

Machine Learning (ML) is now integrated from everyday technologies to sophisticated infrastructures, providing fast, efficient, and scalable decision-making services, with increasing evidence of ML perpetuating invisible harms and biases. The hidden and socio-technical nature of ML biases can make them difficult to detect and prevent without proper literacy. To investigate this, we developed a novel online multiplayer board game Equality Engine, where players learn about various ML biases and debiasing techniques. We conducted a mixed-method formative playtest case study to solicit feedback from post-secondary students (N = 12) with a range of ML experience. We found that students' self-reported ML bias-debias knowledge improved significantly after playing the game. The game was perceived as easy to use because of the social interaction and immersion the game enabled. Students would also use the game in the future because of the self-reported knowledge gained from the game. Although these positive results may be influenced by measurement bias, our study contributes to the design of an approachable game, which not only facilitates exposure, collaboration, and opportunities for critical reflection on ML biases but also provides recommendations for future game designs that can facilitate ML ethics discourse and literacy among a broader audience.

Homelessness presents a long-standing problem worldwide. Like other welfare services, homeless services have gained increased traction in Machine Learning (ML) research. Unhoused persons are vulnerable and using their data in the ML pipeline raises serious concerns about the unintended harms and consequences of prioritizing different ML values. To address this, we conducted a critical analysis of 40 research papers identified through a systematic literature review in ML homelessness service provision research. We found that the values of novelty, performance, and identifying limitations were uplifted in these papers, whereas (in)efficiency, (low/high) cost, fast, (violated) privacy, and (homeless condition) reproducibility valuescollapse. Consequently, unhoused persons were lost (i.e., humans were deprioritized) at multi-level ML abstraction of predictors, categories, and algorithms. Our findings illuminate potential pathways forward at the intersection of data science, HCI and STS by situating humans at the center to support this vulnerable community.

Understanding emerging safety risks in nuanced social VR spaces and how existing safety features are used is crucial for the future development of safe and inclusive 3D social worlds. Prior research on safety risks in social VR is mainly based on interview or survey data about social VR users' experiences and opinions, which lacks "in-situ observations" of how individuals react to these risks. Using two empirical studies, this paper seeks to understand safety risks and safety design in social VR. In Study 1, we investigated 212 YouTube videos and their transcripts that document social VR users' immediate experiences of safety risks as victims, attackers, or bystanders. We also analyzed spectators' reactions to these risks shown in comments to the videos. In Study 2, we summarized 13 safety features across various social VR platforms and mapped how each existing safety feature in social VR can mitigate the risks identified in Study 1. Based on the uniqueness of social VR interaction dynamics and users' multi-modal simulated reactions, we call for further re-thinking and re-approaching safety designs for future social VR environments and propose potential design implications for future safety protection mechanisms in social VR.

To provide seamless wired/wireless ATM networks integration, a flow control framework for ABR traffic traversing both wired and wireless networks should be developed. The major challenges of designing flow control protocols for a combined wired/wireless network are the varying transmission characteristics (bandwidth, error and reliability) of the wireless media. In this paper, we propose a novel flow control scheme for air interface: a weighted ERICA algorithm which considers both the quality of the wireless channel and the status of buffer at base station. Type-I hybrid ARQ is chosen as the error recovery scheme to combat fading effects, while weighted ERICA algorithm is designed to achieve a fair and efficient resource allocation in wireless channels for ABR services. In particular, the weight of a connection used in the algorithm dynamically adapts in terms of varying channel conditions. Simulation is conducted in typical indoor wireless ATM networks. It is shown that the proposed scheme in this paper can achieve a high throughput with minimized buffer size at the base station when compared with the conventional flow control scheme.